Patents by Inventor Luke Robert Gutzwiller

Luke Robert Gutzwiller 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).

  • Classifying data with deep learning neural records incrementally refined through expert input
    Patent number: 9324022
    Abstract: Embodiments are directed towards classifying data using machine learning that may be incrementally refined based on expert input. Data provided to a deep learning model that may be trained based on a plurality of classifiers and sets of training data and/or testing data. If the number of classification errors exceeds a defined threshold classifiers may be modified based on data corresponding to observed classification errors. A fast learning model may be trained based on the modified classifiers, the data, and the data corresponding to the observed classification errors. And, another confidence value may be generated and associated with the classification of the data by the fast learning model. Report information may be generated based on a comparison result of the confidence value associated with the fast learning model and the confidence value associated with the deep learning model.
    Type: Grant
    Filed: March 4, 2015
    Date of Patent: April 26, 2016
    Assignee: Signal/Sense, Inc.
    Inventors: David Russell Williams, Jr., Luke Robert Gutzwiller, Megan Ursula Hazen, Brigham Sterling Anderson, Alan McIntyre, Tom Abeles
  • CLASSIFYING DATA WITH DEEP LEARNING NEURAL RECORDS INCREMENTALLY REFINED THROUGH EXPERT INPUT
    Publication number: 20150254555
    Abstract: Embodiments are directed towards classifying data using machine learning that may be incrementally refined based on expert input. Data provided to a deep learning model that may be trained based on a plurality of classifiers and sets of training data and/or testing data. If the number of classification errors exceeds a defined threshold classifiers may be modified based on data corresponding to observed classification errors. A fast learning model may be trained based on the modified classifiers, the data, and the data corresponding to the observed classification errors. And, another confidence value may be generated and associated with the classification of the data by the fast learning model. Report information may be generated based on a comparison result of the confidence value associated with the fast learning model and the confidence value associated with the deep learning model.
    Type: Application
    Filed: March 4, 2015
    Publication date: September 10, 2015
    Inventors: David Russell Williams, JR., Luke Robert Gutzwiller, Megan Ursula Hazen, Brigham Sterling Anderson, Alan McIntyre, Tom Abeles