Machine Learning Systems and Methods for Real Time Anomaly Detection and Prescriptive Feedback
Summary
USPTO published patent application US20260093977A1 by Zebra Technologies Corporation disclosing a machine learning method for real-time anomaly detection with prescriptive feedback. The method involves receiving query parameters, retrieving datasets, applying machine learning models trained in real-time to detect anomalies, filtering outputs, generating identification instructions, and transmitting anomalous item information. Application No. 18943495 was filed November 11, 2024.
What changed
Zebra Technologies Corporation filed patent application US20260093977A1 disclosing a machine learning system for real-time anomaly detection and prescriptive feedback. The invention covers a method comprising receiving first and second data parameters defining linked queries, retrieving corresponding datasets, selecting predefined filters, analyzing data using a real-time trained machine learning model to detect anomalies, filtering model outputs according to anomaly parameters, generating executable instructions for identifying anomalous items, and transmitting information to a computing device. The application is classified under CPC G06N 3/08 and G06N 3/0455.
Patent applications are informational publications and do not create compliance obligations. Technology companies developing machine learning anomaly detection systems may review this publication for prior art considerations or competitive intelligence. No action is required for compliance purposes as this is not a regulatory requirement.
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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.
Machine Learning Systems and Methods for Real Time Anomaly Detection and Prescriptive Feedback
Application US20260093977A1 Kind: A1 Apr 02, 2026
Assignee
Zebra Technologies Corporation
Inventors
Caleb Popow, Ross Caisse, Aner Gabay, Shreenivasa A. Desai, Gowtham Balan, Robert T. Donald, Ashwini Ardikoppa Shashidhara, Vinay Kumar, Zion Orent, Aman Kumar
Abstract
A method for anomaly detection comprising receiving data parameters defining a first query; retrieving a first dataset corresponding to the data parameters; receiving second data parameters defining a second query linked to the first query; selecting a predefined filter; retrieving a second dataset based on the first dataset, the predefined filter, and the second data parameters; analyzing, using a machine learning model trained in real-time, the second dataset to detect anomalies; selecting anomaly parameters corresponding to the anomalies; filtering an output of the machine learning model according to the anomaly parameters; generating instructions for identifying anomalous items based on the data parameters, the predefined filter, the second data parameters, the anomaly parameters, and detection pattern parameters; executing the set of instructions for identifying anomalous items to identify anomalous items in real-time within the second dataset; and transmitting information about the anomalous items to a computing device.
CPC Classifications
G06N 3/08 G06N 3/0455
Filing Date
2024-11-11
Application No.
18943495
Named provisions
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