Machine Learning Model-Based Anomaly Prediction and Mitigation

• Main Authors: Yeung, Chun Sum, Weyand, Amber E, Wang, Ting-Yen, Tschanz, Michael, Walters, Brian F, Onofre, Thiago Borba
• Format: Patent
• Language: English
• Published: 23.05.2024
• Subjects: CALCULATING; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS; COMPUTING; COUNTING; PHYSICS

A system includes a processor and a memory storing software code and a machine learning (ML) model. The software code is executed to receive contextual data samples each including raw data and a descriptive label, for each contextual data sample: search a database for a data pattern matching the raw data, determine, when the data pattern is detected, whether the data pattern is correlated with an anomalous event, and generate, when the correlation is determined, training data including a label identifying the anomalous event, and the raw data, the data pattern, or both, to provide one of multiple training data samples, wherein the training data samples describe anomalous events corresponding respectively to the raw data, the data pattern, or both. The software code is further executed to train the ML model, using the training data samples, to provide a trained predictive ML model configured to predict the anomalous events.