Automated control of a manufacturing process

US11531907B2 · kind B2 · utility

Assignee

• SAS INSTITUTE, INC. · US

Inventors

• Afshin Oroojlooyjadid · North East, US
• Mohammadreza Nazari · Wayne, US
• Davood Hajinezhad · Cary, US
• Amirhassan Fallah Dizche · Raleigh, US
• Jorge Manuel Gomes da Silva · Durham, US
• Jonathan Lee Walker · Raleigh, US
• Hardi Desai · Morrisville, US
• Robert Blanchard · San Diego, US
• Varunraj Valsaraj · Cary, US
• Ruiwen Zhang · Cary, US
• Weichen Wang · Cary, US
• Ye Liu · Foster City, US
• Hamoon Azizsoltani · Raleigh, US
• Prathaban Mookiah · San Diego, US

Key dates

• Filing date: Jun 30, 2022
• Grant date: Dec 20, 2022
• Expiry date: Jun 30, 2042

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/006
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

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.