Systems and/or methods for dynamic anomaly detection in machine sensor data
US10410135B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | May 21, 2015 |
| Grant date | Sep 10, 2019 |
| Priority date | — |
| Expiry date | Nov 13, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F11/079
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Certain example embodiments relate to techniques for detecting anomalies in streaming data. More particularly, certain example embodiments use an approach that combines both unsupervised and supervised machine learning techniques to create a shared anomaly detection model in connection with a modified k-means clustering algorithm and advantageously also enables concept drift to be taken into account. The number of clusters k need not be known in advance, and it may vary over time. Models are continually trainable as a result of the dynamic reception of data over an unknown and potentially indefinite time period, and clusters can be built incrementally and in connection with an updatable distance threshold that indicates when a new cluster is to be created. Distance thresholds also are dynamic and adjustable over time.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.