Systems and methods for accelerated detection and replacement of anomalous machine learning-based ensembles and intelligent generation of anomalous artifacts for anomalous ensembles

US11841941B2 · kind B2 · utility

Assignee

• Sift Science, Inc. · US

Inventors

• Pradhan Bagur Umesh · San Francisco, US
• Yuan Zhuang · San Francisco, US
• Hui Wang · Upton, US
• Nicholas Benavides · Washington, US
• Chang Liu · Beijing, CN
• Yanqing Bao · San Francisco, US
• Wei Liu · Schenectady, US

Key dates

• Filing date: Jun 16, 2023
• Grant date: Dec 12, 2023
• Expiry date: Jun 16, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N20/00
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

A system and method for accelerated anomaly detection and replacement of an anomaly-experiencing machine learning-based ensemble includes identifying a machine learning-based digital threat scoring ensemble having an anomalous drift behavior in digital threat score inferences computed by the machine learning-based digital threat scoring ensemble for a target period; executing a tiered anomaly evaluation for the machine learning-based digital threat scoring ensemble that includes identifying at least one errant machine learning-based model of the machine learning-based digital threat scoring ensemble contributing to the anomalous drift behavior, and identifying at least one errant feature variable of the at least one machine learning-based model contributing to the anomalous drift behavior; generating a successor machine learning-based digital threat scoring ensemble to the machine learning-based digital threat scoring ensemble based on the tiered anomaly evaluation; and replacing the machine learning-based digital threat scoring ensemble with the successor machine learning-based digital threat scoring ensemble.