Smart Device State Prediction via Usage Pattern Matching
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Solution Overview
Problem
Smart-home devices lack effective solutions for predicting and managing usage patterns, leading to issues like mistakenly left devices on or unlocked, and difficulty in determining which devices to operate based on user requests.
Innovation Solution
A system that analyzes usage patterns of smart devices over time, identifies reference devices with similar patterns, and predicts the current state of target devices by querying their states, enabling notifications and automatic state changes based on probability calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If device states are manually monitored and controlled by users, then device operation reliability is maintained, but user time consumption and operational complexity increase
Solution Approach 1:
The system enables devices to automatically monitor and manage their own states through machine learning models that analyze usage patterns and predict optimal states, eliminating the need for manual user monitoring and intervention
Solution Approach 2:
The system implements continuous feedback loops where device states are monitored, analyzed against usage patterns, and automatically adjusted based on predicted optimal states, creating a self-regulating system that maintains reliability without user intervention
2Loss of energy
If device states are automatically controlled based on usage patterns, then energy consumption is reduced, but system complexity increases
Solution Approach 1:
The system creates simplified digital representations (copies) of device usage patterns through machine learning models that capture essential behavioral characteristics without requiring complex real-time analysis of all device parameters
Solution Approach 2:
The system performs preliminary analysis of usage patterns during off-peak times to build prediction models, so that during operation only simple state comparisons and adjustments are needed, reducing real-time computational complexity
3Measurement precision
If reference devices are used to predict target device states, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system segments the device ecosystem into reference devices and target devices, allowing complex multi-device analysis to be broken down into manageable pairwise comparisons that simplify the overall processing complexity
Solution Approach 2:
The system uses a subset of reference devices with similar usage patterns rather than analyzing all available devices, achieving sufficient prediction accuracy without the computational burden of exhaustive comparison
Data Source
AI summary
Systems and methods for state prediction of devices are disclosed. A group of reference devices may be identified and a subset of the reference devices may be identified and/or determined based at least in part on a degree of similarity between reference usage-patterns associated with the reference devices and a usage pattern of a target device. The current state of the subset of the reference devices may be determined and may be utilized to determine a probability that the target device should be in a given state. The state prediction information may be utilized for one or more actions, such as sending recommendations, target inference operations, and/or device configuration, for example.