Graph-Based Home Routine Monitoring for Anomaly Detection
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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 deteriorates
Solution Approach 1:
The system performs preliminary learning of user routines during a training phase, establishing baseline patterns of normal behavior before actual monitoring begins. This allows the system to detect deviations from established routines without requiring complex real-time analysis of every action, improving reliability while managing complexity through advance preparation
Solution Approach 2:
The system continuously compares observed actions against learned routine patterns and provides feedback through notifications when deviations are detected. This feedback mechanism enables the system to adapt and improve its detection capabilities over time, enhancing reliability through iterative learning while maintaining manageable complexity through pattern-based analysis
2Measurement precision
If the system monitors all actions in detail to detect routine deviations, then detection precision improves, but loss of time increases due to extensive data processing
Solution Approach 1:
The system extracts only the essential features and patterns from action data that are relevant to routine detection, rather than analyzing every detail of each action. By focusing on key behavioral patterns and temporal sequences, the system achieves high detection precision while minimizing processing time through selective analysis
Solution Approach 2:
The system transforms raw action data into standardized parameters and features that capture the essence of routines, such as temporal patterns, sequence relationships, and behavioral metrics. This parameter transformation enables efficient comparison against learned patterns, maintaining high detection precision while reducing processing complexity and time
3Loss of information
If the system provides comprehensive notifications about all deviations, then information completeness improves, but loss of information increases due to user notification fatigue
Solution Approach 1:
The system provides differentiated notification levels based on the significance and type of deviation detected. Critical deviations receive immediate comprehensive notifications, while minor variations receive subdued or aggregated notifications. This local quality approach ensures important information is communicated completely while reducing overall notification volume to prevent user fatigue
Solution Approach 2:
The system maintains continuous monitoring of user routines to detect patterns and deviations, providing a steady stream of valuable insights without overwhelming users. By continuously learning and adapting to user behavior, the system sustains information completeness over time while adjusting notification frequency and intensity to maintain user engagement and prevent fatigue
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 collecting activity data from one or more devices in a home network, generating a graph-based knowledge base including two or more nodes based on the activity data, where each node is an object, person, or routine and wherein each link is a relationship between two nodes, generating, based on the graph-based knowledge base, one or more rules, determining, based in part on the activity data, an occurrence of a particular rule of the one or more rules, generating a notification responsive to the particular rule, presenting the notification to a user, receiving, user feedback responsive to the notification, where the user feedback includes a natural language label for the particular rule, and updating the particular rule based on the user feedback.