Systems and methods for anomaly detection
US10904276B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 14, 2017 |
| Grant date | Jan 26, 2021 |
| Priority date | — |
| Expiry date | Feb 16, 2038 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L43/062
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
The present disclosure describes systems and methods that provide a hybrid framework for augmenting statistical anomaly detection with contextual features, machine learning and human Subject Matter Expert (SME) input to learn significant characteristics of true anomalies for which alerts should be generated. The framework presented herein is domain agnostic and independent of the underlying statistical anomaly detection technique or the machine learning algorithm. The framework described herein is therefore applicable and adaptable to a number of real world service provider systems and applications, such as, for example, detecting network performance degradation in a service provider network or detecting anomalous conditions from data received from a sensor while filtering out false positives.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.