Automated system and method for generating reasons that a court case is cited

• US 20050144169A1
• Filed: 02/14/2005
• Published: 06/30/2005
• Est. Priority Date: 12/21/1999
• Status: Active Grant

• Current Assignee: RELX Inc. (RELX PLC)
• Original Assignee: LexisNexis Group Inc. (RELX PLC)
• Inventors: Wiltshire, James S. Jr., Zhang, Paul, Ahmed, Salahuddin, Lu, Xin Allan, Humphrey, Timothy L., Parhizgar, Afsar, Harmon, Joseph P., Collias, Spiro G., Morelock, John T.
• Granted Patent: US 7,693,704 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/31 Indexing; Data structures therefor; Storage structures
• CPC Class Codes: G06F 16/382 using citations hypermedia G06F16/94
• CPC Class Codes: G06F 40/20 Natural language analysis semantic analysis of natural language G06F40/30
• CPC Class Codes: G06F 40/268 Morphological analysis
• CPC Class Codes: Y10S 707/917 Text
• CPC Class Codes: Y10S 707/942 Legal/academic
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99934 Query formulation, input preparation, or translation
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access
• CPC Class Codes: Y10S 707/99937 Sorting

Abstract

A computer-automated system and method identify text in a first “citing” court case, near a “citing instance” (in which a second “cited” court case is cited), that indicates the reason(s) for citing (RFC). The automated method of designating text, taken from a set of citing documents, as reasons for citing (RFC) that are associated with respective citing instances of a cited document, has steps including: obtaining contexts of the citing instances in the respective citing documents (each context including text that includes the citing instance and text that is near the citing instance), analyzing the content of the contexts, and selecting (from the citing instances'"'"' context) text that constitutes the RFC, based on the analyzed content of the contexts. A related computer-automated system and method selects content words that are highly related to the reasons a particular document is cited, and gives them weights that indicate their relative relevance. Another related computer-automated system and method forms lists of morphological forms of words. Still another related computer-automated system and method scores sentences to show their relevance to the reasons a document is cited. Also, another related computer-automated system and method generates lists of content words. In a preferred embodiment, the systems and methods are applied to legal (especially case law) documents and legal (especially case law) citations.

Claims

1-4. -4. (canceled)

5. An automated method for selecting content words from documents, and for determining content scores for respective content words that indicate the content words'"'"' degree of relevance, the method comprising:

associating paragraphs from the documents;

processing text in the associated paragraphs to eliminate text that conveys little about the content of the paragraphs;

determining common words that are not eliminated by the processing step and that are found in plural paragraphs, while tallying content scores that indicate respective numbers of paragraphs in which the respective common words are encountered; and

forming the content word list as including the common words linked to respective content scores.

6. The method of claim 5, wherein the paragraph associating step includes:

pairing paragraphs from among documents that cite a cited document.

7. The method of claim 5, wherein the paragraph associating step includes:

pairing paragraphs from a cited document with paragraphs from documents that cite the cited document.

8. The method of claim 5, wherein the processing step includes:

removing from the paired paragraphs, noise words that convey little information about the content of the paragraphs; and

stemming the words of the paired paragraphs to a length that preserves their essential character while eliminating characters that convey little information about the word'"'"'s identity.

9-19. -19. (canceled)

20. An apparatus for selecting content words from documents, and for determining content scores for respective content words that indicate the content words'"'"' degree of relevance, the apparatus comprising:

means for associating paragraphs from the documents;

means for processing text in the associated paragraphs to eliminate text that conveys little about the content of the paragraphs;

means for determining common words that are not eliminated by the processing means and that are found in plural paragraphs, while tallying content scores that indicate respective numbers of paragraphs in which the respective common words are encountered; and

means for forming the content word list as including the common words linked to respective content scores.

21. The apparatus of claim 20, wherein the paragraph associating means includes:

means for pairing paragraphs from among documents that cite a cited document.

22. The apparatus of claim 20, wherein the paragraph associating means includes:

means for pairing paragraphs from a cited document with paragraphs from documents that cite the cited document.

23. The apparatus of claim 20, wherein the processing means includes:

means for removing from the paired paragraphs, noise words that convey little information about the content of the paragraphs; and

means for stemming the words of the paired paragraphs to a length that preserves their essential character while eliminating characters that convey little information about the word'"'"'s identity.

24-34. -34. (canceled)

35. A computer-readable memory that, when used in conjunction with a computer, can carry out an automated method for selecting content words from documents, and for determining content scores for respective content words that indicate the content words'"'"' degree of relevance, the computer-readable memory comprising:

computer-readable code for associating paragraphs from the documents;

computer-readable code for processing text in the associated paragraphs to eliminate text that conveys little about the content of the paragraphs;

computer-readable code for determining common words that are not eliminated by the processing computer-readable code and that are found in plural paragraphs, while tallying content scores that indicate respective numbers of paragraphs in which the respective common words are encountered, and

computer-readable code for forming the content word list as including the common words linked to respective content scores.

36. The computer-readable memory of claim 35, wherein the paragraph associating computer-readable code includes:

computer-readable code for pairing paragraphs from among documents that cite a cited document.

37. The computer-readable memory of claim 35, wherein the paragraph associating computer-readable code includes:

computer-readable code for pairing paragraphs from a cited document with paragraphs from documents that cite the cited document.

38. The computer-readable memory of claim 35, wherein the processing computer-readable code includes:

computer-readable code for removing from the paired paragraphs, noise words that convey little information about the content of the paragraphs; and

computer-readable code for stemming the words of the paired paragraphs to a length that preserves their essential character while eliminating characters that convey little information about the word'"'"'s identity.

39-45. -45. (canceled)