Smart Home Scene Automation via Machine Learning
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Solution Overview
Problem
Existing smart home automation systems require significant user effort to maintain and update scene libraries, which can be time-consuming and reduce user experience, often necessitating technical expertise for adding or modifying scenes.
Innovation Solution
A novel technique that utilizes a cloud-system and home-system architecture, employing machine learning to automatically define and adjust scenes based on collected data, minimizing user involvement through the creation of statistical models that predict actions and events, allowing for autonomous operation and fine-tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a user manually maintains and updates scene libraries in existing smart home systems, then the scene configurations can be customized and controlled, but the user effort and time required increases significantly
Solution Approach 1:
The system enables self-service by automatically generating and maintaining scene configurations through machine learning models that analyze user behavior patterns and device states, eliminating the need for manual scene creation and updates while preserving full customization capabilities
Solution Approach 2:
The system performs preliminary action by pre-defining scene templates and configurations that are automatically adapted to user needs, so when users want specific scenes, the system has already prepared and customized appropriate configurations based on collected data
2Adaptability or versatility
If existing smart home systems provide comprehensive scene libraries, then more functionality is available, but the complexity of maintaining and updating these scenes increases
Solution Approach 1:
The system performs self-service by automatically maintaining scene libraries through machine learning models that detect when new scenes should be created or existing scenes should be modified, eliminating the need for technical expertise in scene management while preserving comprehensive functionality
Solution Approach 2:
The system applies parameter changes by dynamically adjusting scene configurations based on learned user preferences and behavior patterns, allowing the scene library to evolve and adapt without manual intervention, thus maintaining high functionality with low complexity
3Manufacturing precision
If manual scene configuration is required, then precise control over device behavior is achieved, but technical expertise is needed which reduces ease of operation
Solution Approach 1:
The system enables self-service by automatically configuring device behavior parameters through machine learning, achieving precise device control without requiring users to have technical expertise in scene configuration or system setup
4Productivity
If a large number of scenes are configured manually, then comprehensive automation coverage is achieved, but the time to define and load parameters increases
Solution Approach 1:
The system performs preliminary action by pre-configuring scene templates and automatically generating comprehensive scene libraries based on analyzed user patterns, so extensive automation coverage is achieved without the time cost of manual scene definition and parameter loading
Data Source
AI summary
A central hub and database for a smart home environment enable the learning of states associated with items within the smart home and the training one or more machine-learned models associated with the items. After training the machine-learned models, the central hub can modify a state of an item based on the machine-learned model associated with the item. For instance, a window can be opened or shut, a light can be dimmed or turned off, and a door can be locked. Each state of the object can be associated with a set of conditions that, when satisfied, cause the central hub to change the state of the object using the corresponding machine-learned model, for instance without receiving an explicit input from a user.