US 10,241,887 B2 · Data-agnostic anomaly detection

Abstract

This disclosure presents computational systems and methods for detecting anomalies in data output from any type of monitoring tool. The data is aggregated and sent to an alerting system for abnormality detection via comparison with normalcy bounds. The anomaly detection methods are performed by construction of normalcy bounds of the data based on the past behavior of the data output from the monitoring tool. The methods use data quality assurance and data categorization processes that allow choosing a correct procedure for determination of the normalcy bounds. The methods are completely data agnostic, and as a result, can also be used to detect abnormalities in time series data associated with any complex system.

Claims36

Dependent claims

A data-anomaly detection system comprising:

one or more processors;
one or more computer-readable media;
and
a routine that executes on the one or more processors to analyze digitally encoded data output from a system monitoring tool and stored in the computer-readable media by
identifying the output data as qualified data or corrupted data;
identifying and sorting the qualified data into categorized data;
calculating normalcy bounds for the categorized data;
discarding the corrupted data from the computer-readable media;
and
inputting the categorized data and normalcy bounds to an alerting engine that generates an alert when the categorized data is outside the normalcy bounds.

7 more elements

Dependencies are inferred from the claim text.

Data-agnostic anomaly detection

Life of this patent

Data-agnostic anomaly detection

Life of this patent

Abstract

Claims36