Home Routine Anomaly Detection via Graph Knowledge Base
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Solution Overview
Problem
Home monitoring systems lack the ability to effectively detect anomalies in daily routines and provide timely notifications to users about deviations or forgotten items, leading to inefficiencies and potential missed events.
Innovation Solution
The system collects activity data from home devices, generates a graph-based knowledge base to identify patterns and relationships, and uses pattern recognition to detect deviations from established routines, providing notifications through visual, audio, or haptic means when anomalies are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If home monitoring systems use traditional anomaly detection methods, then system complexity is reduced, but the ability to detect routine deviations and provide timely notifications is insufficient
Solution Approach 1:
The system performs preliminary actions by establishing baseline routines through cluster analysis of historical activity data before actual monitoring begins. The graph-based knowledge base pre-computes relationships between entities and pre-defines anomaly detection rules, enabling efficient real-time detection without complex runtime computations.
Solution Approach 2:
The patent replaces traditional mechanical anomaly detection systems with data-driven machine learning approaches. Cluster analysis algorithms automatically identify routine patterns from activity data, and graph-based knowledge representations substitute for rule-based systems, enabling more accurate detection of deviations while managing complexity through software-based solutions.
2Measurement precision
If the system collects and analyzes comprehensive activity data from multiple devices, then notification accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system segments activity data by entity types (people, objects, routines) and organizes them in a graph-based knowledge base with hierarchical relationships. This segmentation allows the system to process and analyze specific subsets of data relevant to each monitoring task, reducing overall processing time while maintaining comprehensive monitoring coverage.
Solution Approach 2:
The system performs preliminary clustering and pattern recognition on activity data to establish baseline routines before real-time monitoring. By pre-processing data to identify normal patterns and deviations, the system reduces the computational burden during actual monitoring, enabling faster notification generation without sacrificing accuracy.
3Loss of information
If the system provides detailed notifications about routine deviations, then user awareness of forgotten items improves, but notification frequency and potential user annoyance increase
Solution Approach 1:
The system incorporates feedback mechanisms where user responses to notifications are used to refine and adjust future notification behavior. The graph-based knowledge base learns from user interactions to distinguish between important deviations requiring notification and minor variations that can be ignored, reducing unnecessary notifications while maintaining user awareness of significant events.
Solution Approach 2:
The system applies different notification strategies based on the specific type of deviation detected. Rather than using a uniform notification approach, the system tailors notifications to the local context of each anomaly, providing detailed information for critical deviations (such as forgotten important items) while using simpler or no notifications for minor routine variations.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for machine learning for home understanding and notification. In one aspect, a method includes obtaining reference videos from a camera within a premises of a home, determining, from the reference videos, timing of actions in a routine that a particular person performs before leaving the home, determining from a sample video from the camera within the home that the particular person appears to be out of sync in performing a particular action based on the timing of actions in the routine determined from the reference videos, and in response, providing a notification to the particular person.