The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Dec. 20, 2022
Filed:
Jun. 30, 2022
Sas Institute Inc., Cary, NC (US);
Afshin Oroojlooyjadid, Hellertown, PA (US);
Mohammadreza Nazari, Philadelphia, PA (US);
Davood Hajinezhad, Cary, NC (US);
Amirhassan Fallah Dizche, Raleigh, NC (US);
Jorge Manuel Gomes da Silva, Durham, NC (US);
Jonathan Lee Walker, Raleigh, NC (US);
Hardi Desai, Raleigh, NC (US);
Robert Blanchard, San Diego, CA (US);
Varunraj Valsaraj, Cary, NC (US);
Ruiwen Zhang, Cary, NC (US);
Weichen Wang, Cary, NC (US);
Ye Liu, Morrisville, NC (US);
Hamoon Azizsoltani, Raleigh, NC (US);
Prathaban Mookiah, San Diego, CA (US);
SAS Institute Inc., Cary, NC (US);
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.