Audiovisual detection of expectation violations in disparate home automation systems
US11954900B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 6, 2023 |
| Grant date | Apr 9, 2024 |
| Priority date | — |
| Expiry date | Sep 6, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V20/52
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The invention pertains to methods for monitoring the operational status of a home automation system through extrinsic visual and audible means. Initial training periods involve capturing image and audio data representative of nominal operation, which is then processed to identify operational indicators. Unsupervised machine learning models are trained with these indicators to construct a model of normalcy and identify expectation violations in the system's operational pattern. After meeting specific stopping criteria, real-time monitoring is initiated. When an expectation violation is detected, contrastive collages or sequences are generated comprising nominal and anomalous data. These are then transmitted to an end user, effectively conveying the context of the detected anomalies. Further features include providing deep links to smartphone applications for home automation configuration and the use of auditory scene analysis techniques. The invention provides a multi-modal approach to home automation monitoring, leveraging machine learning for robust anomaly detection.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.