Smart Home Intent Control Using Knowledge Graph Context
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Solution Overview
Problem
Conventional home automation systems require precise user input and rigid schedules, leading to mechanical and unpleasant interactions, as well as failure to adapt to changes in user behavior and environmental conditions.
Innovation Solution
A smart home system that uses a declarative interaction model, interpreting user intents and activities through multiple layers of knowledge, sensors, and metadata to autonomously control devices, allowing for natural interactions, adaptive behavior, and anticipatory actions without explicit user commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional home automation systems use precise user input and rigid schedules, then device control reliability is improved, but ease of operation deteriorates due to mechanical and unpleasant interactions
Solution Approach 1:
The system performs self-service by automatically detecting user presence, activities, and environmental conditions through sensors, then autonomously controlling devices without requiring explicit user commands. The system learns user preferences and behaviors over time, enabling it to anticipate needs and execute appropriate actions independently, thereby improving ease of operation while maintaining reliability through consistent automated responses.
Solution Approach 2:
The patent replaces mechanical interaction systems (physical switches, knobs, and rigid scheduling interfaces) with sensor-based detection and automated control systems. Motion sensors, occupancy sensors, and environmental sensors substitute for manual user input, while automated rule engines and AI algorithms replace rigid schedule configurations, creating a more natural and effortless user experience.
2Adaptability or versatility
If conventional systems use rigid schedules, then adaptability to user behavior changes deteriorates, but device complexity is reduced
Solution Approach 1:
The system implements dynamics by transitioning from static rigid schedules to dynamic adaptive control. Sensors continuously monitor user presence, activities, and environmental conditions, allowing the system to dynamically adjust device control strategies in real-time. The system adapts to changing user behaviors by learning patterns over time and modifying its response accordingly, enabling versatile adaptation without requiring complex reconfiguration of schedules.
Solution Approach 2:
The system employs feedback mechanisms where sensors continuously provide information about user presence, activities, and environmental conditions back to the control system. This feedback loop enables the system to learn from user behaviors and preferences, automatically adjusting its control strategies to adapt to changing patterns. The feedback-driven learning process allows the system to become increasingly adaptive to individual user needs over time.
3Measurement precision
If systems require explicit user commands, then measurement precision of user intent is improved, but loss of time increases due to repeated manual input
Solution Approach 1:
The system performs preliminary actions by proactively detecting user presence and anticipated needs before explicit commands are given. Sensors detect when users are approaching or entering rooms, and the system pre-prepares appropriate device states based on learned preferences and contextual information. This preliminary detection and preparation eliminates the need for repeated manual input, reducing time loss while maintaining accurate understanding of user intent through contextual inference.
Solution Approach 2:
The system performs self-service by autonomously interpreting user intent through sensor data and environmental context without requiring explicit commands. The system learns user preferences and behavioral patterns, enabling it to accurately determine user needs and execute appropriate device control actions independently. This self-service capability eliminates time-consuming manual input while maintaining high precision in understanding and responding to user intent through learned patterns and contextual analysis.
Data Source
AI summary
In one embodiment, a computing system may receive one or more input signals comprising information related to a user of the computing system. The computing system may determine an interpretation of the one or more input signals using a knowledge graph. The knowledge graph may include a number of layers of knowledge about the user or an environment of the computing system. The interpretation of the input signals may be determined based on the knowledge in the knowledge graph. The system may perform one or more execution operations based on the determined interpretation of the one or more input signals. The execution operations may include configuring one or more controllable systems associated with the computing system.