Position-Based Indoor Controller for Multi-Zone Preference Learning
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Solution Overview
Problem
Existing environment control systems require extensive user programming and are limited in functionality, making them expensive and complicated to install and operate across multiple areas, with limited control over environmental variables.
Innovation Solution
A system that learns user behavior, intent, and preferences using machine learning and AI to autonomously control multiple environmental parameters, including lighting, temperature, and other factors, through a network of interconnected controllers and sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a specific environment control system is configured to control a limited set of environment variables in a single room, then the system is simple to install and operate, but the functionality is limited and cannot control multiple environmental variables across multiple areas
Solution Approach 1:
The environment control system is designed to perform multiple functions: it can control lighting, temperature, and other environmental variables across multiple areas simultaneously. The single controller communicates with multiple environmental controls distributed throughout the space, enabling one system to replace what would traditionally require multiple separate control systems.
Solution Approach 2:
The system introduces a central controller as an intermediary that coordinates between multiple environmental controls and the user. This controller learns user preferences and behaviors, then autonomously manages multiple environmental parameters across different zones, simplifying the user interface while expanding control capabilities.
2Adaptability or versatility
If multiple environment control systems are installed to control different aspects of environment variables across multiple areas, then the control capability is improved, but the installation becomes expensive and complicated
Solution Approach 1:
The patent merges multiple separate environment control systems into a single unified system. Instead of installing independent controllers for each room or environmental parameter, one controller manages all environmental controls across multiple areas, reducing installation complexity and cost while maintaining comprehensive control capability.
Solution Approach 2:
The single environment control system is designed with universal functionality to control multiple environmental variables (lighting, temperature, etc.) across multiple areas simultaneously, replacing the need for multiple specialized control systems.
3Measurement precision
If wall-mounted controllers require extensive programming to learn user preferences, then the control precision is improved, but the ease of operation deteriorates
Solution Approach 1:
The environment control system performs self-learning by automatically observing and analyzing user behaviors, preferences, and patterns over time. The controller autonomously programs itself to understand when and how environmental parameters should be adjusted, eliminating the need for users to manually program extensive preferences while maintaining high precision in preference learning.
Solution Approach 2:
The system continuously monitors user interactions with the environment and uses this feedback to refine its understanding of user preferences. By analyzing patterns in user behavior and environmental adjustments, the controller progressively improves its ability to predict and automatically adjust environmental parameters according to user preferences.
Data Source
AI summary
Intelligent environment control systems and methods are described. One embodiment includes a processing system. A sensing system is communicatively coupled to the processing system. One or more devices coupled to the processing system are configured to modify an environment associated with a user. The processing system is configured to control the devices. The processing system is configured to receive a sensor input from the sensing system. The processing system is configured to process the sensor input and determine a user interaction with the environment.