AI classifier training using anomaly detection methods
Summary
The USPTO published patent application US20260093782A1 by inventors Krishnaprasad and Somashekhar describing systems and methods for training AI classifiers using anomaly detection. The application covers a two-phase approach where anomaly detection rules initially label data instances, then a trained classification model takes over once performance thresholds are met. This applies to machine learning model development for anomaly detection in various data sets.
What changed
This patent application discloses a method for training AI classifiers using anomaly detection. The system uses anomaly detection rules to initially label data instances as anomalous or not anomalous, building a labeled training set. Once sufficient labeled data exists, a classification model is trained and periodically retrained until its performance exceeds a specified threshold, after which the model alone is used for labeling.
For technology companies and AI developers, this represents potential prior art in the anomaly detection and AI training space. Organizations developing similar classification systems should review the claims for potential licensing considerations or design-around opportunities. The patent has no compliance deadlines as it is still under examination.
Archived snapshot
Apr 2, 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.
ANOMALY DETECTION-AIDED AI CLASSIFIER TRAINING
Application US20260093782A1 Kind: A1 Apr 02, 2026
Inventors
Ravikiran Vidhyaranya KRISHNAPRASAD, Karthik Gokare SOMASHEKHAR
Abstract
Systems and methods include use of anomaly detection rules may be initially used to detect anomalies in received data instances and to label the instances as anomalous or not anomalous. Once a sufficiently-large set of labeled data instances is available, the labeled data instances are used to train a classification model. The trained classification model and the anomaly detection rules are used to label received data instances as anomalous or not anomalous. The model is re-trained periodically using received labeled data instances until its performance exceeds a threshold. At this point, the trained model only is used to label subsequently-received data instances the instances as anomalous or not anomalous.
CPC Classifications
G06F 18/241 G06N 20/00
Filing Date
2024-10-01
Application No.
18903764
Named provisions
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