Michael J Mcgowan

US Patent:

20110231356, Sep 22, 2011

Filed:

Jul 1, 2010

Appl. No.:

12/829241

Inventors:

Akhileswar Ganesh Vaidyanathan - Landenberg PA, US
Eric N. Jean - Denver CO, US
Mani Thomas - Parsippany NJ, US
David Louis Hample - Newark DE, US
Michael Thomas McGowan - Newark DE, US
Jijun Wang - Hockessin DE, US
Eli T. Faulkner - Wilmington DE, US
Jay Dee Askren - Bear DE, US
Albert Josef Boehmler - Newark DE, US
Durban A. Frazer - Kentfield CA, US

Assignee:

QUANTUM LEAP RESEARCH, INC. - Claymont DE

International Classification:

G06N 5/02

US Classification:

706 52

Abstract:

The present invention relates to a method for generating hypotheses automatically from graphical models built directly from data. The method of the present invention links three key scientific concepts to enable hypothesis generation from data driven hypothesis-models: including the use of information theory based measures to identify informative feature subsets within the data; the automatic generation of graphical models from the informative data subsets identified from step one; and the application of optimization methods to graphical models to enable hypothesis generation. The integration of these three concepts can enable scalable approaches to hypothesis generation from large, complex data environments. The use of graphical models as the model representation can allow prior knowledge to be effectively integrated into the modeling environment.

US Patent:

20120185424, Jul 19, 2012

Filed:

Aug 24, 2010

Appl. No.:

12/862657

Inventors:

Akhileswar Ganesh Vaidyanathan - Landenberg PA, US
Eric N. Jean - Denver CO, US
Mani Thomas - Hillsborough NJ, US
David Louis Hample - Newark DE, US
Michael Thomas McGowan - Newark DE, US
Jijun Wang - Hockessin DE, US
Eli T. Faulkner - Wilmington DE, US
Jay Dee Askren - Bear DE, US
Albert Josef Boehmler - West Grove PA, US
Durban A. Frazer - San Francisco CA, US

Assignee:

QUANTUM LEAP RESEARCH, INC. - Claymont DE

International Classification:

G06N 5/02

US Classification:

706 52

Abstract:

The present invention relates to a method for generating hypotheses automatically from graphical models built directly from data. The method of the present invention links three key scientific concepts to enable hypothesis generation from data driven hypothesis-models: including the use of information theory based measures to identify informative feature subsets within the data; the automatic generation of graphical models from the informative data subsets identified from step one; and the application of optimization methods to graphical models to enable hypothesis generation. The integration of these three concepts can enable scalable approaches to hypothesis generation from large, complex data environments. The use of graphical models as the model representation can allow prior knowledge to be effectively integrated into the modeling environment.