Stephen Charles Tratz

8036876, Oct 11, 2011

Nov 4, 2005

11/267370

Antonio P. Sanfilippo - Richland WA, US
Stephen C. Tratz - Richland WA, US
Michelle L. Gregory - Richland WA, US
Alan R. Chappell - Seattle WA, US
Paul D. Whitney - Richland WA, US
Christian Posse - Seattle WA, US
Robert L. Baddeley - Richland WA, US
Ryan E. Hohimer - West Richland WA, US

Battelle Memorial Institute - Richland WA

G06F 17/28
G06F 17/21

704 9, 704 7, 704 10

Methods of defining ontologies, word disambiguation methods, computer systems, and articles of manufacture are described according to some aspects. In one aspect, a word disambiguation method includes accessing textual content to be disambiguated, wherein the textual content comprises a plurality of words individually comprising a plurality of word senses, for an individual word of the textual content, identifying one of the word senses of the word as indicative of the meaning of the word in the textual content, for the individual word, selecting one of a plurality of event classes of a lexical database ontology using the identified word sense of the individual word, and for the individual word, associating the selected one of the event classes with the textual content to provide disambiguation of a meaning of the individual word in the textual content.

8140464, Mar 20, 2012

Apr 28, 2006

11/414069

Antonio P. Sanfilippo - Richland WA, US
Andrew J. Cowell - Kennewick WA, US
Michelle L. Gregory - Richland WA, US
Robert L. Baddeley - Richland WA, US
Patrick R. Paulson - Pasco WA, US
Stephen C. Tratz - Richland WA, US
Ryan E. Hohimer - West Richland WA, US

Battelle Memorial Institute - Richland WA

G06F 9/44
G06F 17/00
G06N 7/02
G06N 7/06
G06N 5/02

706 52, 706 46, 706911

Hypothesis analysis methods, hypothesis analysis devices, and articles of manufacture are described according to some aspects. In one aspect, a hypothesis analysis method includes providing a hypothesis, providing an indicator which at least one of supports and refutes the hypothesis, using the indicator, associating evidence with the hypothesis, weighting the association of the evidence with the hypothesis, and using the weighting, providing information regarding the accuracy of the hypothesis.

20070083492, Apr 12, 2007

Sep 27, 2005

11/237483

Ryan Hohimer - West Richland WA, US
Judith Thomson - Guelph, CA
William Harvey - Richland WA, US
Patrick Paulson - Pasco WA, US
Mark Whiting - Richland WA, US
Stephen Tratz - Richland WA, US
Alan Chappell - Seattle WA, US
R. Butner - Richland WA, US

Battelle Memorial Institute - Richland WA

G06F 17/30

707003000

Processes, data structures, and apparatuses to represent knowledge are disclosed. The processes can comprise labeling elements in a knowledge signature according to concepts in an ontology and populating the elements with confidence values. The data structures can comprise knowledge signatures stored on computer-readable media. The knowledge signatures comprise a matrix structure having elements labeled according to concepts in an ontology, wherein the value of the element represents a confidence that the concept is present in an information space. The apparatus can comprise a knowledge representation unit having at least one ontology stored on a computer-readable medium, at least one data-receiving device, and a processor configured to generate knowledge signatures by comparing datasets obtained by the data-receiving devices to the ontologies.

20080288425, Nov 20, 2008

May 17, 2007

11/749916

Christian Posse - Seattle WA, US
Nathaniel Beagley - Richland WA, US
Jorge F. Reyes Spindola - Richland WA, US
Stephen C. Tratz - Richland WA, US
Amanda M. White - Kennewick WA, US

G06F 15/18

706 12

Methods and apparatus for reasoning about information fusion approaches are described according to some aspects. In one aspect, a method for reasoning about information fusion approaches comprises selecting one or more information fusion approaches for evaluation, and applying the approaches to two or more sets of data. The selection of information fusion approaches can be based on a taxonomy that classifies information fusion algorithms. The impact of each of the applied information fusion approaches can then be conveyed to a user, wherein the conveyed impact is based on one or more impact measures.

20090254581, Oct 8, 2009

Apr 4, 2008

12/080753

Alan R. Chappell - Seattle WA, US
Christian Posse - Seattle WA, US
Judith R. McCuaig - Guelph, CA
Alan R. Willse - Des Moines IA, US
Alexander A. Donaldson - Seattle WA, US
Stephen C. Tratz - Boise ID, US
David A. Thurman - Kenmore WA, US
Stuart J. Rose - Richland WA, US
Gary R. Danielson - Kennewick WA, US

G06F 7/00
G06F 17/30

707102, 707E17002

An automated method for allowing multiple users to independently analyze a corpus of digital information having discrete elements by providing two or more users access to one or more initial training source of digital information, allowing the users to each define a set of categories, automatically generating a group of digital features associated with at least two of the discrete elements, automatically associating a subset of the discrete elements with at least one of the categories, and automatically determining at least one combination of features and transformed features that identifies at least one of the categories. The automated method allows said two or more users to have the capability to perform the step of defining a set of categories, such that the automated steps of generating a group of digital features, associating a subset of said discrete elements, and determining at least one combination of features and transformed features is in whole or in part determined by the manual input to the automated method.

20110276524, Nov 10, 2011

Jul 20, 2011

13/187275

Ryan E. Hohimer - West Richland WA, US
Judith R. Thomson - Guelph, CN
William J. Harvey - Columbus OH, US
Patrick R. Paulson - Richland WA, US
Mark A. Whiting - Richland WA, US
Stephen C. Tratz - Boise ID, US
Alan R. Chappell - Seattle WA, US
R. Scott Butner - Richland WA, US

BATTELLE MEMORIAL INSTITUTE - Richland WA

G06F 15/18

706 12

Data structures and apparatuses to represent knowledge are disclosed. The processes can comprise labeling elements in a knowledge signature according to concepts in an ontology and populating the elements with confidence values. The data structures can comprise knowledge signatures stored on computer-readable media. The knowledge signatures comprise a matrix structure having elements labeled according to concepts in an ontology, wherein the value of the element represents a confidence that the concept is present in an information space. The apparatus can comprise a knowledge representation unit having at least one ontology stored on a computer-readable medium, at least one data-receiving device, and a processor configured to generate knowledge signatures by comparing datasets obtained by the data-receiving devices to the ontologies.