AI and Virtual Reality Activity Personalization Through Multilevel Analysis
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Solution Overview
Problem
Existing artificial intelligence systems in medicine, such as IBM's Watson, struggle to analyze biological variances effectively, leading to inefficiencies in health services and medical diagnosis.
Innovation Solution
A system incorporating a monitor device and AI system that tracks physical properties using various sensors, performs vision tests, and utilizes retinal imaging for real-time feedback to optimize and personalize health-related activities, treatments, and diagnoses through predictive learning and machine learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional linear AI techniques are used, then the system is simple and easy to implement, but it cannot analyze variances in biologic functions at different levels
Solution Approach 1:
The patent segments the AI analysis into multiple levels corresponding to different biological organization levels (molecular, cellular, tissue, organ, system levels). Each level processes specific types of biological data independently before integrating findings, allowing complex variance analysis while maintaining manageable system architecture through hierarchical processing
Solution Approach 2:
The patent introduces a new dimensional approach by analyzing biological data across multiple hierarchical levels simultaneously. Instead of traditional single-level analysis, the system adds the dimension of biological organization level, enabling comprehensive variance detection that spans from molecular to system-level interactions
2Reliability
If AI systems are used to improve health services accuracy, then diagnosis and treatment accuracy improve, but implementation costs and system complexity increase
Solution Approach 1:
The patent creates a universal AI framework that can process multiple types of biological data (genomic, proteomic, metabolomic, imaging, clinical) through a single multi-level analysis architecture. This multi-functional system handles diverse data types using the same hierarchical processing approach, improving diagnosis accuracy while avoiding the need for separate specialized systems
Solution Approach 2:
The system incorporates feedback mechanisms where analysis results at each biological level are fed back to refine analyses at other levels. This iterative feedback process continuously improves diagnosis accuracy by allowing findings at one level to adjust interpretations at other levels, while the feedback loops are designed to automate refinement processes
3Productivity
If comprehensive health monitoring is implemented, then patient care quality improves, but data processing requirements and system complexity increase
Solution Approach 1:
The patent segments comprehensive health monitoring data into distinct biological levels and types (genomic data, proteomic data, metabolomic data, imaging data, clinical data). Each data type is processed independently through specialized algorithms at its appropriate level, then results are integrated. This segmentation enables efficient processing of comprehensive data without overwhelming complexity in any single processing stage
Data Source
AI summary
Optimizing and/or personalizing activities to a user through artificial intelligence and/or virtual reality.