Patents by Inventor Maksym Zhenirovskyy

Maksym Zhenirovskyy 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).

  • Reducing the numerical complexity of designs
    Patent number: 11934756
    Abstract: A component library having a plurality of design components is received. Designs are predicted using the plurality of components using a machine learning model. The predicted designs comprise a subset of all possible designs using the plurality of components. A set of design criteria is received. At least one design solution is generated based on the set of design criteria and the predicted designs.
    Type: Grant
    Filed: December 4, 2020
    Date of Patent: March 19, 2024
    Assignee: XEROX CORPORATION
    Inventors: Ion Matei, Maksym Zhenirovskyy, Johan de Kleer, Aleksandar B. Feldman
  • Method for classification based diagnosis with partial system model information
    Patent number: 11915112
    Abstract: A classification-based diagnosis for detecting and predicting faults in physical system (e.g. an electronic circuit or rail switch) is disclosed. Some embodiments make use of partial system model information (e.g., system topology, components behavior) to simplify the classifier complexity (e.g., reduce the number of parameters). Some embodiments of the method use a Bayesian approach to derive a classifier structure.
    Type: Grant
    Filed: September 3, 2019
    Date of Patent: February 27, 2024
    Assignee: XEROX CORPORATION
    Inventors: Ion Matei, Johan de Kleer, Alexander Feldman, Maksym Zhenirovskyy
  • Controlling an unmanned aerial vehicle by re-training a sub-optimal controller
    Patent number: 11822345
    Abstract: A nonlinear dynamic control system is defined by a set of equations that include a state vector and one or more control inputs. Via a machine learning method, a sub-optimal controller is derived that stabilizes the nonlinear dynamic control system at an equilibrium point. The sub-optimal controller is retrained to be used as a stabilizing controller for the nonlinear dynamic control system under general operating conditions.
    Type: Grant
    Filed: October 23, 2020
    Date of Patent: November 21, 2023
    Assignee: XEROX CORPORATION
    Inventors: Ion Matei, Rajinderjeet Singh Minhas, Johan de Kleer, Maksym Zhenirovskyy
  • Machine learning based systems and methods for real time, model based diagnosis
    Patent number: 11544422
    Abstract: The disclosure following relates generally to complex simulations, and fault diagnosis. In some embodiments, a component that is causing a delayed simulation time of a system is determined. A component of reduced complexity is designed, and the component of reduced complexity is used to replace the original component in the system. Fault diagnosis may then be conducted using the updated system with the reduced complexity component, thus decreasing the time taken to diagnose the fault.
    Type: Grant
    Filed: September 16, 2019
    Date of Patent: January 3, 2023
    Assignee: Palo Alto Research Center Incorporated
    Inventors: Ion Matei, Maksym Zhenirovskyy, Johan de Kleer, Alexander Feldman
  • REDUCING THE NUMERICAL COMPLEXITY OF DESIGNS
    Publication number: 20220180024
    Abstract: A component library having a plurality of design components is received. Designs are predicted using the plurality of components using a machine learning model. The predicted designs comprise a subset of all possible designs using the plurality of components. A set of design criteria is received. At least one design solution is generated based on the set of design criteria and the predicted designs.
    Type: Application
    Filed: December 4, 2020
    Publication date: June 9, 2022
    Inventors: Ion Matei, Maksym Zhenirovskyy, Johan de Kleer, Aleksandar B. Feldman
  • DYNAMIC SYSTEM CONTROL USING DEEP MACHINE LEARNING
    Publication number: 20220129012
    Abstract: A nonlinear dynamic control system is defined by a set of equations that include a state vector and one or more control inputs. Via a machine learning method, a sub-optimal controller is derived that stabilizes the nonlinear dynamic control system at an equilibrium point. The sub-optimal controller is retrained to be used as a stabilizing controller for the nonlinear dynamic control system under general operating conditions.
    Type: Application
    Filed: October 23, 2020
    Publication date: April 28, 2022
    Inventors: Ion Matei, Rajinderjeet Singh Minhas, Johan de Kleer, Maksym Zhenirovskyy
  • MACHINE LEARNING BASED SYSTEMS AND METHODS FOR REAL TIME, MODEL BASED DIAGNOSIS
    Publication number: 20210081511
    Abstract: The disclosure following relates generally to complex simulations, and fault diagnosis. In some embodiments, a component that is causing a delayed simulation time of a system is determined. A component of reduced complexity is designed, and the component of reduced complexity is used to replace the original component in the system. Fault diagnosis may then be conducted using the updated system with the reduced complexity component, thus decreasing the time taken to diagnose the fault.
    Type: Application
    Filed: September 16, 2019
    Publication date: March 18, 2021
    Applicant: Palo Alto Research Center Incorporated
    Inventors: Ion Matei, Maksym Zhenirovskyy, Johan de Kleer, Alexander Feldman
  • METHOD FOR CLASSIFICATION BASED DIAGNOSIS WITH PARTIAL SYSTEM MODEL INFORMATION
    Publication number: 20210065065
    Abstract: A classification-based diagnosis for detecting and predicting faults in physical system (e.g. an electronic circuit or rail switch) is disclosed. Some embodiments make use of partial system model information (e.g., system topology, components behavior) to simplify the classifier complexity (e.g., reduce the number of parameters). Some embodiments of the method use a Bayesian approach to derive a classifier structure.
    Type: Application
    Filed: September 3, 2019
    Publication date: March 4, 2021
    Applicant: Palo Alto Research Center Incorporated
    Inventors: Ion Matei, Johan de Kleer, Alexander Feldman, Maksym Zhenirovskyy