Patents by Inventor Andreas Kebel

Andreas Kebel 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).

  • Publication number: 20230107475
    Abstract: A computer-implemented method includes obtaining a multi-domain (MD) dataset and training a neural network model using the MD dataset with short-form data withheld (MD-SF). The neural network model includes a plurality of layer each having a plurality of parameters. The method also includes resetting each respective layer in the trained neural network one at a time. For each respective layer in the trained neural network model, and after resetting the respective layer, the method also includes determining a corresponding word error rate of the trained neural network model and identifying the respective layer as corresponding to an ambient layer when the corresponding word error rate satisfies a word error rate threshold. The method also includes transmitting an on-device neural network model to execute on one or more client devices for generating gradients based on the withheld domain (SF) of the MD dataset.
    Type: Application
    Filed: October 4, 2022
    Publication date: April 6, 2023
    Applicant: Google LLC
    Inventors: Dhruv Guliani, Lillian Zhou, Andreas Kebel, Giovanni Motta, Francoise Beaufays