Patents by Inventor Fabian Gobel

Fabian Gobel 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).

  • GESTURE RECOGNITION BASED ON LIKELIHOOD OF INTERACTION
    Publication number: 20230418390
    Abstract: A method for evaluating gesture input comprises receiving input data for sequential data frames, including hand tracking data for hands of a user. A first neural network is trained to recognize features indicative of subsequent gesture interactions and configured to evaluate input data for a sequence of data frames and to output an indication of a likelihood of the user performing gesture interactions during a predetermined window of data frames. A second neural network is trained to recognize features indicative of whether the user is currently performing one or more gesture interactions and configured to adjust parameters for gesture interaction recognition during the predetermined window based on the indicated likelihood. The second neural network evaluates the predetermined window for performed gesture interactions based on the adjusted parameters, and outputs a signal as to whether the user is performing one or more gesture interactions during the predetermined window.
    Type: Application
    Filed: September 8, 2023
    Publication date: December 28, 2023
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Julia SCHWARZ, Bugra TEKIN, Sophie STELLMACH, Erian VAZQUEZ, Casey Leon MEEKHOF, Fabian GOBEL
  • Gesture recognition based on likelihood of interaction
    Patent number: 11768544
    Abstract: A method for evaluating gesture input comprises receiving input data for sequential data frames, including hand tracking data for hands of a user. A first neural network is trained to recognize features indicative of subsequent gesture interactions and configured to evaluate input data for a sequence of data frames and to output an indication of a likelihood of the user performing gesture interactions during a predetermined window of data frames. A second neural network is trained to recognize features indicative of whether the user is currently performing one or more gesture interactions and configured to adjust parameters for gesture interaction recognition during the predetermined window based on the indicated likelihood. The second neural network evaluates the predetermined window for performed gesture interactions based on the adjusted parameters, and outputs a signal as to whether the user is performing one or more gesture interactions during the predetermined window.
    Type: Grant
    Filed: February 1, 2022
    Date of Patent: September 26, 2023
    Assignee: Microsoft Technology Licensing, LLC
    Inventors: Julia Schwarz, Bugra Tekin, Sophie Stellmach, Erian Vazquez, Casey Leon Meekhof, Fabian Gobel
  • GESTURE RECOGNITION BASED ON LIKELIHOOD OF INTERACTION
    Publication number: 20230244316
    Abstract: A method for evaluating gesture input comprises receiving input data for sequential data frames, including hand tracking data for hands of a user. A first neural network is trained to recognize features indicative of subsequent gesture interactions and configured to evaluate input data for a sequence of data frames and to output an indication of a likelihood of the user performing gesture interactions during a predetermined window of data frames. A second neural network is trained to recognize features indicative of whether the user is currently performing one or more gesture interactions and configured to adjust parameters for gesture interaction recognition during the predetermined window based on the indicated likelihood. The second neural network evaluates the predetermined window for performed gesture interactions based on the adjusted parameters, and outputs a signal as to whether the user is performing one or more gesture interactions during the predetermined window.
    Type: Application
    Filed: February 1, 2022
    Publication date: August 3, 2023
    Applicant: Microsoft Technology Licensing, LLC
    Inventors: Julia SCHWARZ, Bugra TEKIN, Sophie STELLMACH, Erian VAZQUEZ, Casey Leon MEEKHOF, Fabian GOBEL