Machine learning systems and methods for real time anomaly detection and prescriptive feedback

A real-time trained machine learning model addresses the limitations of existing anomaly detection systems by enabling efficient detection of unknown anomalies and accurate analysis of multi-step, multi-source queries, enhancing computing device functionality.

GB2701271APending Publication Date: 2026-04-22ZEBRA TECHNOLOGIES CORP
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Patent Information

Authority / Receiving Office
GB · GB
Patent Type
Applications
Current Assignee / Owner
ZEBRA TECHNOLOGIES CORP
Filing Date
2025-09-18
Publication Date
2026-04-22

AI Technical Summary

Technical Problem

Existing anomaly detection processes lack the ability to perform real-time, on-demand detection of anomalies, fail to convert anomaly detection into a persistent process, and are unable to handle multi-step, multi-source queries, leading to inefficiencies and missed detection of previously unknown anomalies.

Method used

A machine learning model trained in real-time is used to analyze multi-step, multi-source queries, generating sets of instructions for identifying anomalous items and executing them in a computing device, ensuring proper query linking and validation for enhanced efficiency and accuracy.

Benefits of technology

The system efficiently detects previously unknown anomalies in real-time and accurately analyzes multiple linked queries, improving the functionality of computing devices by handling multi-step, multi-source queries with reduced code and increased accuracy.

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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
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Citation Information

Patent Citations

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