Patents by Inventor Lingzhi Du

Lingzhi Du 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: 20240303553
    Abstract: This application describes systems and methods for generating machine learning models (MLMs). An exemplary method includes obtaining a sample and user input data characterizing a product or service. A subset of the data is selected from the sample based on sampling the sample according to the user input data. An MLM is trained by applying the data subset as training input to the MLM, thereby providing a trained MLM to emulate a customer selection process unique to the product or service. A user interface (UI) configured to receive other user input data and cause the trained MLM to execute on the other user input data, thereby testing the trained MLM, is presented. A summary of results from the execution of the trained MLM is generated and presented in the UI. The summary of results indicates a contribution to the trained MLM of each of a plurality of features.
    Type: Application
    Filed: April 29, 2024
    Publication date: September 12, 2024
    Inventors: David Sheehan, Siavash Yasani, Bingjia Wang, Yunyan Zhang, Qiumeng Yu, Ruochen Zha, Adam Kleinman, Sean Javad Kamkar, Lingzhi Du, Saar Yalov, Jerome Louis Budzik
  • Patent number: 12058312
    Abstract: A method and an apparatus for video processing are provided. The method includes that a decoding terminal receives a plurality of coded video frames coded using one or more generative adversarial networks (GANs), receives network parameters related to the one or more GANs, and decodes the plurality of coded video frames using GANs based on the network parameters. Further, the one or more GANs respectively implement one or more video coding functions including reference-frame coding, motion-compensated frame prediction, and residue-frame coding.
    Type: Grant
    Filed: October 6, 2021
    Date of Patent: August 6, 2024
    Assignees: KWAI INC., SANTA CLARA UNIVERSITY
    Inventors: Pengli Du, Ying Liu, Nam Ling, Lingzhi Liu, Yongxiong Ren, Ming Kai Hsu
  • Patent number: 11972338
    Abstract: This application describes systems and methods for generating machine learning models (MLMs). An exemplary method includes obtaining a sample and user input data characterizing a product or service. A subset of the data is selected from the sample based on sampling the sample according to the user input data. An MLM is trained by applying the data subset as training input to the MLM, thereby providing a trained MLM to emulate a customer selection process unique to the product or service. A user interface (UI) configured to receive other user input data and cause the trained MLM to execute on the other user input data, thereby testing the trained MLM, is presented. A summary of results from the execution of the trained MLM is generated and presented in the UI. The summary of results indicates a contribution to the trained MLM of each of a plurality of features.
    Type: Grant
    Filed: May 2, 2023
    Date of Patent: April 30, 2024
    Assignee: ZestFinance, Inc.
    Inventors: David Sheehan, Siavash Yasini, Bingjia Wang, Yunyan Zhang, Qiumeng Yu, Ruochen Zha, Adam Kleinman, Sean Javad Kamkar, Lingzhi Du, Saar Yalov, Jerome Louis Budzik
  • Publication number: 20230359944
    Abstract: This application describes systems and methods for generating machine learning models (MLMs). An exemplary method includes obtaining a sample and user input data characterizing a product or service. A subset of the data is selected from the sample based on sampling the sample according to the user input data. An MLM is trained by applying the data subset as training input to the MLM, thereby providing a trained MLM to emulate a customer selection process unique to the product or service. A user interface (UI) configured to receive other user input data and cause the trained MLM to execute on the other user input data, thereby testing the trained MLM, is presented. A summary of results from the execution of the trained MLM is generated and presented in the UI. The summary of results indicates a contribution to the trained MLM of each of a plurality of features.
    Type: Application
    Filed: May 2, 2023
    Publication date: November 9, 2023
    Inventors: David Sheehan, Siavash Yasini, Bingjia Wang, Yunyan Zhang, Qiumeng Yu, Ruochen Zha, Adam Kleinman, Sean Javad Kamkar, Lingzhi Du, Saar Yalov, Jerome Louis Budzik
  • Publication number: 20230105547
    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for machine learning model fairness and explainability. In some implementations, a method includes obtaining data relating to a plurality of potential borrowers; providing the data to the trained machine learning model; obtaining, by the trained machine learning model’s processing of the provided data, the one or more outputs of the trained machine learning model; and automatically generating a report that explains the one or more outputs of the trained machine learning model with respect to one or more fairness metrics and one or more accuracy metrics; and providing the automatically generated report for display on a user device.
    Type: Application
    Filed: September 12, 2022
    Publication date: April 6, 2023
    Inventors: Sean Javad Kamkar, Michael Egan Van Veen, Feng Li, Marc Frederick Eberstein, Jose Efrain Valentin, Jerome Louis Budzik, John Wickens Lamb Merrill, Geoff Ward, Lingzhi Du, Drew Gifford