Home Device Scene Control Using Learned Triggers and Habits
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Solution Overview
Problem
Current home control systems require manual interaction with devices or dedicated remotes for controlling lighting, audio, temperature, and other smart home devices, lacking automation and user convenience in recognizing and executing habitual scenes based on patterns.
Innovation Solution
A distributed system of home device controllers that use touch control grooves, touch sensors, and machine learning to detect user gestures and patterns, allowing for automatic scene execution based on timing, context, and environmental triggers, enabling voice and gesture control of multiple smart devices without manual input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual interaction with devices or remotes is used for controlling smart home devices, then device control functionality is achieved, but user convenience and automation are reduced
Solution Approach 1:
The system automatically detects user presence via sensors, learns user preferences through machine learning algorithms, and executes appropriate scenes without requiring manual interaction. The controller autonomously adjusts lighting, temperature, and other environmental parameters based on detected patterns and triggers, making the system serve itself rather than requiring continuous user input.
Solution Approach 2:
The patent replaces manual mechanical interaction (physical switches, remote controls) with automated sensing and control systems. Sensors detect user presence and gestures, machine learning algorithms process this data to identify patterns, and the system automatically executes control commands, substituting the mechanical interaction paradigm with an automated electronic sensing and actuation system.
2Ease of operation
If automated scene execution is implemented based on machine learning, then user convenience is enhanced, but device complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: sensing modules that detect user presence and gestures, machine learning modules that learn preferences and identify patterns, rule engines that process triggers and conditions, and actuation modules that execute control commands. This segmentation allows each component to be optimized independently while working together to provide automated scene execution.
Solution Approach 2:
The controller serves as an intermediary between sensors and controlled devices, mediating the complex machine learning and scene execution logic. Rather than requiring direct complex interactions between multiple devices and users, the controller translates sensor inputs into appropriate device commands based on learned patterns and configured scenes, simplifying the overall system architecture.
Data Source
AI summary
A distributed system of home device controllers can control a set of home devices. A home device controller of the system can determine a set of configurations for a set of home devices being repeatedly configured by a user. The controller can automatically display a selectable feature indicating a suggested scene corresponding to the set of configurations for the set of home devices. The system can receive one or more inputs to select the suggested scene, and based at least in part on the one or more inputs, associate the suggest scene with a set of triggers. In response to detecting the set of triggers, the controller can automatically transmit a set of commands that correspond to the suggested scene to the set of home devices to execute the suggested scene.