Machine learning model abstraction layer for runtime efficiency

US11861472B2 · kind B2 · utility

Assignee

• Zscaler, Inc. · US

Inventors

• Rex Shang · Los Altos, US
• Dianhuan Lin · Sunnyvale, US
• Changsha Ma · Campbell, US
• Douglas A. Koch · Santa Clara, US
• Shashank Gupta · Sammamish, US
• Parnit Sainion · معلمی نژاد, US
• Visvanathan Thothathri · Santa Clara, US
• Narinder Paul · Sunnyvale, US
• Howie Xu · Palo Alto, US

Key dates

• Filing date: Sep 29, 2022
• Grant date: Jan 2, 2024
• Expiry date: Jan 7, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/045
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Systems and methods include receiving a trained machine learning model that has been processed with training information removed therefrom, wherein the training information is utilized in training of the trained machine learning model; monitoring traffic, inline at the node, including processing the traffic with the trained machine learning model; obtaining a verdict on the traffic based on the trained machine learning model; and performing an action on the traffic based on the verdict.