Machine learning-based anomaly detection for human presence verification
US11461441B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 2, 2019 |
| Grant date | Oct 4, 2022 |
| Priority date | — |
| Expiry date | May 22, 2039 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG08B29/186
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are provided for machine learning-based anomaly detection in a monitored location. One method comprises obtaining data from multiple data sources associated with a monitored location for storage into a data repository; processing the data to generate substantially continuous time-series data for multiple distinct features within the data; applying the substantially continuous time-series data for the distinct features to a machine learning baseline behavioral model to obtain a probability distribution representing a behavior of the monitored location over time; and evaluating a probability score generated by the machine learning baseline behavioral model to identify an anomaly at the monitored location. The machine learning baseline behavioral model is trained, for example, to identify anomalies in correlations between the plurality of distinct features at each timestamp. A presence verification is optionally provided based on a deviation from the machine learning baseline behavioral model at the monitored location.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.