Patents by Inventor David DeBarr

David DeBarr 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).

  • TRIGGERING EXECUTION OF MACHINE LEARNING BASED PREDICTION OF DOCUMENT METADATA
    Publication number: 20250173369
    Abstract: A system predicts metadata attributes associated with documents using machine learning models. The document may represent an interaction between entities. The system trains machine learning models to predict scores indicating whether a token or a sequence of token of a document represents a metadata attribute. The metadata prediction is used to annotate the document and display to users. The system receives user feedback via the user interface and uses the user feedback to evaluate or retrain the model. The system generates training data by receiving a set of annotated documents and comparing the annotated documents against other documents to identify matching documents. The system determines when to execute the machine learning based metadata prediction based on steps of document workflow executed by the system.
    Type: Application
    Filed: January 27, 2025
    Publication date: May 29, 2025
    Applicant: Docusign, Inc.
    Inventors: Kaushik Narayanan, David Matthew Wong, David Lange, Vinay Jethava, Qing Zheng, Mohammad Mehdi Ghanimifard, Pontus Lindstrom, Gowtham Rangarajan Raman, David DeBarr, Yan He
  • Triggering execution of machine learning based prediction of document metadata
    Patent number: 12222975
    Abstract: A system predicts metadata attributes associated with documents using machine learning models. The document may represent an interaction between entities. The system trains machine learning models to predict scores indicating whether a token or a sequence of token of a document represents a metadata attribute. The metadata prediction is used to annotate the document and display to users. The system receives user feedback via the user interface and uses the user feedback to evaluate or retrain the model. The system generates training data by receiving a set of annotated documents and comparing the annotated documents against other documents to identify matching documents. The system determines when to execute the machine learning based metadata prediction based on steps of document workflow executed by the system.
    Type: Grant
    Filed: January 13, 2023
    Date of Patent: February 11, 2025
    Assignee: Docusign, Inc.
    Inventors: Kaushik Narayanan, David Matthew Wong, David Lange, Vinay Jethava, Qing Zheng, Mohammad Mehdi Ghanimifard, Pontus Lindstrom, Gowtham Rangarajan Raman, David DeBarr, Yan He
  • TRAINING OF MACHINE LEARNING MODELS FOR PREDICTING DOCUMENT METADATA
    Publication number: 20240242108
    Abstract: A system predicts metadata attributes associated with documents using machine learning models. The document may represent an interaction between entities. The system trains machine learning models to predict scores indicating whether a token or a sequence of token of a document represents a metadata attribute. The metadata prediction is used to annotate the document and display to users. The system receives user feedback via the user interface and uses the user feedback to evaluate or retrain the model. The system generates training data by receiving a set of annotated documents and comparing the annotated documents against other documents to identify matching documents. The system determines when to execute the machine learning based metadata prediction based on steps of document workflow executed by the system.
    Type: Application
    Filed: January 13, 2023
    Publication date: July 18, 2024
    Inventors: Kaushik Narayanan, David Matthew Wong, David Lange, Vinay Jethava, Qing Zheng, Mohammad Mehdi Ghanimifard, Pontus Lindstrom, Gowtham Rangarajan Raman, David DeBarr, Yan He
  • MACHINE LEARNING BASED PREDICTION OF DOCUMENT METADATA
    Publication number: 20240242018
    Abstract: A system predicts metadata attributes associated with documents using machine learning models. The document may represent an interaction between entities. The system trains machine learning models to predict scores indicating whether a token or a sequence of token of a document represents a metadata attribute. The metadata prediction is used to annotate the document and display to users. The system receives user feedback via the user interface and uses the user feedback to evaluate or retrain the model. The system generates training data by receiving a set of annotated documents and comparing the annotated documents against other documents to identify matching documents. The system determines when to execute the machine learning based metadata prediction based on steps of document workflow executed by the system.
    Type: Application
    Filed: January 13, 2023
    Publication date: July 18, 2024
    Inventors: Kaushik Narayanan, David Matthew Wong, David Lange, Vinay Jethava, Qing Zheng, Mohammad Mehdi Ghanimifard, Pontus Lindstrom, Gowtham Rangarajan Raman, David DeBarr, Yan He
  • TRIGGERING EXECUTION OF MACHINE LEARNING BASED PREDICTION OF DOCUMENT METADATA
    Publication number: 20240241901
    Abstract: A system predicts metadata attributes associated with documents using machine learning models. The document may represent an interaction between entities. The system trains machine learning models to predict scores indicating whether a token or a sequence of token of a document represents a metadata attribute. The metadata prediction is used to annotate the document and display to users. The system receives user feedback via the user interface and uses the user feedback to evaluate or retrain the model. The system generates training data by receiving a set of annotated documents and comparing the annotated documents against other documents to identify matching documents. The system determines when to execute the machine learning based metadata prediction based on steps of document workflow executed by the system.
    Type: Application
    Filed: January 13, 2023
    Publication date: July 18, 2024
    Inventors: Kaushik Narayanan, David Matthew Wong, David Lange, Vinay Jethava, Qing Zheng, Mohammad Mehdi Ghanimifard, Pontus Lindstrom, Gowtham Rangarajan Raman, David DeBarr, Yan He
  • Estimating engagement of consumers of presented content
    Patent number: 8943526
    Abstract: Technologies described herein relate to estimating engagement of a person with respect to content being presented to the person. A sensor outputs a stream of data relating to the person as the person is consuming the content. At least one feature is extracted from the stream of data, and a level of engagement of the person is estimated based at least in part upon the at least one feature. A computing function is performed based upon the estimated level of engagement of the person.
    Type: Grant
    Filed: April 19, 2013
    Date of Patent: January 27, 2015
    Assignee: Microsoft Corporation
    Inventors: Javier Hernandez Rivera, Zicheng Liu, Geoff Hulten, Michael Conrad, Kyle Krum, David DeBarr, Zhengyou Zhang
  • ESTIMATING ENGAGEMENT OF CONSUMERS OF PRESENTED CONTENT
    Publication number: 20130232515
    Abstract: Technologies described herein relate to estimating engagement of a person with respect to content being presented to the person. A sensor outputs a stream of data relating to the person as the person is consuming the content. At least one feature is extracted from the stream of data, and a level of engagement of the person is estimated based at least in part upon the at least one feature. A computing function is performed based upon the estimated level of engagement of the person.
    Type: Application
    Filed: April 19, 2013
    Publication date: September 5, 2013
    Applicant: Microsoft Corporation
    Inventors: Javier Hernandez Rivera, Zicheng Liu, Geoff Hulten, Michael Conrad, Kyle Krum, David DeBarr, Zhengyou Zhang