Home Automation Expectation Violations via Audiovisual Collages
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Solution Overview
Problem
Home automation systems face challenges in detecting anomalies and conveying complex operational issues to users, as they often lack awareness of device failures and environmental changes, relying on textual messages that are difficult to relate to physical devices and environments.
Innovation Solution
The system employs unsupervised machine learning models trained on audiovisual data to detect anomalies by generating contrastive collages or sequences that visually and audibly convey expectation violations, using XR-viewable spatial representations and machine learning algorithms to integrate disparate home automation systems and provide remedial actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If home automation systems use textual messages to convey operational issues, then information can be transmitted to users, but users find it difficult to relate textual messages to physical devices and environments
Solution Approach 1:
The system creates visual copies (photographs) and audio copies (sound recordings) of the actual physical state of devices and environments. These sensory copies are transmitted to users alongside or instead of textual messages, enabling direct visual and auditory recognition of the problematic state without requiring users to interpret abstract text descriptions and map them to physical devices.
2Reliability
If home automation systems lack awareness of device failures and environmental changes, then system complexity is reduced, but anomaly detection capability is insufficient
Solution Approach 1:
The system implements feedback loops where sensors continuously monitor device operations and environmental conditions, compare observed states against expected states, and trigger anomaly detection when deviations are identified. This feedback mechanism enables the system to automatically become aware of device failures and environmental changes without requiring complex centralized awareness, as each component contributes to the overall detection capability through localized sensing and reporting.
3Adaptability or versatility
If home automation systems rely on non-communicative devices, then device compatibility is improved, but audiovisual capture and anomaly detection are limited
Solution Approach 1:
The system introduces audiovisual capture devices (cameras, microphones) as intermediaries that observe and record the operational states of non-communicative devices. These intermediary sensors do not require the target devices to have communication capabilities or be integrated into the automation network. Instead, the intermediaries passively capture sensory data that is then analyzed for anomalies, enabling detection across heterogeneous device ecosystems without requiring device-to-device communication or deep system integration.
Data Source
AI summary
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.