User-Context Color Palette Generation for Coordinated Multi-Device Lighting
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Solution Overview
Problem
Existing information handling systems lack the ability to dynamically adapt lighting effects in multi-room environments based on user context, particularly in gaming scenarios, leading to inconsistent and non-personalized experiences across different devices and spaces.
Innovation Solution
A machine learning algorithm is employed to determine a color palette based on user context, analyzing inputs such as camera feeds, audio, and gaming history to generate an aggregate color palette, which is then applied to connected peripherals and lighting devices to provide personalized lighting effects across various environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional lighting systems are used in multi-room environments, then device simplicity is maintained, but lighting effects are static and non-personalized
Solution Approach 1:
The system automatically retrieves user context, analyzes it to determine colors, generates color palettes, and applies lighting effects without manual intervention. The machine learning algorithm autonomously processes user context components (gaming application, camera feed, audio input) and generates appropriate lighting configurations, allowing the system to serve itself rather than requiring user configuration for each lighting scenario.
Solution Approach 2:
The patent replaces manual lighting configuration mechanisms with machine learning-based automated generation. Instead of users manually selecting colors or configuring lighting parameters, a machine learning algorithm processes user context and generates color palettes automatically. This substitution of mechanical/user-driven processes with intelligent automation resolves the contradiction by providing high adaptability through AI while maintaining operational simplicity for users.
2Ease of operation
If manual color palette configuration is used, then system complexity is reduced, but user experience personalization is lost
Solution Approach 1:
The system automatically retrieves and processes user context information including gaming application state, camera feeds, audio input, and user preferences. The machine learning algorithm autonomously analyzes these context components and generates appropriate color palettes without requiring users to manually configure lighting settings, thereby preserving user context information while maintaining ease of operation.
Solution Approach 2:
The system continuously monitors user context components and uses this feedback to dynamically adjust lighting effects. By processing real-time information from gaming applications, camera feeds, and audio inputs, the system adapts lighting configurations based on current user activities and preferences, ensuring personalized experiences without manual intervention.
3Adaptability or versatility
If dynamic lighting effects are implemented across multiple devices, then user experience is enhanced, but coordination complexity between devices increases
Solution Approach 1:
The patent consolidates the lighting control functionality into a centralized hub that receives user context from various sources and distributes coordinated lighting commands to multiple peripheral devices. By merging the intelligence and decision-making capabilities into a single central system rather than distributing it across all devices, the system achieves high environmental adaptability while simplifying multi-device coordination through centralized control.
Solution Approach 2:
The hub device performs multiple functions including retrieving user context from various sources (gaming applications, camera feeds, audio inputs), analyzing context data, generating color palettes through machine learning, and distributing lighting commands to diverse peripheral devices. This universal hub consolidates complex multi-device coordination into a single multi-functional system, reducing overall system complexity while maintaining high adaptability across different environments and devices.
Data Source
AI summary
Systems and methods described herein may provide a system that utilizes a machine learning algorithm to determine a color palette based on user context and assigning colors of the color palette to connected systems or peripherals to apply lighting effects. A method may include retrieving, by an information handling system, user context; analyzing, by the information handling system, user context to determine a plurality of colors associated with the user context; generating, by the information handling system, a set of color palettes based, at least in part, on the user context; and generating, by the information handling system, an aggregate color palette based, at least in part, on the set of color palettes. Other aspects are also disclosed.