Context-Aware Lighting Control via Sensor-Driven Adaptation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing smart lighting systems face challenges in designing and controlling multiple lights due to cumbersome user interfaces, leading to user frustration and limited adoption.
Innovation Solution
A computer system that dynamically learns an individual's lighting preferences by analyzing non-verbal physical responses and environmental contexts, using image-processing techniques and machine-learning models to adjust lighting states and configurations in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If smart lighting systems are implemented with multiple lights and computing capabilities, then the impact on users throughout the home is enhanced, but the difficulty of designing, integrating and controlling the lights increases
Solution Approach 1:
The lighting system automatically learns user preferences through machine learning algorithms that analyze sensor data and adjust lighting parameters without requiring manual user configuration. The system performs self-configuration and self-optimization, eliminating the need for complex user setup procedures while providing personalized lighting experiences across multiple lights throughout the home.
Solution Approach 2:
The system dynamically adjusts lighting parameters such as intensity, color temperature, and timing based on learned user preferences and environmental conditions. By automatically modifying these parameters based on sensor data and machine learning models, the system provides adaptability across multiple lights without requiring users to manually configure each parameter.
2Ease of operation
If traditional user interfaces are used for controlling smart lights, then the system structure is simple, but the user experience degrades and user frustration increases
Solution Approach 1:
The system eliminates the need for traditional user interfaces by automatically learning and adapting to user preferences through machine learning. The lighting system self-configures based on sensor data and observed user behavior, completely removing the burden of complex user interfaces while delivering personalized lighting experiences.
Solution Approach 2:
The system continuously monitors sensor data and user responses to lighting conditions, using this feedback to automatically adjust and refine lighting parameters. This closed-loop feedback mechanism enables the system to learn user preferences over time and optimize lighting performance without requiring manual user input or complex control interfaces.
3Loss of time
If manual control methods are used for lighting, then the system is easy to implement, but user effort and time consumption increase
Solution Approach 1:
The lighting system automatically adjusts parameters such as intensity, color temperature, and timing based on learned user preferences and environmental sensor data. This self-adjusting capability eliminates the need for manual user intervention, significantly reducing time consumption while the machine learning algorithms handle the complexity of automatic optimization.
Solution Approach 2:
The system pre-learns user preferences and environmental patterns through continuous monitoring and machine learning, enabling it to automatically configure optimal lighting conditions in advance. This preliminary learning action allows the system to anticipate user needs and adjust lighting parameters proactively, eliminating the need for manual control and reducing user time consumption.
Data Source
AI summary
During operation, a computer obtains information specifying a lighting configuration of one or more lights in an environment, where the lighting configuration includes the one or more lights at predefined or predetermined locations in the environment. Then, the computer receives sensor data associated with the environment. Moreover, the computer analyzes the sensor data to determine a context associated with the environment. Then, based at least in part on the lighting configuration, a layout of the environment, and the determined context, the computer automatically determines the dynamic lighting states of the one or more lights, where a dynamic lighting state of a given light includes an intensity and a color of the given light. Next, the computer provides instructions corresponding to the dynamic lighting states to the one or more lights. Note that the dynamic lighting states may be based at least in part on a transferrable profile of the individual.