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| United States Patent Application |
20080154873
|
| Kind Code
|
A1
|
|
Redlich; Ron M.
; et al.
|
June 26, 2008
|
Information Life Cycle Search Engine and Method
Abstract
Search engine process operates on data collections to expand a user's
knowledge about select content (SC) words, data objects, etc. A
prioritized hierarchical taxonomic system (H-tax-sys) encompasses some SC
is set, input data is classified thereto, and non-matching data (non-SC)
is identified. The non-SC are used as a search terms.
Additionally-optionally, the process identifies input data as SC per n
priority H-tax, and uses these as search terms. Additionally-optionally,
input data matching SC is added as search terms. Additionally-optionally,
search terms are extracted from the input based upon H-tax. Supplemental
documents are gathered, H-tax classified, mapped and represented with and
without mapped search terms.
| Inventors: |
Redlich; Ron M.; (Miami Beach, FL)
; Nemzow; Martin A.; (Miami Beach, FL)
|
| Correspondence Name and Address:
|
ROBERT C. KAIN, JR.
750 SOUTHEAST THIRD AVENUE, SUITE 100
FT LAUDERDALE
FL
333161153
US
|
| Serial No.:
|
614186 |
| Series Code:
|
11
|
| Filed:
|
December 21, 2006 |
| U.S. Current Class: |
707/5; 707/E17.017; 707/E17.108 |
| U.S. Class at Publication: |
707/5; 707/E17.017 |
| Intern'l Class: |
G06F 17/30 20060101 G06F017/30 |
Claims
1. A method of computerized searching through electronic data collections,
represented by databases or data structures, to expand a user's knowledge
about select content represented by one or more predetermined words,
characters, images or data objects, with a prioritized hierarchical
taxonomic system encompassing some select content, comprising:selecting n
priorities from said prioritized hierarchical taxonomic system;gathering
input data represented by a source document or a data stream, said input
data having data elements therein;identifying data elements in said input
data with said hierarchical taxonomic system and identifying data
elements representing supplemental select content corresponding to said n
priorities therefrom;searching said data collections with said
supplemental select content data elements and gathering supplemental
documents based thereon.
2. A method of searching as claimed in claim 1 including:identifying
non-matching data elements from said input data which do not match said
select content;searching said data collections with said non-matching
data elements and gathering additional supplemental documents based
thereon.
3. A method of searching as claimed in claim 2 including:identifying said
select content with said hierarchical taxonomic system;identifying data
elements in said input data which match said select content;identifying
select content with said n priorities in said hierarchical taxonomic
system and searching said data collections with said non-matching data
elements and said matching data elements for said n priorities and said
supplemental select content data elements;gathering further supplemental
documents based thereon.
4. A method of searching as claimed in claim 3 including:identifying data
elements in said supplemental documents and said additional supplemental
documents and said further supplemental documents with said hierarchical
taxonomic system; andcompiling at least one representation from the group
of representations including the data elements from all supplemental
documents grouped based upon said hierarchical taxonomic system, all
supplemental documents grouped based upon said hierarchical taxonomic
system and supplemental select content grouped based upon said
hierarchical taxonomic system.
5. A method of searching as claimed in claim 4 wherein identifying data
elements in said input data tags said input data elements with priorities
and classes based upon said hierarchical taxonomic system.
6. A method of searching as claimed in claim 3 including ending said
searching of said data collections upon an end search event from the
group of end search events which include a predetermined time, a
predetermined number of supplemental documents, a predetermined number of
further supplemental documents, a predetermined number of additional
supplemental documents, a predetermined number of data elements from said
supplemental documents which fulfill a predetermined number of priorities
in said hierarchical taxonomic system, a predetermined number of data
elements from said further supplemental documents which fulfill a
predetermined number of priorities in said hierarchical taxonomic system,
a predetermined number of data elements from said additional supplemental
documents which fulfill a predetermined number of priorities in said
hierarchical taxonomic system, a predetermined number of matches of
select content in said supplemental documents, a predetermined number of
matches of select content in said additional supplemental documents and a
predetermined number of matches of select content in said further
supplemental documents.
7. A method of searching as claimed in claim 6 including resetting said n
priorities in said hierarchical taxonomic system to m priorities in said
hierarchical taxonomic system and repeating the step of gathering
supplemental documents if one expanding search parameter is not met from
the group of expanding search parameters including said supplemental
documents do not exceed a predetermined number, a predetermined number of
data elements from said further supplemental documents which do not
fulfill a predetermined number of priorities in said hierarchical
taxonomic system, said supplemental documents do not include a data
elements matching a predetermined number of select content therein, and
data elements in said supplemental documents do not met predetermined
contextual rules or algorithms related to said select content.
8. A method of searching as claimed in claim 3 including:identifying data
elements in said input data which match said select content and further
identifying the matching select content data elements with said
hierarchical taxonomic system;identifying matching data elements which
fulfill predetermined contextual rules or algorithms in said input data
and incrementing, decrementing or otherwise tagging contextually matching
data elements;wherein contextually matching data elements occurs prior to
said identifying data elements with n priorities in said hierarchical
taxonomic system and said searching said data collections with said
non-matching data elements and said matching data elements.
9. A method of searching as claimed in claim 3 including increasing the
bandwidth of said searching said data collections by adding p number of
select content to said search in addition to said non-matching data
elements.
10. A method of searching as claimed in claim 3 including filtering out
noise data elements from said non-matching data elements prior to
searching said data collections.
11. A method of searching as claimed in claim 3 including providing a
client computer and a server computer, communicatively coupled together,
in a web-based server-client computer system and the method includes
gathering input data using said client computer in said web-based
server-client computer system; forwarding input data to said server
computer; compiling all supplemental documents on said server computer
and permitting access thereto via said client computer; and, employing
said server computer to:identify input data elements with said
hierarchical taxonomic system, identify non-matching data elements,
identify matching data elements, search said data collections, and gather
supplemental documents.
12. A method of computerized searching through electronic data
collections, represented by databases or data structures, to expand a
user's knowledge about select content represented by one or more
predetermined words, characters, images or data objects, with a
prioritized hierarchical taxonomic system encompassing some select
content, comprising:gathering input data represented by a source document
or a data stream, said input data having data elements
therein;identifying data elements in said input data with said
hierarchical taxonomic system and identifying non-matching data elements
which do not match said select content;searching said data collections
with said non-matching data elements and gathering supplemental documents
based thereon.
13. A method of searching as claimed in claim 12 including:identifying
data elements in said supplemental documents with said hierarchical
taxonomic system; andcompiling either the data elements from said
supplemental documents or the supplemental documents into a
representation of said hierarchical taxonomic system in some
predetermined order.
14. A method of searching as claimed in claim 12 wherein identifying data
elements in said input data tags said input data elements with priorities
and classes based upon said hierarchical taxonomic system.
15. A method of searching as claimed in claim 12 including:identifying
said select content with said hierarchical taxonomic system;identifying
data elements in said input data which match said select
content;identifying data elements with n priorities in said hierarchical
taxonomic system and searching said data collections with said
non-matching data elements and said matching data elements for said n
priorities;gathering further supplemental documents based thereon.
16. A method of searching as claimed in claim 12 includingidentifying data
elements in said supplemental documents with said hierarchical taxonomic
system; andending said searching of said data collections upon an end
search event from the group of end search events which include a
predetermined time, a predetermined number of supplemental documents, a
predetermined number of data elements from said supplemental documents
which fulfill a predetermined number of priorities in said hierarchical
taxonomic system, and a predetermined number of matches of select content
in said supplemental documents.
17. A method of searching as claimed in claim 15 includingidentifying data
elements in all said supplemental documents with said hierarchical
taxonomic system; andending said searching of said data collections upon
an end search event from the group of end search events which include a
predetermined time, a predetermined number of supplemental documents, a
predetermined number of further supplemental documents, a predetermined
number of data elements from said supplemental documents which fulfill a
predetermined number of priorities in said hierarchical taxonomic system,
a predetermined number of data elements from said further supplemental
documents which fulfill a predetermined number of priorities in said
hierarchical taxonomic system, a predetermined number of matches of
select content in said supplemental documents and a predetermined number
of matches of select content in said further supplemental documents.
18. A method of searching as claimed in claim 17 including resetting said
n priorities in said hierarchical taxonomic system to m priorities in
said hierarchical taxonomic system and repeating the step of gathering
further supplemental documents if one expanding search parameter is not
met from the group of expanding search parameters including said further
supplemental documents do not exceed a predetermined number, said further
supplemental documents do not include a data elements matching a
predetermined number of select content, a predetermined number of data
elements from said further supplemental documents which do not fulfill a
predetermined number of priorities in said hierarchical taxonomic system,
and data elements in said further supplemental documents do not met
predetermined contextual rules or algorithms related to said select
content.
19. A method of searching as claimed in claim 18 wherein m is greater than
n priorities.
20. A method of searching as claimed in claim 15 including:identifying
data elements in said input data which match said select content and
further identifying the matching select content data elements with said
hierarchical taxonomic system;identifying matching data elements which
fulfill predetermined contextual rules or algorithms in said input data
and incrementing, decrementing or otherwise tagging contextually matching
data elements;wherein contextually matching data elements occurs prior to
said identifying data elements with n priorities in said hierarchical
taxonomic system and said searching said data collections with said
non-matching data elements and said matching data elements.
21. A method of searching as claimed in claim 12 including:identifying
data elements in said supplemental documents with said hierarchical
taxonomic system;compiling either the data elements from said
supplemental documents or the supplemental documents into a map, display
or tree representation of said hierarchical taxonomic system.
22. A method of searching as claimed in claim 21 including compiling said
map or tree and changing said prioritized hierarchical taxonomic when
said map or tree representations of supplemental document data elements
or supplemental documents exceeds a certain map or tree parameter.
23. A method of searching as claimed in claim 22 wherein said map or tree
parameter is a number of branches.
24. A method of searching as claimed in claim 12 including increasing the
bandwidth of said searching said data collections by adding p number of
select content to said search in addition to said non-matching data
elements.
25. A method of searching as claimed in claim 12 including filtering out
noise data elements from said non-matching data elements prior to
searching said data collections.
26. A method of searching as claimed in claim 12 including providing a
client computer and a server computer, communicatively coupled together,
in a web-based server-client computer system and the method includes
gathering input data using said client computer in said web-based
server-client computer system.
27. A method of searching as claimed in claim 26 includes compiling said
data elements from said supplemental documents and said supplemental
documents on said server computer in said web-based server-client
computer system and permitting access thereto via said client computer.
28. A method of searching as claimed in claim 26 including forwarding
input data to said server computer and, employing said server computer
to:identify input data elements with said hierarchical taxonomic system,
identify non-matching data elements, search said data collections, and
gather supplemental documents.
29. A method of searching as claimed in claim 15 including:identifying
data elements in all said supplemental documents with said hierarchical
taxonomic system;compiling either the data elements from said
supplemental documents or the supplemental documents into a map, display
or tree representation of said hierarchical taxonomic system.
30. A method of searching as claimed in claim 29 wherein identifying data
elements in said input data tags said input data elements with priorities
and classes based upon said hierarchical taxonomic system.
31. A method of searching as claimed in claim 30 including ending said
searching of said data collections upon an end search event from the
group of end search events which include a predetermined time, a
predetermined number of supplemental documents, a predetermined number of
further supplemental documents, a predetermined number of data elements
from said supplemental documents which fulfill a predetermined number of
priorities in said hierarchical taxonomic system, a predetermined number
of data elements from said further supplemental documents which fulfill a
predetermined number of priorities in said hierarchical taxonomic system,
a predetermined number of matches of select content in said supplemental
documents and a predetermined number of matches of select content in said
further supplemental documents.
32. A method of searching as claimed in claim 31 including resetting said
n priorities in said hierarchical taxonomic system to m priorities in
said hierarchical taxonomic system and repeating the step of gathering
further supplemental documents if one expanding search parameter is not
met from the group of expanding search parameters including said further
supplemental documents do not exceed a predetermined number, said further
supplemental documents do not include a data elements matching a
predetermined number of select content, a predetermined number of data
elements from said further supplemental documents which do not fulfill a
predetermined number of priorities in said hierarchical taxonomic system,
and data elements in said further supplemental documents do not met
predetermined contextual rules or algorithms related to said select
content.
33. A method of searching as claimed in claim 32 including:identifying
data elements in said input data which match said select content and
further identifying the matching select content data elements with said
hierarchical taxonomic system;identifying matching data elements which
fulfill predetermined contextual rules or algorithms in said input data
and incrementing, decrementing or otherwise tagging contextually matching
data elements;wherein contextually matching data elements occurs prior to
said identifying data elements with n priorities in said hierarchical
taxonomic system and said searching said data collections with said
non-matching data elements and said matching data elements.
34. A method of searching as claimed in claim 33 including increasing the
bandwidth of said searching said data collections by adding p number of
select content to said search in addition to said non-matching data
elements.
35. A method of searching as claimed in claim 34 including filtering out
noise data elements from said non-matching data elements prior to
searching said data collections.
36. A method of searching as claimed in claim 35 including providing a
client computer and a server computer, communicatively coupled together,
in a web-based server-client computer system and the method includes
gathering input data using said client computer in said web-based
server-client computer system; forwarding input data to said server
computer; compiling said data elements from said supplemental documents
and said supplemental documents on said server computer and permitting
access thereto via said client computer; and, employing said server
computer to:identify input data elements with said hierarchical taxonomic
system, identify non-matching data elements, search said data
collections, gather supplemental documents, identify supplemental
document data elements with said hierarchical taxonomic system, and
compile supplemental document data elements and supplemental documents
into said hierarchical taxonomic system.
37. A method of computerized searching through electronic data
collections, represented by databases or data structures, to expand a
user's knowledge about select content represented by one or more
predetermined words, characters, images or data objects therein,
comprising:employing a prioritized hierarchical taxonomic system which
encompasses some select content;gathering input data represented by a
source document or a data stream, said input data having data elements
therein;tagging each data element in said input data based upon said
hierarchical taxonomic system;searching through said data collections and
gathering supplemental documents having non-matching data elements from
said data input which do not match said select content;tagging each data
element in said supplemental documents based upon said hierarchical
taxonomic system; andcompiling either the data elements from said
supplemental documents or the supplemental documents into a
representation of said hierarchical taxonomic system.
38. A method of searching as claimed in claim 37 including:identifying and
tagging said select content based upon said hierarchical taxonomic
system;identifying and tagging data elements in said input data which
match said select content;identifying and tagging data elements with n
priorities in said hierarchical taxonomic system and searching said data
collections with said non-matching data elements and said matching data
elements for said n priorities;gathering further supplemental documents
based thereon;identifying and tagging data elements in said further
supplemental documents with said hierarchical taxonomic system;
andcompiling either the data elements from said further supplemental
documents or the further supplemental documents into said representation
of said hierarchical taxonomic system.
39. A method of searching as claimed in claim 38 said compiling is listing
of supplemental document data elements or the supplemental documents
themselves, from a higher to a lower priority based upon said
hierarchical taxonomic system.
40. A method of searching as claimed in claim 38 wherein identifying and
tagging data elements in said input data tags elements with priorities
and classes based upon said hierarchical taxonomic system.
41. A method of searching as claimed in claim 38 including ending said
searching of said data collections upon an end search event from the
group of end search events which include a predetermined time, a
predetermined number of supplemental documents, a predetermined number of
further supplemental documents, a predetermined number of data elements
from said supplemental documents which fulfill a predetermined number of
priorities in said hierarchical taxonomic system, a predetermined number
of data elements from said further supplemental documents which fulfill a
predetermined number of priorities in said hierarchical taxonomic system,
a predetermined number of matches of select content in said supplemental
documents and a predetermined number of matches of select content in said
further supplemental documents.
42. A method of searching as claimed in claim 41 including resetting said
n priorities in said hierarchical taxonomic system to m priorities in
said hierarchical taxonomic system and repeating the step of gathering
further supplemental documents if one expanding search parameter is not
met from the group of expanding search parameters including said further
supplemental documents do not exceed a predetermined number, said further
supplemental documents do not include a data elements matching a
predetermined number of select content, a predetermined number of data
elements from said further supplemental documents which do not fulfill a
predetermined number of priorities in said hierarchical taxonomic system,
and data elements in said further supplemental documents do not met
predetermined contextual rules or algorithms related to said select
content.
43. A method of searching as claimed in claim 42 including:identifying and
tagging data elements in said input data which match said select content
and further identifying and tagging the matching select content data
elements with said hierarchical taxonomic system;identifying matching
data elements which fulfill predetermined contextual rules or algorithms
in said input data and incrementing, decrementing or otherwise tagging
contextually matching data elements;wherein contextually matching data
elements occurs prior to said identifying data elements with n priorities
in said hierarchical taxonomic system and said searching said data
collections with said non-matching data elements and said matching data
elements.
44. A method of searching as claimed in claim 38 including increasing the
bandwidth of said searching said data collections by adding p number of
select content to said search in addition to said non-matching data
elements.
45. A method of searching as claimed in claim 38 including filtering out
noise data elements from said non-matching data elements prior to
searching said data collections.
46. A method of searching as claimed in claim 38 including providing a
client computer and a server computer, communicatively coupled together,
in a web-based server-client computer system and the method includes
gathering input data using said client computer in said web-based
server-client computer system; forwarding input data to said server
computer; compiling said data elements from said supplemental documents
and said supplemental documents on said server computer and permitting
access thereto via said client computer; and, employing said server
computer to:identify input data elements with said hierarchical taxonomic
system, identify non-matching data elements, search said data
collections, gather supplemental documents, identify supplemental
document data elements with said hierarchical taxonomic system, and
compile supplemental document data elements and supplemental documents
into said hierarchical taxonomic system.
47. A computer readable medium containing programming instructions for
computerized searching through electronic data collections, represented
by databases or data structures, to expand a user's knowledge about
select content represented by one or more predetermined words,
characters, images or data objects, with a prioritized hierarchical
taxonomic system encompassing some select content, comprising:gathering
input data represented by a source document or a data stream, said input
data having data elements therein;identifying data elements in said input
data with said hierarchical taxonomic system and identifying non-matching
data elements which do not match said select content;searching said data
collections with said non-matching data elements and gathering
supplemental documents based thereon.
48. A computer readable medium containing programming instructions for
searching as claimed in claim 47 including identifying data elements in
said supplemental documents with said hierarchical taxonomic system;
andcompiling either the data elements from said supplemental documents or
the supplemental documents into a representation of said hierarchical
taxonomic system in some predetermined order.
49. A computer readable medium containing programming instructions for
searching as claimed in claim 47 wherein identifying data elements in
said input data tags said input data elements with priorities and classes
based upon said hierarchical taxonomic system.
50. A computer readable medium containing programming instructions for
searching as claimed in claim 47 including:identifying said select
content with said hierarchical taxonomic system;identifying data elements
in said input data which match said select content;identifying data
elements with n priorities in said hierarchical taxonomic system and
searching said data collections with said non-matching data elements and
said matching data elements for said n priorities;gathering further
supplemental documents based thereon.
51. A computer readable medium containing programming instructions for
searching as claimed in claim 47 includingidentifying data elements in
said supplemental documents with said hierarchical taxonomic system;
andending said searching of said data collections upon an end search
event from the group of end search events which include a predetermined
time, a predetermined number of supplemental documents, a predetermined
number of data elements from said supplemental documents which fulfill a
predetermined number of priorities in said hierarchical taxonomic system,
and a predetermined number of matches of select content in said
supplemental documents.
52. A computer readable medium containing programming instructions for
searching as claimed in claim 50 includingidentifying data elements in
all said supplemental documents with said hierarchical taxonomic system;
andending said searching of said data collections upon an end search
event from the group of end search events which include a predetermined
time, a predetermined number of supplemental documents, a predetermined
number of further supplemental documents, a predetermined number of data
elements from said supplemental documents which fulfill a predetermined
number of priorities in said hierarchical taxonomic system, a
predetermined number of data elements from said further supplemental
documents which fulfill a predetermined number of priorities in said
hierarchical taxonomic system, a predetermined number of matches of
select content in said supplemental documents and a predetermined number
of matches of select content in said further supplemental documents.
53. A computer readable medium containing programming instructions for
searching as claimed in claim 52 including resetting said n priorities in
said hierarchical taxonomic system to m priorities in said hierarchical
taxonomic system and repeating the programming step of gathering further
supplemental documents if one expanding search parameter is not met from
the group of expanding search parameters including said further
supplemental documents do not exceed a predetermined number, said further
supplemental documents do not include a data elements matching a
predetermined number of select content, a predetermined number of data
elements from said further supplemental documents which do not fulfill a
predetermined number of priorities in said hierarchical taxonomic system,
and data elements in said further supplemental documents do not met
predetermined contextual rules or algorithms related to said select
content.
54. A computer readable medium containing programming instructions for
searching as claimed in claim 53 wherein m is greater than n priorities.
55. A computer readable medium containing programming instructions for
searching as claimed in claim 50 including:identifying data elements in
said input data which match said select content and further identifying
the matching select content data elements with said hierarchical
taxonomic system;identifying matching data elements which fulfill
predetermined contextual rules or algorithms in said input data and
incrementing, decrementing or otherwise tagging contextually matching
data elements;wherein contextually matching data elements occurs prior to
said identifying data elements with n priorities in said hierarchical
taxonomic system and said searching said data collections with said
non-matching data elements and said matching data elements.
56. A computer readable medium containing programming instructions for
searching as claimed in claim 47 including:identifying data elements in
said supplemental documents with said hierarchical taxonomic
system;compiling either the data elements from said supplemental
documents or the supplemental documents into a map, display or tree
representation of said hierarchical taxonomic system.
57. A computer readable medium containing programming instructions for
searching as claimed in claim 56 including compiling said map or tree and
changing said prioritized hierarchical taxonomic when said map or tree
representations of supplemental document data elements or supplemental
documents exceeds a certain map or tree parameter.
58. A computer readable medium containing programming instructions for
searching as claimed in claim 57 wherein said map or tree parameter is a
number of branches.
59. A computer readable medium containing programming instructions for
searching as claimed in claim 47 including increasing the bandwidth of
said searching said data collections by adding p number of select content
to said search in addition to said non-matching data elements.
60. A computer readable medium containing programming instructions for
searching as claimed in claim 47 including filtering out noise data
elements from said non-matching data elements prior to searching said
data collections.
61. A computer readable medium containing programming instructions for
searching as claimed in claim 47 including providing a client computer
and a server computer, communicatively coupled together, in a web-based
server-client computer system and the method includes gathering input
data using said client computer in said web-based server-client computer
system.
62. A computer readable medium containing programming instructions for
searching as claimed in claim 61 includes compiling said data elements
from said supplemental documents and said supplemental documents on said
server computer in said web-based server-client computer system and
permitting access thereto via said client computer.
63. A computer readable medium containing programming instructions for
searching as claimed in claim 61 including forwarding input data to said
server computer and, employing said server computer to:identify input
data elements with said hierarchical taxonomic system, identify
non-matching data elements, search said data collections, and gather
supplemental documents.
64. A computer readable medium containing programming instructions for
searching as claimed in claim 50 including:identifying data elements in
all said supplemental documents with said hierarchical taxonomic
system;compiling either the data elements from said supplemental
documents or the supplemental documents into a map, display or tree
representation of said hierarchical taxonomic system.
65. A computer readable medium containing programming instructions for
searching as claimed in claim 64 wherein identifying data elements in
said input data tags said input data elements with priorities and classes
based upon said hierarchical taxonomic system.
66. A computer readable medium containing programming instructions for
searching as claimed in claim 65 including ending said searching of said
data collections upon an end search event from the group of end search
events which include a predetermined time, a predetermined number of
supplemental documents, a predetermined number of further supplemental
documents, a predetermined number of data elements from said supplemental
documents which fulfill a predetermined number of priorities in said
hierarchical taxonomic system, a predetermined number of data elements
from said further supplemental documents which fulfill a predetermined
number of priorities in said hierarchical taxonomic system, a
predetermined number of matches of select content in said supplemental
documents and a predetermined number of matches of select content in said
further supplemental documents.
67. A computer readable medium containing programming instructions for
searching as claimed in claim 66 including resetting said n priorities in
said hierarchical taxonomic system to m priorities in said hierarchical
taxonomic system and repeating the programming step of gathering further
supplemental documents if one expanding search parameter is not met from
the group of expanding search parameters including said further
supplemental documents do not exceed a predetermined number, said further
supplemental documents do not include a data elements matching a
predetermined number of select content, a predetermined number of data
elements from said further supplemental documents which do not fulfill a
predetermined number of priorities in said hierarchical taxonomic system,
and data elements in said further supplemental documents do not met
predetermined contextual rules or algorithms related to said select
content.
68. A computer readable medium containing programming instructions for
searching as claimed in claim 67 including:identifying data elements in
said input data which match said select content and further identifying
the matching select content data elements with said hierarchical
taxonomic system;identifying matching data elements which fulfill
predetermined contextual rules or algorithms in said input data and
incrementing, decrementing or otherwise tagging contextually matching
data elements;wherein contextually matching data elements occurs prior to
said identifying data elements with n priorities in said hierarchical
taxonomic system and said searching said data collections with said
non-matching data elements and said matching data elements.
69. A computer readable medium containing programming instructions for
searching as claimed in claim 68 including increasing the bandwidth of
said searching said data collections by adding p number of select content
to said search in addition to said non-matching data elements.
70. A computer readable medium containing programming instructions for
searching as claimed in claim 69 including filtering out noise data
elements from said non-matching data elements prior to searching said
data collections.
71. A computer readable medium containing programming instructions for
searching as claimed in claim 70 including providing a client computer
and a server computer, communicatively coupled together, in a web-based
server-client computer system and the method includes gathering input
data using said client computer in said web-based server-client computer
system; forwarding input data to said server computer; compiling said
data elements from said supplemental documents and said supplemental
documents on said server computer and permitting access thereto via said
client computer; and, employing said server computer to:identify input
data elements with said hierarchical taxonomic system, identify
non-matching data elements, search said data collections, gather
supplemental documents, identify supplemental document data elements with
said hierarchical taxonomic system, and compile supplemental document
data elements and supplemental documents into said hierarchical taxonomic
system.
72. A computer readable medium containing programming instructions for
computerized searching through electronic data collections, represented
by databases or data structures, to expand a user's knowledge about
select content represented by one or more predetermined words,
characters, images or data objects therein, comprising:employing a
prioritized hierarchical taxonomic system which encompasses some select
content;gathering input data represented by a source document or a data
stream, said input data having data elements therein;tagging each data
element in said input data based upon said hierarchical taxonomic
system;searching through said data collections and gathering supplemental
documents having non-matching data elements from said data input which do
not match said select content;tagging each data element in said
supplemental documents based upon said hierarchical taxonomic system;
andcompiling either the data elements from said supplemental documents or
the supplemental documents into a representation of said hierarchical
taxonomic system.
73. A computer readable medium containing programming instructions for
searching as claimed in claim 72 including:identifying and tagging said
select content based upon said hierarchical taxonomic system;identifying
and tagging data elements in said input data which match said select
content;identifying and tagging data elements with n priorities in said
hierarchical taxonomic system and searching said data collections with
said non-matching data elements and said matching data elements for said
n priorities;gathering further supplemental documents based
thereon;identifying and tagging data elements in said further
supplemental documents with said hierarchical taxonomic system;
andcompiling either the data elements from said further supplemental
documents or the further supplemental documents into said representation
of said hierarchical taxonomic system.
74. A computer readable medium containing programming instructions for
searching as claimed in claim 73 said compiling is listing of
supplemental document data elements or the supplemental documents
themselves, from a higher to a lower priority based upon said
hierarchical taxonomic system.
75. A computer readable medium containing programming instructions for
searching as claimed in claim 73 wherein identifying and tagging data
elements in said input data tags elements with priorities and classes
based upon said hierarchical taxonomic system.
76. A computer readable medium containing programming instructions for
searching as claimed in claim 73 including ending said searching of said
data collections upon an end search event from the group of end search
events which include a predetermined time, a predetermined number of
supplemental documents, a predetermined number of further supplemental
documents, a predetermined number of data elements from said supplemental
documents which fulfill a predetermined number of priorities in said
hierarchical taxonomic system, a predetermined number of data elements
from said further supplemental documents which fulfill a predetermined
number of priorities in said hierarchical taxonomic system, a
predetermined number of matches of select content in said supplemental
documents and a predetermined number of matches of select content in said
further supplemental documents.
77. A computer readable medium containing programming instructions for
searching as claimed in claim 76 including resetting said n priorities in
said hierarchical taxonomic system to m priorities in said hierarchical
taxonomic system and repeating the programming step of gathering further
supplemental documents if one expanding search parameter is not met from
the group of expanding search parameters including said further
supplemental documents do not exceed a predetermined number, said further
supplemental documents do not include a data elements matching a
predetermined number of select content, a predetermined number of data
elements from said further supplemental documents which do not fulfill a
predetermined number of priorities in said hierarchical taxonomic system,
and data elements in said further supplemental documents do not met
predetermined contextual rules or algorithms related to said select
content.
78. A computer readable medium containing programming instructions for
searching as claimed in claim 77 including:identifying and tagging data
elements in said input data which match said select content and further
identifying and tagging the matching select content data elements with
said hierarchical taxonomic system;identifying matching data elements
which fulfill predetermined contextual rules or algorithms in said input
data and incrementing, decrementing or otherwise tagging contextually
matching data elements;wherein contextually matching data elements occurs
prior to said identifying data elements with n priorities in said
hierarchical taxonomic system and said searching said data collections
with said non-matching data elements and said matching data elements.
79. A computer readable medium containing programming instructions for
searching as claimed in claim 73 including increasing the bandwidth of
said searching said data collections by adding p number of select content
to said search in addition to said non-matching data elements.
80. A computer readable medium containing programming instructions for
searching as claimed in claim 73 including filtering out noise data
elements from said non-matching data elements prior to searching said
data collections.
81. A computer readable medium containing programming instructions for
searching as claimed in claim 73 including providing a client computer
and a server computer, communicatively coupled together, in a web-based
server-client computer system and the method includes gathering input
data using said client computer in said web-based server-client computer
system; forwarding input data to said server computer; compiling said
data elements from said supplemental documents and said supplemental
documents on said server computer and permitting access thereto via said
client computer; and, employing said server computer to:identify input
data elements with said hierarchical taxonomic system, identify
non-matching data elements, search said data collections, gather
supplemental documents, identify supplemental document data elements with
said hierarchical taxonomic system, and compile supplemental document
data elements and supplemental documents into said hierarchical taxonomic
system.
82. A computer readable medium containing programming instructions for
computerized searching through electronic data collections, represented
by databases or data structures, to expand a user's knowledge about
select content represented by one or more predetermined words,
characters, images or data objects, with a prioritized hierarchical
taxonomic system encompassing some select content, comprising:selecting n
priorities from said prioritized hierarchical taxonomic system;gathering
input data represented by a source document or a data stream, said input
data having data elements therein;identifying data elements in said input
data with said hierarchical taxonomic system and identifying data
elements representing supplemental select content corresponding to said n
priorities therefrom;searching said data collections with said
supplemental select content data elements and gathering supplemental
documents based thereon.
83. A computer readable medium containing programming instructions for
searching as claimed in claim 82 including:identifying non-matching data
elements from said input data which do not match said select
content;searching said data collections with said non-matching data
elements and gathering additional supplemental documents based thereon.
84. A computer readable medium containing programming instructions for
searching as claimed in claim 82 including:identifying said select
content with said hierarchical taxonomic system;identifying data elements
in said input data which match said select content;identifying select
content with said n priorities in said hierarchical taxonomic system and
searching said data collections with said non-matching data elements and
said matching data elements for said n priorities and said supplemental
select content data elements;gathering further supplemental documents
based thereon.
85. A computer readable medium containing programming instructions for
searching as claimed in claim 84 said compiling is listing of all
supplemental document data elements or all supplemental documents
themselves, from a higher to a lower priority based upon said
hierarchical taxonomic system.
86. A computer readable medium containing programming instructions for
searching as claimed in claim 85 including:identifying data elements in
said supplemental documents and said additional supplemental documents
and said further supplemental documents with said hierarchical taxonomic
system; andcompiling at least one representation from the group of
representations including the data elements from all supplemental
documents grouped based upon said hierarchical taxonomic system, all
supplemental documents grouped based upon said hierarchical taxonomic
system and supplemental select content grouped based upon said
hierarchical taxonomic system.
87. A computer readable medium containing programming instructions for
searching as claimed in claim 84 including ending said searching of said
data collections upon an end search event from the group of end search
events which include a predetermined time, a predetermined number of
supplemental documents, a predetermined number of further supplemental
documents, a predetermined number of additional supplemental documents, a
predetermined number of data elements from said supplemental documents
which fulfill a predetermined number of priorities in said hierarchical
taxonomic system, a predetermined number of data elements from said
further supplemental documents which fulfill a predetermined number of
priorities in said hierarchical taxonomic system, a predetermined number
of data elements from said additional supplemental documents which
fulfill a predetermined number of priorities in said hierarchical
taxonomic system, a predetermined number of matches of select content in
said supplemental documents, a predetermined number of matches of select
content in said additional supplemental documents and a predetermined
number of matches of select content in said further supplemental
documents.
88. A computer readable medium containing programming instructions for
searching as claimed in claim 87 including resetting said n priorities in
said hierarchical taxonomic system to m priorities in said hierarchical
taxonomic system and repeating the programming step of gathering
supplemental documents if one expanding search parameter is not met from
the group of expanding search parameters including said supplemental
documents do not exceed a predetermined number, said supplemental
documents do not include a data elements matching a predetermined number
of select content therein, a predetermined number of data elements from
said supplemental documents which do not fulfill a predetermined number
of priorities in said hierarchical taxonomic system, and data elements in
said supplemental documents do not met predetermined contextual rules or
algorithms related to said select content.
89. A computer readable medium containing programming instructions for
searching as claimed in claim 84 including:identifying data elements in
said input data which match said select content and further identifying
the matching select content data elements with said hierarchical
taxonomic system;identifying matching data elements which fulfill
predetermined contextual rules or algorithms in said input data and
incrementing, decrementing or otherwise tagging contextually matching
data elements;wherein contextually matching data elements occurs prior to
said identifying data elements with n priorities in said hierarchical
taxonomic system and said searching said data collections with said
non-matching data elements and said matching data elements.
90. A computer readable medium containing programming instructions for
searching as claimed in claim 84 including increasing the bandwidth of
said searching said data collections by adding p number of select content
to said search in addition to said non-matching data elements.
91. A computer readable medium containing programming instructions for
searching as claimed in claim 84 including filtering out noise data
elements from said non-matching data elements prior to searching said
data collections.
92. A computer readable medium containing programming instructions for
searching as claimed in claim 84 including providing a client computer
and a server computer, communicatively coupled together, in a web-based
server-client computer system and the method includes gathering input
data using said client computer in said web-based server-client computer
system; forwarding input data to said server computer; compiling all
supplemental documents on said server computer and permitting access
thereto via said client computer; and, employing said server computer
to:identify input data elements with said hierarchical taxonomic system,
identify non-matching data elements, identify matching data elements,
search said data collections, and gather supplemental documents.
93. A method of computerized searching through electronic data collections
to expand a user's knowledge about select content represented by one or
more predetermined words, characters, images or data objects, with a
ranked categorical classification system encompassing some select
content, comprising:selecting a plurality of classifications from said
classification system;gathering input data represented by a source
document or a data stream, said input data having data elements
therein;identifying input data elements corresponding to said plurality
of classifications to derive supplemental select content;searching said
data collections with said supplemental select content and gathering
supplemental documents based thereon;compiling said supplemental
documents based upon said classification system.
94. A method of searching as claimed in claim 93 including:filtering
non-matching input data elements which do not match said select
content;searching said data collections with said non-matching data
elements and gathering additional supplemental documents based thereon;
andcompiling the additional supplemental document with said
classification system.
95. A method of searching as claimed in claim 94 including:searching said
data collections with some select content and gathering further
supplemental documents based thereon; andcompiling said further
supplemental documents with said classification system.
96. A method of searching as claimed in claim 93 including one or
expanding and contracting a bandwidth of said search by adding select
content, altering the identification of input data elements corresponding
to said plurality of classifications, and applying contextual filters to
said supplemental select content.
97. A method of searching as claimed in claim 93 wherein the number and
classification of said supplemental documents adaptively effects the
identification of supplemental select content with said input data
elements and said plurality of classifications.
98. A method of searching as claimed in claim 97 including one of a
feedback attenuation filter to narrow said searching said data
collections and a feedback expansion filter to broaden said searching
said data collections, said feedback controlled by one of:said
supplemental documents exceed or do not exceed a predetermined
number,said supplemental documents include or do not include a data
elements matching a predetermined number of select content therein,a
predetermined number of data elements from said supplemental documents
which do or do not fulfill a predetermined number of priorities in said
hierarchical taxonomic system, anddata elements in said supplemental
documents which do or do not met predetermined contextual rules or
algorithms related to said select content.
99. A method of computerized searching through electronic data collections
to expand a user's knowledge about select content represented by one or
more predetermined words, characters, images or data objects, with a
ranked categorical classification system encompassing some select
content, comprising:selecting a plurality of classifications from said
classification system;gathering input data represented by a source
document or a data stream, said input data having data elements
therein;identifying input data elements corresponding to said plurality
of classifications to derive supplemental select content;searching said
data collections with said supplemental select content and gathering
supplemental documents based thereon;compiling said supplemental
documents based upon said classification system;and one or more of the
following expansion and attenuation functions including:filtering
non-matching input data elements which do not match said select content
and searching said data collections with said non-matching data elements
and gathering supplemental documents based thereon;searching said data
collections with some select content and gathering supplemental documents
based thereon;expanding a bandwidth of said search by adding select
content, altering the identification of input data elements corresponding
to said plurality of classifications, and applying contextual filters to
said supplemental select content;contracting a bandwidth of said search
by adding select content, altering the identification of input data
elements corresponding to said plurality of classifications, and applying
contextual filters to said supplemental select content;narrowing said
searching said data collections with a feedback attenuation filter
controlled by one of: said supplemental documents exceed a predetermined
number, said supplemental documents exceed data elements matching a
predetermined number of select content therein, a predetermined number of
data elements from said supplemental documents exceed a predetermined
number of priorities in said classification system, and data elements in
said supplemental documents exceed predetermined contextual rules or
algorithms related to said select content; andexpanding said searching
said data collections with a feedback expansion filter controlled by one
of: said supplemental documents do not exceed a predetermined number,
said supplemental documents do not include a data elements matching a
predetermined number of select content therein, a predetermined number of
data elements from said supplemental documents do not fulfill a
predetermined number of priorities in said hierarchical taxonomic system,
and data elements in said supplemental documents do not met predetermined
contextual rules or algorithms related to said select content.
100. A method of searching as claimed in claim 99 including ending said
searching upon an end search event from the group of end search events
which include a predetermined time, a predetermined number of
supplemental documents, a predetermined number of data elements from said
supplemental documents which fulfill a predetermined number of priorities
in said classification system, a predetermined number of matches of
select content in said supplemental documents.
101. A method of searching as claimed in claim 93 including providing a
client computer and a server computer, communicatively coupled together,
in a web-based server-client computer system and the method includes
gathering input data using said client computer in said web-based
server-client computer system; forwarding input data to said server
computer; compiling said supplemental documents on said server computer
and permitting access thereto via said client computer.
102. A method of searching as claimed in claim 99 including providing a
client computer and a server computer, communicatively coupled together,
in a web-based server-client computer system and the method includes
gathering input data using said client computer in said web-based
server-client computer system; forwarding input data to said server
computer; compiling said supplemental documents on said server computer
and permitting access thereto via said client computer.
103. A method of searching as claimed in claim 99 wherein said expansion
and attenuation functions are modules selected upon initialization.
104. A method of computerized searching through electronic data
collections, represented by databases or data structures, utilizing a
prioritized hierarchical classification system comprising:selecting n
priorities from said prioritized hierarchical classification
system;extracting search terms from input data represented by a source
document or a data stream, by identifying data elements in said input
data with said classification system corresponding to said n priorities
therefrom;searching said data collections with said search terms and
gathering supplemental documents based thereon;resetting said n
priorities in said classification system to m priorities in said
classification system;extracting secondary search terms from input data
elements with said classification system corresponding to said m
priorities and repeating the search of said data collections with said
secondary search terms and gathering secondary supplemental documents;
and,determining whether said supplemental documents and said secondary
supplemental documents exhibit convergent or divergent characteristics.
105. A method of searching as claimed in claim 104 wherein extracting
secondary search terms and repeating resetting priorities is based upon a
convergent or divergent search command.
106. A method of searching as claimed in claim 104 including repeating
extracting search terms and searching if one search end parameter is not
met from the group of search end parameters including:all supplemental
documents do not exceed a predetermined number,a predetermined number of
data elements from all supplemental documents do not fulfill a
predetermined number of priorities in said classification system,a lapse
of a predetermined time,a predetermined number of data elements from all
supplemental documents do not fulfill a predetermined number of
priorities in said classification system.
107. A method of searching as claimed in claim 106 including filtering out
noise data elements from said input data represented by a source document
or a data stream prior to searching said data collections.
108. A method of searching as claimed in claim 107 including providing a
client computer and a server computer, communicatively coupled together,
in a web-based server-client computer system and the method
includesgathering said input data using said client computer in said
web-based server-client computer system;forwarding said input data to
said server computer;compiling all supplemental documents on said server
computer and permitting access thereto via said client computer;
and,employing said server computer to:extract search terms and gather
supplemental documents.
109. A method of searching as claimed in claim 108 wherein said
prioritized hierarchical classification system is a hierarchical
taxonomic system.
110. A method of computerized searching through electronic data
collections, represented by databases or data structures, utilizing a
prioritized hierarchical classification system comprising:selecting n
priorities from said prioritized hierarchical classification
system;extracting search terms from input data represented by a source
document or a data stream, by identifying data elements in said input
data with said classification system corresponding to said n priorities
therefrom;searching said data collections with said search terms and
gathering supplemental documents based thereon; and,extracting secondary
search terms as data elements from said supplemental documents with said
classification system corresponding to said n priorities and repeating
the search of said data collections with said secondary search terms and
gathering secondary supplemental documents.
111. A method of searching as claimed in claim 110 including repeating
extracting search terms and searching if one search end parameter is not
met from the group of search end parameters includingall supplemental
documents do not exceed a predetermined number,a predetermined number of
data elements from all supplemental documents do not fulfill a
predetermined number of priorities in said classification system,a lapse
of a predetermined time,a predetermined number of data elements from all
supplemental documents do not fulfill a predetermined number of
priorities in said classification system.
112. A method of searching as claimed in claim 111 including filtering out
noise data elements from said input data represented by said source
document or data stream prior to searching said data collections.
113. A method of searching as claimed in claim 112 including providing a
client computer and a server computer, communicatively coupled together,
in a web-based server-client computer system and the method
includesgathering said input data using said client computer in said
web-based server-client computer system;forwarding said input data to
said server computer;compiling all supplemental documents on said server
computer and permitting access thereto via said client computer;
and,employing said server computer to:extract search terms and gather
supplemental documents.
114. A method of searching as claimed in claim 113 wherein said
prioritized hierarchical classification system is a hierarchical
taxonomic system.
115. A method of computerized searching through electronic data
collections, represented by databases or data structures, utilizing a
prioritized hierarchical classification system comprising:selecting n
priorities from said prioritized hierarchical classification
system;extracting search terms from input data represented by a source
document or a data stream, by identifying data elements in said input
data with said classification system corresponding to said n priorities
therefrom;searching said data collections with said search terms and
gathering supplemental documents based thereon.
116. A method of searching as claimed in claim 115 includingdetermining
whether supplemental documents for n priorities and a second search for m
priorities with extracted search terms exhibit convergent or divergent
characteristics.
117. A method of searching as claimed in claim 115 including extracting
secondary search terms and resetting n priorities to m priorities is
based upon a convergent or divergent search command.
118. A method of searching as claimed in claim 117 including repeating
extracting search terms and searching if one search end parameter is not
met from the group of search end parameters including:all supplemental
documents do not exceed a predetermined number,a predetermined number of
data elements from all supplemental documents do not fulfill a
predetermined number of priorities in said classification system,a lapse
of a predetermined time,a predetermined number of data elements from all
supplemental documents do not fulfill a predetermined number of
priorities in said classification system.
119. A method of searching as claimed in claim 118 including filtering out
noise data elements from said input data represented by a source document
or a data stream prior to searching said data collections.
Description
[0001]The present invention relates to an information life cycle search
engine and method to process, search, expand and secure information and
data objects in an electronic format from and in computer systems and
networks.
BACKGROUND OF THE INVENTION
[0002]The extensive use of computers and the continued expansion of
telecommunications networks, particularly the Internet, enable
businesses, governments and individuals to create documents and data
streams (whether text, characters, icons, images or a combination
thereof, sound, video, and data objects in general, sometimes referred to
generally herein as "data objects") and distribute those documents and
data streams widely to others. Although the production, distribution and
publication of source documents (which includes data streams and other
input data) is generally beneficial to society, there is a need to expand
the user's knowledge relative to certain select content ("SC") such as
critical words, terms, images, characters or security sensitive words,
characters, images or sound. Concerns regarding SC and security sensitive
items (for example, an individual's social security number, credit
history, medical history, business trade secrets and financial data) is
an important issue in society. In another words, individuals and
businesses have a greater concern regarding maintaining the secrecy of
certain information in view of the increasing ease of distribution of
documents through computer networks and the Internet. Equally, the user
may want to increase his or her knowledge about SC beyond the common
input of search terms into a search engine such as Google.
The Etiology of Information
[0003]Security, privacy and information sharing is predicated by the
representation of the structure information. The structure has evolved in
usage over time from simple linear formats to complex hierarchical trees
typified by tags, metadata and modifiers. Although the predominant
information delivery and information storage format is in a linear data
stream, the internal structure or representations include all possible
linear, field defined, metric, tree, compound and combined layouts. In
other words, while data is delivered in a linear stream, the complexity
of internal structure resolves into specific documented patterns,
self-documenting meta data formats like HTML or XML, defined utilitarian
and purpose-oriented formats like database management system (DBMS), ODF
(open document format) or proprietary document object models (such as the
Microsoft DOM model). The combination and recombination of metadata in
source documents or data streams complicates finding, location, and
expanding one's knowledge base of SC. The issue of the internal format is
important to the regulation, interpretation and application of
information.
OBJECTS OF THE INVENTION
[0004]It is an object of the invention to provide an expansive search
engine which can be used to gather additional documents to expand the
user's knowledge about select content, terms, words, ideas and data
objects.
[0005]It is another object of the invention to provide a number of search
engine tools which can be selected and built up into a lego-type search
engine to gather information.
[0006]It is an additional object to employ convergent search tools,
divergent search tools, no-match searches, select content tools, content,
contextual and hierarchical taxonomic system tools in a search engine.
SUMMARY OF THE INVENTION
[0007]The computerized search method operates on electronic data
collections, represented by databases or data structures, to expand a
user's knowledge about select content represented by one or more
predetermined words, characters, images or data objects. The method or
engine uses a prioritized hierarchical taxonomic system encompassing some
select content. The SC falls within the taxonomic classification system.
Input data is gathered, data elements therein are identified and
classified with the hierarchical taxonomic system and non-matching data
elements which do not match the select content (SC) are identified. Data
collections are searched, in one embodiment, with the non-matching data
elements. In another embodiment, or as an adjunct to the knowledge
expander (KE) search engine process, the system identifies input data
elements as supplemental select content search terms which supplement
(supple) SC corresponds to said n number of SC, preferably n top
priorities from the hierarchical taxonomic system. In a further
development, certain SC, particularly n top prioritized SC from the
hierarchical taxonomic system, are added as search terms to the no-match
search and supple SC search. Therefore, search tools of no-match, SC from
input data and SC from the taxonomic classes are employed. As another
search tool, the hierarchical taxonomic system is applied on the input
data and the process extracts n top priority search terms from the input
data, without reference to the SC. Supplemental documents are gathered
from the data collections, are classified with the hierarchical taxonomic
system and mapped represented or charted based thereon. To compliment the
KE search process, contextual rules or algorithms operating on the SC in
the input data are employed and the hierarchical taxonomic class priority
for the supple SC is incremented, decremented, or otherwise tagged as
being important to the search, which search is then automatically
processed by the system. A computerized medium carrying programming
instructions is also discussed herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008]Further objects and advantages of the present invention can be found
in the detailed description of the preferred embodiments when taken in
conjunction with the accompanying drawings in which:
[0009]FIG. 1 diagrammatically illustrates deconstruction of an input
document into segments.
[0010]FIG. 2 diagrammatically illustrates the information life cycle
knowledge expander (KE) search engine process.
[0011]FIG. 3 diagrammatically illustrates the KE process of using search
terms, data collection and depth-of-search control.
[0012]FIG. 4 diagrammatically illustrates the KE process for the primary
search results.
[0013]FIG. 5 diagrammatically illustrates the KE process for the output
representation of search term data and search result data of supplemental
documents.
[0014]FIG. 6 diagrammatically illustrates a filter program.
[0015]FIG. 7 diagrammatically illustrates an editor program.
[0016]FIGS. 8A, 8B. 8C and 8D diagrammatically illustrate extraction of
select content (SC) and a hierarchical taxonomic system based upon TS, T,
C and UC security code classifications.
[0017]FIG. 9 diagrammatically illustrates a data object tree or map for a
document model (DOM).
[0018]FIG. 10 diagrammatically illustrates varies metadata levels in the
DOM.
[0019]FIG. 11 diagrammatically illustrates a DOM editor process.
[0020]FIG. 12 diagrammatically illustrates another DOM editor process.
[0021]FIG. 13 diagrammatically illustrates a basic KE search process.
[0022]FIG. 14 diagrammatically illustrates an expanded KE search process.
[0023]FIG. 15 diagrammatically illustrates KE search process options.
[0024]FIG. 16 diagrammatically illustrates a hardware implementation for
the KE search system.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0025]As discussed above, the etiology of information involves a complex
hierarchical trees of various types of data such as tags, metadata, and
modifiers. Specifically to the issue of semiotic (words and objects in
language) and pragmatic (words relationship to user) meaning, the
internal format of information is important to its regulation,
interpretation and the further use and application of the information. A
discussion of the differentiation of the data as to content, context and
concept is discussed later herein. The abstraction of information is also
relevant here. For example, the database scheme DBMS and the DOM style
sheets and the internal metadata all modify the encoding, format,
structure, purpose and usage of the information. Links and references
establish contextual meaning, just as the environment establishes meaning
relevant to the multiple granular data in the information stream or
document. Metadata can validate or invalidate meaning in the text of the
data, for example, a format or a replace meta data element could indicate
the complete deletion of material, the connection between topic and
footnote, or modification with non-usage of the data. To perceive data
only in context without the effects of format, purpose, reference and
access rights, potentially misinterprets the importance of context and
concept which may result in a misunderstanding of the information.
Metasearch Engine for Knowledge Discovery: Search Convergence and
Divergence
[0026]As an overview, the system's metasearch engine is a novel and useful
service to uncover unknown or concealed relationships, delivery of higher
relevancy search results and connecting the dots. Although metasearch is
derived from the technology for security and information sharing, it is
useful to augment standard Internet search engines because it enables
both search divergence for knowledge discovery and search convergence for
assessing information integrity, the full process necessary for
connecting the dots. Search divergence extends search with aggregation,
inference and data-to-data interaction beyond simple content into the
realm of context and concept. The system is a standalone system or can be
integrated to process results of other search engines. Presented is a
process and a technology for knowledge discovery, security and
information sharing that is based on management and control granular data
content to secure information while also providing interdomain
information sharing. The basic assumption is that all data/content must
be monitored, analyzed and categorized at the granular data level for
basic security risk management and control. Risk measurement is driven by
information recognition, multi-tier overlapping hierarchical meta-tagging
technology, and granularization. This same technology, as a standalone
deliverable product or service-oriented architecture, is applicable to
knowledge discovery, uncovering unknown or concealed relationships, and
for connecting the dots. The metasearch process is a serialized and
repetitive process of knowledge discovery, usage of the innovation's
secure engine, and the application of granular data content, as a
standalone service for knowledge discovery. Because the risk measurement
is driven by 1) content, 2) context, and 3) concept, it is essential that
the system recognize content not only in isolation to itself as a data
stream or document but also its value when combined with other private
and public sources through aggregation, inference and data-to-data
interaction. The metasearch is useful to augment standard Internet search
engines because it enables both: (1) search divergence for knowledge
discovery and (2) search convergence for assessing information integrity.
It completes the process necessary for discovering new knowledge to
connect the dots.
Initial Process--Metasearch Engine and Knowledge Expander
[0027]The automated metasearch KE search engine takes as source any data
stream or document.
[0028]The metasearch engine automatically parses the source for content
recognition and assessment of granular content. This granular content is
automatically metatagged by the search engine--system for range
(contextual relevancy), sensitivity level (prioritized),
compartmentalization (sensitivity levels at the same hierarchical level,
but laterally or orthogonally disposed to one another, see TS-Navy
compared to TS-Army wherein the Navy and the Army are compartments),
categories (hierarchical taxonomic classes), relevancy (a type of
sensitivity), and other multi-tiered overlapping hierarchical factors.
The granularized data content becomes the search terms, while these
metatags become metafilters for the knowledge discovery process. These
search terms are submitted to any number of extant tools and services
(other search engines, such as Google, Yahoo), for locating other
relevant supplemental data streams, documents, databases, local,
Intranet, Internet, and public or private data stores. Likely tools
include Internet search engines, data mining tools, database query
engines, data collections, indices and other knowledge management (KM)
applications or services, which are generally classified as data
structures or data collections herein. Inductive and deductive tools are
advantageous too. Most tools can be easily integrated with the system,
linked through service-oriented architectures (SOA), or their results
piped into the source data steam or document (through Web 2.0 mashups).
The metasearch engine also works with standard--a priori--search terms or
keywords, like any desktop, web-enabled, or Internet search tool. This
search operation may bypass the system's secure assessment of a relevant
or representative source data stream or document.
Simple Metasearch Engine
[0029]The results from the extant tools (the results sometimes called
supplemental documents or supple does) are logged for auditing, dispersed
to maintain a legal chain-of-custody, and aggregated into a single
collated result. A List of Abbreviations is set forth in a later section.
This collated result (the source, results, reports and links or
references) is automatically submitted into the metasearch engine. Inputs
include the configuration for either 1) search convergence or 2)--search
divergence. Search convergence conforms to the standard purpose for using
Internet search engines like Google or Alta Vista because it defines,
authenticates, and validates the search terms. The first level search
results provide for an integrity check for the initial information
results in that it does not aggregate or inference the information
therein.
[0030]The divergence function, on the other hand, is all about
aggregation, inference, and data-to-data interaction because it
specifically searches for links, references, relationships, outliers, and
social networking associations to the search terms. The divergence
function will likely ignore anything overlapping the search terms in
order to expand the search. In other words, convergence increases the
depth of knowledge where divergence increases the breadth of knowledge
represented by the collated result. The divergence function explores new,
novel, unknown, and hidden connections. If you want to connect the dots,
divergence dredges new but factually uncertain information where
convergence thereafter authenticates.
[0031]The system is automatically run. The source (which is now the
aggregated result from the extant tool processes) is automatically parsed
by the metasearch engine with secondary recognition and assessment of
granular content.
Automatic Metatagging
[0032]This granular content of the source doc is automatically metatagged
for: (1) Content categories, (name, location, date, dollar amount etc);
(2) sensitivity level, ("Top Secret", "Private", "level 9"); (3)
compartmentalization, ("top secret--Finance" "Top secret logistics"); and
(4) relevancy, and other multi-tier hierarchical factors.
Automatic Secondary Tagging of a "Range"
[0033]A secondary level of metatagging may be created to define the
"range" or "the area within the document" from which the keywords for the
search will be selected. This is a simple contextual select content (SC)
operation. To create a higher level of relevancy in the search results
the system controls the granular data within the document. Only
"areas"/"ranges" with a high relevancy will become the basis for locating
keywords for the next search. Range defines the area or areas within a
document (characters from the target selected content, lines away, within
the same paragraph, on the same page, etc.), which will be the base and
platform for locating key words for the next search cycle. An analogy is
the defining the range/area for oil drilling in a suburb of the city of
Houston. All the drilling will take place only in that location. As an
example of defining a "range," we present the system's creation of
automated tear lines.
[0034]Each paragraph (the "range" can be also configured to a "sentence"
or "line" or "page") is automatically tagged based on an algorithm. The
algorithm may be "tag the paragraph based on the highest sensitivity
level assigned to any of its words; or if a group of specific words
appear in a paragraph," ("ATTA", "Hamburg" "New York" all tagged as
Secret "S" security level or importance) and tag the paragraph at a
higher sensitivity level than those words (i.e. Top Secret "TS").
[0035]The granularized selected content becomes the search terms and the
metatags become the metafilters for the systems meta search. The
automated results of the meta search process is an augmentation of
existing knowledge or a breakthrough to unknown or covert relationships.
The convergence search vets the integrity of a priori search terms, while
the divergence search generates new search terms and new references. The
metasearch is repeated, as configured, for a fixed number of times, for a
fixed period of time, until results reach an asymptote, or indefinitely.
The metasearch inputs also include the metafilters created prior to the
presentation of the aggregated search result. These metafilters define
the direction of the search by introducing "fine adjustments" to the
search process. In essence the metafilters narrow the scope of all
subsequent and serial submissions to the extant tools and services. The
search may be configured to include one or a combination of filters as
follows:
[0036](1) Selection of keywords for search based on their categories. The
system automatically categorizes each word and character. In one
embodiment, there are over 50 different categories or hierarchical
taxonomic classes. Categories may include name, location, date, dollar
amount, credit card number etc. As an example the system may be
configured to feed the search engine with words that where automatically
categorized by the system as "locations" and "people" in such an example
"Rome" "London" and "Muhammad Atta" may be selected automatically as the
keywords for the next search cycle.
[0037](2) Selection of keywords for search based on their sensitivity
level or important or assigned weight. This is the sensitivity level of
the hierarchical taxonomic classes. The system may be configured to
select as keywords for its next search only data elements that where
classified/tagged with a specific sensitivity classification/tagging. A
possible example, is an instruction to use as keywords only words that
where given "Top Secret" classification, or "private" classification or
assigned with a specific weight (select words that where assigned weight
9 out of 10).
[0038](3) Selection of keywords for search based on the specific
importance of their content. For example, use as keywords, words that
belong to a list of specific grouping of words. A list of associated
words that convey a concept or a contextual relationship and other
features and compartmentalization. Metafilters for standard search terms,
which bypass the initial system's automatic review of a source, are
optional.
MLS, Multi-Level Security-Compliant Search and Distributions
[0039]Because the system and all the extant knowledge management tools may
run in a secure system high environment, results are likely to be system
high too. However, the point of any search or knowledge exploration is to
share information and distribute it to the users at the edge. The
advantage of the system is that the new search terms, the aggregate
result, and all intermediate reports and documents are processed for
Multi Level MLS-compliant security and information sharing. Each user
will get a result based on his security level (for example, TS or S or C
or UC). Specifically, the list of new words, phrases, and other content
is either automatically assessed or manually reviewed for addition to the
system's dictionaries. Ownership sensitivity level, compartment, group,
categories, mission, relevancy, and other multitier overlapping
hierarchical metatags are applied to each discovery and distributed to
users subject to MLS compliance. The aggregate result are secured with
multiple MLS-compliant versions and distributed to the edge on a: (1) per
sensitivity-level basis version, or (2) a base redacted document with
objective security compliance for universal distribution and pushed to
the edge with the system's reconstitution services. In other words,
information sharing is implemented either through the delivery of: (1)
Multi Level Security--MLS-compliant versions, or (2) Through a base
redacted document with objective security compliance for universal
distribution with reconstitution.
[0040]Reconstitution is supported in whole or in part as: (a)
MLS-compliant results, or as (b) Step-wise reconstruction, with
defenses-in-depth MLS-compliant results i.e. controlled release layer by
layer.
[0041]These two options are also applied to all intermediate results,
reports, lists, linked or referenced sources. Of note, all links and
references, even access to public data sources can be optionally
restricted to minimize subsequent risks from aggregation, inference, and
data-to-data interaction. An agency with specific intent and knowledge is
able to use aggregation, inference, and data-to-data interaction on
public sources to create classified results. For example, if unclassified
search terms including "president assassination British visit" returned
news clippings and RSS feeds of a forthcoming presidential visit to 10
Downing Street, these public results are still unclassified but the
context is clear and may be classified.
[0042]The systems metasearch is a novel and useful standalone service to
uncover unknown or concealed relationships, and connect the dots. It is
useful to augment standard Internet search engines because it enables
both search divergence for knowledge discovery and search convergence for
assessing information integrity, the full process necessary for
connecting the dots. Search divergence extends search with aggregation,
inference and data-to-data interaction beyond simple content into the
realm of context and concept.
[0043]One of the main tools in the present knowledge expander (KE) engine
is its ability to control granular data with automatic content
analysis/filtering and tagging of all the select content SC data and
unknown data elements in every document, data stream or input document.
[0044]The knowledge expander engine: (1) automatically creates a list of
search terms; (2) automatically cleans the noise from the search term
list; (3) automatically selects from the list of search terms those that
will be sent to the next search cycle (preexisting rules for doing the
selection)--example: select for search only the items found which are in
the "names category" and "locations category"--from the list of search
terms--for example the names "Redhouse," "Kohn," "Miami Beach," and "Ft
Lauderdale" will be sent to the next search cycle; (4) conduct a
divergence search--limit set at 200 supplemental documents (supple doc).
Each supple doc goes through an automatic content analysis/filtering and
tagging of all the data elements in the document. Key words are located
in the supple docs by filtering of categories. The process cleans noise
from the extract key words from the supple does. Rules select which key
words will be fed into next search cycle. For example, if 1 of the 200
documents had the name "Bob Smith" location "Sarasota" (as long as the
rules for selection were not changed the next search will be fed with
search terms--"Bob Smith" and "Sarasota") search is expanding--a
diverging search. The last process wherein Bob Smith is added to the KE
engine is a convergence filtering process because the search results will
converge on Bob Smith.
Benefits of the Metasearch--Knowledge Expander
[0045]Typical search and data mining tools presupposes that the user
already knows part of the answer. The user must know how to phrase the
question (search terns ir query) in order to get a proper answer.
However, this is only partial solution to knowledge management in that
does not begin to address the real issues needed to connect the dots.
This knowledge management technique as called herein "convergent" because
the operator starts with a known entity and the search confirms or denies
the basic entity or gathers additional information in depth on the entity
topic. The present knowledge expander search, with various modules
operating together, automates the convergent process until no additional
information is found on the topic.
[0046]The present system and process allows knowledge management as a
divergent exploration. The basic starting point is any data stream (RSS,
blog, documents, a fire hose of data) or multiple data streams, or a
collection of search terms for an automated process. A single term
defines a manual process with a primary search term. The automated
process redacts the data stream(s) and looks for content and context of
importance creating a list of primary search terms, metadata contextual
filters, and noise reduction tools in order to expand the breath of
knowledge with new links and references. Alternate spellings,
misspellings, partial matches, duplicates, and other fuzzy technology is
used to graft depth to a convergent search or prune a divergent search.
In the case of a divergent search, the process is specifically exploring
for secondary search terms and information that is not part of the
primary search terms. Noise and duplicates are pruned. All hits and
information located with URLs, X-links, or other pointers is retained for
a chain-of-custody source-indicating data, reproducibility, and human
intelligence analysis. Consolidated results are insufficient for legal
exploration, search warrants, or plans of action, so the retention is
critical to build a rationale for action and review the relationships,
connections, and networks of people.
[0047]The search is extended with the primary search terms, the secondary
search terms, and combinations and permutations thereof. Duplicates and
non-duplicates are pruned per the selection of convergence or divergence.
Tertiary search terms and information is aggregated in the same manner as
the secondary search terms and secondary information. This process would
repeat unless manually interrupted, terminated by a timer, manually
reviewed midstream and grafted or pruned, or terminated by repetition or
lack of new information. When no new information is found both the
convergent and divergent search are stopped. The termination rationale is
inverted. In the case of convergence, the latest results are only new
information that is not wanted and adds no depth to the knowledge. In the
case of divergence, the latest results are only old information and
represent no new breadth to the knowledge.
Information Life Cycle Engine
[0048]The information life cycle engine has an input configuration which
is saved, an indication of the source of the information and text,
metadata, data streams, blogs, RSS (Release Simple Syndication or Rich
Site Summary), or a compound document. The process is a reduction of the
information input into the system into its major elements by format,
style, type and data type. Compound documents must be taken apart to
basic identified types with tags and metadata separated out.
[0049]FIG. 1 shows an input file 2001 having therein text A, Text B,
various tags relative to the information in the text (such as paragraph
numbers), metadata associated with items and data objects in the document
and image elements. The input file is subjected to a reduction or
deconstruction step 2002 which creates an expanded information document
which includes source data, and file map of the various elements, text A,
text B, metadata, tags, audio and image. Of course, multiple audio files,
image files, metadata files, tags etc. may be included in the expanded
information document represented at 2004. The Editor as an Information
Expander section and following sections provides greater detail of the
reduction step 2002.
[0050]Further defining the source as an information file or data stream is
important. "Text" is a basic defined type. The information life cycle
engine automatically processes the information document. The process
includes selection, extraction and categorization. For example, a
document having security sensitive words or selected content (sel. cont.
or SC), phrases, images or sounds, identified as secured content by
Department of Defense pre-classification must be processed with a step of
filtering, identifying tear lines between classified material (contextual
filter or parsing technique) and ultimate identification to a base level
of classification. Words, phrases, user defined words and a group list of
words, phrases, combination and occurrences within a defined range are
employed to identify the security (priority) level of the information
document being processed. Pattern and categorization, dictionary
categorization, and syntactic categorization is employed.
[0051]The information document is also parsed or separated apart by
syntax, that is, words, sentences, quotations, parenthesis, other types
of textual delineation, and instruction. Complex phrases are resolved for
overlapping security levels and complex meanings. Categorization is
employed to determine the level of security of the information. Multi
tier-overlapping and hierarchical tagging is employed. Any dispute
automatically identified by the system upgrades the entire information
document 2004 to the next highest security level.
[0052]The next step in information life cycle engine is to extract and
disperse various versions of the document 2004 elements along tear lines
or other types of delineation specified by the operator.
[0053]Extracted data objects or elements may be replaced by tags, codes,
or null field indicators. The system may create various versions of
redacted output documents. The base document, redacted of all critical or
security sensitive information, should be stored along with a recovery
file. The recovery file may include maps to permit a party with a
preferred security clearance to recover completely the base or original
source document. A metadata table is created with an intermediate
resultant document. Reports are generated showing encoding, dispersion of
the data and storage location. Lists are also created showing selections
of data redacted from the source document, encoding of the data and
synopsis of the data. Additional outputs include a recovery file and a
categorized meta search file. Displays are permitted either locally or to
test the discovery results.
[0054]FIG. 2 shows information life cycle search. At the beginning of the
program flow in FIG. 2, an input function 2006 is noted which includes
information regarding the source of the information document or "source
document" which source doc may, at the operator's discretion, include
search terms 2008, data stream 2010 or a source information document file
2012. The search terms may include one or more SC or select content which
is of interest to the user. If search terms are input, these terms are
supplied to a summation or discrimination function 2014 (AND, OR or NOT
AND operator). In other words, the operator (or the system automatically
by default) could conduct an AND conjunctive search obtaining additional
information from other documents or an OR exclusionary search showing and
processing only the items that are identical are substantially similar
from source doc and filter outputs. The AND operation may operate on the
terms applied to discrimination function 2014 (e.g., the terms from input
2008 must match one output of the simple filter 2018) or may refer to a
summation of all terms from input 2008 and simple filter 2018 and
metasearch term engine 2020. The OR function has the same options. The
NOT AND discriminator operates only to extract and use terms output from
metasearch term engine 2020 which do not match search terms 2008 and the
output of simple filter 2018. Some select content SC must fall within the
prioritized hierarchical taxonomic class or classes function 2030 in the
NOT AND search. The non-matching search term engine is discussed later in
connection with the Expansive Unknown Search Engine. A substantial
similarity test, as a subroutine in the discriminator 2014, may ignore
duplicates and may truncate search terms by eliminating suffixes (and
possibly prefixes) from the search term processor. Input IN 2016
recognizes that the operator may provide a manual input into
discrimination function 2014. If data stream 2010 or an information
source file or document 2012 are input, these information documents may
be processed through one or more simple filters 2018 extracting white
list terms or black list terms or terms not found in dictionaries, the
result of which is supplied to the summation-differentiation function
2014. See the Simple Filter and Complex Filter sections below. In
addition, data stream 2010 and document source file 2012 are submitted to
a metasearch term engine 2020. The more highly organized metasearch term
engine 2020 filters the data stream or source document with a contextual
filter 2022, a conceptual filter 2024, a sensitivity level filter 2026, a
hierarchical taxonomic or category analysis 2030 and a parsing algorithm
for word count, line, paragraph, or DOL 2032. The order of these
metasearch filters may be changed. The filters are contextual, semiotic
and taxonomic. Semiotic include syntactic, semantic and pragmatic
features (discussed later). Sensitivity level 2026 contemplates potential
manual input IN 2028 which establishes the depth or expansive nature of
either the contextual filter or conceptual filter. The sensitivity filter
and the hierarchical taxonomic filters are used to focus the search in
that the operator can select hierarchical taxonomic levels or concern,
for example, names of terrorists--priority 1, terrorist
location--priority 2, date of source doc--one week--priority 3, temporal
(date)--1 month--priority 4, terrorist organization--priority 5. Each
"priority" is a SC selection sensitivity factor and the class of the
information is a taxonomic analysis, the result of which is a prioritized
hierarchical taxonomic classification system and filter for the search.
The result of this meta or more highly organized search term engine 2020
is supplied to the summation-discrimination function 2014.
[0055]The system then operates on convergent or divergent function 2034
which includes, optionally, input IN 2036 from an operator. The
convergent or divergent function 2034 enables the operator to only search
for converging search terms or select a more divergent search routine.
Function 2038 results in a primary set of search terms.
[0056]A major element of the information life cycle engine is parsing the
original source document. The structure of the source document DOM
metadata and compound file format must be simplified and the source
document must be broken into its atomic types such as markup data, tags,
metadata, links, hyperlinks, references, comment, differing data types,
purpose and format. This parsing is discussed later in connection with
the DOM data structure. Categorization is applied as a resolution or
coding to a single overriding security level. Priority is given to the
most complex or highest security level. For example, a document with "Bin
Laden" as compared with "Bin" next to "Laden" results in "Bin" being
identified at the secret "S" level and "Laden" being identified at the
classified "C" level wherein "Bin Laden" is classified at the top secret
"TS" level. (The security classes including TS top secret, S secret, C
classified and UC unclassified). The resulting document would be
identified as top secret TS because "Bin Laden" represents the highest
level in that paragraph (format or contextual indicator). Adjacent
permutations of a linear nature could also be employed. For example,
"Khalid Sheik Mohamed of Syria" results in unknown [Khalid], Sheik [title
of middle eastern person], and Mohamed [name] "of" [preposition] and
"Syria" [geographic territory]. The resulting analysis results in an up
coding of categorization to a next higher security level (e.g., S to TS)
due to the contextual range grouping of the SC.
[0057]The system may employ a "My Group" list or profile which identifies
key items and triggers a certain result based upon the "My Group" pre-set
profile list.
[0058]The My Group list would include a profile name such as "first strike
capacity" and a list of key terms under that profile such as "nuclear,
ballistic, submarine" and a "range" such that anytime that key word is
found in a paragraph, a certain classification is assigned. The "range"
being the paragraph in the source document. The Profile Name could be
used as a search term for a divergence search (discussed later) or a link
to a dictionary of search terms. The search terms are either
content-derived in nature or conceptual in nature. Certain
contextually-derived results, see Bin Laden example above, may
automatically trigger use of a specially designated My Group search
profile. Further, the Profile Name can be linked to another list of
synonyms associated with the specific terms nuclear, ballistic,
submarine. Contextual and conceptual terms are employed in this manner.
Information Life Cycle Searches
[0059]The input into the information life cycle search (FIG. 2) may be one
or more search terms, a data stream such as a blog, RSS, or a string of
data, or information document (DOM). FIG. 2 graphically shows the
procedure for the information life cycle search. The system pre-processes
the input and obtains metasearch terms both in a contextual manner and
conceptual manner. These are identified with filters discussed later on.
Sensitivity levels are set and the information is categorized both in a
contextual manner and a conceptual manner. Ranges of data representing
format choices are used such as lines, sentences, DOL's (lines of data)
and paragraphs. Thereafter, the information life cycle search engine
modifies the search terms and the user inputs either a convergent
instruction or a divergent instruction. The resulting search terms are
then supplemented according to the convergent or divergent system.
[0060]The search for the primary search terms (convergent or divergent)
results in a federated or confederated as well as a distributed
supplemental search term group. Hyperlinks, URL, network references, SQL
are submitted to one or more of the following: search engines, databases,
data warehouses, addressable data elements, artificial intelligence, data
mining sources, text storage, method data storage, indexes, libraries,
catalogs as well as other data structures.
[0061]The next step involves ascertaining the depth of the search by
identifying the number of hits for the first search term and the last
search term, the time involved to compile the search, the time involved
until no changes, the number of iterations involved as based upon a
divergence search as separate from a convergence search. The output is a
consolidated search result list.
[0062]FIG. 3 starts with an input being primary search terms 2038. The
search engine then executes function 2040 which is either a federated,
consolidated, or distributed search. A federated search unites the search
terms and additional supplemental documents generated and located from
those search terms together. A consolidated search is simply a composite
of all the search documents obtained without any relationship or
affiliation. A distributed search seeks to widely engage a large number
of data structures for the search. Operator input IN 2042 directs the
selection of the search function 2040. Search function 2040 operates by
sending the primary search terms to additional data collection targets
such as search engines 2044 (Google, Yahoo, etc.), databases 2046
(representing a plurality of databases), data warehouses such as
dictionaries or other sources 2048, addressable data structures 2049,
artificial intelligence or mining operation functions 2050 and other data
collections, data structures, indices, etc. The mining operation 2050 may
access further tertiary data structures 2052. The search function 2040
applies these primary search terms to text stores 2054, metadata stores
2056, indexes 2058, libraries of words, terms, images, data or data
objects 2060 and catalogs 2062. The results are compiled back to search
function 2040. Step 2064 is a depth or search depth control in which the
operator by input IN 2066 delineates either as a pre-set or a default or
an active real-time control the number of hits needed from each of the
data structures accessed by search function 2040, the amount of time for
the search, the amount of time when no change is found from the retrieved
documents, the number of iterations (repetitions), and whether the
primary goal is a divergent search or a convergent search. Search depth
control 2064 provides control information CNTLR 2068 back to search
function 2040. The search results 2070 (supplemental documents or supple
docs) are obtained either from output of search 2040 or after the depth
control 2064 is applied to the search.
[0063]The third step is to take the secondary search result or the
consolidated search result and apply them in a convergence manner or
divergence manner. The output is either a convergent compilation of
documents which are obtained via the supplemental search terms (adding
known SC to the search term group) or a divergent compilation of
documents employing the same search terms. A similar output result can be
obtained by using hyperlinks, URLs and other metadata aspects.
[0064]The fourth step (see FIGS. 4 and 5) is to build a tree or a table
listing the details. The tree shows the hierarchy of the data retrieved
in the convergent or divergent search and the table lists the items such
as profile, time, username, link, context, etc. FIG. 4 shows that the
primary search term generates a certain supplemental group of documents
whereas the secondary search results in a different group of documents
and the tertiary search generates a third level group of documents. The
number of searches is keyed to the time involved and the criticality of
the information sought to be searched. The object of the tree building
and table building (FIGS. 4, 5) is an exploration of information linked
or associated with the initial information provided. Further, a social
relationship or connect-the-dots matrix display can be obtained based
upon this iterative information life cycle search.
[0065]The output or yield of the information life cycle engine results in
a convergence search wherein additional supplemental information data is
obtained to corroborate or validate the initial search terms and initial
information document supplied. A divergent search yields more data which
is loosely associated or related, coupled or referenced to the initial
information document. Initial search terms result in a divergent
documents can be expanded upon by the hierarchical tree with a list of
details.
[0066]At some point, the operator must prune the hierarchical tree, reduce
unnecessary items and augment others. Some of this pruning can be
automated with an inference engine or artificial intelligence module.
Otherwise, the operator may manually engage in the information life cycle
engine.
[0067]The last step is a relationship map between the initial document and
the supplemental documents generated through the convergent or divergent
search. The final result is a map of the hierarchical search terms at
various levels source, search 1, search 2, search 3 . . . search n. See
FIG. 4. A further result is shown in FIG. 5 wherein a social relationship
or process relationship between the various documents is established.
Some people would call this connecting the dots or relationship mapping.
The relationship mapping changes the degree of separation or similarity
between each information document.
[0068]FIG. 4 begins with the primary search results 2070 and function step
2072 executes a convergent test and a divergent test on the primary
search result. The basis 2074 could either be the search results
themselves, a search term generation which is search terms extracted from
the documents located during the search, hyperlink or x-link and URLs.
The convergent test determines whether the search and recovered documents
converged into a certain pattern whereas a divergent test shows that the
documents, search results, hyperlinks or whatever spread to wider
results. The build and display function 2076 takes the primary search
terms and builds a search tree and builds a table. Operator input IN 2078
optionally enables the operator to set the scope and format of the search
tree and the table. Tree 2080 is a display showing the primary search
terms n, n+1, n+2 and secondary search terms m, m+1, m+2 and tertiary
search terms o, o+1, o+2, etc. Iterative search function 2084 establishes
how many paths or branches are executed in building the primary search
tree. This may be one of the input IN 2078 functions for build and
display function 2076. The iterative search 2084 may be a number of times
counted in branches or may be certain time unit g, h, i, j. Table 2082 is
a detail showing the search term including profile, time, username, link
and context of the search term. Function 2086 is this first level output
report showing the search terms for the information life cycle engine.
[0069]FIG. 5 begins with the first level output for the search term 2086.
Function 2090 is a convergent display which highlights the same, similar
or closely related documents. Also, an output ratio 2092 may show how
many of the documents are related by word, term, image segment, hyperlink
or metadata factors. Step 2094 is a divergent display which is the
opposite of the convergent display. Output factors 2096 may show how many
divergent documents having little or no connection to the primary search
terms have been discovered by the search engine. Function 2098 permits
the operator by input IN 2100 to truncate the search or supplement the
search. Function 2102 repeats the search on command of the operator. Step
2104 categorizes the search results by key terms (taxonomic
classification) and generates a map. One map is shown by tree 2106 which
shows the search results or information documents as document o, o+1, o+2
as well as secondary documents p, p+1. Step 2108 develops a relationship
map with associated terms, phrases, documents or hyperlinks. Relational
tree 2110 shows that point or document p+1 is related to document o+1, p
and o+2. In contrast, document o is not related to any document other
than o+1. The relationship tree 2110 is sometimes called a
"connect-the-dots" map.
Search with Prioritized Classifications
[0070]FIG. 2 shows that the operator or user can input search terms,
source document or a data stream 2006 into the system. Search terms
function 2008 represents the use of a search term string, document or
data stream into the metasearch engine 2020 and through the
classification or category analysis filter (hierarchical taxonomic
system) function 2030. The sensitivity function 2026 permits the operator
or user to pre-select the priority for the hierarchical taxonomic or
class system. Alternatively, "My Profile" or pre-set prioritized classes
may be used. The user may also select the priority and the class/subclass
of the search terms immediately prior to the search. The process, in FIG.
2, extracts search terms from input data represented by a source document
or a data stream, and identifies data elements in the input data with the
classification system corresponding to n priorities therein. In one
embodiment, the operator selects either a convergent search or a
divergent search in function 2034. Optionally, the convergent-divergent
search function 2034 may be omitted. The process then applies the
prioritized and classified search terms as primary search terms in FIG.
3, term function 2038, gathers documents and then resets n priorities in
said classification system to m priorities in said classification system.
This is part of the depth control function 2064 and control function
2068. Secondary search terms from the input data elements (terms 2008),
classified corresponding to m priorities are again used in function 2040
and the search is repeated to gather secondary supplemental documents.
The system then applies convergent-divergent test function 2072 (FIG. 4)
on all supplemental documents to find convergent or divergent
characteristics of the gathered documents. The search repeats until a
search end parameter is met, such as all supplemental documents exceed a
predetermined number, a predetermined number of data elements from all
supplemental documents fulfill a predetermined number of priorities in
said classification system, a lapse of a predetermined time, a
predetermined number of data elements from all supplemental documents
fulfill a predetermined number of priorities in said classification
system, among other search end parameters discussed herein.
[0071]Optionally, the system and process may omit convergence-divergence
function input 2034, 2036, and select n priorities from the prioritized
hierarchical classification system, extract search terms from input data
(functions 2006, 2008, 2010, 2012) represented by a source document or a
data stream or a search term string, and identify data elements in said
input data with said classification system corresponding to the n
priorities. The process then searches data collections with said search
terms and gathering supplemental documents based thereon. FIG. 3. The
process then extracts secondary search terms as data elements from the
supplemental documents with the classification system corresponding to n
priorities and repeats the search of data collections with the secondary
search terms. Secondary supplemental documents are then gathered. See
FIG. 5, truncate, supplement and search function 2098, and repeat search
function 2102. Such an optional search ends as described above.
Simple Filters
[0072]The user, prior to initiating the knowledge expander select content
(SC) engine, may be given a choice of filtering out or identifying all SC
data objects with white lists or black lists or a combination thereof as
a content filter. Uncommon words, terms or data elements not found in the
dictionary, geographic term lists or name lists and other lists are
located with a negative content filters used to discover unknown data
elements. The negative content filters can be added to the metasearch
term engine such that these unknown words, terms or data elements are
tagged "unknown" and assigned to the hierarchical taxonomic class as an
unknown data element at the hierarchical taxonomic classification level
(assigned to a class of words, terms, etc., and assigned an SC
sensitivity level--that is--a hierarchical marker). The search terms
extracted from the source doc are supplemented with white lists or black
lists words, terms, etc. (content filters additions) and the supplemented
SC or select content words or terms are filtered out to identify the
uncommon or unknown words. Of course, the user may be required to
manually input all SC words or download the SC word filter from the
Internet or another secure network system or LAN. A "profile" of SC with
the hierarchical taxonomic markers can be employed by the user-operator.
For select content systems having multiple levels of importance (which
may be multiple security levels or multiple levels of SC importance in an
organization or related to the organization, for example, primary
competitors compared with secondary competitors), a plurality of filters
would be created, each filter associated with a different SC level.
Further, multiple SC levels may require, remainder SC document or data
stores (unknown data stores) and a plurality of extracted data stores.
[0073]The ability of the program to locate select content or SC words or
characters can be enhanced by using a telephone book, properly dissected,
to identify a collection of last names. Cities and towns and street names
can also be identified in this manner. The compilation of last names and
cities, towns and streets can be used as a list of critical, SC words.
The filter is represented by this compilation of words. Similar
techniques may be used to create filters for scientific words, or words
unique to a certain industry, or country.
Complex Filters
[0074]There is a need to construct filters which supplement the initial
list or compilation of SC words, characters, icons and data objects
(herein "word" or "data object" or "word/object"). The need arises either
due to the fact that the initial SC search term word/object list is
incomplete, or that the author of the initial list is concerned that the
list is too limited or in order to defeat an attack or an inference
engine "reverse engineering" at the resultant expanded SC document.
Further, the incorporation of a filter generator for SC search terms
enhances the user friendliness of the program. In one embodiment, the
program is configured as an editor compiler to screen and build enhanced
SC doc or doc collection from a source document. The user selects, at his
option, functional aspects which include: compliance with laws (an
application of a type of filter, e.g. HIPAA, GLB, Oxley-Sarbanes, EU
privacy, executive orders); privacy (another type of filter which locates
SC terms, for example, social security numbers, see also, EU policy);
search for and supplement filter; pay per view (which enables the user to
buy missing sensitive information (for commercial purposes); survival
(which creates a distributed and dispersed copy of the user's source
document and other stored documents and items using predetermined storage
facilities); security (which triggers the various security routines); and
storing (which permits the user to select which of the several storage
options the extracted SC data/objects should be employed in the
dispersal.
[0075]The filter routine diagrammatically illustrated in FIG. 6 is useful
in compiling a SC search term filter which separates both the sensitive
word/objects and contextual and semiotic and taxonomic aspects of the
initial list of SC word/objects. The filter works in conjunction with a
compilation of data, typically located on a network which could be
private or public. In low level SC situations, the filter may access
Internet databases to gather additional data for the filter. In more
robust SC systems, the filter could access private data bases (one
located at the same organization level as the user) and build or compile
the additional SC word/objects. The filter program 950 in FIG. 6 begins
with step 952 which compiles the initial list of SC word/objects. In 954,
the initial list is supplemented with dictionaries, phone books,
corporate records (to obtain subsidiary data and trade names) and
thesaurus data. This is a content filter. Each of these represent
different compilations of data and the added data is added to the initial
list of SC word/objects. In 956 a search is conducted on a network,
usually through a search engine, to gather excerpts near and abut the
keywords. This is a range or contextual filtering aspect. These keywords
are the initial SC word/objects. Statistical algorithms are applied to
gather non-common word/objects which are associated with the keywords as
found in the additional data compilations. The goal of the adaptive
complex filter is to obtain contextual, semiotic and taxonomic words,
characters or data objects from the compilation of additional data
related to the SC words, characters or data objects. Semiotic is a
general philosophical theory of signs and symbols (read language and
words and objects) that especially deals with their function. Semiotics
include syntactics, semantics and pragmatics. Syntactics is the formal
relationship between signs. Semantics is the meaning of signs and
pragmatics is the relationship between signs and their users, such as the
relationship of sentences to their environment. Taxonomy is the
scientific classification and categorization of items. Therefore as an
example, a search through the Internet on Google search engine under "Bin
Laden" may show a number of uncommon (non-dictionary words)(content
filter-based search) within 200 words of the target "Bin Laden" (a
contextual filter-based search). This search string would gather
documents from the Google search and copy 200 words on either side of
"Bin Laden" and then extract only non-dictionary words into a
supplemental SC term list. This type of filter algorithm looks for
contextual matters close or near to the target. The search is semiotic
and statistical in nature. Additionally, the initial supplemental list
would identify the Bin Laden is an Arab and this classification (a
taxonomic aspect) can be used to expand the list for the filter. The
algorithm may include a simple command to gather all 10 words on either
side of Bin Laden. This is a pure contextual search and the "10 word"
range or format aspect is a statistical number. From the supplemental
list, all pronouns, prepositions and conjunctions may be eliminated.
Spiders or robots may be used in the gathering of the contextual and
semiotic filter data. The contextual, semiotic and taxonomic words,
characters or data objects from the compilation of additional data are
all related to the initial list of SC words, characters or data objects.
[0076]Step 958 compiles the adaptive or complex filter. The above noted
contextual, semiotic and taxonomic filter is adaptive since it can be
used to expand (and potentially contract or reduce) and adapt an existing
list of SC word/objects to a larger list which better expands the
information content of the source document and supplies SC term data to
an inference engine. Step 959 repeats the filter gathering and
compilation for various levels of SC inquiries. Higher SC inquiries
require a broader search (1000 uncommon words near Bin Laden and add all
Arabic and sub-Asian continent cities). Orthogonal SC groups (those
groups having the same level, e.g. S Secret, with each other but being
different organizations, e.g, Department of Defense compared to the FBI)
often have different methods to keep SC data separate between
compartments.
[0077]The adaptive filter can be set to automatically gather additive SC
word/objects. The system, with a basic filter, may identify a SC word in
a paragraph being scanned by the initial filter. This SC word may be a
special word in the existing filter or may be a non-common word not found
in the initial filter. The adaptive filter system may then obtain this
"unknown" or "special" word (a negative filter in that the word-object is
not matched to a word-object having the same content), and conduct a
search through a compilation or data base of additional words, etc. Any
new word/objects falling within the contextual, semiotic and taxonomic SC
words, characters or data objects from the compilation of additional data
(database) related to said SC words, characters or data objects are then
added to the filter. The expanded filter is then used to supplement the
source document.
[0078]Step 960 compiles a supplemental filter with random words, phrases,
etc. in order to further defeat an inference engine reverse engineering
assault on the SC supplement document matrix. In some sense, the
production and use of a random filter is an encryption technique since
the resultant filtered product, in order to be understood by others, must
be reverse filtered or decrypted to reveal the source doc and enhanced
doc matrix at the appropriate SC level. Nonsense words may be added to
this supplemental filter. Step 962 applies the primary filter (with the
SC word/objects and the additive word/objects from the contextual et al.
filter) to the source document. Step 964 extracts the SC word/objects per
SC organizational level. It is noted that several filters are used, on
one for each SC level, whether hierarchical or orthogonal. The extracted
SC word/objects are stored as a supplemental search term doc and the
system gathers supplemental documents (supple docs) which expand the
knowledge base about the SC word/object. Step 966 applies the
supplemental filter to the supple docs returned per search level 1 with
SC search terms. The system then repeats the process of SC search term
generation on primary retrieved supple docs level 1, generates SC search
terms per level 2, and retrieves SC level 2 supple docs. A tertiary
search term generation and supple doc retrieval is possible based upon
the operator's initial set-up or upon operator input at steps 964, 970.
Step 968 stores the supplemental doc to permit information enhancement of
the source doc. Step 970 publishes, distributes or pushes the source and
all supple docs and search term data to others having a need to know.
The Editor as an Information Expander
[0079]FIGS. 7-8D diagrammatically illustrate an editor which may be
employed to locate SC word/objects in a source document and expand the
knowledge base with supple docs. In one embodiment, the editor is a
standalone application or a module to add onto other applications for
plain text and media creation, editing, and sensitivity SC level tagging.
Other types of tagging, wherein the editor supplements the initial group
or subset of select content SC sensitive words, characters, icons and
data objects by categorization, taxonomy classification, privacy,
security, compliance, and semiotic meaning, are also available. The
editor supports a full range of document management and can be integrated
into a unified infrastructure, from creation, editing, document markup,
tagging, tag conversion, tag removal, context sensitivity level
redaction, context reconstitution, support for complex process work
flows, and expanding the knowledge base by adding supplemental documents
(supple docs) to the initial collection of source doc. The architecture
assures separation of data from metadata so that no security lapses are
introduced into the traditional word processing and document management
cycle.
[0080]From the user's standpoint, the Editor is not much different from
other information processors such as Vi, Word, Notepad, and other desktop
tools. However, behind the scenes (that is, automatically and with
nominal operator input (after the editor is initialized)), this
application separates the data stream from all markup and tagging
word/objects for SC knowledge generation purposes.
[0081]The interlacing of user content with metadata creates significant
process, storage, distribution, and workflow security failures that are
not resolved with current technologies. Current technologies include
encryption, firewalls, intrusion detection, perimeter guards, and locked
distribution packages.
[0082]The Editor enables text and media creation. However, all additions,
deletions, changes, insertions, and reorganizations and reordering are
tracked as metadata that does not become part of the document. The
document as seen and shown to the user represents the deliverable format.
Since formatting is metadata, it is not included in the representation.
Formatting, such font sizing, colors, font selection, footnotes, headers,
subscripts, superscripts, line numbering, indexing, and other features
characteristic of standard document preparation can be supported but are
represented only as metadata. Tagging, including SC sensitivity level,
categorization, taxonomy classification, privacy, security, compliance,
and semiotic meaning are also represented only as metadata. This
separation of representation from meta-representation is critical for
creating the infrastructure for SC knowledge expansion, secure
information sharing, privacy, security, and compliance.
[0083]The editor is currently set in a WINDOWS environment. Pulldown menus
provide access to formatting and tagging features. The document, from
source, precursor (marked and tagged but not yet filtered or extracted)
and resultant final versions for each SC sensitivity level, as seen and
represented to the user as is distributed in is resultant final form,
thereby assuring SC knowledge level compliance. No hierarchical, hidden,
encapsulated, linked, associated, or referential information is part of
the data stream, file, or storage.
[0084]Metadata (such as formatting, such font sizing, colors, font
selection, footnotes, headers, subscripts, superscripts, line numbering,
indexing, and other features characteristic of standard document
preparation) is usually hidden from the user. This supplemental metadata
information contains all markup, tagging, formatting, and process support
information for the editing process and enables immediate granular
distribution of the data stream subject to the needed SC compliance
rules. In other words, the data stream can be automatically processed
with other functions to satisfy multiple competing requirements and SC
sensitivity levels.
[0085]FIGS. 7, 8A-8D are discussed concurrently herein. FIG. 7 is a basic
flow chart for one embodiment of the Editor. Editor program 972 begins
with obtaining the source document 974. Of course, the source document
may be any type of document or may be a data stream. Of course, the data
stream is typically delimited by start and stop characters or codes.
Hence, the term "data stream" is similar to "source document" herein and
in the claims. Step or function 976 obtains one or more filters for one
or more SC security or sensitivity levels. Step 978 screens or processed
the source document with the filter(s). For example, the source document
in FIG. 8A in window 991 has text regions 993, 994, 995 and 996. In step
979, the Editor displays, in situ (in the displayed document), the
filtered identified SC material and conforms the precursor document to
the SC sensitivity protocols. As shown, the SC sensitivity protocols are
security level protocols for the system within which the Editor is
employed as an information processing tool. SC sensitivity levels 1, 2,
3, etc. correlate to security levels TS, S, C and UC for top secret,
secret, classified and unclassified. FIG. 8B shows that the address data
993 is marked TS (top secret), region 994 is displayed in color A for TS
coding (please note that the addressee data may also be so marked) and is
"red-lined" or struck out. In an information-expansion system, the
process marks, labels or tags each word. This is a content filter and tag
regime. The user may manually upgrade or downgrade SC levels. Region 995
is displayed as presented in the source document and is labeled U
(unclassified) and region 996 is shown in color B, is redlined and is
labeled S. Labels TS, S, C (classified) and U are the established
security labeling protocol used by the organization employing the Editor.
The same tagging for each word or character in the source doc can be
used. Other labeling schemes for SC elements may be employed. Color is
used to assist the user to select (and in some non-standard cases,
deselect) the SC data marked by the editor. Redline is used to inform the
user that the filter(s) will tag and extract the marked data. Labels are
used to permit the entity using the editor to employ standard tear line
protocol. Tear line protocol is a contextual tool. Any data beneath a
security classification of the user is under the tear line and the data
is permitted to be distributed to the lower security cleared user. Of
course, electronic distribution of secure data need not use the hard copy
or print version of the tear line. However, this nomenclature referring
to the tear line is used in the prior art systems.
[0086]Step 980 accepts the user's manual changes (typically upgrades) to
the precursor document. These manual changes are displayed, redlined,
colored and labeled. Step 982 inserts the SC tags (or security label TS,
S, C and U for the sample doc in FIGS. 8A-8D) as discussed above. Step
984 notes that the system takes certain meta data such as author,
date-time, version history, change history, etc. and converts this meta
data into ordinary text, marks that data at the necessary security level
or SC sensitivity level and labels the meta data. Step 986 permits the
user to add (or omit) placeholders into the final document. FIG. 8C shows
placeholders as black lines or as XXXXX symbols (or other symbols)
wherein the sensitive SC text is not shown but some replacement markers
are shown. The byline in region 1003 show "sanitized document." The
byline 1003 in FIG. 8B lists the security level (SC level of priority)
and the color representation.
[0087]In a specific knowledge expander engine, each word, term and
character is labeled or tagged with a content filter to show data object
type (name, town, credit card number, etc.) and an SC sensitivity level
(a "tax" or taxonomic classification and sensitivity level). See step
982. The user may add tags (step 979, 980). Meta data is also tagged or
labeled.
[0088]Step 988 activates the filter, extracts the sensitive SC data and
temporarily stores the extracted SC data. The expansive knowledge engine
also stores negative filter results which are those words, terms or
characters, images, not found in the context filters. Step 990 displays
the filtered document and the user may view the filtered document at each
SC level. Therefore, the user, before transmitting a secured email (or
letter) doc may look at the source (FIG. 8A), may look at the TS level
(FIG. 8A) without the redline strike out but with security labels and
colors, may look at the T level revealing regions 996 and 994 but not
regions 993 and 994 (which are TS coded regions), and look at U versions
as shown in FIG. 8C. Step 992 disperses the extracted data and the
remainder data or disperses partial versions of the document (those
partial versions formatted and containing only data at or above the
target security level (all TS level data (which includes TS, S, C and U
data), or all S data (comprising S, C and U) or all C data and U)).
[0089]In step 979, the SC level protocol determines whether single words
are granularly classified (TS, S, etc.) or whether a line is classified
(context filter), or whether an entire paragraph is classified (see FIG.
8B). If a commercial/privacy filter is used to exclude all social
security numbers, the organizational protocol is set at a granular level
to exclude just social security numbers. Different group protocols use
algorithms to mark, filter and extract adjunctive security sensitive
words, characters, icons and data objects near the target SC sensitive
words, characters, icons and data objects. The SC words may be security
sensitive words, characters or data objects defined by compliance with
law, regulation or policy, privacy, national, organizational or private
security concerns. For example, Bin Laden is the target sensitive word in
FIG. 8B and this classifies the entire paragraph as TS level. The other
words in the paragraph are adjunctive word/objects.
[0090]In a knowledge expander mode, the SC filters are applied in a
negative manner in that the following process is noted: (a) in the user
set-up, the user establishes taxonomic categories or classifications and
sets the selection priority of the classes; (b) the source document or
source data stream is broken apart to separate all metadata; (c) the
source document (may be a data stream) is processed by a taxonomic filter
which tags or labels each word or data element with the taxonomic
classification and the sensitivity or priority label (multiple "tax"
tags, overlapping "tax" tags and "unknown" tags are possible); (d) after
labeling, a content and contextual filter is used to separate out the
unknown words or data elements (a negative filter). The resulting
negative list of search terms is used in various search engines for both
public and private data sources, to compile a compilation of supplemental
documents (supple docs) and, thereafter, the supple docs are re-cycled
through the H-tax and priority filter, then the content and contextual
filter and a secondary supple doc collection is obtained. The primary and
secondary supple doc collection represents the expanded knowledge search
not typically found with commonly available search engines and processing
techniques.
Document Object Model (DOM)--Protection and Processing
[0091]The search for expanding the knowledge base from a single source
document to a larger compilation of docs has changed from locating
content (see the prior art GOOGLE search engine) to the battle for
expanding concept and context. Sequential text files are the exception
rather than the norm. Flat, plain, and sequential files would have
disappeared entirely from all but transitional processing steps except
for the recent success of HTML web sites and the desire for storage of
complex data into sequential XML formats. In spite of the apparent
linearity of HTML and XML, in practice these flat files participate in a
greater complex hierarchy of structured data mapped by object models. The
object models blur the lines between content, concept, and context such
that effective security requires a broader stroke than merely
encapsulating content with encryption and limiting access with tokens or
encrypted certificates.
[0092]Linkages to external files, style sheets, and embedded applications
or scripts undermine the simplicity of HTML and XML flat formats and
compromise point security. Even structured field or line and
record-oriented file formats have given way to more complex data storage
models. It is insufficient to view security of content and files in terms
of encryption and encapsulation alone. Structured object models mix
content with metadata and methods such that non-granular access--that is,
either/or barrier-based access through encryption keys, dongles, and
passwords--undermines any concept of effective security.
[0093]Furthermore, simplistic document management and access control
overlook the multiple purposes for each compound data document and the
adverse impact on organizational processes and work flows. Barrier-based
security also fails from any Pacman-style attack, where the barrier, once
breached not only provides full access to the once-protected interior
also interferes with analysis of the attack and observation of how to
prevent the ongoing attack. Granular multi-level control of user data,
metadata, data stored through the specifications of a hierarchical data
object model, and methods underscores the new security paradigm. This
transition is most pronounced in Microsoft Office documents, such as
Word, Outlook, or Excel given the indiscreet distribution of source
files. Office document publishing and Adobe PDF creation represents a
minimal solution to the object model and metadata security risk.
[0094]All data sources important to data process workflow are non-linear,
non-sequential, and not standalone in that the data sources are
interconnected to or required by other data sources. This includes
databases, structured documents, desktop application user files,
hierarchies of data structures, and work flows. The most advanced data
workflow and the focus of attention is the object-oriented models used in
data processing today which comprise a cascade of events rather than a
single point operation. This complicates SC data expansion activities to
promote security, survivability, privacy, confidentiality, and anonymity.
The present invention improves the security of complex document object
models and interdependent workflow by expanding the knowledge base form a
source document, thereby testing the classifications levels and generally
expanding the knowledge base of a user form the simple source doc.
[0095]There are only a handful of counterexamples to complex data
structures, mostly monolithic file structures and simplistic processes.
This includes text files, raw binary image files, and lists. These are
typically inputs to older or uncomplicated computer activities; they do
not reflect the complexity and interrelationships consistent with and
necessary for most critical networked data processing activities.
Examples of flat files are text files, binary images, and lists.
Plain-text documents are used only as temporarily or as conversion paths
for other activities. Binary graphics are employed for their specific
simplicity, speed of display, and small size. It should be noted that
they (BMP, GIF, and other formats represent the bulk of web images) are
usually stored in an inverted backward last-to-first sequence. List files
are rarely important and standalone files are often a temporary part of
another process. One of the most ubiquitous of plain-text files, the HTML
web page, is rarely a simple text file, but a circular connection to many
other like files and one part of a more complex hierarchy. A relative of
lists is the field-oriented record structure. This is web page usually a
grid-like storage of linear data. However, even a table grid,
multi-dimensional indexing, SQL query concept is giving way to
object-oriented post-relational database storage methods based on object
models in order to augment functionality, speed of performance,
cross-platform and application functionality, and compete with easier to
use user and developer products. Even the image files are becoming
increasingly complex. Hierarchical images formats with vector graphics
compress motion and curves into small packages. Examples include Corel
Draw, Macromedia Flash, Adobe Photoshop, and Microsoft Photo. These of
course contain proprietary and unintentionally-distributed information.
Increased reliance on reliable data storage infrastructure and networked
storage technologies is enabling the transition to data storage based on
object models.
[0096]FIG. 9 shows the root, branch, and leaf paradigm of this principal
data storage structure. See root 1012, content leaf 1014, branches 1016,
1018 and leaf 1020. The object model refers to the layout or the map (a
blueprint supplied by the document object model (DOM) vendor) of how the
data is potentially stored in what is definitely a linear file. The
stored file is the document object structure containing the data whereas
the model is the schema representation. The model FIG. 22x is just a
blueprint for an empty data structure.
[0097]The data structure is stored as a binary file populated with data
representing a subset of that blueprint. The data file is often referred
to as the document binary file so as to make clear that it is not a
plain-text file, not in user-friendly format, and generally readable by
an ASCII reader only in discontinuous chunks. The model and the structure
are not the same. The model (FIG. 9) does not represent a security threat
in itself, it just represents how to find and define data stored within
an actual data structure. It is the data structure in memory (the source
document) or stored as a file that is the security threat. Usually, the
file containing the data structure gives enough clues to the purpose,
methods, and sources unless addressed by a multi-level security scheme
attuned to the complexity of the object model. Although this "file" is
stored as linear flat file, the extended structures is dependent on the
hierarchical collection of potentially infinite branch and leaf
references. Despite this complexity, there are clear reasons based on
simplicity for this hierarchical structure, not the least of which is
flexibility, self-documentation, and backwards/forwards compatibility.
[0098]The subtle differences between a plain-text file, a file containing
lightly structured data, the schema, and a file containing data within an
object structure becomes very important for security. When files are
distributed and those files each contain data within object structures,
workflow is complex and cannot be effectively protected with
barrier-based security without complicating or disrupting operations. For
these reasons, internalized security reflecting leaf content, structural
paths, and the mesh of inter-relatedness among the paths, leaves, and
external sources becomes the next paradigm for implementing effective
content-level and application-level security. Consider the data structure
defined by an object model as an organizing container. The contents
within can be empty, or collections of containers, with more containers
within. It is a security sieve with traditional encryption and the
requisite inter-process work flows. The leafs and the security of the
leaves does not secure a chain of evidence increasingly necessary in
modern data processing activity.
[0099]Enhanced security must reflect this distributed requirement since
the data sources are not single point sources, but complex relational,
object-oriented, or hierarchical. In addition, data access and processing
is approaching a worldwide distributed infrastructure, and completion
transcends single places, times, and events. When the security problem is
dispersed, the security solution cannot be monolithic either but must
reflect the dispersed distribution and hierarchical complexity of the
data and process. Location is not the problem, so metaphorical perimeter
walls are not the answer. To treat security too as a monolithic, static,
and walled solution when the security problem is granular and dispersed
within a flexible time frame misses its true need. Effective data
security must reflect five informational attributes in a newer paradigm
for security. The five informational attributes are listed below and
examples of the attributes are also listed. For each select content or SC
sensitive organization, the data structure must be analyzed and the five
attributes must be applied to each root, branch and leaf to ascertain the
level of SC sensitivity for that item. For example applying a security
schema to the SC knowledge expander problem, a TS level may be
established by applying the five attributes that all audio files are
"security safe" for that SC level but these audio files will not be
downgraded or released to a lower SC level. Therefore the meta data
representing the audio file is designated TS. Another example is that all
machines at the SC level S (3.sup.rd level of the TS-S-C-UC schema) are
2004 machines and programs. The organization may set, as a policy, that
all MS Office program meta data need not be backward compatible beyond
2004. This organizational protocol then reduces security issues relative
to the backward compatibility issue.
TABLE-US-00001
Informational Attributes for Security
Purpose
Sources and methods
Ownership
Date or timeliness
Content
Purpose Classification - Exemplary Table
.backwards compatibility (purpose: communication across
machine platforms
.background color (purpose: visual presentation)
.font size (purpose: visual presentation)
.image
.video
.audio
.version control (purpose: source identification)
.etc.
Sources and Methods Classification - Exemplary Table
.origin plain text
.origin entire document
.image
.video
.audio
Ownership Classification - Exemplary Table
.source, author
.security level initial document
.security level generating modifications to initial document
.hierarchical, orthogonal security classification
Date or Time lines - Exemplary Table
.version control
.source identification (includes all contributing
authors supplying modifications)
[0100]These five security attributes reflect not only the data content but
also the point processes, embedded resources, and work flows. In a
similar manner, SC sensitivity levels can be applied based upon time
(temporal) issues, competitor or size of company, type of product
(critical, sub-critical, or ancillary), etc.
[0101]This metaphor fractures complex data processing workflow.
Traditional search methods point only to defined search terms, maybe with
a simple truncating algorithm which deletes "s" and plural suffixes from
the defined search terms. A monolithic approach to prior art searching
ignores metadata, process, multi-tiered files, delivery, and storage.
Data sources are not monolithic, and certainly data is not either.
Distributed data, distributed processing, and widespread distribution
defeats common search techniques. Access and search techniques need to be
granular and multi-level, and represent the five informational attributes
presented above.
Recognizing the Document Object Model (MS Office)
[0102]Every MS Office binary document contains confidential information,
typically metadata. This ranges--from small amounts of information about
authorship--to the editing history complete with deletions, reviewer
comments, file attributes, and source and routing information--to
extraneous baggage from documents previously edited during the same
session.
[0103]A multi-faceted SC workflow process becomes an issue over control of
distribution by document type, recognition and categorization of all user
content defined by security exons (discussed later), removal of
non-coding or non-activating security introns (discussed later),
preparation and distribution by SC sensitive levels, content
certification and accreditation (C&A) subject to conversion to primitive
and certifiable file formats, distribution in print-representative-like
packages, with guarded ingress and egress of Office files. Finally,
implementation of a knowledge expander search through granularity of MS
Office node elements by analysis for inclusion and exclusion is a far
more effective method, permitting collaboration within a multiple-usage
infrastructure.
Office Versions, Releases, and the Data Object Models (DOM)
[0104]MS Office is a complex document structure because of the interaction
among the MS Office applications and documents, the creation of metadata
in binary document file formats, and the shift from one of results to
that of a reentrant and ongoing process. Document data has expanded from
simple linear files to complex object-oriented structures. FIG. 9, 10. MS
documents are black holes in that what goes into them at any point
usually stays there. Additions, deletions, system information, redlining,
reviewer comments, and routing become indelible parts of each document as
metadata. Many different versions of MS Windows, server extensions, and
many releases of MS Office or its constituents complicate security.
Application features, bug fixes, security patches, and 3.sup.rd party
add-ins complicate the nightmare when assessing and ascertaining the
exact composition of the MS Office environment. Client-based
applications, such as InfoPath, Outlook, Outlook Express, Internet
Explorer, the various scripting languages, plus server-based applications
including Exchange, SharePoint Server, Net Meeting and Live Meeting
Whiteboard, Live Communications Server enhance the collaborative physical
coverage of MS Office but also correspondingly increase security and
privacy risks.
[0105]The MS Office document is forwards and backwards compatible across
MS Office releases. Cut and paste among the Office applications adds
non-native structures too. Therefore, results from file conversion, raw
data, metadata, links, macro code, and structural elements can be hidden
accidentally or purposefully. It also possible for a sophisticated user
to create new and undefined covert structures ignored by all extant MS
Office versions and tools, visible or activated only by complex steps,
since MS Office does not validate the integrity and applicability of
internal structures within a document binary file.
[0106]A typical commercial installation will include any, all, or
additional components as listed in FIG. 10. This chart does not included
ASCII file formats, printers, printer drivers, FAX drivers, HTML, XML,
Adobe Postscript or Acrobat drivers, Outlook or Exchange databases, and
OLE document objects, plus other COTS products that integrate with
Office, expect Windows or Internet Explorer components, use dynamic data
exchange (DDE), object linking and embedding (OLE), or exploit the
kernels of Windows and Office. These all pertain to the process of
implementing MS Office document metadata. The metadata, by its nature,
defines a context within which the content of the words and terms in the
doc file are employed.
[0107]It is important to recognize that there are many file types and
document structures associated with MS Office, specifically defined by
the formal MS Office documentation at msdn.microsoft.com but also those
shared with other MS Windows applets and competing products. Each MS
Office application, such as Word or Excel, create file binaries or binary
files with different object structures but interchangeably read/write and
import/export each other's file types, embed portions as formatted text
or complete objects, or link through remote procedure calls to these
other file types. These object model structures are generically called
the Document Object Model (DOM). The DOM is another term for an
object-oriented data storage package. The purpose for the DOM with
hierarchical storage of metadata is three-fold. First, it is useful for
backwards and forwards version compatibility. Second, metadata extends
the document creation session from one-time event into an ongoing
revisional process. Third, metadata provides order and structure
otherwise notoriously difficult for inherently free-form and flexible
documents.
[0108]Metadata provides backwards and forwards version compatibility, a
problem that plagued the software market of the 1980s as upgrades were
frequent and disruptive. This is specifically missing with Access and its
.MDB table space/workspace metaphor. Frequently, software upgrades
included old data upgrade routines to convert old formats to new. This
was both risky and prevented reversion to the older software version once
the converted data was used in the newer application. Metadata provides
the necessary blueprint, format, and structure retention so documents can
be revised in future editing sessions. Format information is part of the
Office metadata, although style sheets and schemas maintained in a
different storage channel are valuable in HTML and XML and might aid the
future transition to a different MS Office DOM.
[0109]It is incorrect to assume a static basis for any MS Office
application document structure, as a monolithic MS DOS-based file, or as
an in-memory object. For example, the Excel DOM can be embedded inside a
Word DOM, which selectively can then be pasted as a formatted object into
a PowerPoint presentation. Another concern that arises in almost every
Office document is imports, pastes, and OLE imbedding of other Office
documents and aspects of the object model corresponding to that
application type. For example, a base Word document with a spreadsheet
and Project waterfall chart now includes editable components referencing
a different Office applications with data in a structure referenced by
that corresponding application object model, in this case Word, Excel,
and Project.
[0110]FIG. 9 shows each branch or leaf can be replicated indefinitely
until reaching the limits of Windows RAM or file size. Each MS Office
application has a different DOM. Because of DOM evolution, with the MS
Office assertion of backwards and forwards compatibility, realize that
some nodes might exist in the binary document file but not every function
appears within each published output because it is not used by the
author.
[0111]A notepad text file in a corresponding word document has a 40
character file is stored by FAT32 in minimum 1 KB blocks, although its 1
KB storage block only uses 40 characters (use a hex editor). In contrast,
the basic Word document file requires 18 KB on initial saving, but a full
28 KB with edits and deletions, metadata, and redlining, as shown.
Footnotes, font changes, hidden text, additional changes, headers, and
footers, table of content, indexing, an index, macros, .DLL add-ins, .OCX
add-ins, and formulae could arbitrarily increase the file size
indefinitely. This shows that MS Office security risks are reproducible
at any user desktop. A hex editor used in conjunction with an initial raw
ASCII file and the corresponding .DOC file also shows risks. ASCII text
has only 40 characters despite the directory display of the 1 KB FAT32
block. The internal encoding of the .DOC file with initial content, the
binary object structure and additional metadata are partially encoded in
a padded form of ASCII. The metadata displays the source location of the
document, removing possible doubts of file directory structures, security
based on location obscurity, and other rational workflow techniques for
securing user files within the context of a network infrastructure.
[0112]Microsoft has identified thirteen key categories of metadata: Name;
Initials; Organization name; Name of originating computer (desktop); Name
of network server and/or hard drive; File properties and summary
information; Non-visible embedded documents; Names of previous authors;
Document revisions; Document versions; Template; Hidden text; and Author
comments. Some of this metadata is accessible through the Office
application menu interface through menus and dialog boxes. The
exploitation of this metadata in an knowledge expander search is useful.
There are also the document file properties exposed by the Tools/Options
pulldown menu and the User Information tab. This is not the complete list
of metadata. Other visible metadata with potential search characteristics
include: Footnotes; Cross-references; Table of Contents tags; Indexing
tags; Hyperlinks; and Smart tags. Expect x-link and x-pointers plus style
sheets and schemas within documents saved in the XML format. In addition,
other undocumented structures are part of the extended and expanding
Office document object models. Consider fields and mail-merge fields,
which are markers for information automatically inserted by Office or by
a user when opening, saving, printing, or emailing documents. These
fields create a built-in facility for careless information disclosure or
overt hacking. There are also the document file properties exposed by the
File/Properties pulldown menu. This includes: File/properties; General;
Summary; Statistics; Contents; and Custom.
[0113]The knowledge expander search also contemplates finding and using
other items not specific to MS Office. Techniques for information
camouflage can be used in an knowledge expander search. These are equally
valid in most any desktop application, and are most relevant to
presentation output rather than binary file delivery. Information
camouflaged in a source document includes text set to small font sizes,
such as 0 or 1, fonts set to type unlikely to be installed on the system
which map to symbols or line drawing, PostScript or Unicode font sets
with alternate encoding, and font color set to match the paper color or
an applied background. White font on white paper hides text, black font
on a black border or shading hides text too. Text can also be hidden with
graphics when the graphics are anchored to a specific location congruent
with the text. Color games with text and graphics also hides the text.
Macros, VBA (Visual Basic Application) codes, VBA add-ins, and applets
also represent a search asset. Anything than anyone can imagine as an
application can run from within MS Office, productive or destructive.
Usually, these bits of code are stored as part of the document metadata.
However, they also can be out-of-channel files. Either way, they can be
compromised by a new code that overwrites the original. They also can be
inserted through fields, formulae, or menu add-ins. Collaborative tools
are the most obvious entree.
[0114]New features in Windows and other Microsoft digital rights
management (DRM) applications, such as ORAPI, ADSI, and MS IRM provide
for collaboration, resiliency, and complex versioning and backup far
beyond the capabilities of MS Office.
Content
[0115]The differentiation of content within an MS Office document based on
initial owner and target distribution is important for search expansion.
Some content will be strategic and some tactical. Content of MS Office
documents transcends the actual presentation as a printed page, slide,
spreadsheet, database report, email message, an index of documents, UML:
or project waterfall, or organization chart. Microsoft Corporation is
positioning Office as a platform for delivery of new services; it is not
just about a PowerPoint presentation or a Word document printed to a
facsimile. The DOM is a project plan, with a structure, with components
that do things and are sensitive of their own.
[0116]For these reasons, it is important to explore the MS Office DOM
factors: Content classification; Tagging; Clearance level; Data mining;
Traffic analysis; Inference; Encryption; Digital Signature; Document
access linked to Fortezza (an encryption program/system), PC Crypto
cards, smartcards, and n-factor authentication; Granularity; Strategic
information; Tactical information; Common Criteria or NIST analysis;
Covert channels; and Bell-LaPadula model conformance.
[0117]Content classification with taxonomic classes occurs with tagging
for formatting with bold, indexing, and paragraph marking, explicit
element tagging for HTML and XML or database and spreadsheet table,
field, ranges, row, and column designations, as well as authorship
techniques. Formulae and macros define ranges with informational content
(contextual algorithms which link content), as well as indicate purpose
and intent of the process as well as the target data. When content is
tagged at the sideline, as in "eyes-only," or within-the text with any
label name for clearance level, as in "<1>," this attests to a SC
sensitivity level with an importance factor. For example, a subtotal of
employee salaries within a pro form a business plan matched against a
list of employee names compared to a bank check ledger gives away each
employee's salary level; each document in isolation does not give away
information until several are merged and analyzed together. Direct
analysis through record relationships and sorting is one type of data
mining, human intelligence through inference or statistical inference
with set theory or Bayesian methods is yet another. For example, because
you know that 6 employees are traveling to a conference in D.C. and two
others are not in the office, you can approach a particular person who by
inference is manning the station desk with a very specific social
engineering attack. OneNote, InfoShare, Net Meeting and/or Live Meeting,
Outlook, and Exchange with MS Project also enable workflow routing, group
editing, and acceptance signoff. This information becomes part of the
document metadata so that traffic analysis shows where the document
originated, what changes were made and by whom, how it was routed by
username, network, and IP address, who has seen it and has access to it,
and all process flow and comments. One of the secure prizes of
organization information thus unintentionally published is the names of
people within the organization and functional roles.
[0118]Designing a knowledge expander search engine through granularity of
MS Office node elements by analysis for inclusion and exclusion is an
effective method. Multiple source documents create structure and semiotic
meaning not in evidence with subsets. This process breaks the context to
prevent useful data mining, routing inferences, and the more powerful
semiotic information methods. It allows for the separation of strategic
information from the tactical, so that access is granular by role, user,
and other discriminators. Many academic and implemented security models
are in use today, both as a straw man and for certification processes.
DOM Process Editor
[0119]Document object model (DOM) source documents, and particularly
Office document modules, comprise the blueprints, process, external data
sources and linkages, and materials for building the resulting
presentation; the presentation content is usually the ultimate end
product. The blueprints and process often are immaterial to the
presentation and represent proprietary and confidential material. This
DOM object model complexity and diverse accessibility creates an
opportunity for the knowledge expander search engine.
[0120]Effective DOM (Microsoft) and metadata searches use the object
hierarchy structure as variously described as a binary tree, category
structure, or hive. In any event, the entry point is the root or base,
containing a potentially infinite number of subcategories, each with a
potentially infinite number of leaf items. See FIG. 9. The structure can
be pruned, deleted, or rearranged. The items represent object-oriented
information, from entire subdocuments, to relational databases, layered
graphics with vector elements, to simple plain-text, to a single binary
numerical element.
[0121]The process requires a parse of all branches to each and every leaf.
This process is not recursive, just extensive. Each path is examined for
context, each leaf for content, all nodes for external references, and
everything must be viewed within the context of sources and methods, not
just obvious content. The obvious content is what the user created and
sees, but as you now know, that is a minor portion of the data contained
within the document object structure. This is a paradigm shift is shown
in the hierarchy below:
TABLE-US-00002
Table for Processing DOM
For each document (the file and structure)
Access the root
For each limb
For each branch
For each sub-branch
For each leaf (item)
Process each leaf
[0122]Preservation of the path to each leaf is important as it defines the
access to that data element. The existence and/or null value of the leaf
represents a security control point. The model defines, with supplemental
external knowledge of the object model, potential search opportunities.
The model and the content are not separate from external knowledge of
sources and methods. The model and the content are part of a contextual
analysis of the source document or data stream itself. The leaf the
significant search control point. It is possible to review and alter the
contents of the leaf within the context of the purpose of the leaf to
retain functional access with multi-level SC sensitivity.
Five Informational Attributes
[0123]The five information attributes of SC sensitivity in context to
processing include the leaf, purpose, sources and methods, ownership,
date or timeliness, and content. The entity must establish protocols
which rate or prioritize the five information attributes on each root,
branch and leaf in the DOM source document. With the system initialized
in this manner, the processing of the DOM document within the parameters
of the knowledge expander search is accomplished.
[0124]Purpose
[0125]How does the purpose of the leaf provide context, purpose, or
informational reference to the document as a whole or the individual
leaf? Does it provide source, destination, authorship, viability,
validity, verification, or integrity to the document as a whole or the
individual leaf? Consider the value of processes imbedded in the document
as cell formulae, a help file, or other complex routing wizard. Does it
show linkages or references to other documents? What is its status or
position within the document? What is its element position, as a
headline, footnote, or redlined status? These seemingly minor details
transcend actual content but provide clues to the following attributes.
[0126]Sources and Method
[0127]Intelligence agencies stress the confidentially of the sources and
methods used to gather information. The information itself might or might
not be important, but the ongoing care of the sources and methods is
important for future information gathering activities and retention of
any status quo until action is initiated. In addition, the viability,
validity, verification, or integrity of the document is predicated by the
viability, validity, verification, or integrity of the sources and
methods used to create it. In terms of the Office document, this type of
information is both contextual, leaf content, and metadata. To presume
that the SC search uses only doc content at the leaf misses the value of
metadata and the inherent value of the object-oriented document format.
For example, authorship, source, source dates, editing dates, deletions,
redlining, notes, footnotes, MS hidden text, links, and other structural
elements in the source doc describe when, how, where, and who created the
document. This speaks to the viability, validity, verification, or
integrity of the document as a whole, and can compromise past, ongoing,
or future data collection efforts and operations.
[0128]Ownership
[0129]Ownership is reflected both in leaf-level content--that is obvious
when a document is presented or published--but also in the metadata.
Ownership is also a characteristic of file storage properties, in ring
rights, file storage position, linkages, SMB or network file access
rights, and HTML references. Ownership, particular the number of links,
the times access and edited, numbers of hits, and the level of churning,
suggests the relative importance and merit in the document.
[0130]Date-Timeliness
[0131]Date or timeliness reflects currency. The dates, in terms of edit
times, access times, and frequencies suggest the relative importance and
merit in the document. Touch and other file-level commands can only mask
the overt date and timestamp of a file, not its purpose or content, true
timeliness, or merit. This information is spread through the metadata and
leaf content. In some hierarchical structures, this information is stored
in tables or other structures apart from the immediate document root.
When a document is a relational data structure, as in Access or SQL,
hidden system fields and hidden security data define edit and deletion
times. It is also important to recognize that in databases, records which
are deleted by the user are only marked as deleted but persist until the
database is purged, packed, cleaned, compressed, or otherwise processed
in a maintenance mode. When relational technology with transactional logs
and rollback facilities are enabled, data can be recreated or dated
despite many types or natural of instigated disasters. This supplemental
metadata defines date and timeliness too.
[0132]Security
[0133]Security of content can be compared to erecting a barrier around
that content and may be viewed as an important SC sensitivity issue.
However, when content becomes a collection of simple data elements along
with data objects, dispersed and distributed sources, effected by
embedded events and triggered methods, a search which ignores the
security content characteristic fails to acknowledge that the SC
sensitivity may be impacted by such omission.
[0134]While content is king in most search systems, it is not the only
critical aspect of a source document. In terms of the knowledge expander
processing of an Office document, each leaf must be processed and
assessed for its metadata. Note again that each leaf may be another
object-oriented structure in its own right or a simple element. It will
need to be processed and assessed accordingly.
Select Content Introns and Exons
[0135]Terminology employed in connection with the operation DNA
(deoxyribonucleic acid) provides an appropriate metaphor for the MS
Office document object model or any other DOM model. While the DOM is
separate from an MS Office binary file, it defines the purpose of that
file and maps its activation. The DOM "genes" are expressed into the file
binaries only as specifically referenced, and frequently diverge from the
pure MS Office application as genes from other OLE (object linking and
embedding) applications are embedded into the document. The DOM and the
expressed document can mutate for better or worse, and both backwards and
forwards the document is adaptable just like DNA, with unforeseen
consequences including the profound security flaws evident within the MS
Office workflow.
[0136]In genetics, an intron is any non-coding or non-activating sequence
of DNA initially copied into RNA but cut from the final RNA transcript or
unknown as to singular or recombinant purposes. Introns are excluded or
ignored in the DNA process. An exon is a coding or activating sequence
with a known purpose that is actually used or one that is unknown as to
purpose but nonetheless still used. DNA is, of course, the blueprint for
life. RNA is the functional transcript of the DNA blueprint used for cell
division and replication. Exons are the useful portions in the DNA cycle.
[0137]In the object model, the DOM is metaphorically the DNA blueprint for
an MS Office document whereas the actual Word, Excel, or Outlook message
is an expression of the RNA as a functional transcript. Correspondingly,
the SC or select content intron is any document branch, leaf, or node
element with a non-coding, non-activated, or even unknown control utility
for the document. From a select content-search standpoint, each and every
intron represents a non-qualified element that is a potential search term
that may lead to an expanded knowledge expander supple doc compilation.
Rather than ignoring SC introns as most common search engines do, the
current knowledge expander search engine employs the SC introns to expand
the scope of the search. A SC exon is any document branch, leaf, or node
element serving an end purpose. Each SC exon in a MS Office document
becomes a certifiable data element.
[0138]Unless each such SC intron and SC exon in the source document DOM is
vetted for credentials, which vetting includes a prioritized taxonomic
label or tag, the knowledge expander search, whether through conventional
search engines or the novel search engines described herein, will be
incomplete. This is effective for DOM, HTML. XML, databases, and any
structured file binaries. The standard 2-phrase process transforms into a
3-phase process where DOM node elements are coded either as exons or
introns, and thereafter processed accordingly for inclusion or exclusion.
[0139]The improved accuracy of 3-phase scanning of documents within the
context of an object model is apparent. While the traditional 2-phase
method find SC introns and SC extrons within a source document, it also
miscodes several other sequences as introns. The accuracy of such process
will always include statistically measurable false negatives and
positives, thereby missing true threats and removing non-threats. The
3-phase process improved on the 2-phase process with granular
deconstruction of the document and subsequent recoding of both false
positives and false negatives to yield a higher rate of accuracy. SC
introns are added to the search term list in the knowledge expander
search engine and are scalable numerically. Better DOM maps mean better
intron handling.
[0140]FIG. 11 shows the General DOM Editor program 1022 in a flow chart
form. Step 1024 is the initialization that is employed by the security
entity or SC sensitivity supervisor to set up the program. Step 1026
obtains the DOM layout or blueprint from the DOM vendor. Step 1028 notes
that for each root, branch and leaf in the DOM, the information
attributes must be prioritized with the SC or select content and
organizational informational attributes (a taxonomic routine with
prioritization). SC introns are identified and SC exons are classified,
placed in a hierarchical structure and labeled. This step, of course,
uses a content filter to identify the SC exons. A contextual filter or
algorithm is used to taxonomically classify SC exons at a higher priority
level. For example, when "Bin" is next to "Laden" the SC exon
combination--Bin Laden--is classified TS or the top level for the SC
H-tax class.
[0141]Step 1030 obtains the source document DOM. Step 1032 maps the DOM
source document as a binary file populated with content data and meta
data as a subset of the DOM blueprint. Step 1034 identifies SC exons and
SC Introns. Step 1036 labels or tags SC exons per the tax class
(taxonomic classification) and per priority, to be included in the
further processing of the item and identifies SC introns to be used as
search terms in the knowledge expander search. Multiple tags and
overlapping tags are possible and a ruled-based system is employed to
upcode multiple tags and overlapping tags to the next higher tax
class--priority level. Step 1036 converts SC exons at each root, branch
and leaf into SC level save doc, form and format (for example, a safe DOM
template), and generates a save blueprint or map, drops (or alternatively
stores in a remainder store) all low priority SC exons, and expands the
search term list with the SC introns and the top group of the SC exons.
Step 1038 processes the save DOM doc, that is, the top level SC exons and
all SC introns as search terms through public and/or private databases,
indices, search engines, etc. A divergent search uses SC intron. A
convergent search uses SC exon. Supplemental documents are gathered form
the search, the filter H-- tax class priority tag process is repeated on
the supple docs, the SC introns are identified and selected top priority
SC exons are identified and the search and a second tier supple docs
compilation is processed. Supple docs are stored and a relational map is
generated both with the search terms and the supple docs.
DOM Template Editor
[0142]The following tables present the current collection of methods for
offsetting MS Office security flaws. The same method are applied to
deconstruct the MS Office document to locate, map and tag data elements
and metadata therein.
TABLE-US-00003
Template - Editing - Publishing Table
1. Start with a clean template
2. Write-protect templates
Attached template(s) or styles for other MS Office documents
Normal .DOT
Clean up .DOT
3. Edit cleanly
Disable Versioning
Disable Change Tracking
Periodically "Accept Changes" to purge change
log and save or save as
Disable Fast Save
Install Patches for "Unwanted Data"
Do use comments, not hidden text
Do not use footnotes, end notes, table of contents, index, links,
4. Remove References - Convert into Safe Text and Function mode
URL (covert to non-function form, such as "www and pto.gov")
Hyperlinks
Pointers
References
hidden text, headers, footers, footnotes, endnotes, tables
of contents, index, links, can establish a context or cerate a
semiotic inference to other documents or sources (copy
content and paste into safe DOM, for example, all footnotes
and endnotes are shown as [data here] where the footnote
appears int eh text)
5. Paste... do not embed
6. Publish... do not send a file
Print
Fax as image (not as binary document in WinFax or eFax, etc)
[0143]FIG. 12 shows a flow chart of a basic application for a DOM
Editor--knowledge expander search for MS Office document. Step 1042
initializes the system. Step 1044 obtains the source DOM document and
notes that all processing occurs after a spam and virus filter. Step 1046
notes that the program creates or is provided with a security safe or
clean DOM document and map. All levels of SC are labeled or tagged and
saved in the "save doc" routine. This hierarchical taxonomic (H-tax)
tagging labels all SC words, terms, etc. and locates and maps exons and
introns in the source doc. Step 1048 notes that a template is opened and
SC exons and SC introns (non-standard SC and unknown terms, characters,
words, etc.) are copied from the source doc into the clean DOC. A clean
.DOC template (Word) or whatever the new document type is opened for the
specific application. The Normal.DOC or New Spreadsheet.XLS on the MS
Office distribution CD is safe. In Visio, for example, start with a new
chart. Older documents saved as templates tend to accumulate
unanticipated metadata. If one must use a non-standard template, clean it
up. View it as both a printed document, as a binary file, and as a
document object. Write protect the templates, or store the templates as
non-modifiable networked volume. If the templates are used throughout the
organization, create a network store for them and configure each user's
installation of MS Office to look there for the templates. Search the
network for duplicate copies of these templates and eliminate them.
[0144]If changes from any version of MS Office to another version are
made--this can be a regularly upgrade or a even a downgrade--create new
documents and cut-and-paste parts of prior documents into new ones. Lose
the older files and templates. If you receive or open an Office document
in either an older or newer version, create new documents and
cut-and-paste parts of prior documents into new ones consistent with the
MS Office version that you use.
[0145]Step 1050 disables edit controls and step 1052 copies SC exon and SC
intron content. The point is one must edit cleanly. This is not a single
step but rather a process, both one time and ongoing. Disable versioning
in step 1050 to prevent a buildup of past versions of the document. With
versioning, prior sessions will be stored as document.doc 1, document.doc
2, and so on. These tend to get entwined with the latest version. If
workflow with InfoPath, Outlook, or other collaborative workflow tools
creates duplicate copies of the source document file for each user. Step
1050 includes the concept that the system is configured to store a single
network copy instead. Preserve an audit trail and backup with a system
backup rather than versioning. Disable change tracking in step 1050 to
curtail the buildup of additions, deletions, and changes that transcend
the publishing intent of the document. If redlining is necessary,
establish guidelines for periodically accepting changes to purge change
log. Use the command to save the document without the change log with
File/Save or File/Save As. Do not use nor rely on fast saves, timed
saves, or file recovery after a MS Office crash to purge the dirty
metadata. After a crash, reopen the document, save the document under a
new name. Close the Office application. Delete the old file precipitating
the crash. Rename the new file under the old name. Reopen the Office
application.
[0146]Step 1054 locates text in footnotes, hidden text, etc and labels or
tags that content as SC exons or SC introns and maps those data elements
to the SC save doc and copies the elements into the Clean DOC. Use
comments instead of hidden text. It is documented as a feature so it can
be found rather than accidentally uncovered. Hidden text with a font
color change or font size change looks like an artifact that most users
will ignore or delete. Avoid the use of headers, footers, footnotes,
endnotes, inserts for table of contents, index and the like. These appear
only in the printed output unless specifically viewed from the View
pulldown menu. Such links also create a lot of dirty metadata beyond what
is visible even during editing that persists until changes are accepted.
Remove references from the source document. This is subtle, but very
important when documents are specifically posted or even inadvertently
listed on web sites. References include other files, documents,
hyperlinks, and other possible embedded formatted materials. These
references create the ability to infer quite a lot about the purpose of
the document from other related documents in the same directory, by the
same authors, and the types of other documents. For example, a
spreadsheet stored with a report that is not overtly included in the
report suggests that is source material that has not been reviewed with
an eye towards privacy, security, or client privilege.
[0147]Paste and copy images, cut text, formatted text, pie charts, record
sets, slides, waterfalls, milestones, organizational charts as plain text
or an image rather than formatted Office objects. If the embed commend is
used, all the metadata baggage from the other Office application is now
added to the metadata in the target document. Since that metadata baggage
is not native to the target document application, it is inaccessible and
truly hidden. Tools, such as Metadata Assistant will not find Excel
metadata within a Word Document, Word metadata within an Excel
spreadsheet, and none of them within an Outlook note or message.
[0148]Step 1056 notes that a clean map for the meta data cleared DOM
document must be created.
[0149]Step 1058 executes the intron search and/or granular search routine,
gathers and compiles supple. docs and the processes those supple docs
through the granular filters discussed earlier to locate, with the
hierarchical taxonomic filter process tags new words, terms, symbols,
which are related to the original SC or select content, thereby expanding
the knowledge base of the meaning of the SC source document.
Hierarchical Taxonomic Class Examples
[0150]Various type of classification systems (taxonomic systems) may be
employed. For example, a dictionary classifies all words as nouns, verbs,
adverbs, etc. This is one taxonomic system. A prioritized H-tax system
classifies nouns into a name subclass and this name subclass may be
priority or sensitivity level 1. Geographic locations (class nouns) may
be priority 2. The operator of the present knowledge expander search
process may place a high priority or sensitivity on "noun" class, thereby
converting the simple taxonomic structure into a hierarchical taxonomic
system. Identifying categories and subcategories for SC sensitive words,
etc. or critical data, creating a compilation of pre-existing data,
comparing the compiled pre-existing data to the target data and labeling
or tagging the SC terms is one of many important aspects of the present
invention. Table 1 which follows provides categorical identifiers for
personal identity. These categories, which may be utilized to identify a
person, are not meant to be all encompassing but are mainly provided as
examples.
TABLE-US-00004
TABLE 1
Categorical Identifiers For Personal Identity
name association(s)
address(es) and variants frequent flyer/buyer club info
telephone number(s) and variants remittance advice
username investigation evidence
biometrics court evidence
gene typing EDI/EDIFACT records
photograph applications
date of birth personal web sites
age Chain of trust forms
marital status Chain of custody forms
gender skill set
sexual orientation religion
sexual proclivities personal interests
disabilities travel log
tattoos number of siblings
scars business partners
visible or functional injuries business name
age/age range profession
hair color account numbers (banking, services, suppliers)
eye color service providers (physicians, insurers, hospitals,
race profile clinics, etc.)
educational history X-rays
employment history surveillance
home price dental charts
ethnicity medical records
personal interests account balances
personal descriptive information (e.g., SWHM account transfer or
transaction amounts
38, Professional) income range
physical stigmata neighborhood/city/region/country
skill set license (driver, occupational, professional)
credit history vehicle registration (license, tag, plate, etc.)
credit reports (formal NCR, etc.) vehicle identification
social security number vehicle make, type, model, color, year
patient ID or other location- or process- date of life events
specific user assignment incident reports (legal, criminal, health
insurance number services, news)
credit card numbers accident reports (auto, OSHA, EPA, EEOC,
birthplace etc.)
heritage criminal convictions
health history court records
political party abuse records
political beliefs divorce proceedings
bankruptcy records news reports
organization registrations family history
Corporation officers and registrations family relationships
tax records (chattel, land, local, state, Federal, family health history
and special use taxes) legal documents
property ownership consent forms
permit applications newsgroup postings
donor lists
[0151]After categories are identified for the critical data, it is
important to create the hierarchical taxonomic system against which the
target data is tested. Ranking or qualifying the categories at SC
sensitivity levels is next. Table 2 which follows is a general attempt to
quantify the categories for personal identification from a high risk
value beginning with "name" to a low risk value ending with "personal
interests". Again, the Ranked Identity Category Table 2 is not meant to
be limiting but is meant to be an example. Individuals skilled in
identifying a person may alter the ranking of the identity categories in
Table 2.
TABLE-US-00005
TABLE 2
Ranked Identity Category
name disabilities
address tattoos
telephone scars
username injuries
biometrics age range
gene typing hair color
photograph eye color
date of birth race profile
age education
marital status employment
sex personal interests
sexual orientation
sexual proclivities
[0152]The present invention can be employed to use a hierarchical
taxonomic system for a business. Table 3 set forth below provides
examples of categorical identifiers for a manufacturing business. Again,
this list is not meant to be exhaustive or complete, but is only provided
as an example of the types of categories and subcategories which a
manufacturing business would employ in order to establish the risk
monitor of the present invention.
TABLE-US-00006
TABLE 3
Categorical Identifiers for Manufacturing Business
Manufacturing
product brand names
product generic name
drawings
tools (brand names and generic names)
hand tools
software
machines
software, computer programs, etc.
Research and Development
competitors products, competitor names, patent numbers,
patent titles, project names, project personnel
Sales
personnel
competitors
sales data
quantity
geographic distribution
customers
names, addresses, contacts
sales projections
Financial
chart of accounts
ledgers
financial statements
tax returns
Human Resources
see categorical identifiers for personal identity
[0153]With this list, the manufacturing business may assign a SC
sensitivity level to each category (class, subclass and sub-subclass)
thereby creating a prioritized hierarchical taxonomic system.
Knowledge Expander (KE) Basic Program
[0154]As discussed earlier, the information life cycle or knowledge
expander search can be configured with many different modules in order to
achieve the objective of the operator. The knowledge expander or KE basic
program 3001 in FIG. 13 is one basic implementation of the expander
search program. FIG. 13, In step 3002, the operator or user sets various
filters and sets the search targets. The search targets may be publicly
available search engines or private data bases, data collections, indices
or any item that the user wants the KE search to access. Filters are
described earlier as hierarchical taxonomic filters or taggers, content,
contextual and other types of filters. Filters are used to identify
class, subclass and priority in the hierarchical taxonomic or H-tax
system, to apply contextual rules or algorithms ("bomb" within 10 words
of "arab"), and content (select content or SC verses non-matching SC).
Step 3004 obtains the source data or the source is input by the operator.
Data input may be from a client computer in a server-client computer
system. The source data may be a data stream, a source document or other
item. Step 3004 contemplates that if a document is utilized, the document
is deconstructed to its basic data elements and appropriately mapped. See
the discussion above in connection with DOM branch, leaf and tree
deconstruction. In step 3006, the hierarchical taxonomic classification
(H-tax) occurs and each data element in the input document is tagged
based upon the classification system which has been prioritized by the
user (or the user uses a pre-set prioritized H-tax). A note to
deconstruct the source document into data elements (see DOM
deconstruction into its object hierarchical structure above) is found in
step 3006. In step 3008, for non-matching data elements, a search is
conducted through the search targets. Non-matching data elements form the
input doc are those which do not match SC. In step 3010, the system
gathers the documents from the search (supplemental documents) and
repeats the H-tax tagging operation for b number of supple docs. A
relationship mapping function and display function is activated if c
number of hits occur within the H-tax top priority range. In other words,
if the search for non-matching data elements returns 50 select content or
SC terms and of those 50 SC terms in the supple docs, 20 SC terms fall
within priority ranges 1-5 of the hierarchical taxonomic classification
set by the user (n priority H-tax levels), then the system and process
maps the results and displays the representations of the H-tax recovered
from the supple docs. C is less than 20. If less than b number of H-tax
tags are noted in the supplemental documents, the system repeats steps
3006 and 3008 and gathers a second tier of supplemental documents.
[0155]Steps 3012, 3014, 3016 are optional. As an option to be set by the
user or as an option to be set by the system operator as a default, step
3012 changes the H-tax top range bandwidth from n priorities to be
searched to m priorities. This change may be needed to expand the search
bandwidth from b to b-10 to b+20. A reduction (m less than n) is needed
if too many supple docs are recovered by the search. If too few are
recovered, then m is made greater than n priorities in the H-tax. Option
3014 changes the output supplemental document bandwidth to b+y if too few
supple docs are recovered. Of course, the output supplemental bandwidth
could be reduced with b-y. Optional step 3016 mixes n number of select
content search terms with the no-match data element before the search.
This effectively is a convergent filter. By adding the known SC to the
search terms (initially, search terms are generated from the input doc
based upon priority H-tax), the search is biased or is set to converge on
the known SC added to the search term group. By using the no-match (NOT
AND) function, the KE search diverges to gather a wider range of docs. Of
course, the user may always employ common search engines in addition to
the search plans set forth herein. Step 3018 repeats the process until b
supplement documents or c hits (SC sensitivity matches) are noted within
the H-tax top range. Step 3020 maps, displays, stores and ends the
program.
[0156]FIG. 14 shows an expansive or expansion unknown search engine 3031.
Step 3032 initializes the program with preferably user input, sets up the
hierarchical taxonomic H-tax classification system along with a priority
or sensitivity level assigned to each level and branch and leaf in the
H-tax system. Select content or SC filter for content and contextual
range or algorithm tests are also set. At least some select content
should fall within the prioritized hierarchical taxonomic system. The
select content SC encompasses words, terms and data objects that the user
wants to expand his or her knowledge base with the search engine. The
user sets a no-match search bandwidth nms and sets search targets such as
search engines, public or private databases, data compilations, indices,
data collections and data structures or whatever. Step 3034 obtains a
source or data stream or input document. Step 3036 processes each data
element (which may include a document deconstruction) with the H-tax,
content and context filters, and tags each data element with an H-tax
marker. Multiple, singular, and a default "unknown" H-tax classification
indicators are linked or tagged on every data element. The system in step
3036 may upgrade or downgrade the priority or sensitivity H-tax level for
a particular word, term, etc., due to contextual rules in the context
filter. In step 3038, the system conducts a search for nms number of not
matching or unknown data elements. If nms is 10, the system takes the top
10 priority H-tax supple SC terms obtained from the input doc and uses
the top 10 supple SC as search terms in the target data collections. The
search is conducted through search targets identified in the set-up step
3032. The no-match search 3038 is sometimes identified as step A herein.
Step 3040 gathers supplemental documents. Sometimes, step 3040 is step B
herein. In step 3042, the system processes the supplemental documents
through the H-tax tagger. The supple docs are classified with the H-tax.
This is sometimes step C. In step 3044, the system determines if p hits
have been noted in the H-tax top range levels. If YES, the system stops
and maps the select content SC sensitivity sel. levels to the H-tax
mapping structure and displays that SC hierarchical representation map to
the user. Further, the system in step 3044 maps the supplemental
documents to the H-tax map structure and displays that to the user.
Sometimes, step 3044 is step D herein. In step 3046, if p number of hits
are NOT identified in step 3044, steps A,B,C,D are again executed and
repeated unless the repeat cycle is more than R. The nms bandwidth may
automatically change (increase) or the n priority H-tax may change to m
H-tax levels to increase the supple docs or the hit count for supple SC.
The same techniques may be used in reverse if too many supple docs are
recovered. The system ends after step 3046.
[0157]FIG. 15 shows knowledge expander KE search options 3051. Step 3052
is an option to add a noise filter after the H-tax tagger step 3036 in
the expansive unknown search engine routine 3031. The noise filter
reduces the number of unknown data element and operates on the unknown
tags for those unknown data elements. For example, terms "and" and "or"
and "the" may be deleted by the noise filter. Optional step 3054 adds to
step D, if p hits is equal to zero, then the system resets the no-match
bandwidth nms+u and repeats step A,B,C,D and repeats the entire process
until p hits in step D or R iterative cycles have been noted (counted).
In optional step 3056, a convergence filter is added to the expansive,
unknown search engines 3031 by adding w select content SC search terms to
the no-match search step A. In optional step 3058, a divergence filter is
added by changing, either increasing or decreasing, nms value if p hits
do not exceed p+x. In optional step 3059, a content filter is added to
detect temporal relationships such as time and date ranges. Time may be
time of length of search or time may be a time-date range limit on
recovered supple docs. The temporal contextual filter (time and date is a
matter of context) would be added to step 3036 the H-tax step. In
optional step 3060, the H-tax map is output to the user so the user sees
the classification and the prioritization of that classification. In step
3062 the operator can change the repeat cycle R, the time the search
runs, the number of cycles, and the search target databases, data sets,
spreadsheets or public or private search engines. In optional step 3064,
the operator can limit the supplemental documents based upon a temporal
factor such as time, the total number of SC terms located in the
supplemental documents, and the total number of supplemental documents.
General System Comments
[0158]It is important to know that the embodiments illustrated herein and
described herein are only examples of the many advantageous uses of the
innovative teachings set forth herein. In general, statements made in the
specification of the present application do not necessarily limit any of
the various claimed inventions. Moreover, some statements may apply to
some inventive features but not to others. In general, unless otherwise
indicated, singular elements may be in the plural and vice versa with no
loss of generality. In the drawings, like numerals refer to like parts or
features throughout the several views. The section titles are not meant
to limit the detailed description of the system and process described
therein.
[0159]The present invention could be produced in hardware or software, or
in a combination of hardware and software, and these implementations
would be known to one of ordinary skill in the art. The system, or
method, according to the inventive principles as disclosed in connection
with the preferred embodiment, may be produced in a single computer
system having separate elements or means for performing the individual
functions or steps described or claimed or one or more elements or means
combining the performance of any of the functions or steps disclosed or
claimed, or may be arranged in a distributed computer system,
interconnected by any suitable means as would be known by one of ordinary
skill in the art.
[0160]According to the inventive principles as disclosed in connection
with the preferred embodiment, the invention and the inventive principles
are not limited to any particular kind of computer system but may be used
with any general purpose computer, as would be known to one of ordinary
skill in the art, arranged to perform the functions described and the
method steps described. The operations of such a computer, as described
above, may be according to a computer program contained on a medium for
use in the operation or control of the computer as would be known to one
of ordinary skill in the art. The computer medium which may be used to
hold or contain the computer program product, may be a fixture of the
computer such as an embedded memory or may be on a transportable medium
such as a disk, as would be known to one of ordinary skill in the art.
[0161]The invention is not limited to any particular computer program or
logic or language, or instruction but may be practiced with any such
suitable program, logic or language, or instructions as would be known to
one of ordinary skill in the art. Without limiting the principles of the
disclosed invention any such computing system can include, inter alia, at
least a computer readable medium allowing a computer to read data,
instructions, messages or message packets, and other computer readable
information from the computer readable medium. The computer readable
medium may include non-volatile memory, such as ROM, flash memory, floppy
disk, disk drive memory, CD-ROM, and other permanent storage.
Additionally, a computer readable medium may include, for example,
volatile storage such as RAM, buffers, cache memory, and network
circuits. Furthermore, the computer readable medium may include computer
readable information in a transitory state medium such as a network link
and/or a network interface, including a wired network or a wireless
network, that allow a computer to read such computer readable
information.
[0162]In the drawings, and sometimes in the specification, reference is
made to certain abbreviations. The following Abbreviations Table provides
a correspondence between the abbreviations and the item or feature.
Abbreviations Table
[0163]bd board [0164]CD-RW compact disk drive with read/write feature
for CD disk [0165]comp computer [0166]CPU central processing unit
[0167]DB or db data base or structured data file [0168]doc document
[0169]dr drive, e.g., computer hard drive [0170]e encryption [0171]F
floppy computer drive or flash drive memory [0172]H-tax hierarchical
taxonomic system, a prioritized classification system [0173]I/O
input/output [0174]KE knowledge expander, such as a knowledge expander
search [0175]loc location [0176]mem memory [0177]opt optional [0178]PC
personal computer or any other type of general computer [0179]recon
reconstruct [0180]rel release [0181]req request [0182]SC select
content--e.g., words, terms, images, sound that is of particular interest
to the user [0183]sec security [0184]sec. level TS top secret, S secret,
C classified, UC unclassified [0185]sel SC sensitivity level, sometimes
SC sel. level [0186]sel cont select content [0187]sel levels SC levels,
hierarchical taxonomic classification levels of SC [0188]sch search
[0189]supple supplemental [0190]supple doc supplemental document of data
object [0191]sys system [0192]t time [0193]tax taxonomic or
classification system [0194]tele-com telecommunications system or network
[0195]unkn unknown item or data element or data object
[0196]Data," as used herein, includes any data object, e.g., text, image,
icons, audio, video, still images, etc. and data is sometimes referred to
herein as a "data object." A source document is either a document, any
data structure, or a data stream. Since a data stream has a start bit or
term and an end bit or term, the data stream is structured data, and
reference to a "document" refers to any document, data structure, or data
stream. Likewise a "supplemental document" is any document, data
structure, or data stream. The select content (SC) is any critical or
security sensitive word, character, image, or data object as pre-defined
or as established by the user. The user may specifically design the
filter, begin with a dictionary to define common terms, identify any
additional SC words, letters, images, icons, partial versions of the
foregoing or any other granular aspect of the source document.
[0197]FIG. 16 diagrammatically illustrates a personal computer or PC
computer system 140, a second PC or computer 142, and a third PC-3. PCs
140, 142 and PC-3 are connected together via a network 145 (LAN or WAN)
and connected to one or more private databases or data structures which
are represented by Db A. The LAN 145 is also connected to an input/output
device 146 that may be generally described as a router or an I/O device
to an outside communications system. The input/output device 146 is
connected to a telecommunications system 148 which leads to Internet 150.
The Internet is a global computer network. Internet 150 is coupled to a
plurality of servers, one of which is server 152. Server 152 may be
designated as an application service processor ASP. The ASP provides
various tools to the user on computer 142 such a Google search through
other data bases, indices and data structures. Internet 150 also includes
various computer memory storage devices such as computer storage B 154,
computer storage C 156 accessible through Gate C 157 (via password or
other control device) and computer storage D 158 accessible via Gate D
159. Access to computer storage C and D is permitted via, in one
instance, security level clearance modules or Gate C and D 157, 159. Maps
to the computer stores C and D may require security level clearance.
[0198]Typically, PCs include a keyboard or data input device 161, a
display 163, a central processing unit CPU 165, a video board 167 having
video board memory 169, a fixed disc hard drive 168, a RAM 166, and
input/output device 164, a removable memory F media (flash or floppy)
drive 162 and a removable compact disk (CD) read-write (CD-RW) device or
drive 160. The system may include other removable disk drives, tape
drives, or flash memory units. Internal units CPU 165, video board 167,
hard drive 168, RAM 166 input/output device 164, floppy drive 162 and
CD-ROM device 160 are all coupled together via an internal bus 171. Bus
171 represents a plurality of buses as is known to persons of ordinary
skill in the art.
[0199]One methodology of implementing the present invention utilizes
filters on the PC or on removable memory F. The present invention may
also be embodied utilizing an Application Service Provider (ASP) on
server 152 and in a client-server network. The user or operator on the PC
142 calls up an ASP on system 152 and operates the KE process on the
computer system 152. Filters and rules may be located with a uniform
research locator or URL to find filters, data collections, target files
from computer store B, C and D. In a client-server environment, server
152 acts as a server generally cooperating with data operations with
client computer 140. Of course, persons of ordinary skill in the art
recognize that the server may be located on the local area network 145
rather than being interconnected with Internet 150 as shown in FIG. 16.
The claims appended hereto are meant to cover the alternative
embodiments.
[0200]As an example of a client-server or web-based implementation of the
present invention, the user at computer 140 may define the SC 2018 as
described above, and input data via keyboard 161 or load source data from
F drive 162 or CD-ROM drive 160 into RAM 166. Alternatively, the data
stream on LAN 145 may be monitored by PC 140. In any event, whether the
source data is input via keyboard 161 or copied or accessed in or from F
drive 162 or CD-RW drive 160, the source data is filtered as discussed
above in connection with FIGS. 1-2. Prior to filtering, it is appropriate
for the user at computer 140 to identify the hierarchical taxonomic
system and the location of SC filters and My Group Profiles. Off site
data storage and processes are available permitting activation of server
152 and enabling the server to take over the process directly from user
140. In other words, the user at computer 140 could call up the URL of
the server 152, the server could request certain user information (user
name, password), and would request data from the client computer to
establish the SC filter and H-tax classes pursuant to input selected by
the user. The client compute may (a) filter the source doc thereat or (b)
send the data to the server for filtering. The server could store data
either locally on computer 140 or remotely at computer memories 154, 156.
Either the PC or the server could conduct the knowledge expander search
and return the compiled data, supple does, and map results to the PC.
[0201]The claims appended hereto are meant to cover modifications and
changes within the scope and spirit of the present invention.
* * * * *
