Patents by Inventor Zachary Babcock

Zachary Babcock 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).

  • 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
  • Publication number: 20240150488
    Abstract: Antibody molecules that specifically bind to C5aR1 are disclosed. The antibody molecules can be used to treat, prevent, and/or diagnose disorders, such as ANCA-vasculitis.
    Type: Application
    Filed: September 29, 2023
    Publication date: May 9, 2024
    Inventors: Karthik Viswanathan, Brian Booth, Boopathy Ramakrishnan, Andrew Wollacott, Gregory Babcock, Zachary Shriver
  • Publication number: 20240141334
    Abstract: Polypeptides, such as antibody molecules and TCR molecules, and methods of making the same, are disclosed. The polypeptides can be used to treat, prevent, and/or diagnose disorders.
    Type: Application
    Filed: October 5, 2023
    Publication date: May 2, 2024
    Inventors: Zachary Shriver, Gregory Babcock, Luke Robinson
  • Publication number: 20240092921
    Abstract: Antibody molecules that specifically bind to APRIL are disclosed. The antibody molecules can be used to treat, prevent, and/or diagnose disorders, such as IgA nephropathy.
    Type: Application
    Filed: April 24, 2023
    Publication date: March 21, 2024
    Inventors: David William Oldach, James R. Myette, Zachary Shriver, Karthik Viswanathan, Andrew M. Wollacott, Hedy Adari-Hall, Boopathy Ramakrishnan, Gregory Babcock, Jill Yarbrough, Asher Schachter, Mohit Mathur
  • Publication number: 20240076398
    Abstract: Antibody molecules that specifically bind to C5aR1 are disclosed. The antibody molecules can be used to treat, prevent, and/or diagnose disorders, such as ANCA-vasculitis.
    Type: Application
    Filed: August 18, 2023
    Publication date: March 7, 2024
    Inventors: Karthik Viswanathan, Brian Booth, Boopathy Ramakrishnan, Andrew Wollacott, Gregory Babcock, Zachary Shriver
  • Patent number: 11912781
    Abstract: The present disclosure provides, among other things, two different formats of humanized antibodies against human complement component 5a receptor I. The disclosure also provides a method of treating a subject having dysfunctions of C5a/C5aR1 axis pathway, including but not limited to ANCA-associated vasculitis, comprising administering to the subject in need thereof a an effective amount of antibody or a nucleic encoding an antibodies binding to C5aR1 described herein, and wherein administering results in a decrease in symptoms associated with C5a/C5aR1 associated dysfunction in the subject.
    Type: Grant
    Filed: January 13, 2022
    Date of Patent: February 27, 2024
    Assignee: Visterra, Inc.
    Inventors: Karthik Viswanathan, Brian Booth, Boopathy Ramakrishnan, Andrew Wollacott, Gregory Babcock, Zachary Shriver, Lauren Olinski
  • 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
  • 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
  • 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