Prediction Model Training Using Detected Anomalies
Summary
USPTO published patent application US20260099735A1 for a system and method of training prediction models using detected anomalies. The system trains multiple models using historical data, selects the best-performing model based on test data, generates forecasts, identifies anomalies between forecasts and model outputs, and incorporates user feedback to retrain and improve prediction accuracy.
What changed
USPTO published Application US20260099735A1, covering methods and systems for training prediction models using detected anomalies. The invention involves receiving historical data, training multiple models, selecting the best-performing model using test data, generating prediction models with optimized hyperparameters, detecting anomalies based on differences between forecasts and outputs, and retraining models based on user feedback indicating false detections.
For technology companies and AI developers, this published patent application establishes prior art in the field of anomaly detection and adaptive prediction model training. While it does not impose immediate compliance obligations, it may influence patent strategy and freedom-to-operate analysis for companies developing similar AI-based predictive analytics systems.
Archived snapshot
Apr 17, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
PREDICTION MODEL TRAINING USING DETECTED ANOMALIES
Application US20260099735A1 Kind: A1 Apr 09, 2026
Inventors
Kiran Prabhakara, Arun Krishnaswamy, Venu Kasyap Tangirala, Changsheng Chen, Roy Sturgeon, Ganesh Rajaratnam
Abstract
An interface is configured to receive historical data. A processor is configured to determine a training and a test data set; train models using the training data set to obtain trained models; determine a best trained model of the trained models using the test data set; select hyperparameters associated with the best trained model; generate a prediction model using the hyperparameters and the historical data to obtain a trained prediction model; determine a detected anomaly based on a difference between a forecast and the output of the trained prediction model; provide the forecast, the output of the trained model, and the detected anomaly to an interface; receive user feedback from the interface, wherein the user feedback comprises a false detected anomaly indication indicating that the detected anomaly is not an anomaly; and retrain the trained prediction model using the hyperparameters and the user feedback to obtain a retrained prediction model.
CPC Classifications
G06N 5/04 G06N 5/01 G06N 5/022 G06N 20/00
Filing Date
2025-10-17
Application No.
19362050
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