Stephen R Poteet, Age 7117116 28Th St, Bellevue, WA 98008

6701305, Mar 2, 2004

Oct 20, 2000

09/693114

Fredrick Baden Holt - Seattle WA
Anne Shu-Wan Kao - Bellevue WA
Daniel John Pierce - Woodinville WA
Stephen Robert Poteet - Bellevue WA
Yuan-Jye Wu - Issaquah WA

The Boeing Company - Seattle WA

G06F 1700

706 45, 706 12, 706 46

Methods, apparatus and computer program products are provided for retrieving information from a text data collection and for classifying a document into none, one or more of a plurality of predefined classes. In each aspect, a representation of at least a portion of the original matrix is projected into a lower dimensional subspace and those portions of the subspace representation that relate to the term(s) of the query are weighted following the projection into the lower dimensional subspace. In order to retrieve the documents that are most relevant with respect to a query, the documents are then scored with documents having better scores being of generally greater relevance. Alternatively, in order to classify a document, the relationship of the document to the classes of documents is scored with the document then being classified in those classes, if any, that have the best scores.

7403932, Jul 22, 2008

Jul 1, 2005

11/173600

Anne S-W Kao - Bellevue WA, US
Stephen R. Poteet - Bellevue WA, US
Yuan-Jye Wu - Issaquah WA, US
William Ferng - Sammamish WA, US
Robert E. Cranfill - Seattle WA, US

The Boeing Company - Chicago IL

G06N 5/00

706 45, 706 47

Provided are improved methods, apparatus, and computer program products for text differentiation which involves identifying differences between documents with similar content, not merely similar terms, and generating results. Text differentiation provides the ability to find non-similar, or different, content hidden within documents with similar overall content, but not exactly the same content. Text differentiation may be used to quickly identify key differences between similar documents.

7673234, Mar 2, 2010

Mar 11, 2002

10/095673

Anne S. W. Kao - Bellevue WA, US
Stephen R. Poteet - Bellevue WA, US
Lesley Hong-Minh Quach - Seattle WA, US
Steven D. Woods - Seattle WA, US

The Boeing Company - Chicago IL

G06F 17/00

715255, 715256

A method and system are provided for determining metadata to be assigned to a document. A document is identified to be published to a collection of documents. A category for publishing the document to the collection is automatically recommended using a text classifier. At least one category for the document may be selected, and the selected category is assigned as metadata for the document. A method and system are also provided for constructing a search for a document in a collection of documents. A search query is entered for a document in a collection of documents. A search category for searching for the document in the collection of documents is automatically recommended using a text classifier. At least one search category for searching for the document is selected.

7752204, Jul 6, 2010

Nov 18, 2005

11/281499

Anne S-W Kao - Bellevue WA, US
Stephen R. Poteet - Bellevue WA, US
Yuan-Jye Wu - Issaquah WA, US
William Ferng - Sammamish WA, US
Robert E. Cranfill - Seattle WA, US

The Boeing Company - Chicago IL

G06F 7/00
G06F 17/30

707736, 707772, 707917, 715254

A text summarizer identifies relevant terms in a document, weights the terms and extracts one or more segments to produce a summary or abstract. The various terms in a particular are weighted in relation to an existing document collection. A term weight computer computes term weights for terms in the document, and a threshold comparator compares the term weights to determine if the corresponding terms are relevant to the document collection. Next, a term weight summer adds the term weights for each occurrence of each relevant term in the various segments of the document, and a summation comparator compares the summations to identify a text summarization segment representative of the document. Optionally, relevant terms can be highlighted in the term summarization segment.

7831597, Nov 9, 2010

May 4, 2006

11/417196

Yuan-Jye Wu - Issaquah WA, US
Anne S-W Kao - Bellevue WA, US
Stephen R. Poteet - Bellevue WA, US
William Ferng - Sammamish WA, US
Robert E. Cranfill - Seattle WA, US

The Boeing Company - Chicago IL

G06F 7/00
G06F 17/30

707736, 707772, 707917, 715254

A text summarizer identifies relevant terms in a document, weights the terms and extracts one or more segments to produce a summary or abstract. The various terms in a particular are weighted in relation to an existing document collection. A term weight computer computes term weights for terms in the document, and a threshold comparator compares the term weights to determine if the corresponding terms are relevant to the document collection. Next, a term weight summer adds the term weights for each occurrence of each relevant term in the various segments of the document, and a summation comparator compares the summations to identify a text summarization segment representative of the document. Optionally, relevant terms can be highlighted in the term summarization segment.

8234279, Jul 31, 2012

Oct 11, 2005

11/246195

Yuan-Jye Wu - Issaquah WA, US
Anne S-W Kao - Bellevue WA, US
Stephen R. Poteet - Bellevue WA, US
William Ferng - Sammamish WA, US
Robert E. Cranfill - Seattle WA, US

The Boeing Company - Chicago IL

G06F 7/00
G06F 17/00

707737, 707736, 707739, 706 45

A streaming text data comparator performs real-time text data mining on streaming text data. The comparator receives a streaming text data document and generates a vector representation of the term frequencies relating to an existing document collection. The comparator then transforms the term frequency vector into a projection in a precomputed multidimensional subspace that represents the original document collection. The comparator further calculates a relationship value representing the similarities or differences between the vector representation and the subspace, and compares the relationship value to a predetermined threshold to determine whether the streaming text data document is related to the original document collection. If the streaming text data document is related, the streaming text data comparator intercalates the new document into the document collection. If the new document is not related, the comparator may store or delete the unrelated document.

8272064, Sep 18, 2012

Nov 16, 2005

11/280610

Anne S-W Kao - Bellevue WA, US
Stephen R. Poteet - Bellevue WA, US
Nicholas J. Multari - Snoqualmie WA, US

The Boeing Company - Chicago IL

H04L 29/06

726 27

A system generates rules for classifying documents are generated by building a vocabulary of features (e. g. , words, phrases, acronyms, etc. ) that are related to classifying concepts. The system includes a security document reader receives a security document that defines security concepts for a particular project and parses the security document to separate the security concepts. A vocabulary builder receives samples provided by the user that contain information related to the project. For each security concept, the vocabulary builder uses statistical analysis techniques to find features in the samples that are related to that concept. A rule generation assistant, for each security concept, generates rules based on the built vocabulary and the samples. The rule generation assistant uses statistical analysis techniques on the vocabulary and samples to determine features that optimally predict a particular concept. The rules can be used by a downgrader to process information to be distributed.

8577884, Nov 5, 2013

May 13, 2008

12/119697

Stephen R. Poteet - Bellevue WA, US
Anne Kao - Bellevue WA, US
Shan Luh - Kenmore WA, US

The Boeing Company - Chicago IL

G06F 17/30

707737, 707738, 707776

Technologies are described herein for providing automated analysis and summarization of free-form comments in survey response data. A number of topic words are identified from the survey response comments, and a numeric weight is calculated for each topic word that reflects the relevance of the topic word to each comment. Each topic word is associated with one or more topics and the comments relevant to each topic is then determined based on the weights of the associated topic words in each comment. A report is generated which summarizes the topics and their relative importance in the survey response comments based upon the number of comments relevant to each.