Patents by Inventor Michael Thomas McGowan

Michael Thomas McGowan has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • FlexSCAPE: Data Driven Hypothesis Testing and Generation System
    Publication number: 20120185424
    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.
    Type: Application
    Filed: August 24, 2010
    Publication date: July 19, 2012
    Applicant: QUANTUM LEAP RESEARCH, INC.
    Inventors: Akhileswar Ganesh Vaidyanathan, Eric N. Jean, Mani Thomas, David Louis Hample, Michael Thomas McGowan, Jijun Wang, Eli T. Faulkner, Jay Dee Askren, Albert Josef Boehmler, Durban A. Frazer
  • FlexSCAPE: Data Driven Hypothesis Testing and Generation System
    Publication number: 20110231356
    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.
    Type: Application
    Filed: July 1, 2010
    Publication date: September 22, 2011
    Applicant: QUANTUM LEAP RESEARCH, INC.
    Inventors: Akhileswar Ganesh Vaidyanathan, Eric N. Jean, Mani Thomas, David Louis Hample, Michael Thomas McGowan, Jijun Wang, Eli T. Faulkner, Jay Dee Askren, Albert Josef Boehmler, Durban A. Frazer