United States Patent [191
`Egger
`
`111111111111111111111111111111111111111111111111111111111111111111111111111
`US005544352A
`[Ill Patent Number:
`[451 Date of Patent:
`
`5,544,352
`Aug. 6, 1996
`
`[54] METHOD AND APPARATUS FOR INDEXING,
`SEARCHING AND DISPLAYING DATA
`
`[75]
`
`Inventor: Daniel Egger, Washington, D.C.
`
`[73] Assignee: Libertech, Inc., Durham, N.C.
`
`[21] Appl. No.: 76,658
`
`[22] Filed:
`
`Jun. 14, 1993
`
`Int. Cl.6
`•••.••• ; .............................................. G06F 17/30
`[51]
`[52] U.S. Cl ............... 395/600; 364/419.19; 364/DIG. 1;
`364/282.1; 364/283.3
`[58] Field of Search ....................... 395/600; 364/419.19,
`364/419.13
`
`[56]
`
`References Cited
`
`U.S. PATENT DOCUMENTS
`
`4,839,853
`4,945,476
`5,122,951
`5,157,783
`5,206,949
`5,241,671
`5,243,655
`5,301,109
`5,325,298
`5,418,948
`
`6/1989 Deerwester et al ..................... 395/600
`711990 Bodick et al ...................... 364/413.02
`6/1992 Kamiya .............................. 364/419.13
`10/1992 Anderson et al. . ..................... 395/600
`4/1993- Cochran et al. ........................ 395/600
`8/1993 Reed et al. ..•........................... 395/600
`9/1993 Wang ........................................ 380/51
`4/1994 Landauer et al ................... 364/419.19
`6/1994 Gallant ............................... 364/419.19
`5/1995 Turtle ............................•......... 395/600
`
`OTHER PUBLICATIONS
`
`Agosti, et al., "A Two-Level Hypertext Retrieval Model for
`Legal Data," SIGIR '91 (1991).
`Fowler, et al., "Integrating Query, Thesaurus and Documents
`Through a Commn Visual Representation," SIGIR '91
`(1991).
`Rose & Belew, "Legal Information Retrieval: a Hybrid
`Approach," ICAIL '89 (1989).
`Belew, Richard, "A Connectionist Approach to Conceptual
`Information Retrieval," !CAlL '87 (1987).
`
`Gelbart & Smith, "Beyond Boolean Search: FLEXICON, A
`Legal Text-Based Intelligent System," ICAIL '91 (1991).
`Lin, "A Self-Organizing Semantic Map for Information
`Retrieval," SIGIR '91 (1991).
`Turtle & Croft, "Inference Networks for Document
`Retrieval," SIGR '90 (1990).
`
`Primary Examiner-Thomas G. Black
`Assistant Examiner-Wayne Amsbury
`Attorney, Agent, or Firm-Dorsey & Whitney PLLP
`
`[57]
`
`ABSTRACT
`
`A computer research tool for indexing, searching and dis(cid:173)
`playing data is disclosed. Specifically, a computer research
`tool for performing computerized research of data including
`textual objects in a database and for providing a user
`interface that significantly enhances data presentation is
`described. Textual objects and other data in a database are
`indexed by creating a numerical representation of the data.
`The indexing technique called proximity indexing generates
`a quick-reference of the relations, patterns and similarity
`found among the data in the database. Proximity indexing
`indexes the data by using statistical techniques and empiri(cid:173)
`cally developed algorithms. Using this proximity index, an
`efficient search for pools of data having a particular relation,
`pattern or characteristic can be effectuated. The Computer
`Search program, called the Computer Search Program for
`Data represented in Matrices (CSPDM), provides efficient
`computer search methods. The CSPDM rank orders data in
`accordance with the data's relationship to time, a paradigm
`datum, or any similar reference. The user interface program,
`called the Graphical User Interface (GUI), provides a user
`friendly method of interacting with the CSPDM program
`and prepares and presents a visual graphical display. The
`graphical display provides the user with a two dimensional
`spatial orientation of the data.
`
`52 Claims, 24 Drawing Sheets
`
`EXHIBIT 2006
`Facebook,Inc.etal
`v.
`Software Rights Archive, LLC
`CASE IPR2013-00479
`
`

`

`U.S. Patent
`
`Aug. 6, 1996
`
`Sheet 1 of 24
`
`5,544,352
`
`26
`
`34
`
`~
`
`~ a 30
`I t:48
`
`58
`
`/
`
`Separate
`Memory
`
`Database
`
`~54
`
`Computer
`Processor
`
`38~
`
`Display
`
`/
`
`28
`
`42
`
`Separate
`Computer
`Processor
`
`l
`
`50
`
`46
`
`FIG.l
`
`

`

`U.S. Patent
`
`Aug. 6, 1996
`
`Sheet 2 of 24
`
`5,544,352
`
`60
`
`~
`PROXIMITY v
`INDEXING
`/
`APPLICATION
`PROGRAM
`
`6 2
`
`8 6
`
`/
`
`....._//
`
`CSPDM
`
`/
`
`70
`
`GUI
`PROGRAM /
`
`FIG .. 2
`
`

`

`U.S. Patent
`
`Aug. 6, 1996
`
`Sheet 3 of 24
`
`5,544,352
`
`INITIALIZE DATA /
`
`J
`
`74
`
`78
`/
`DETERMINE
`TEXTUAL OBJECT /'\../'
`RELATlONSHIPS
`
`72
`
`80
`/
`DETERMINE
`PARAGRAPH TO ~/
`TEXTUAL OBJECT /
`RELATIONSHJ PS
`
`84
`/
`
`/
`/\../'
`
`CLUSTER
`AND
`SECTION
`
`DETERMINE
`
`/
`
`·RELATIONSHIPS V'
`
`BETWEEN
`SECTIONS
`
`/
`
`88
`
`2
`
`GOTO CSPDM
`ROUTINE
`
`/
`/v·
`
`FIG.3A
`
`

`

`U.S. Patent
`
`Aug. 6, 1996
`
`Sheet 4 of 24
`
`5,544,352
`
`96
`
`v
`
`/
`
`INITIAL
`EXTRACTOR
`
`OPINION
`PATTERNER
`
`100
`
`/ /
`
`OPINION v
`
`WEAVER
`
`/
`
`10 4
`
`10 8
`
`PARAGRAPH
`PATTERNER
`(OPTIONAL)
`
`/it
`
`PARAGRAPH v
`
`WEAVER
`
`/
`
`11 2
`
`SECTION
`COMPARISON
`SUBROUTINE
`
`11 6
`
`/
`
`/
`
`FIG. 3B
`
`

`

`U.S. Patent
`
`Aug. 6, 1996
`
`Sheet 5 of 24
`
`5,544,352
`
`120 /
`
`I
`
`Sectloner
`(Geographic)
`
`Sectloner
`(To plea!)
`
`Section
`12s
`Extractor /v
`
`Section
`PaHerner ,...V
`
`132
`
`Section V
`Weaver
`-
`
`~lr_
`
`136
`
`FIG. 3C
`
`

`

`U.S. Patent
`
`Aug. 6, 1996
`
`INITIAUZE CORE
`EN GUSH WORDS
`
`/
`
`Sheet 6 of 24
`
`v40
`
`144
`
`/
`
`5,544,352
`
`CREATEpxw
`BOOLEAN INDEX
`MATRIX
`
`148
`NITIAL ~
`EXTRACfOR
`
`/ ~
`
`POOLPATTERNER
`
`POOL WEAVER
`
`POOL
`SECfiONER
`
`SECTION
`EXTRACTOR
`
`SEcriON
`PAITERNER
`
`152
`
`/ /
`
`US6
`
`v/
`
`/
`
`160
`,/
`
`,_,./
`
`/
`
`128
`.. /
`-
`ft..//
`
`/
`
`132
`
`/
`
`/
`;....-/
`~
`
`SEcriON
`WEAVER
`
`136
`
`/
`
`/
`
`FIG. 3D
`
`

`

`U.S. Patent
`
`Aug. 6, 1996
`
`Sheet 7 of 24
`
`5,544,352
`
`200
`
`Enter
`CSPDU
`(text)
`
`Eucule
`Booltln --~
`Se .. chor
`Alternate
`Pool·
`Genen~tion
`Method
`
`;v
`228
`
`232
`
`~n
`Sullroutne
`
`c...
`Afllr
`Subroutine
`
`Similar·
`CUes
`SuJroulne
`
`Pooi(cid:173)
`Simlllrlty
`Subroudne
`
`Pool-
`Pool-
`lmporta'lce
`Paradigm
`Subroutine SUbroutine
`
`224
`
`GUt
`
`70
`
`\
`
`212
`
`F1G. 4A
`
`

`

`U.S. Patent
`
`Aug. 6, 1996
`
`Sheet 8 of 24
`
`5,544,352
`
`Execute
`Boolean S..-c:h
`crAltem•
`Pool Generdon
`Me1hod
`
`N'lalyzt Info
`
`Analyze~
`
`y
`
`FIG. 4B
`
`

`

`U.S. Patent
`
`Aug. 6, 1996
`
`Sheet 9 of 24
`
`5,544,352
`
`RECEIVE SELECTION OF A
`SINGLE TEXTUAL OBJECT
`FROM RESEARCHER
`
`EXAMINE nxn OPINION
`CITATION MATRIX AND
`OTHER FACTORS
`
`232
`
`400
`
`/ v
`
`4 04
`
`/
`
`/
`
`408
`
`RETRIEVE CITED ~
`
`TEXTUAL OBJECTS
`
`/
`
`FIG. 4C
`
`

`

`U.S. Patent
`
`Aug. 6, 1996
`
`Sheet 10 of 24
`
`5,544,352
`
`236
`
`/
`/ v
`
`40 0
`
`RECEIVE SELECTION OF A
`SINGLE TEXTUAL OBJECT
`FROM RESEARCHER
`
`4 12
`
`EXAMINE n x n OPINION v
`
`CITATION MATRIX AND
`OTHER FACTORS
`
`/
`
`RETRIEVE CITING
`TEXTUAL OBJECTS
`
`18
`
`v
`
`/
`
`FIG. 4D
`
`

`

`U.S. Patent
`
`Aug. 6, 1996
`
`Sheet 11 of 24
`
`5,544,352
`
`240
`
`400
`
`RECEIVE SELECTION OF A IV
`
`SINGLE TEXTUAL OBJECT
`FROM RESEARCHER
`
`EXAMINE q x q SECTION
`SIMILARITY MATRIX AND
`OTHER FACTORS
`
`RETRIEVE SIMILAR
`TEXTUAL OBJECTS
`
`4 20
`
`/
`
`/
`
`4 24
`
`/
`
`/
`
`FIG.- 4E
`
`

`

`U.S. Patent
`
`Aug. 6, 1996
`
`Sheet 12 of 24
`
`5,544,352
`
`244
`
`Receive AddrtJonal
`Textual Object that
`Researcher
`Selected from
`Outside of Pool
`
`428
`
`400
`
`436
`
`440
`
`Receive Pool of
`Textual ObJects
`Identified by
`Researcher
`
`Evaluate nxn
`OpJnlon Similarity
`Matrix for Selected
`Textual Object and
`Other Textual
`Objects In Pool
`
`Determine Sfmllarlty
`of Other Textual
`Objects In Pool to
`Selected Textual
`Object
`
`FIG. 4F
`
`

`

`U.S. Patent
`
`Aug. 6, 1996
`
`Sheet 13 of 24
`
`5,544,352
`
`248
`
`/ .:
`
`428
`
`/
`
`448
`
`452
`
`RECEIVE POOL
`OF TEXTUAL
`OBJECTS
`IDENTIFIED BY
`RESEARCHER
`
`EVALUATE nxn
`OPINION
`PROXIMITY MATRIX
`FOR POOL OF
`TEXTUAL OBJECTS
`
`DETERMINE
`PARADIGM TEXTUAL
`OBJECT BY
`CALCULATING THE
`MEAN OF EUCLIDEAN
`DISTANCES OF
`TEXTUAL OBJECTS IN
`POOL
`
`DETERMINE
`SIMILARITY BETWEEN
`PARADIGM TEXTUAL
`OBJECT AND OTHER
`TEXTUAL OBJECTS IN
`POOL
`
`456
`
`FIG. 4G
`
`

`

`U.S. Patent
`
`Aug. 6, 1996
`
`Sheet 14 of 24
`
`5,544,352
`
`428
`_/!//
`
`/
`
`252
`
`RECEIVE POOL
`OF TEXTUAL
`OBJECTS
`IDENTIFIED BY
`RESEARCHER
`
`I
`
`EVALUATE nxn
`OPINION MATRIX
`FOR POOL OF
`TEXTUAL OBJECTS
`
`/
`
`/
`
`44 8
`
`I
`
`EVALUATE NUMERICAL
`FACTORS AND
`RESULTS OF WEIGHING
`ALGORITHM
`/
`CALCULATED BY THE
`OPINION PATTERNER
`SUBROUTINE FOR THE
`POOL OF TEXTUAL
`OBJECTS
`
`4 60
`
`v
`
`I
`
`DETERMINE
`IMPORTANCE
`OF EACH
`TEXTUAL
`OBJECT IN
`POOL
`
`4
`64
`
`/
`
`/
`
`FIG. 4H
`
`

`

`U.S. Patent
`
`Aug. 6, 1996
`
`Sheet 15 of 24
`
`5,544,352
`
`256
`
`Receive Pool of
`Textual ObJects
`Selected by
`Researcher
`
`428
`
`Evaluate nxn
`Proximity Matrix
`
`Evaluate nxn
`Pattern Matrix
`
`468
`
`S.lec:t Column•
`of Proximity Matrix
`~
`Corrupondlng to Pool o
`Textual ObJect•
`
`484
`
`Select Columns
`of P•tt•m Matrix
`Corre~ndlng to Pool of
`Textual Objtc:ta
`
`488
`
`492
`
`496
`
`472
`
`Create PO
`vector
`
`Create PF
`vector
`
`476
`
`Create
`(n+1) x (n+1)
`Proximity Matrix
`
`Create
`(n+1) x (n+1)
`Pattern Matrix
`
`-·-------,
`I I
`i
`Create
`(n+1) x (n+1)
`i
`Proximity Matrix
`i
`L _________ _j
`
`Create
`(n+1) x (n+1)
`Similarity Matrix
`
`Await Search
`From Similar
`cases
`Subroutine
`
`480
`
`500
`
`FIG. 141
`
`

`

`U.S. Patent
`
`Aug. 6, 1996
`
`Sheet 16 of 24
`
`5,544,352
`
`400
`
`I
`S.Jacted Pool of
`Taxtual Objects
`
`244
`
`/
`
`I
`
`PDol-
`Sknllltlty
`Sear ell
`Subroutine
`
`Pliradlgm
`Subroutine
`
`\ Pool-
`Importance
`Subroutine
`
`\ '
`f /a.~s~ 25\ \ ~ ~
`r Poo ...
`
`256
`\
`
`Pool- I
`Pliradlgm-
`Slmllerlty
`Subroutine
`
`,,.
`5S~
`
`Pool-'
`P.radlgm-
`SimiSirlty
`DJapLiy
`Subroutine
`
`508
`
`/
`S.lectad Single
`Textual Object
`
`232
`
`l
`
`r
`) I 23~ ,,
`
`'
`\.
`
`240
`
`\
`SlrnJiar-
`ea ...
`Subloutlne
`
`\
`
`ca ....
`Att.r
`SUbtoutlne
`
`ea .. ..,
`Subroutine
`
`512
`
`,, ~
`
`\
`Cue.-ln
`Dl1play
`SUbroutine
`
`' I
`i6 7 GUI 1124 .7
`, r
`yl/ ,
`
`212
`
`632
`
`,, ~
`
`Slmlla;f
`ca ...
`DlapJay
`Subrout!M
`
`Pool-'
`Similarity
`Dt.pa.y
`SUbroutine
`
`Pool-'
`Pliradlgm
`Dt.pa.y
`Subroutine
`
`Pool· I
`lmportanct
`Display
`Subroutine
`
`.. ,.
`
`I
`c. .. a-Attw
`Dleplay
`SUbroutine
`
`504~~,./0
`
`540 ~,_ .
`
`INTEGRATOR
`
`38~
`r-
`
`DISPLAY
`
`1
`
`70
`
`FIG. 5A
`
`

`

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`U.S. Patent
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`Aug. 6, 1996
`
`Sheet 23 of 24
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`

`U.S. Patent
`
`Aug. 6, 1996
`
`Sheet 24 of 24
`
`5,544,352
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`

`5,544,352
`
`1
`METHOD AND APPARATUS FOR INDEXING,
`SEARCHING AND DISPLAYING DATA
`
`TECHNICAL FIELD
`
`This invention pertains to computerized research tools.
`More particularly, it relates to computerized research on
`stored databases. Specifically, the invention indexes data,
`searches data, and graphically displays search results with a
`user interface.
`
`BACKGROUND
`
`5
`
`2
`happens to mention "search and seizure" one time, even if
`the case has nothing to do with a Fourth Amendment issue.
`Since the researcher cannot possibly know all of the group(cid:173)
`ings of text within all the textual objects in the database, the
`researcher is unable to phrase his request to only retrieve the
`textual objects that are relevant.
`Aside from the inefficiency of Boolean searches, the
`present systems for computerized searching of data are
`inadequate to serve the needs of a researcher for several
`10 other reasons. Even if one assumes that all the textual
`objects retrieved from a Boolean search are relevant, the
`listing of the textual objects as done by WestlawTM or
`LexisTM does not convey some important and necessary
`information to the researcher. The researcher does not know
`which textual objects are the most significant (i.e., which
`textual object is referred to the most by another textual
`object) or which textual objects are considered essential
`precedent (i.e., which textual objects describe legal doc(cid:173)
`trines).
`In addition, both WestlawTM and LexisTM have a Shepar-
`dizingTM feature that enables the researcher to view a list of
`textual objects that mention a particular textual object. The
`shepardizing feature does not indicate how many times a
`listed textual object mentions the particular textual object.
`Although the shepardizing feature uses letter codes to indi(cid:173)
`cate the importance of a listed textual object (e.g. an "f'
`beside a listed textual object indicates that the legal rule
`contained in particular textual object was followed in the
`listed textual object), data on whether a listed textual object
`followed the rule of a particular textual object is entered
`manually by employees of Shepard'sTMfMcGraw Hill, Inc.,
`Div. of McGraw-Hill Book Co., 420 N. Cascade Ave.,
`Colorado Springs, Colo. 80901, toll free 1-800-525-2474.
`Therefore, such process is subjective and is prone to error.
`Another legal research system that is available is the
`WestlawTM key number system. The WestlawTM key number
`system has a problem similar to the shepardizing feature on
`the LexisTM and WestlawTM systems. West key numbers are
`groups of textual objects organized by topic. The West key
`numbers enable a researcher to search for textual objects on
`40 a computerized system via the key numbers. However, the
`employees of WesfrM manually determine which cases
`should be categorized under which key number. Therefore,
`such a numbering process is subjective and is prone to error.
`Furthermore, many people in the legal profession have
`criticized the West key number system because the system is
`45 very slow to recognize new topic areas, very rigid and very
`difficult to keep up to date. In addition, the WesfrM key
`number system, like Boolean searches, produces pools of
`cases that are over-inclusive or under-inclusive.
`The video displays of both the WesfrM and LexisTM
`systems are difficult to use. The simple text displays of these
`systems do not provide a researcher with all the information
`that is available in the database.
`Computerized research tools for legal opinions and
`related documents are probably the most sophisticated com(cid:173)
`puter research tools available and therefore form the back(cid:173)
`ground for this invention. However, the same or similar
`computer research tools are used in many other areas. For
`example, computer research tools are used for locating prior
`art for a patent application. The same problems of ineffi(cid:173)
`ciency discussed above exist for computer research tools in
`many areas of our society.
`What is needed is a system for computerized searching of
`data that is faster than the available systems of research.
`What is needed is a system for computerized searching of
`data that enables attorneys to research in a manner in which
`they are familiar.
`
`15
`
`Our society is in the information age. Computers main(cid:173)
`taining databases of information have become an everyday
`part of our lives. The ability to efficiently perform computer
`research has become increasingly more important. The area
`in our society in which this is most evident is the legal
`profession. A major problem in the legal profession today is 20
`the great deal of time spent performing legal research. Many
`aspects of legal research are tedious and time consuming.
`Therefore, performing legal research detracts from the
`amount of time the attorney is able to spend on tasks that
`actually require him to utilize his legal judgment and rea- 25
`soning. Recent efforts in the art of computer research have
`been aimed at reducing the time required to accomplish legal
`research. Current computer search programs use a text-by(cid:173)
`text analysis procedure (Boolean Search) to scan a database
`and retrieve items from a database. The attorney must input 30
`a string of text, and the computer evaluates this string of text.
`Then the computer retrieves items from the database that
`match the string of text. The two most popular systems for
`computerized searching of data used in the legal profession
`are WestlawTM, a service sold by West Publishing Company, 35
`50 W. Kellogg Blvd., P.O. Box 64526, St. Paul, Minn.
`55164-0526, and LexisTM, a service sold by Mead Data
`Central, P.O. Box 933, Dayton, Ohio 45401.
`However, Boolean searches of textual material are not
`very efficient. Boolean searches only retrieve exactly what
`the computer interprets the attorney to have requested. If the
`attorney does not phrase his or her request in the exact
`manner in which the database represents the textual object,
`the Boolean search will not retrieve the desired textual
`object. For example, if the attorney desires to retrieve cases
`in which a judge decided the issue before the jury could
`decide it, the attorney may enter "Summary Judgment" as
`his textual string. However, such a request will not retrieve
`cases that were decided by the judge under a motion to
`dismiss. Therefore, the researcher may effectively be denied 50
`access to significant cases, statutes, laws or other textual
`objects that may be crucial to the project on which the
`attorney is working. A second problem encountered with
`Boolean searches is that the search retrieves a significant
`amount of irrelevant textual objects. (It should be noted that 55
`in the context of legal research, a textual object could be any
`type of written legal material such as a judicial opinion, a
`statute, a treatise, a law review article, etc. The term textual
`object is used to stress the fact that the present invention
`applies to all types of databases, and not just legal research 60
`databases). The only requirement that a textual object must
`satisfy in order to be selected by a Boolean search program
`is that part of the textual object match the particular request
`of the researcher. For example, if the researcher desires to
`recover all cases that relate to a Fourth Amendment issue, 65
`the researcher may input "search and seizure" as his textual
`string. However, the computer will retrieve every case that
`
`

`

`3
`What is needed is a computerized research tool that will
`reorganize, re-index or reformat the data into a more efficient
`format for searching.
`What is needed are more sophisticated methods to search
`data.
`What is needed is a system for computerized searching of
`data that will significantly reduce the number of irrelevant
`textual objects it retrieves.
`What is needed is a user friendly computerized research 10
`tool.
`What is needed is a visual user interface which can
`convey information to a user conveniently.
`What is needed is a system for computerized searching of
`data that easily enables the attorney himself to classify the
`textual object according to his or her own judgment.
`What is needed is a system for computerized searching of
`data that provides a visual representation of "lead" textual
`objects and "lines" of textual objects, permitting a broad
`overview of the shape of the relevant legal "landscape."
`What is needed is a system for computerized searching of
`data that provides an easily-grasped picture or map of vast
`amounts of discrete information, permitting researchers
`(whether in law or other databases) to "zero in" on the most
`relevant material.
`What is needed is a system for computer searching of data
`that provides a high degree of virtual orientation and track(cid:173)
`ing, the vital sense of where one has been and where one is
`going, and that prevents researchers from becoming con(cid:173)
`fused while assimilating a large amount of research mate(cid:173)
`rials.
`Accordingly, there is an unanswered need for a user
`friendly computerized research tool. There is a need for
`"intelligent" research technology that emulates human
`methods of research. There is a need in the marketplace for
`a more efficient and intelligent computerized research tool.
`The present invention is designed to address these needs.
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`SUMMARY OF THE INVENTION
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`This invention is a system for computerized searching of
`data. Specifically, the present invention significantly aids a
`researcher in performing computerized research on a data(cid:173)
`base. The invention simplifies the research task by improv(cid:173)
`ing upon methods of searching for data including textual
`objects and by implementing a user interface that signifi(cid:173)
`cantly enhances the presentation of the data. Simplifying
`such research reduces the amount of human time that must
`be allocated to research.
`The invention begins with an existing database and
`indexes the data by creating a numerical representation of
`the data. This indexing technique called proximity indexing
`generates a quick-reference of the relations, patterns, and
`similarity found among the data in the database. Using this
`proximity index, an efficient search for pools of data having
`a particular relation, pattern or characteristic can be effec(cid:173)
`tuated. This relationship can then be graphically displayed.
`There are three main components to the invention; a data
`indexing applications program, a Computer Search Program
`for Data Represented by Matrices ("CSPDM"), and a user
`interface. Various indexing application programs, CSPDMs,
`and user interface programs can be used in combination to
`achieve the desired results. The data indexing program
`indexes data into a more useful format. The CSPDM pro- 65
`vides efficient computer search methods. The preferred
`CSPDM includes multiple search subroutines. The user
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`interface provides a user friendly method of interacting with
`the indexing and CSPDM programs. The preferred user
`interface program allows for easy entry of commands and
`visual display of data via a graphical user interface.
`The method which the invention uses to index textual
`objects in a database is called Proximity Indexing. Proximity
`Indexing is a method of preparing data in a database for
`subsequent searching by advanced data searching programs.
`Proximity Indexing indexes the data by using statistical
`techniques and empirically developed algorithms. The
`resulting search by an advanced data searching program of
`the Proximity Indexed data is significantly more efficient
`and accurate than a simple Boolean search.
`The Proximity Indexing Application Program indexes the
`15 database into a more useful format to enable the Computer
`Search Program for Data Represented by Matrices
`(CSPDM) to efficiently search the database. The Proximity
`Indexing Application Program of the preferred embodiment
`has several subroutines, including the Extractor, the Pat-
`terner, and the Weaver. The Proximity Indexing Application
`Program indexes data in a locally located database or
`remotely located database. The database can contain any
`type of data including text, alphanumerics, or graphical
`information.
`In the preferred embodiment, the database is located
`remotely from the Computer Processor and contains data in
`the form of textual objects. The Proximity Indexing Appli(cid:173)
`cation Program indexes the textual objects by determining
`how each full textual object (e.g., whole judicial opinion,
`30 statute, etc.) relates to every other full textual object by using
`empirical data and statistical techniques. Once each full
`textual object is related to each other full textual object, the
`Proximity Indexing Application Program compares each
`paragraph of each full textual object with every other full
`textual object as described above. The Proximity Indexing
`Application Program then clusters related contiguous para(cid:173)
`graphs into sections. Subsequently, the Proximity Indexing
`Application Program indexes each section and the CSPDM
`evaluates the indexed sections to determine which sections
`to retrieve from the database. Such organization and clas