Patents by Inventor Soheil Khorram

Soheil Khorram 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: 20230096805
    Abstract: A method includes receiving a plurality of unlabeled audio samples corresponding to spoken utterances not paired with corresponding transcriptions. At a target branch of a contrastive Siamese network, the method also includes generating a sequence of encoder outputs for the plurality of unlabeled audio samples and modifying time characteristics of the encoder outputs to generate a sequence of target branch outputs. At an augmentation branch of a contrastive Siamese network, the method also includes performing augmentation on the unlabeled audio samples, generating a sequence of augmented encoder outputs for the augmented unlabeled audio samples, and generating predictions of the sequence of target branch outputs generated at the target branch. The method also includes determining an unsupervised loss term based on target branch outputs and predictions of the sequence of target branch outputs. The method also includes updating parameters of the audio encoder based on the unsupervised loss term.
    Type: Application
    Filed: December 14, 2021
    Publication date: March 30, 2023
    Applicant: Google LLC
    Inventors: Jaeyoung Kim, Soheil Khorram, Hasim Sak, Anshuman Tripathi, Han Lu, Qian Zhang
  • Patent number: 11545173
    Abstract: A method of predicting a mood state of a user may include recording an audio sample via a microphone of a mobile computing device of the user based on the occurrence of an event, extracting a set of acoustic features from the audio sample, generating one or more emotion values by analyzing the set of acoustic features using a trained machine learning model, and determining the mood state of the user, based on the one or more emotion values. In some embodiments, the audio sample may be ambient audio recorded periodically, and/or call data of the user recorded during clinical calls or personal calls.
    Type: Grant
    Filed: August 30, 2019
    Date of Patent: January 3, 2023
    Assignee: THE REGENTS OF THE UNIVERSITY OF MICHIGAN
    Inventors: Emily Mower Provost, Melvin McInnis, John Henry Gideon, Katherine Anne Matton, Soheil Khorram
  • Publication number: 20220067384
    Abstract: Video and audio from a computer simulation are processed by a machine learning engine to identify candidate segments of the simulation for use in a video summary of the simulation. Text input is then used to reinforce whether a candidate segment should be included in the video summary.
    Type: Application
    Filed: November 25, 2020
    Publication date: March 3, 2022
    Inventors: Lakshmish Kaushik, Saket Kumar, Jaekwon Yoo, Kevin Zhang, Soheil Khorram, Sharath Rao, Chockalingam Ravi Sundaram
  • Publication number: 20220067385
    Abstract: Video and audio from a computer simulation are processed by a machine learning engine to identify candidate segments of the simulation for use in a video summary of the simulation. Text input is then used to reinforce whether a candidate segment should be included in the video summary. Metadata can be added to the summary showing game summary information.
    Type: Application
    Filed: August 25, 2021
    Publication date: March 3, 2022
    Inventors: Lakshmish Kaushik, Saket Kumar, Jaekwon Yoo, Kevin Zhang, Soheil Khorram, Sharath Rao, Ravi Sundaram
  • Patent number: 11072344
    Abstract: A method includes receiving acoustic features and phonetic features associated with an utterance from a driver in a vehicle, providing the acoustic features and the phonetic features to a feature fusion sub-network, receiving a feature fusion utterance representation from the feature fusion sub-network, providing one of the acoustic features or the phonetic features to a non-fusion sub-network trained using supervised learning, receiving a non-fusion utterance representation from the non-fusion sub-network, generating an intermediate utterance representation based on the feature fusion utterance representation and the non-fusion utterance representation, providing at least a portion of the intermediate utterance representation to a fully-connected sub-network trained using supervised learning, receiving a valence vector from the fully-connected sub-network, and causing a vehicle control system to perform a vehicle maneuver based on the valence vector.
    Type: Grant
    Filed: March 18, 2019
    Date of Patent: July 27, 2021
    Assignee: The Regents of the University of Michigan
    Inventors: Emily Mower Provost, Biqiao Zhang, Soheil Khorram
  • Publication number: 20200298873
    Abstract: The present disclosure provides a method that includes receiving acoustic features and phonetic features associated with an utterance from a driver in a vehicle with a vehicle control system, providing the plurality of acoustic features and the plurality of phonetic features to a feature fusion sub-network, receiving a feature fusion utterance representation from the feature fusion sub-network, providing one of the plurality of acoustic features or the plurality of phonetic features to a non-fusion sub-network trained using supervised learning, receiving a non-fusion utterance representation from the non-fusion sub-network, generating an intermediate utterance representation based on the feature fusion utterance representation and the non-fusion utterance representation, providing at least a portion of the intermediate utterance representation to a fully-connected sub-network trained using supervised learning, receiving a valence vector from the fully-connected sub-network, and causing the vehicle control sys
    Type: Application
    Filed: March 18, 2019
    Publication date: September 24, 2020
    Inventors: EMILY MOWER PROVOST, BIQIAO ZHANG, SOHEIL KHORRAM
  • Publication number: 20200075040
    Abstract: A method of predicting a mood state of a user may include recording an audio sample via a microphone of a mobile computing device of the user based on the occurrence of an event, extracting a set of acoustic features from the audio sample, generating one or more emotion values by analyzing the set of acoustic features using a trained machine learning model, and determining the mood state of the user, based on the one or more emotion values. In some embodiments, the audio sample may be ambient audio recorded periodically, and/or call data of the user recorded during clinical calls or personal calls.
    Type: Application
    Filed: August 30, 2019
    Publication date: March 5, 2020
    Inventors: Emily Mower Provost, Melvin McInnis, John Henry Gideon, Katherine Anne Matton, Soheil Khorram