US 2026/0093977 A1Published application

Machine Learning Systems and Methods for Real Time Anomaly Detection and Prescriptive Feedback

AssigneeZebra Technologies Corporation

InventorsCaleb Popow, Ross Caisse, Aner Gabay, Shreenivasa A. Desai, Gowtham Balan, Robert T. Donald, Ashwini Ardikoppa Shashidhara, Vinay Kumar, Zion Orent, Aman Kumar

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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 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.

Claims20

Dependent claims

A method for anomaly detection, the method comprising:

receiving, via one or more processors, a set of data parameters defining a first query;
retrieving, via the one or more processors, a first dataset corresponding to the set of data parameters from a database;
receiving, via the one or more processors, a second set of data parameters defining a second query linked to the first query;
selecting, via the one or more processors, a predefined filter;
retrieving, via the one or more processors, a second dataset based on the first dataset, the predefined filter, and the second set of data parameters corresponding to the second query linked to the first query;
analyzing, via the one or more processors and using a machine learning model trained in real-time, the second dataset to detect one or more anomalies in the second dataset;
selecting, via the one or more processors, a set of anomaly parameters corresponding to the detected one or more anomalies;
filtering, via the one or more processors, an output of the machine learning model according to the set of anomaly parameters;
generating, via the one or more processors, a set of instructions for identifying one or more anomalous items based on the set of data parameters, the predefined filter, the second set of data parameters, the set of anomaly parameters, and a set of detection pattern parameters;
executing, via the one or more processors, the set of instructions for identifying anomalous items to identify one or more anomalous items in real-time within the second dataset responsive to updates to the second dataset;
and
transmitting, via the one or more processors, information about the one or more anomalous items to a user computing device or another computing device.

9 more elements

A system for anomaly detection, the system comprising:

one or more processors, and
one or more memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the system to:
receive a set of data parameters defining a first query;
retrieve a first dataset corresponding to the set of data parameters from a database;
receive a second set of data parameters defining a second query linked to the first query;
select a predefined filter;
retrieve a second dataset based on the first dataset, the predefined filter, and the second set of data parameters corresponding to the second query linked to the first query;
analyze, using a machine learning model trained in real-time, the second dataset to detect one or more anomalies in the dataset;
select a set of anomaly parameters corresponding to the detected one or more anomalies;
filter an output of the machine learning model according to the set of anomaly parameters;
generate a set of instructions for identifying one or more anomalous items based on the set of data parameters, the predefined filter, the second set of data parameters, the set of anomaly parameters, and a set of detection pattern parameters;
execute the set of instructions for identifying anomalous items to identify one or more anomalous items in real-time within the second dataset responsive to updates to the second dataset;
and
transmit information about the one or more anomalous items to a user computing device or another computing device.

11 more elements

Dependencies are inferred from the claim text.