Patents by Inventor Jason Adam DEICH

Jason Adam DEICH 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: 20220414231
    Abstract: An adversarial reinforcement learning system is used to simulate a spatial environment. The system includes a simulation engine configured to simulate a spatial environment and various objects therein. The system further includes a first model configured to control objects in the simulation and a second model configured to control objects in the simulation. The first model generates a threat-mitigation input to control one or more objects in the simulation, and the second model generates a threat input to control one or more objects in the simulation. The system then executes a first portion of the simulation based at least in part of the threat mitigation input and the threat input.
    Type: Application
    Filed: August 22, 2022
    Publication date: December 29, 2022
    Applicant: NOBLIS, INC.
    Inventors: Brian Jacob LEWIS, Jason Adam DEICH, Stephen John MELSOM, Kara Jean DODENHOFF, William Tyler NIGGEL
  • Patent number: 11423157
    Abstract: An adversarial reinforcement learning system is used to simulate a security checkpoint. The system includes a simulation engine configured to simulate a security checkpoint and various threat objects and threat-mitigation objects therein. The system further includes an attack model configured to control threat objects in the simulation and a defense model configured to control threat-mitigation objects in the simulation. A first portion of the simulation is executed by the simulation engine in order to generate an outcome of the first portion of the simulation. The defense model then generates a threat-mitigation input to control threat-mitigation objects in a subsequent portion of the simulation, and the attack model then generates a threat input to control threat objects in the subsequent portion of the simulation, wherein the inputs are based in part on the outcome of the first portion of the simulation.
    Type: Grant
    Filed: May 1, 2020
    Date of Patent: August 23, 2022
    Assignee: NOBLIS, INC.
    Inventors: Brian Jacob Lewis, Jason Adam Deich, Stephen John Melsom, Kara Jean Dodenhoff, William Tyler Niggel
  • Publication number: 20200364347
    Abstract: An adversarial reinforcement learning system is used to simulate a security checkpoint. The system includes a simulation engine configured to simulate a security checkpoint and various threat objects and threat-mitigation objects therein. The system further includes an attack model configured to control threat objects in the simulation and a defense model configured to control threat-mitigation objects in the simulation. A first portion of the simulation is executed by the simulation engine in order to generate an outcome of the first portion of the simulation. The defense model then generates a threat-mitigation input to control threat-mitigation objects in a subsequent portion of the simulation, and the attack model then generates a threat input to control threat objects in the subsequent portion of the simulation, wherein the inputs are based in part on the outcome of the first portion of the simulation.
    Type: Application
    Filed: May 1, 2020
    Publication date: November 19, 2020
    Applicant: NOBLIS, INC.
    Inventors: Brian Jacob LEWIS, Jason Adam DEICH, Stephen John MELSOM, Kara Jean DODENHOFF, William Tyler NIGGEL