Metadata Centric AI Class Reassignment Patent
Summary
USPTO published patent application US20260099765A1 for a metadata-centric AI system that reassigns data classifications based on performance metrics and confusion matrix analysis. The invention derives group-specific thresholds from prior classification instances to evaluate and update predicted classifications. Inventors include Madhusoodhana Chari Sesha, Pradeep Kumar Surenran, Ankush Anshuman, Akshay Jain, and Surya Thankamony Somanathan.
What changed
USPTO published patent application US20260099765A1 for a machine learning classification system that dynamically reassigns predicted classifications based on performance metrics. The system generates confusion matrices clustering classification instances into groups, derives group-specific thresholds from performance metrics, and updates predictions when performance falls below thresholds.
For technology companies developing AI classification systems, this published application indicates potential prior art in metadata-driven classification reassignment techniques. Companies should monitor for patent prosecution updates and potential claims that could affect product development strategies.
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- Monitor for patent grant or examination updates
Archived snapshot
Apr 16, 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.
META-DATA CENTRIC APPROACH FOR PREDICTED CLASS REASSIGNMENT
Application US20260099765A1 Kind: A1 Apr 09, 2026
Inventors
Madhusoodhana Chari Sesha, Pradeep Kumar Surendran, Ankush Anshuman, Akshay Jain, Surya Thankamony Somanathan
Abstract
Systems and methods are provided for reassigning classifications based on performance metrics obtained from prior instances of classifications. Examples include obtaining performance metrics of instances of classifications performed by a classification model in classifying data samples and generating a confusion matrix for the classification model that clusters the instances of classifications into a plurality of groups. For each group of the plurality of groups, examples derive a threshold from the performance metrics for instances of data samples constituting the respective group. The examples determine whether or not a classification predicted for an input data sample is correct based on a prediction performance metric of the predicted classification and one or more of the thresholds for the plurality of groups of the confusion matrix. Examples can update the predicted classification based on the determination.
CPC Classifications
G06N 20/00
Filing Date
2024-12-03
Application No.
18967020
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