PCT
`INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT)
`wo 95/00896
`
`(51) International Patent Classification 6 :
`G06F
`
`(11) International Publication Number:
`
`A2
`
`(43) International Publication Date:
`
`5 January 1995 (05.01.95)
`
`WORLD INTElLECTUAL PROPERTY ORGANIZATION
`International Bureau
`
`(21) International Application Number:
`
`PCr/US94/06705
`
`(22) International Filing Date:
`
`13 June 1994 (13.06.94)
`
`(30) Priority Data:
`08/076,658
`
`14 June 1993 (14.06.93)
`
`us
`
`(81) Designated States: AT, AU, BB, BG, BR, BY, CA, CH, CN,
`CZ, DE, DK, ES, FI, GB, GE, HU, JP, KE, KG, KP, KR,
`KZ, LK, LU, LV, MD, MG, MN, MW, NL, NO, NZ,
`PL, PT, RO, RU, SD, SE, SL SK, TJ, IT, UA, UZ, VN,
`European patent (AT, BE, CH, DE, DK, ES, FR, GB, GR,
`IE, IT, LU, MC, NL, PT, SE), OAPI patent (BF, BJ, CF,
`CG, Cl, CM, GA, GN, ML, MR, NE, SN, TD, TG).
`
`(71) Applicant: LIBERTECH, INC. [US/US]; Suite 4005, 3622 Published
`Without international search report and to be republished
`Lyckan Parkway, Durham. NC 27707 (US).
`upon receipt of that report.
`
`(72) Inventor: EGGER, Daniel; Libertech, Inc., Suite 4005, 3622
`Lyckan Parkway, Durham, NC 27007 (US).
`
`(74) Agents: NOTO, Aldo; Dorsey & Whitney, Suite 200, 1330
`Connecticut Avenue, N.W., Washington, DC 20036 (US) et
`al.
`
`(54) Title: METHOD AND APPARATUS FOR INDEXING SEARCHING AND DISPLAYING DATA
`
`28
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`(57) Abstract
`
`A computer research tool (26) for indexing, searching and displaying data is disclosed. Textual objects and other data in a database
`(54) are indexed by creating a nmnerical representation of the data. An indexing technique called proximity indexing indexes the data
`by using statistical techniques and empirically developed algorithms. Using proximity indexing, an efficient search for pools of data can
`be effectuated. The Computer Search progam. 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 datmn, 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.
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`FOR THE PURPOSES OF INFORMATION ONLY
`
`Codes used to identify States party to the PCT on the front pages of pamphlets publishing international
`applications under the PCT.
`
`AT
`AU
`BB
`BE
`BF
`BG
`BJ
`BR
`BY
`CA
`CF
`CG
`CH
`CI
`CM
`CN
`cs
`cz
`DE
`DK
`ES
`FI
`FR
`GA
`
`Austria
`Australia
`Barbados
`Belgium
`Bwtinafaso
`Bulgaria
`Benin
`Brazil
`Belarua
`Canada
`Central African Republic
`Congo
`Switzerland
`COte d'Ivoirc
`Cameroon
`Cbina
`Czccboslovalda
`Czccb Republic
`Germany
`Demnarlt
`Spain
`Finland
`fraDcc
`Gabon
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`GB
`GE
`GN
`GR
`HU
`IE
`IT
`JP
`KE
`KG
`KP
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`KR
`KZ
`u
`LK
`LU
`LV
`MC
`MD
`MG
`ML
`MN
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`United Kingdom
`GeagiA
`Guinea
`Greece
`Hungazy
`Ireland
`Italy
`Japan
`Kelly a
`Kyrgystan
`Demoaatic People's Republic
`of Korea
`Republic of Korea
`Kazakhstan
`Uccbtenstein
`Sri Lanka
`Luxembourg
`Latvia
`Monaco
`Republic of Moldova
`Madagascar
`Mali
`Mongolia
`
`MR
`MW
`NE
`NL
`NO
`NZ
`PL
`PT
`RO
`RU
`SD
`SE
`SI
`SK
`SN
`TD
`TG
`TJ
`TT
`UA
`us
`UZ
`VN
`
`Mauritania
`Malawi
`Nig~
`Netherlands
`Norway
`NewZcaland
`Poland
`Portugal
`Romania
`Russian federation
`Sudan
`Sweden
`Slovenia
`Slovakia
`Senegal
`Cbad
`Togo
`Tajikistan
`liinidad and Tobago
`Ukraine
`United States of America
`Uzbekistan
`VietNam
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`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
`Our society is in the information age. Computers
`maintaining 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 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
`reasoning. 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-text analysis
`procedure (Boolean Search) to scan a database and retrieve items
`from a database. The attorney must input 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 Westlaw™, a service sold by West Publishing
`Company, 50 W. Kellogg Blvd., P.O. Box 64526, St. Paul, Minnesota
`55164-0526, and Lexis™, 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
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`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 by denied 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 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 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, the researcher may input "search and seizure"
`as his textual string. However, the computer will retrieve every
`case that 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 groupings 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.
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`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 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 Westlaw™
`or Lexis™ 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 doctrines).
`
`In addition, both Westlaw™ and Lexis™ have a
`Shepardizing™ 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 indicate the importance of a
`listed textual object (e.g. an 11£'' 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's™/McGraw Hill, Inc., Div. of
`McGraw-Hill Book Co., 420 N. Cascade Ave., Colorado Springs, CO.
`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
`Westlaw™ key number system. The Westlaw™ key number
`system has a problem similar to the shepardizing feature on the
`Lexis™ and Westlaw™ systems. West key numbers are groups of
`textual objects organized by topic. The West key numbers enable a
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`researcher to search for textual objects on a computerized system via
`
`the key numbers. However, the employees of West™ 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
`very slow to recognize new topic areas, very rigid and very difficult
`to keep up to date. In addition, the West™ key number system, like
`
`Boolean searches, produces pools of cases that are over-inclusive or
`
`under-inclusive.
`The video displays of both the West™ and Lexis™ 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 computer research
`tools available and therefore form the background 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 inefficiency 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.
`What is needed is a computerized research tool that will
`reorganize, re-index or reformat the data into a more efficient
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`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 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 '1ead" 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 tracking, the
`vital sense of where one has been and where one is going, and that
`prevent researchers from becoming confused while assimilating a
`large amount of research materials.
`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
`This invention is a system for computerized searching of
`data. Specifically, the present invention significantly aids a
`researcher in performing computerized research on a database. The
`invention simplifies the research task by improving upon methods
`of searching for data including textual objects and by implementing
`a user interface that significantly 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(cid:173)
`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 effectuated. 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 provides efficient computer search methods. The
`preferred CSPDM includes multiple search subroutines. The user
`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.
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`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
`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 Pattemer, 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 Application
`Program indexes the textual objects by determining how each full
`textual object (e.g., whole judicial opinion, 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 paragraphs
`into sections. Subsequently, the Proximity Indexing Application
`Program indexes each section and the CSPDM evaluates the indexed
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`sections to determine which sections to retrieve from the database.
`Such organization and classification of all of the textual objects in
`the database before any given search commences significantly limits
`the irrelevant textual objects that the CSPDM program retrieves
`during the subsequent search and allows retrieval of material based
`on its degree of relevancy.
`Legal research searches on systems like Westlaw™ and
`Lexis™ only use a series of interrelated Boolean searches of actual
`text to retrieve textual objects from databases. These searches
`unnecessarily consume valuable time and retrieve a significant
`number of irrelevant textual objects.
`Again, this method of computerized research can be used for
`nearly any database including those containing non-textual
`material, graphical material, newspapers material, data on personal
`identification, data concerning police records, etc.
`The remaining two programs in the present invention are
`the CSPDM and the GUI Program. The CSPDM has seven
`subroutines that each search for different pools of textual objects.
`The GUI Program also has seven subroutines. Each subroutine
`performs a different type of search. Each of the subroutines of the
`GUI uses the results of the corresponding subroutine of the CSPDM
`to create the proper display on the display.
`After the Proximity Indexing Application Program indexes a
`database, the CSPDM application program is used to search the
`indexed database. The CSPDM program can either be located in
`memory that is remote from the Computer Processor or local to the
`Computer Processor. In addition, the CSPDM program can either be
`remote or local in relation to the database.
`The subroutines of the CSPDM that utilize the matrix
`coefficients and other data created by the Proximity Indexing
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`Application Program to facilitate its search. However, if the
`researcher does not have the particular textual object citation
`available, the researcher can perform a Boolean search to retrieve
`and organize a pool of textual objects. Alternatively, the researcher
`can subsequently search for related textual objects by using the Pool(cid:173)
`Similarity Subroutine, the Pool-Paradigm Subroutine, the Pool(cid:173)
`Importance Subroutine or the Pool-Paradigm-Similarity Subroutine
`as defined below.
`H the researcher already has the citation of a particular textual
`object available, the researcher can search for related textual objects
`
`by utilizing the Cases-In Subroutine, Cases-After Subroutine or
`Similar-Cases Subroutine. The Cases-In Subroutine retrieves all of
`the textual objects from the database to which a selected textual
`object refers. In addition, the subroutine determines the number of
`times the selected textual object refers to each retrieved textual
`object and other characteristics of each textual object, including its
`importance, and degree of relatedness to the selected textual object.
`The Cases-After Subroutine retrieves all of the textual objects
`from the database that refer to the selected textual object. Also, the
`subroutine determines the number of times each retrieved textual
`object refers to the selected textual object and other characteristics of
`each textual object, including its importance, and degree of
`relatedness to the particular textual object to which it refers.
`The Similar-Cases Subroutine determines the degree of
`similarity between the retrieved textual objects and the selected
`textual object. Similarity is defined, in the context of legal cases, as
`the extent to which the two textual objects lie in the same lines of
`precedent or discuss the same legal topic or concept.
`In addition, if the researcher does not know of a certain
`particular textual· object on which to base his or her search, the
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`researcher may execute a Boolean word search. After a standard
`Boolean word search has been run, the researcher may run the
`Pool-Similarity Subroutine to retrieve information containing the
`degree of similarity between each textual object in the pool and a
`particular textual object selected by the user. Similarly, the Pool(cid:173)
`Importance Subroutine can be used to determine the degree of
`importance (i.e., whether a judicial opinion is a Supreme Court
`opinion or a District Court opinion) and other characteristics of each
`textual object retrieved using the Boolean word search.
`The Pool-Paradigm Subroutine calculates the geographic
`center in vector space of the pool of textual objects retrieved by the
`Boolean word search or other pool generating method. It then
`orders the retrieved textual objects by their degree of similarity to
`that center or "paradigm." The researcher can then evaluate this
`"typical textual object'' and utilize it to help him or her find other
`relevant textual objects. In addition, the researcher can scan
`through neighboring "typical textual objects" to evaluate legal
`subjects that are closely related to the subject of the researcher's
`search.
`The Pool-Paradigm-Similarity Subroutine similarly creates a
`paradigm textual object from the retrieved textual objects.
`However, the subroutine calculates the similarity of all textual
`objects in the database to the paradigm textual object in addition to
`the similarity of the retrieved textual objects to the paradigm textual
`object.
`After the CSPDM has retrieved the desired textual objects, the
`Graphical User Interface (GUI) Program may be used to display the
`results of the search on the display. The GUI is a user interface
`program. The GUI Program contains three main subroutines:
`Cases-In Display Subroutine (CIDS), Cases-After Display Subroutine
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`(CADS) and Similar-Cases Display Subroutine (SCDS). THe main
`subroutines receive information from the corresponding
`subroutines Cases-In, Cases-Mter and Similar-Cases of the CSPDM .
`The GUI Program also contains four secondary subroutines: Pool-
`Similarity Display Subroutine ("PSDS"), Pool-Paradigm Display
`Subroutine ("PPDS"), Pool-Importance Display Subroutine ("PIDS"),
`and the Pool-Paradigm-Similarity Subroutine (PPSDS). The
`secondary subroutines also receive information from the
`corresponding subroutines Pool-Similarity Subroutine, Pool-
`Paradigm Subroutine, Pool-Importance Subroutine and the Pool(cid:173)
`Paradigm Similarity Subroutine of the CSPDM.
`The CIDS subroutine receives information gathered from the
`Cases-In Subroutine of the CSPDM. The CIDS subroutine displays
`user friendly active boxes and windows on the display 38 which
`represent the textual objects retrieved from the database represented
`in Euclidean space. The display depicts the appropriate location of
`textual objects in Euclidean space on a coordinate means. The
`coordinate means may have one or more axis, but the present
`embodiment contains two axis. The horizontal axis of the
`coordinate means represents the time of textual object creation. The
`vertical axis represents a weighted combination of the number of
`sections in which that particular retrieved text is cited or discussed,
`its degree of importance, and its degree of similarity to the host
`textual object. The CIDS also enables the researcher to open up
`various active boxes on the display by entering a command into the
`computer processor with the input means. Mter entering the
`proper command, the active box transforms into a window
`displaying additional information about the selected textual object.
`These windows can be moved about the display and stacked on top
`or placed beside each other via the input means to facilitate viewing
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`of multiple windows of information simultaneously. Since the
`number of textual objects retrieved in a single search may exceed
`the amount which could be displayed simultaneously, the GUI
`Program enables the researcher to "zoom in" or "zoom out'' to
`different scales of measurement on both the horizontal and vertical
`axis.
`
`The CADS receives information gathered by the Cases-Mter
`Subroutine of the CSPDM. The CADS creates a display similar to
`the CIDS display. However, the active boxes representing the
`retrieved textual objects indicate which textual objects in the
`database refer to a selected textual object as opposed to which textual
`objects a selected textual object refers.
`The SCDS receives information gathered by the Similar(cid:173)
`Cases Subroutine of the CSPDM. The SCDS causes a similar display
`on the display as the CIDS and the CADS except that the vertical axis
`'
`indicates the degree of similarity between the retrieved textual
`objects and the selected textual object.
`The GUI Program contains four secondary subroutines: Pool(cid:173)
`Search Display Subroutine (PSDS), Pool-Paradigm Display
`Subroutine (PPDS), Pool-Importance Display Subroutine (PIDS) and
`the Pool-Paradigm-Similarity Display Subroutine (PPSDS). The
`PSDS receives the results gathered by the Pool-Search Subroutine of
`the CSPDM. The PPDS receives the results gathered by the Pool(cid:173)
`Paradigm Subroutine of the CSPDM. The PIDS receives the results
`gathered by the Pool-Importance Subroutine of the CSPDM. The
`PPSDS receives the results gathered by the Pool-Paradigm-Similarity
`Subroutine of the CSPDM. The results of the PSDS, PPDS, PIDS and
`PPSDS are then displayed in a user friendly graphical manner
`similar to the results of the CIDS, CADS and SCDS. A researcher
`can access the PSDS, PIDS, PSDS or PPSDS from any of the three
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`main or four secondary subroutines of the GUI to gather
`information corresponding to the active boxes that represent the
`pool of textual objects retrieved by the corresponding subroutine of
`the CSPDM.
`By using the graphical display, the researcher can view
`immediately a visual representation of trends developing in the
`law and current and past legal doctrines. In addition, the researcher
`can immediately identify the important precedent and which
`textual object serving as the precedent is most important to the
`project on which the researcher is working. This visual
`representation is a vast improvement over the current
`computerized research tools. Furthermore, the researcher using
`the present invention does not have to rely on the interpretation of
`another person to categorize different textual objects because the
`researcher can immediate visualize the legal trends and categories
`of law. In addition, new topic areas can be recognized without direct
`human intervention. The current research programs require a
`researcher to read through the actual text of a number of textual
`objects in order to determine which textual objects are important,
`interrelated, or most closely related to the topic at hand and which
`ones are not.
`It is an object of this invention to create an efficient and
`intelligent system for computerized searching of data that is faster
`than available systems of research.
`It is an object of the invention to integrate the system of
`computerized searching into the techniques to which researchers
`are already accustomed.
`It is an object of the invention to utilize statistical techniques
`along with empirically generated algorithms to reorganize, re-index
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`and reformat data in a database into a more efficient model for
`searching.
`It is an object of the invention to utilize statistical techniques
`along with empirically generated methods to increase the efficiency
`of a computerized research tool.
`It is an object of the invention to create a system of
`computerized searching of data that significantly reduces the
`number of irrelevant textual objects retrieved.
`It is an object of this invention to create a user friendly
`interface for computer search tools which can convey a significant
`amount of information quickly.
`It is an object of the invention to enable the researcher to
`easily and immediately classify retrieved textual objects according to
`the researcher's own judgment.
`It is an object of the invention to provide a visual
`representation of "lead" textual objects and "lines" of textual objects,
`permitting a broad overview of the shape of the relevant legal
`"landscape."
`It is an object of the invention to provide 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.
`It is an object of the invention to provide a high degree of
`virtual orientation and tracking that enables a researcher to keep
`track of exactly what information the researcher has already
`researched and what information the researcher needs to research.
`These and other objects and advantages of the invention will
`become obvious to those skilled in the art upon review of the
`description of a preferred embodiment, and the appended drawings
`and claims.
`
`>
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`DESCRIPTION OF THE DRAWINGS
`FIG. 1 is a high level diagram of the hardware for the system
`for computerized searching of data.
`FIG. 2 is high level diagram of the software for the system for
`computerized searching of data. The three main programs are the
`Proximity Indexing Application Program, the Computer Search
`Program for Data Represented by Matrices (CSPDM)
`Application Program and the Graphical User Interface (GUI)
`Program.
`FIG. 3A is a flow chart illustrating a possible sequence of
`procedures that are executed during the Proximity Indexing
`Application Program.
`FIG. 3B is a flow chart illustrating a possible sequence of the
`specific subroutines that are executed during one stage of the
`Proximity Indexing Application Program. The subroutines are the
`Initial Extractor Subroutine, Opinion Pattemer Subroutine, the
`Opinion Weaver Subroutine, the Paragraph Pattemer Subroutine
`(Optional), the Paragraph Weaver Subroutine and the Section
`Comparison Subroutine.
`FIG. 3C is flow chart illustrating a possible sequence of
`subroutines that are executed after the Section Comparison
`Subroutine. The Section Comparison Subroutine may comprise the
`Sectioner-Geographic Subroutine and the Section-Topical
`Subroutine (Optional). The sequence of subroutines executed after
`the Section Comparison Subroutine are the Section Extractor
`Subroutine, the Section Patterner Subroutine and the Section
`Weaver Subroutine.
`FIG. 3D is a high level flow chart illustrating a possible
`sequence of subroutines that comprise the Boolean Indexing
`Subroutine which are executed during another stage of the
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