Adaptive AR Visual Aid System for Autonomous User Configuration
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Solution Overview
Problem
Existing wearable visual aids for augmented and virtual reality struggle to autonomously adjust settings in real-time to address user-specific visual impairments and environmental changes, requiring manual optimization and complex control manipulations.
Innovation Solution
An adaptive system integrating machine learning (ML) with augmented and virtual reality, comprising three ML systems (ML1, ML2, ML3) that autonomously analyze camera inputs, learn user preferences, and adjust device settings to enhance visual acuity and functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual optimization of device settings is implemented, then initial user-dependent technical configuration can be achieved, but the process becomes painstaking and infeasible for untrained or vision-impaired users
Solution Approach 1:
The system performs self-configuration by automatically capturing user responses to diagnostic questions and using machine learning algorithms to determine optimal device settings without requiring manual user input or technical knowledge from the user
Solution Approach 2:
The patent replaces manual mechanical adjustment of device parameters with an automated electronic system that uses camera-based eye tracking and machine learning to automatically configure settings based on user responses and visual behavior analysis
2Reliability
If sophisticated image processing operations are performed to address disease state and environmental factors, then visual aid effectiveness is improved, but control manipulation complexity increases
Solution Approach 1:
The patent extracts the complex image processing and parameter optimization functions from manual user control and places them into an autonomous machine learning system that automatically analyzes environmental conditions and adjusts parameters without user intervention
Solution Approach 2:
The system introduces an intermediary machine learning layer that translates complex image processing operations and parameter adjustments into automatic responses based on environmental analysis, shielding users from the complexity of control manipulations while maintaining effective visual aid performance
3Extent of automation
If autonomous machine learning systems are implemented to adjust settings in real-time, then manual intervention is reduced, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the autonomous system into modular components: a camera for capturing eye movements, a machine learning module for analyzing patterns, and a control module for adjusting parameters, allowing each component to be optimized independently while reducing overall system complexity
Solution Approach 2:
The system performs preliminary training during an initial setup phase where user responses and eye tracking data are collected and analyzed to pre-determine optimal parameters, enabling the autonomous system to operate with reduced computational requirements during normal use
Data Source
AI summary
Adaptive control systems using augmented and virtual reality systems chimerically integrated with Artificial Intelligence and the like Homunculi fix vision issues with data based and user tuned solutions in real time.