Patents by Inventor Rex Shen

Rex Shen 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).

  • Graph convolutional anomaly detection
    Patent number: 12008584
    Abstract: There is a need for more effective and efficient anomaly detection. This need can be addressed by, for example, solutions for performing/executing graph convolutional anomaly detection. In one example, a method includes identifying related graph database input data associated with a predictive entity; generating related graph feature data for the predictive entity; generating, based on the related graph feature data and using a graph convolutional neural network model, an anomaly detection score for the predictive entity, wherein at least a portion of the graph convolutional neural network model is trained using confirmation feedback data; performing an anomaly confirmation to generate the confirmation feedback data object for the predictive entity, and integrating the confirmation feedback data object for the predictive entity into the confirmation feedback data associated with the graph convolutional anomaly detection.
    Type: Grant
    Filed: October 3, 2022
    Date of Patent: June 11, 2024
    Assignee: OPTUM, INC.
    Inventors: Parker J. Erickson, Gerald Liu, Rex Shen, Devin Uner, George L Williams, Zachary Babcock, Lydia M. Narum
  • GRAPH CONVOLUTIONAL ANOMALY DETECTION
    Publication number: 20230025252
    Abstract: There is a need for more effective and efficient anomaly detection. This need can be addressed by, for example, solutions for performing/executing graph convolutional anomaly detection. In one example, a method includes identifying related graph database input data associated with a predictive entity; generating related graph feature data for the predictive entity; generating, based on the related graph feature data and using a graph convolutional neural network model, an anomaly detection score for the predictive entity, wherein at least a portion of the graph convolutional neural network model is trained using confirmation feedback data; performing an anomaly confirmation to generate the confirmation feedback data object for the predictive entity, and integrating the confirmation feedback data object for the predictive entity into the confirmation feedback data associated with the graph convolutional anomaly detection.
    Type: Application
    Filed: October 3, 2022
    Publication date: January 26, 2023
    Inventors: Parker J. Erickson, Gerald Liu, Rex Shen, Devin Uner, George L. Williams, Zachary Babcock, Lydia M. Narum
  • Graph convolutional anomaly detection
    Patent number: 11494787
    Abstract: There is a need for more effective and efficient anomaly detection. This need can be addressed by, for example, solutions for performing/executing graph convolutional anomaly detection. In one example, a method includes identifying related graph database input data associated with a predictive entity; generating related graph feature data for the predictive entity; generating, based on the related graph feature data and using a graph convolutional neural network model, an anomaly detection score for the predictive entity, wherein at least a portion of the graph convolutional neural network model is trained using confirmation feedback data; performing an anomaly confirmation to generate the confirmation feedback data object for the predictive entity, and integrating the confirmation feedback data object for the predictive entity into the confirmation feedback data associated with the graph convolutional anomaly detection.
    Type: Grant
    Filed: June 30, 2020
    Date of Patent: November 8, 2022
    Assignee: Optum, Inc.
    Inventors: Parker J. Erickson, Gerald Liu, Rex Shen, Devin Uner, George L. Williams, Zachary Babcock, Lydia M. Narum
  • GRAPH CONVOLUTIONAL ANOMALY DETECTION
    Publication number: 20210406917
    Abstract: There is a need for more effective and efficient anomaly detection. This need can be addressed by, for example, solutions for performing/executing graph convolutional anomaly detection. In one example, a method includes identifying related graph database input data associated with a predictive entity; generating related graph feature data for the predictive entity; generating, based on the related graph feature data and using a graph convolutional neural network model, an anomaly detection score for the predictive entity, wherein at least a portion of the graph convolutional neural network model is trained using confirmation feedback data; performing an anomaly confirmation to generate the confirmation feedback data object for the predictive entity, and integrating the confirmation feedback data object for the predictive entity into the confirmation feedback data associated with the graph convolutional anomaly detection.
    Type: Application
    Filed: June 30, 2020
    Publication date: December 30, 2021
    Inventors: Parker J. Erickson, Gerald Liu, Rex Shen, Devin Uner, George L. Williams, Zachary Babcock, Lydia M. Narum