Patents by Inventor Amirhassan Fallah Dizche

Amirhassan Fallah Dizche 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).

  • Automated control of a manufacturing process
    Patent number: 11531907
    Abstract: A computing device trains a machine state predictive model. A generative adversarial network with an autoencoder is trained using a first plurality of observation vectors. Each observation vector of the first plurality of observation vectors includes state variable values for state variables and an action variable value for an action variable. The state variables define a machine state, wherein the action variable defines a next action taken in response to the machine state. The first plurality of observation vectors successively defines sequential machine states to manufacture a product. A second plurality of observation vectors is generated using the trained generative adversarial network with the autoencoder. A machine state machine learning model is trained to predict a subsequent machine state using the first plurality of observation vectors and the generated second plurality of observation vectors. A description of the machine state machine learning model is output.
    Type: Grant
    Filed: June 30, 2022
    Date of Patent: December 20, 2022
    Assignee: SAS Institute Inc.
    Inventors: Afshin Oroojlooyjadid, Mohammadreza Nazari, Davood Hajinezhad, Amirhassan Fallah Dizche, Jorge Manuel Gomes da Silva, Jonathan Lee Walker, Hardi Desai, Robert Blanchard, Varunraj Valsaraj, Ruiwen Zhang, Weichen Wang, Ye Liu, Hamoon Azizsoltani, Prathaban Mookiah
  • AUTOMATED CONTROL OF A MANUFACTURING PROCESS
    Publication number: 20220374732
    Abstract: A computing device trains a machine state predictive model. A generative adversarial network with an autoencoder is trained using a first plurality of observation vectors. Each observation vector of the first plurality of observation vectors includes state variable values for state variables and an action variable value for an action variable. The state variables define a machine state, wherein the action variable defines a next action taken in response to the machine state. The first plurality of observation vectors successively defines sequential machine states to manufacture a product. A second plurality of observation vectors is generated using the trained generative adversarial network with the autoencoder. A machine state machine learning model is trained to predict a subsequent machine state using the first plurality of observation vectors and the generated second plurality of observation vectors. A description of the machine state machine learning model is output.
    Type: Application
    Filed: June 30, 2022
    Publication date: November 24, 2022
    Inventors: Afshin Oroojlooyjadid, Mohammadreza Nazari, Davood Hajinezhad, Amirhassan Fallah Dizche, Jorge Manuel Gomes da Silva, Jonathan Lee Walker, Hardi Desai, Robert Blanchard, Varunraj Valsaraj, Ruiwen Zhang, Weichen Wang, Ye Liu, Hamoon Azizsoltani, Prathaban Mookiah
  • Tabular data generation with attention for machine learning model training system
    Patent number: 11416712
    Abstract: A computing device generates synthetic tabular data.
    Type: Grant
    Filed: December 23, 2021
    Date of Patent: August 16, 2022
    Assignee: SAS Institute, Inc.
    Inventors: Amirhassan Fallah Dizche, Ye Liu, Xin Jiang Hunt, Jorge Manuel Gomes da Silva