JOHN THAYER MORELOCK
Pilots at Homeway Dr, Dayton, OH

6772149, Aug 3, 2004

Sep 23, 1999

09/401725

John T. Morelock - Beavercreek OH
Salahuddin Ahmed - Miamisburg OH
Timothy Lee Humphrey - Kettering OH
Xin Allan Lu - Springboro OH

Lexis-Nexis Group - Miamisburg OH

G06F 1730

707 6, 707 7, 704 9, 704 1, 706 12, 706 16, 706 45, 715500, 715531, 715535

A computer-implemented method of gathering large quantities of training data from case law documents (especially suitable for use as input to a learning algorithm that is used in a subsequent process of recognizing and distinguishing fact passages and discussion passages in additional case law documents) has steps of: partitioning text in the documents by headings in the documents, comparing the headings in the documents to fact headings in a fact heading list and to discussion headings in a discussion heading list, filtering from the documents the headings and text that is associated with the headings, and storing (on persistent storage in a manner adapted for input into the learning algorithm) fact training data and discussion training data that are based on the filtered headings and the associated text. Another method (of extracting features that are independent of specific machine learning algorithms needed to accurately classify case law text passages as fact passages or as discussion passages) has steps of: determining a relative position of the text passages in an opinion segment in the case law text, parsing the text passages into text chunks, comparing the text chunks to predetermined feature entities for possible matched feature entities, and associating the relative position and matched feature entities with the text passages for use by one of the learning algorithms. Corresponding apparatus and computer-readable memories are also provided.

6502081, Dec 31, 2002

Aug 4, 2000

09/633266

John T. Morelock - Beavercreek OH
Timothy L. Humphrey - Kettering OH
James M. Peck - Rockville MD
Salahuddin Ahmed - San Diego CA

Lexis Nexis - Dayton OH

G06F 1518

706 12, 707500

An economic, scalable machine learning system and process perform document (concept) classification with high accuracy using large topic schemes, including large hierarchical topic schemes. One or more highly relevant classification topics is suggested for a-given document (concept) to be classified. The invention includes training and concept classification processes. The invention also provides methods that may be used as part of the training and/or concept classification processes, including: a method of scoring the relevance of features in training concepts, a method of ranking concepts based on relevance score, and a method of voting on topics associated with an input concept. In a preferred embodiment, the invention is applied to the legal (case law) domain, classifying legal concepts (rules of law) according to a proprietary legal topic classification scheme (a hierarchical scheme of areas of law).

6684202, Jan 27, 2004

May 31, 2000

09/583867

Timothy L. Humphrey - Kettering OH
John T. Morelock - Beavercreek OH
Spiro G. Collias - Springboro OH
Salahuddin Ahmed - San Diego CA

Lexis Nexis - Miamisburg OH

G06N 504

706 45, 707 2, 707 5

A system and method for binary classification of text units such as sentences, paragraphs and documents as either a rule of law (ROL) or not a rule of law (˜ROL). During a training phase of the system and method of the present invention, an initialized knowledge base and labeled or pre-classified sentences are used to build a trained knowledge base. The trained knowledge base contains an equation, a threshold, and a plurality of statistical values called Z values. When inputting text documents for classification, a Z value is generated for each term or token in the input text. The Z values are input to the equation which calculates a score for each sentence. Each calculated score is then compared to the threshold to classify each sentence as either ROL or ˜ROL.

7693704, Apr 6, 2010

Feb 14, 2005

11/056121

Timothy L. Humphrey - Kettering OH, US
Xin Allan Lu - Springboro OH, US
Afsar Parhizgar - Dayton OH, US
Salahuddin Ahmed - Miamisburg OH, US
John T. Morelock - Beavercreek OH, US
Joseph P. Harmon - Centerville OH, US
Spiro G. Collias - Springboro OH, US
Paul Zhang - Springboro OH, US

Lexis-Nexis Group, a division of Reed Elsevier Inc. - Miamisburg OH

G06F 17/27

704 9, 707 2, 707 3, 707 4, 707 5, 707 6, 704 1, 704 10

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.

2005014, Jul 7, 2005

Feb 14, 2005

11/056102

Timothy Humphrey - Kettering OH, US
Xin Lu - Springboro OH, US
Afsar Parhizgar - Dayton OH, US
Salahuddin Ahmed - Miamisburg OH, US
James Wiltshire - Springboro OH, US
John Morelock - Beavercreek OH, US
Joseph Harmon - Centerville OH, US
Spiro Collias - Springboro OH, US
Paul Zhang - Springboro OH, US

G06F017/30, G06F007/00

707007000, 707006000

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.

6856988, Feb 15, 2005

Dec 21, 1999

09/468785

Timothy L. Humphrey - Kettering OH, US
Xin Allan Lu - Springboro OH, US
Afsar Parhizgar - Dayton OH, US
Salahuddin Ahmed - Miamisburg OH, US
John T. Morelock - Beavercreek OH, US
Joseph P. Harmon - Centerville OH, US
Spiro G. Collias - Springboro OH, US
Paul Zhang - Springboro OH, US

Lexis-Nexis Group - Miamisburg OH

G06F017/30

707 7, 707 3, 704 1, 704 4, 704 7, 704 9

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.

7464025, Dec 9, 2008

Feb 14, 2005

11/056200

Timothy L. Humphrey - Kettering OH, US
Xin Allan Lu - Springboro OH, US
Afsar Parhizgar - Dayton OH, US
Salahuddin Ahmed - Miamisburg OH, US
John T. Morelock - Beavercreek OH, US
Joseph P. Harmon - Centerville OH, US
Spiro G. Collias - Springboro OH, US
Paul Zhang - Springboro OH, US

Lexis-Nexis Group - Miamisburg OH

G06F 17/27

704 9, 707 2, 707 3, 707 4, 707 5

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.