Patents by Inventor Aaron Benjamin Greenblatt

Aaron Benjamin Greenblatt 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).

  • Hypercomplex deep learning methods, architectures, and apparatus for multimodal small, medium, and large-scale data representation, analysis, and applications
    Patent number: 11645835
    Abstract: A method and system for creating hypercomplex representations of data includes, in one exemplary embodiment, at least one set of training data with associated labels or desired response values, transforming the data and labels into hypercomplex values, methods for defining hypercomplex graphs of functions, training algorithms to minimize the cost of an error function over the parameters in the graph, and methods for reading hierarchical data representations from the resulting graph. Another exemplary embodiment learns hierarchical representations from unlabeled data. The method and system, in another exemplary embodiment, may be employed for biometric identity verification by combining multimodal data collected using many sensors, including, data, for example, such as anatomical characteristics, behavioral characteristics, demographic indicators, artificial characteristics.
    Type: Grant
    Filed: August 30, 2018
    Date of Patent: May 9, 2023
    Assignee: Board of Regents, The University of Texas System
    Inventors: Aaron Benjamin Greenblatt, Sos S. Agaian
  • HYPERCOMPLEX DEEP LEARNING METHODS, ARCHITECTURES, AND APPARATUS FOR MULTIMODAL SMALL, MEDIUM, AND LARGE-SCALE DATA REPRESENTATION, ANALYSIS, AND APPLICATIONS
    Publication number: 20190087726
    Abstract: A method and system for creating hypercomplex representations of data includes, in one exemplary embodiment, at least one set of training data with associated labels or desired response values, transforming the data and labels into hypercomplex values, methods for defining hypercomplex graphs of functions, training algorithms to minimize the cost of an error function over the parameters in the graph, and methods for reading hierarchical data representations from the resulting graph. Another exemplary embodiment learns hierarchical representations from unlabeled data. The method and system, in another exemplary embodiment, may be employed for biometric identity verification by combining multimodal data collected using many sensors, including, data, for example, such as anatomical characteristics, behavioral characteristics, demographic indicators, artificial characteristics.
    Type: Application
    Filed: August 30, 2018
    Publication date: March 21, 2019
    Inventors: Aaron Benjamin Greenblatt, Sos S. Agaian