Hierarchical Context Framework for Smart Home Mode Transitions
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Solution Overview
Problem
Current smart home systems lack an effective framework to manage and transition between operational modes based on user inputs and sensor data, leading to inconsistent and unreliable automation experiences due to the complex and personal nature of human presence and activities within the home environment.
Innovation Solution
A hierarchical framework of contexts that defines people and areas within the home, incorporating presence states, modes, and activities, along with rules to orchestrate state transitions, allowing for intuitive and reliable home automations by combining pattern-based predictions with sensor inputs and explicit user inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a hierarchical framework with multiple input types is implemented, then automation reliability is improved, but system complexity increases
Solution Approach 1:
The system segments automation inputs into three distinct types: sensor inputs (environmental data), learned inputs (patterns from historical data), and explicit inputs (user-defined rules). This segmentation allows each input type to be processed independently through specific modules, improving reliability while managing complexity through modular architecture.
Solution Approach 2:
The framework dynamically weights and combines different input types based on current context and confidence levels. The system adapts the influence of each input type (sensor, learned, explicit) depending on the situation, enabling reliable automation decisions while maintaining flexibility to handle varying levels of complexity.
2Measurement precision
If pattern-based predictions are combined with sensor inputs and user inputs, then mode transition accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary learning of usage patterns from historical sensor data during off-peak times, building predictive models in advance. When mode transitions are needed, these pre-learned patterns are quickly applied alongside current sensor inputs and explicit user rules, achieving high accuracy without real-time processing delays.
Solution Approach 2:
The framework implements feedback loops where the results of mode transitions are fed back into the learning module. This continuous feedback refines the learned patterns over time, improving transition accuracy while the system learns to predict user intentions, reducing the need for complex real-time processing.
Data Source
AI summary
Techniques and devices for a hierarchical framework of contexts for the smart home are described for managing modes in a smart home system by an electronic device. The electronic device receives a first input of a model for a second operational mode of a smart home system and receives a second input of the model for the second operational mode of the smart home system. Based on the first input and the second input, the electronic device determines an effective time interval for the second operational mode that is effective to cause the smart home system to transition from a first operational mode to the second operational mode during the effective time interval.