Source: http://www.google.fr/patents/US7571174?hl=fr
Timestamp: 2013-05-20 08:15:11
Document Index: 176865942

Matched Legal Cases: ['application No. 60', 'art 200', 'art 200', 'art 900', 'art 900', 'Application No. 02828266', 'Application No. 2003']

Brevet US7571174 - Systems, methods, interfaces and software for automated collection and ... - Google�BrevetsRecherche Images Maps Play YouTube Actualit�s Gmail Drive Plus » Recherche avanc�e dans les brevets | Historique Web | Connexion Recherche avanc�e dans les brevets BrevetsAn information-retrieval system includes a server that receives queries for documents from client devices and means for outputting results of queries to the client devices, with the results provided in association with one or more interactive control features that are selectable to invoke display of...http://www.google.fr/patents/US7571174?utm_source=gb-gplus-shareBrevet US7571174 - Systems, methods, interfaces and software for automated collection and integration of entity data into online databases and professional directories Num�ro de publicationUS7571174 B2Type de publicationOctroi Num�ro de demande11/028,464 Date de publication4 ao�t 2009 Date de d�p�t3 janv. 2005 Date de priorit�31 d�c. 2003Autre r�f�rence de publicationCN1934567ACN100472520CEP1704497A1EP1704497B1EP2270688A1US8001129US20050234968WO2005066848A1 InventeursYohendran ArumainayagamChristopher C. Dozier Cessionnaire d'origineThomson Reuters Global ResurcesThomson Reuters Global ResourcesWest Services, Inc.Thomson Global Resources Classification aux �tats-Unis1/1707/999.101707/999.4707/999.1707/999.7 Classification internationaleG06N7/00G06F17/30G06N5/02 Classification coop�rativeG06N7/005G06F17/30011G06N5/022G06F17/30014 Classification europ�enneG06F17/30D4G06N5/02KG06F17/30DG06N7/00PR�f�rencesCitations de brevets (108)Citations hors brevets (72) R�f�renc� par (1)Liens externesUSPTO Cession USPTO EspacenetSystems, methods, interfaces and software for automated collection and integration of entity data into online databases and professional directoriesUS 7571174 B2 R�sum� An information-retrieval system includes a server that receives queries for documents from client devices and means for outputting results of queries to the client devices, with the results provided in association with one or more interactive control features that are selectable to invoke display of information regarding entities, such as professionals, referenced in the results.
1. A hardware system comprising a processor and a memory coupled to the processor and comprising:
means for extracting one or more entity reference records from documents;
means for defining one or more entity records by merging one or more of the entity reference records with one or more other entity reference records;
means for categorizing one or more of the defined entity records based on a taxonomy;
means for defining links between one or more of the defined entity records and other documents or data sets;
graphical user interface means for defining a query related to an entity, for viewing one or more documents resulting from the query, and for selecting one or more of the defined links within a legal, financial, healthcare, scientific, or educational document and causing retrieval and display of at least a portion of the one defined entity record; and
means for defining profile records from the one or more entity reference records, wherein the means for defining the profile records comprises means for merging entity reference records that refer to a same person to create a profile record for that person;
wherein the means for merging comprises:
means for sorting the entity reference records by last name;
means for selecting an unmerged entity reference record and means for creating a profile record from the selected unmerged entity reference record; and
means for analyzing the unmerged entity reference record for determining a probability that a person in a profile record is the same person as referenced in the selected unmerged entity reference record.
2. The system of claim 1, wherein one or more of the recited means include one or more processors, a tangible computer-readable storage medium, display devices, and network communications, with the tangible computer-readable storage medium including coded instructions and data structures.
3. The system of claim 1, comprising means for categorizing the profile records by expertise of the persons in the profile records.
4. The system of claim 1, wherein the means for analyzing includes Bayesian matching.
5. The system of claim 4, comprising means for marking the entity reference record as merged when a computed match probability exceeds a threshold.
6. The system of claim 1, comprising means for adding information to the entity reference record including means for harvesting information from other databases and sources.
7. The system of claim 6, comprising means for computing a Bayesian match probability means for determining if the harvested information refers to a person of a particular entity reference record.
means for determining types of the documents; and
means for extracting one or more entity reference records as a function of the types of the documents.
9. The system of claim 8, wherein the means for determining the types of the documents include means for identifying keywords in the documents.
10. The system of claim 1, wherein the entity reference record includes a personal name record, an organizational name record, a product name record, or a fact profile record.
11. The system of claim 10, comprising means for extracting information from the entity reference record as a function of one or more of a personal name, an organization name, a product name, or a particular fact.
extracting one or more entity reference records from documents;
defining one or more entity records by merging one or more of the entity reference records with one or more other entity reference records;
categorizing one or more of the defined entity records based on a taxonomy;
defining links between one or more of the defined entity records and other documents or data sets;
providing network accessible graphical user interface means for defining a query related to an entity, for viewing one or more documents resulting from the query, and for selecting one or more of the defined links within a legal, financial, healthcare, scientific, or educational document and causing retrieval and display of at least a portion of the one defined entity record; and
defining a profile record from the one or more entity reference records, wherein defining the profile record comprises merging entity reference records that refer to a same person to create the profile record for that person;
sorting the entity reference records by last name; selecting an unmerged entity reference record and creating a profile record from the selected unmerged entity reference record; and analyzing the unmerged entity reference record to determine a probability that a person in a profile record is the same person as referenced in the selected unmerged entity reference record.
13. The computer-implemented method of claim 12, comprising categorizing the profile record by expertise of the person in the profile record.
14. The computer-implemented method of claim 12, wherein the analyzing includes Bayesian matching.
15. The computer-implemented method of claim 12, comprising marking the entity reference record as merged when a computed match probability exceeds a threshold.
16. The computer-implemented method of claim 12, comprising adding information to the entity reference record by harvesting information from other databases and sources.
17. The computer-implemented method of claim 16, comprising computing a Bayesian match probability to determine if the harvested information refers to a person of a particular entity reference record.
18. The computer-implemented method of claim 12, comprising:
determining types of the documents; and
extracting one or more entity reference records as a function of the types of the documents.
19. The computer-implemented method of claim 18, wherein the determining the types of the documents includes identifying keywords in the documents.
20. The computer-implemented method of claim 12, wherein the entity reference record includes a personal name record, an organizational name record, a product name record, or a fact profile record.
21. The computer-implemented method of claim 20, comprising extracting information from the entity reference record as a function of one or more of a personal name, an organization name, a product name, or a particular fact.
a processor configured for extracting one or more entity reference records from documents
a processor configured for defining one or more entity records by merging one or more of the entity reference records with one or more other entity reference records;
a processor configured for categorizing one or more of the defined entity records based on a taxonomy;
a processor configured for defining links between one or more of the defined entity records and other documents or data sets; and
a graphical user interface processor for defining a query related to an entity, for viewing one or more documents resulting from the query, and for selecting one or more of the defined links within a legal, financial, healthcare, scientific, or educational document and causing retrieval and display of at least a portion of the one defined entity record; and
a processor configured for defining profile records from the one or more entity reference records, wherein defining the profile records comprises a processor for merging entity reference records that refer to a same person to create a profile record for that person;
wherein the processor for merging comprises:
a processor configured for sorting the entity reference records by last name;
a processor configured for selecting an unmerged entity reference record and for creating a profile record from the selected unmerged entity reference record; and
a processor configured for analyzing the unmerged entity reference record to determine a probability that a person in a profile record is the same person as referenced in the selected unmerged entity reference record.
23. The system of claim 22, comprising a processor configured for categorizing the profile records by expertise of the persons in the profile records.
24. The system of claim 22, wherein the processor configured for analyzing includes Bayesian matching.
25. The system of claim 22, comprising a processor configured for marking the entity reference record as merged when a computed match probability exceeds a threshold.
26. The system of claim 22, comprising a processor configured for adding information to the entity reference record by harvesting information from other databases and sources.
27. The system of claim 26, comprising a processor configured for computing a Bayesian match probability to determine if the harvested information refers to a person of a particular entity reference record.
28. The system of claim 22, comprising:
a processor configured for determining types of the documents; and
a processor configured for extracting one or more entity reference records as a function of the types of the documents.
29. The system of claim 28, wherein the processor configured for determining the types of the documents include identifying keywords in the documents.
30. The system of claim 22, wherein the entity reference record includes a personal name record, an organizational name record, a product name record, or a fact profile record.
31. The system of claim 22, comprising a processor configured for extracting information from the entity reference record as a function of one or more of a personal name, an organization name, a product name, or a particular fact.
a memory coupled to the one or more processors, wherein the memory includes instructions that when executed on the one or more processors execute a process comprising:
defining links between one or more of the defined entity records and other documents or data sets; and
defining a query related to an entity, for viewing one or more documents resulting from the query, and for selecting one or more of the defined links within a legal, financial, healthcare, scientific, or educational document and causing retrieval and display of at least a portion of the one defined entity record;
defining profile records from the one or more entity reference records, wherein the means for defining the profile records comprises means for merging entity reference records that refer to a same person to create a profile record for that person;
CROSS-REFERENCE TO RELATED APPLICATION This application claims priority to U.S. provisional application No. 60/533,588 filed on Dec. 31, 2003. The provisional application is incorporated herein by reference.
COPYRIGHT NOTICE AND PERMISSION A portion of this patent document contains material subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyrights whatsoever. The following notice applies to this document: Copyright � 2003, Thomson Global Resources AG.
TECHNICAL FIELD Various embodiments of the present invention concerns information-retrieval systems, such as those that provide legal documents or other related content.
BACKGROUND In recent years, the fantastic growth of the Internet and other computer networks has fueled an equally fantastic growth in the data accessible via these networks. One of the seminal modes for interacting with this data is through the use of hyperlinks within electronic documents.
The hyperlinks in these judicial opinions are generated automatically, using a system that extracts first, middle, and last names; law firm name, city, and state; and court information from the text of the opinions and uses them as clues to determine whether to link the named attorneys and judges to their corresponding entries in the professional directory. See Christopher Dozier and Robert Haschart, �Automatic Extraction and Linking of Person Names in Legal Text� (Proceedings of RIAO 2000: Content Based Multimedia Information Access. Paris, France. pp. 1305-1321. Apr. 2000), which is incorporated herein by reference. An improvement to this system is described in Christopher Dozier, System, Methods And Software For Automatic Hyperlinking Of Persons' Names In Documents To Professional Directories, WO 2003/060767A3 Jul. 24, 2003.
BRIEF DESCRIPTION OF DRAWINGS FIG. 1 is a diagram of an exemplary information-retrieval system 100 corresponding to one or more embodiments of the invention;
FIGS. 10 is a flow chart corresponding to one or more additional embodiment of the invention.
DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS This description, which references and incorporates the above-identified Figures, describes one or more specific embodiments of an invention. These embodiments, offered not to limit but only to exemplify and teach the invention, are shown and described in sufficient detail to enable those skilled in the art to implement or practice the invention. Thus, where appropriate to avoid obscuring the invention, the description may omit certain information known to those of skill in the art.
Exemplary Information-Retrieval System FIG. 1 shows an exemplary online information-retrieval system 100. System 100 includes one or more databases 110, one or more servers 120, and one or more access devices 130.
Caselaw database 111 generally includes electronic text and image copies of judicial opinions for decided cases for one or more local, state, federal, or international jurisdiction. Expert witness directory 112, which is defined in accord with one or more aspects of the present invention, includes one or more records or database structures, such as structure 1121. Structure 1121 includes an expert identifier portion 1121A which is logically associated with one or more directory documents or entries 1121B, one or more verdict documents or entries 1121C, and one or more articles 1121D. Some embodiments logically associate the expert identifier with court filings documents, such as briefs and expert reports and/or other documents.
Server 120, which is generally representative of one or more servers for serving data in the form of webpages or other markup language forms with associated applets, ActiveX controls, remote-invocation objects, or other related software and data structures to service clients of various �thicknesses.� More particularly, server 120 includes a processor 121, a memory 122, a subscriber database 123, one or more search engines 124 and software module 125.
Subscription database 123 includes subscriber-related data for controlling, administering, and managing pay-as-you-go- or subscription-based access of databases 110. Subscriber database 123 includes subscriber-related data for controlling, administering, and managing pay-as-you-go or subscription-based access of databases 110.
Server 120 is communicatively coupled or couplable via a wireless or wireline communications network, such as a local�, wide�, private�, or virtual�private network, to one or more accesses devices, such as access device 130.
Specifically, access device 130 includes one or more processors (or processing circuits) 131, a memory 132, a display 133, a keyboard 134, and a graphical pointer or selector 135. Memory 132 stores code (machine-readable or executable instructions) for an operating system 136, a browser 137, and a graphical user interface (GUI) 138. In the exemplary embodiment, operating system 136 takes the form of a version of the Microsoft Windows operating system, and browser 137 takes the form of a version of Microsoft Internet Explorer. Operating system 136 and browser 137 not only receive inputs from keyboard 134 and selector (or mouse) 135, but also support rendering of GUI 138 on display 133. Upon rendering, GUI 138 presents data in association with one or more interactive control features (or user-interface elements). (The exemplary embodiment defines one or more portions of interface 138 using applets or other programmatic objects or structures from server 120.)
Display portion 1382B displays at least a portion of the full text of a first displayed or user-selected one of the profiles identified within listing portion 1382A, EXP 2 in the illustration. (Some embodiments present regions 1382A and 1382B as selectable tabbed regions.) Portion 1382B also includes features 2B1, 2B2, 2B3, and 2B4. User selection of feature 2B1 initiates retrieval and display of the profile text for the selected expert, EXP 2; selection of feature 2B2 initiates retrieval and display of licensing data for any licenses or other credentials held by the selected expert or professional image copy of the document displayed in region 1382B in a separate window; selection of feature 2B3 initiates display and retrieval of verdict data related to the expert or professional; and selection of feature 2B4 initiates retrieval and display of articles (from database 115) that are related to, for example authored by, the expert or professional. Other embodiments include additional control features for accessing court-filing documents, such as briefs, and/or expert reports authored by the expert or professional, or even deposition and trial transcripts where the expert or testimony was a participant. Still other embodiments provide control features for initiating an Internet search based on the selected expert and other data and for filtering results such search based on the profile of the expert or professional.
Exemplary Methods of Operation FIG. 2 shows a flow chart 200 of one or more exemplary methods of operating an information-management system, such as system 100. Flow chart 200 includes blocks 210-290, which are arranged and described in a serial execution sequence in the exemplary embodiment. However, other embodiments execute two or more blocks in parallel using multiple processors or processor-like devices or a single processor organized as two or more virtual machines or sub processors. Other embodiments also alter the process sequence or provide different functional partitions to achieve analogous results. For example, some embodiments may alter the client-server allocation of functions, such that functions shown and described on the server side are implemented in whole or in part on the client side, and vice versa. Moreover, still other embodiments implement the blocks as two or more interconnected hardware modules with related control and data signals communicated between and through the modules. Thus, this (and other exemplary process flows in this description) apply to software, hardware, and firmware implementations.
Exemplary Method of Building Expert Directory In FIG. 9, flow chart 900 shows an exemplary method of building an expert directory or database such as used in system 100. Flow chart 900 includes blocks 910-960.
At block 910, the exemplary method begins with extraction of entity reference records from text documents. In the exemplary embodiment, this entails extracting entity references from approximately 300,000 jury verdict settlement (JVS) documents using finite state transducers. JVS documents have a consistent structure that includes an expert witness section or paragraph, such as that exemplified in Table 1.
TABLE 1 Expert Witness Section of Jury Verdicts and Settlements (JVS) Document EXPERTS: Plaintiff: Neal Benowitz MD, pharmacologist, UCSF Medical Center, San Francisco. David M. Burns, pulmonologist, UC San Diego, Div. of Pulmonary and Critical Care Medicine, La Jolla. wDefendant: Jerry Whidby PhD., chemist, Philip Morris Co., Richmond, VA. The exemplary embodiment uses a parsing program to locate expert-witness paragraphs and find lexical elements (that is, terms used in this particular subject area) pertaining to an individual. These lexical elements include name, degree, area of expertise, organization, city, and state. Parsing a paragraph entails separating it into sentences, and then parsing each element using a separate or specific finite state transducer. The following example displays regular expressions from the finite state transducer used for the organization element. (Variables are prefixed by $.)
$ORG=($UNIVERSITY|$COMPANY|$FIRM . . . ) $UNIVERSITY=($UNIVERSITY1|$UNIVERSITY2) $UNIVERSITY1=(University|College . . . ) (of) [A-Z] [a-z]+ $UNIVERSITY2=([A-Z][a-z]+$SPACE)+(University|College . . . )
Typically one expert is listed in a sentence along with his or her area of expertise and other information. If more than one expert is mentioned in a sentence, area of expertise and other elements closest to the name are typically associated with that name. Each JVS document generally lists only one expert witness; however, some expert witnesses are references in more than one JVS document. Table 2 shows an example of an entity reference records.
TABLE 2 Structured Expert-Witness Reference Record Created by Regular Expression Parsers fname ARTHUR mname lname ABLIN suffix degree MD org Expertise Pediatric hematology/oncology city SAN FRANCISCO state CA Once the entity reference records are defined, execution continues at block 220.
Block 920 entails defining profile records from the entity reference records: In the exemplary embodiment, defining the profile records entails merging expert-witness reference records that refer to the same person to create a unique expert-witness profile record for the expert. To this end, the exemplary embodiment sorts the reference records by last name to define a number of lastname groups. Records within each �last-name� group are then processed by selecting an unmerged expert reference record and creating an new expert profile record from this selected record.
The new expert reference record is then marked as unmerged and compared to each unmerged reference record in the group using Bayesian matching to compute the probability that the expert in the profile record refers to the same individual referenced in the record. If the computed match probability exceeds a match threshold, the reference is marked as �merged.� If unmerged records remain in the group, the cycle is repeated.
Note that it is still possible for duplicate records to reside in the profile file if two or more reference records pertain to one individual (for example, because of a 10 misspelled last name). To address this possibility, a final pass is made over the merged profile file, and record pairs are flagged for manual review. Table 3 shows an exemplary expert profile record created from expert reference records.
Expert-Profile Record in which Middle Name,
Address, and ZIP-code Fields
Filled or Harvested from Professional License Record
43 Culloden Pk Road
The exemplary taxonomy includes the following top-level categories: Accident & Injury; Accounting & Economics; Computers & Electronics; Construction & Architecture; Criminal, Fraud and Personal Identity; Employment & Vocational; Engineering & Science; Environmental; Family & Child Custody; Legal & Insurance; Medical & Surgical; Property & Real Estate; Psychiatry & Psychology; Vehicles, Transportation, Equipment & Machines. Each categories includes one or more subcategories. For example, the �Accident & Injury� category has the following subcategories: Aerobics, Animals, Apparel, Asbestos, Boating, Bombing, Burn/Thermal, Child Care, Child Safety, Construction, Coroner, Cosmetologists/Beauticians/Barbers/Tattoos, Dog Bites, Entertainment, and Exercise.
T=f(S)
where T denotes a set of taxonomy nodes, and S is the professional descriptor. The exemplary function fuses a lexicon of 500 four-character sets that map professional descriptors to expertise area. For example, experts having the �onco� professional descriptor are categorized to the oncology specialist, oncologist, and pediatric oncologist subcategories. Other taxonomies are also feasible. The exemplary embodiment allows descriptors to map to more than one expertise area (that is, category or subcategory) in the taxonomy. For example, �pediatric surgeon� can be mapped to both the �pediatrics� node and �surgery� nodes. Table 5 shows an example of an expert profile record in which the expertise field has been mapped to the category �Medical & Surgical� and to the subcategories �pediatrics,� �blood & plasma,� and �oncology.�
Expert Profile Record with Expertise Area Mapped
to �Medical & Surgical�
AUTHORS: Bertucio C S; Wara W M; Matthay K K; Ablin A R; Johnston
J O; O'Donnell R J; Weinberg V; Haas-Kogan D A
At block 1010, the exemplary method begins with receipt of a document. In the exemplary embodiment, this entails receipt of an unmarked document, such an a judicial opinion or brief. However, other embodiments receive and process other types of documents. Execution then advances to block 1020.
Block 1030 entails extracting one or more entity reference records from the received document based on the determined type of the document. In the exemplary embodiment, four types of entity records are extracted: personal names, such as attorneys, judges, expert witnesses; organizational names, such as firms and companies; product names, such as drugs and chemicals; and fact profiles (�vernacular� of subject area). Specialized or configurable parsers (finite state transducers), which are selected or configured on the basis of the determined document type and the entity record being built, identify and extract entity information for each type of entity.
Parsers extract information by specifically searching for a named entity (person, address, company, etc.) or by relationships between entities. Parser text-extraction is based on the data's input criteria. For example, the more structured (tagged) data enables a �tighter� set of rules to be built within a parser. This set of rules allows more specific information to be extracted about a particular entity. A more �free� data collection, such as a web site, is not as conducive to rule-based parsers. A collection could also include a combination of structured, semi-structured, and free data. More specifically, parsers are developed through �regular-expression� methods. The regular expressions serves are �rules� for parsers to find entity types and categories of information.
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