System, method, and software for identifying historically related legal opinions

• US 7,620,626 B2
• Filed: 05/30/2006
• Issued: 11/17/2009
• Est. Priority Date: 04/04/2001
• Status: Active Grant

• Current Assignee: Thomson Reuters Enterprise Centre GmbH (The Woodbridge Co. Ltd.)
• Original Assignee: West Services
• Inventors: Jackson, Peter, Al-Kofahi, Khalid
• Primary Examiner(s): Fleurantin; Jean B.
• Assistant Examiner(s): Ly; Anh
• Application Number:
  US11/421,043
• Publication Number:
  US 20060206467A1
• Time in Patent Office: 1,267 Days
• Field of Search: 707 3- 6, 707/10, 707100-102, 707/104.1, 706 12- 13, 706 15- 21, 706/45, 706/7, 704/1
• US Class Current: 1/1
• CPC Class Codes: G06F 16/30 of unstructured textual data document management systems G06F16/93
• CPC Class Codes: G06F 16/3334 Selection or weighting of terms from queries, including natural language queries
• CPC Class Codes: G06F 16/334 Query execution G06F16/335 takes precedence
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access

Abstract

The American legal system, judges and lawyers are continually researching an ever-expanding body of past judicial opinions, or case law, for the ones most relevant to resolution of new disputes. To facilitate these searches, some companies collect and publish the judicial opinions of courts across the United States in both paper and electronic forms, with some of the cases containing references to prior cases from other courts that have previously ruled on all or part of the same dispute. Identifying the prior cases is problematic, because, for example, conventional computer text-matching not only suggests too many non-prior cases, but also misses too many actual prior cases. Accordingly, the present inventors devised systems, methods, and software that generally facilitate identification of one or more documents that are related to a given document, and particularly facilitate identification of prior cases for a given case. One specific embodiment retrieves prior-case candidates based on information extracted from an input case, and then uses a support vector machine to determine which of the prior-case candidates are most probably prior cases for the input case.

Claims

1. A computerized method implemented using a processor and memory, the method comprising:

extracting information from a first document;

retrieving a set of one or more second documents based on the extracted information;

identifying one or more of the set of second documents as more probably related to the first document than one or more others of the second documents using a learning machine; and

defining a multi-dimensional feature vector for each candidate document, with a set of features including a similarity feature indicating similarity of at least a portion of the candidate legal case to a portion of the document and the multi-dimensional feature vectors using support-vector processing.

2. The method of claim 1, wherein the learning machine comprises a support vector machine.

3. The method of claim 1, wherein the documents are legal opinions.

4. The method of claim 1, wherein extracting information from the first document comprises extracting information identifying one or more persons, places, or legal entities.

5. A computerized method for retrieving documents, the method implemented using at least one processor and memory and comprising:

searching for a set of one or more documents based on a set of one of more queries;

identifying one or more of the set of documents as more probably related to the query than one or more of other of the documents using a learning machine; and

defining a multi-dimensional feature vector for each candidate document, with a set of features including a similarity feature indicating similarity of at least a portion of the candidate legal case to a portion of the document and the multi-dimensional feature vectors using support-vector processing.

6. The method of claim 5, wherein the learning machine comprises a support vector machine.

7. The method of claim 5, wherein the documents are legal opinions.

8. The method of claim 5, wherein searching for a set of one or more documents based on a query, comprises:

parsing an input document;

defining one or more queries based results of parsing; and

executing the one or more queries against one or more databases.

9. The method of claim 5, wherein parsing the input document comprises:

identifying one or more parties in the document.

10. The method of claim 5, wherein identifying one or more of the documents as more probably related to the query than one or more of other of the documents using a learning machine, comprises

defining a set of feature vectors, with each feature vector based on information related to the query and a respective one of the set of documents;

and

communicating the feature vectors to the support vector machine.

11. The method of claim 5, wherein the query is based on an input document, and each of the feature vectors is based on similarity score for one or more portions of the document and the respective one of the set of documents.

12. A computerized method for identifying related documents, the method implemented using a processor and memory and comprising:

receiving an input document;

searching at least one database for a set of one or more related documents based on content of the input document;

identifying one or more of the related documents as more probably related to the input document than one or more of other of the related documents using a support vector machine; and

defining a multi-dimensional feature vector for each candidate document, with a set of features including a similarity feature indicating similarity of at least a portion of the candidate legal case to a portion of the document and the multi-dimensional feature vectors using support-vector processing.

13. The computerized method of claim 12, wherein the input document is a judicial opinion.

14. The computerized method of claim 12, wherein searching the database for a set of one or more related documents based on content of the input document comprises:

extracting one or more party entities from the input document; and

searching the database based on one or more of the party entities.

15. A system comprising:

means, including a processor and memory, for extracting information from a first document;

means, including a processor and memory, for retrieving a set of one or more second documents based on the extracted information;

a learning machine for identifying one or more of the set of second documents as more probably related to the first document than one or more others of the second documents; and

means, including a processor and memory, for defining a multi-dimensional feature vector for each candidate document, with asset of features including a similarity feature indicating similarity of at least a portion of the candidate legal case to a portion of the document and the multi-dimensional feature vectors using support-vector processing.

16. The system of claim 15, wherein the learning machine comprises a support vector machine.

17. The system of claim 15, wherein the documents are legal opinions.

18. The system of claim 15, wherein the means for extracting information from the first document extracts information identifying one or more persons, places, or legal entities.

19. A computerized system for retrieving documents, comprising:

means, including a processor and memory, for searching for a set of one or more documents based on a set of one of more queries;

a learning machine for identifying one or more of the set of documents as more probably related to the query than one or more of other of the documents; and

means, including a processor and memory, for defining a multi-dimensional feature vector for each candidate document, with a set of features including a similarity feature indicating similarity of at least a portion of the candidate legal case to a portion of the document and the multi-dimensional feature vectors using support-vector processing.

20. The system of claim 19, wherein the learning machine comprises a support vector machine.

21. The system of claim 19, wherein the documents are legal opinions.

22. The system of claim 19, wherein the means for searching for a set of one or more documents based on a query, comprises:

means for parsing an input document;

means for defining one or more queries based on results of parsing; and

means for executing the one or more queries against one or more databases.

23. The system of claim 19, wherein the means for parsing the input document comprises means for identifying one or more parties in the document.

24. The system of claim 19, further comprising means for defining a set of feature vectors, with each feature vector based on information related to the query and a respective one of the set of documents.

25. The system of claim 24, wherein the query is based on an input document, and each of the feature vectors is based on similarity score for one or more portions of the document and the respective one of the set of documents.

26. A system for identifying related documents, the method comprising:

means, including a processor and memory, for searching at least one database for a set of one or more related documents based on content of a document;

a support vector machine for identifying one or more of the related documents as more probably related to the input document than one or more of other of the related documents; and

means, including a processor and memory, for defining a multi-dimensional feature vector for each candidate document, with a set of features including a similarity feature indicating similarity of at least a portion of the candidate legal case to a portion of the document and the multi-dimensional feature vectors using support-vector processing.

27. The system of claim 26, wherein the document is a judicial opinion.

28. The system of claim 26, wherein the means for searching the database for a set of one or more related documents based on content of the document comprises:

means for extracting one or more party entities from the input document; and

means searching the database based on one or more of the party entities.