Context-Aware Lighting Effects With AI Trigger Detection
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Solution Overview
Problem
Existing VR systems lack individualized, home-use devices that can deliver targeted haptic effects with high realism and low latency, and do not leverage generative AI for dynamic lighting effects tailored to user context, leading to repetitive and disconnected experiences.
Innovation Solution
A modular system using generative AI to process audio/video inputs for real-time haptic commands, integrating peripheral devices with computer vision processing and machine learning to deliver customizable lighting and haptic effects based on user context and environmental data, reducing latency and enhancing immersion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional VR systems use pre-programmed lighting effects, then the system complexity is reduced, but the adaptability and personalization capability deteriorates
Solution Approach 1:
The system automatically captures contextual data from microphones and cameras, processes it through machine learning models, and generates lighting effects without requiring manual programming. The AI model autonomously determines when and how to trigger lighting effects based on real-time environmental understanding, eliminating the need for complex pre-programming while maintaining high adaptability
Solution Approach 2:
The system dynamically adjusts lighting parameters (intensity, color, duration) based on processed contextual data. The machine learning model continuously modifies lighting effect parameters in response to changing environmental conditions, user reactions, and event types, enabling adaptability without fixed programming
2Adaptability or versatility
If the system processes contextual data in real-time, then the personalization capability is improved, but the processing time and latency increases
Solution Approach 1:
The system performs preliminary processing by continuously capturing and pre-analyzing contextual data from environmental sensors, microphones, and cameras. Machine learning models pre-process audio and video streams to identify potential trigger events before they occur, reducing the computational burden during actual effect delivery and minimizing latency
Solution Approach 2:
The system replaces traditional rule-based processing mechanisms with machine learning models that can quickly classify and interpret contextual data. The AI-based event detection and lighting effect selection occurs in milliseconds through neural network inference, significantly faster than sequential rule evaluation while maintaining high personalization capability
3Measurement precision
If the system integrates multiple contextual data sources, then the accuracy of lighting effect selection is improved, but the device complexity increases
Solution Approach 1:
The system merges multiple contextual data sources (audio from microphones, video from cameras, environmental sensor data) into a unified event detection framework. The machine learning model integrates these diverse inputs to comprehensively understand environmental context, improving event detection accuracy while managing complexity through unified processing architecture
Solution Approach 2:
The machine learning model serves as an intermediary layer between raw contextual data from multiple sources and the lighting effect control system. It processes and synthesizes information from microphones, cameras, and sensors, translating complex multi-source data into simplified event classifications that drive lighting decisions, thereby improving accuracy while shielding the control system from data complexity
4Adaptability or versatility
If the system uses generative AI for lighting control, then the creativity and variety of effects is improved, but the computational power required increases
Solution Approach 1:
The system uses generative AI selectively only when creative or non-routine lighting effects are needed, rather than continuously. For standard events with established lighting patterns, traditional rule-based control is used. This partial application of computationally intensive generative AI reduces overall energy consumption while maintaining effect variety and creativity when beneficial
Data Source
AI summary
The present invention relates to a system and method for generating lighting effects based on contextual data. The system includes one or more sources, including microphones, cameras, biometric sensors, environmental sensors, and digital feeds providing information including time, date, season, geolocation, weather, calendar events, or current events. A processor analyzes the contextual data to determine a contextual state associated with an ongoing event or user condition. The processor further identifies secondary trigger conditions through computer vision analysis of screen content, developer-scripted events, generative AI-derived cues, optical character recognition, gameplay events, or transformation of on-screen 2D or 3D visual regions. Based on the contextual or composite contextual state, the processor determines one or more lighting effects, which are then triggered on one or more output devices. This enables dynamic and contextually adaptive lighting responses to both real-world and digital stimuli.