AI-Generated Wellness Content with Real-Time Biometric Adaptation
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Solution Overview
Problem
Existing wellness apps lack personalization, engagement, and accessibility, failing to dynamically respond to individual user needs and preferences, and are limited by static content, language barriers, and high production costs.
Innovation Solution
An AI-driven system that uses a Large Language Model and Neural Network to generate personalized audio and video content, integrating biometric data for real-time customization and adaptation, and supporting offline use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static content is used in wellness apps, then production costs are reduced, but personalization and adaptability to user needs deteriorate
Solution Approach 1:
The patent implements dynamic content generation where wellness scripts are automatically adapted based on real-time biometric data (heart rate, respiratory rate, skin conductance) and user profile information. The system transitions from static pre-recorded content to dynamic AI-generated content that responds to individual user states, achieving personalization without requiring manual production for each user scenario.
Solution Approach 2:
The system employs self-service through automated AI processing where user biometric data and preferences are automatically processed to generate customized wellness content without human intervention. The AI system independently analyzes user responses, adjusts script parameters, and generates personalized content, eliminating the need for manual content creation for each user.
2Adaptability or versatility
If AI-driven personalized content generation is implemented, then personalization and engagement are improved, but device complexity and processing requirements worsen
Solution Approach 1:
The patent segments the AI system into distinct functional modules: biometric data collection module, data processing module, AI script generation module, and content delivery module. Each module handles specific tasks independently, making the complex system more manageable and easier to implement. The segmentation allows for modular development where each component can be optimized separately.
Solution Approach 2:
The patent introduces an intermediary data processing layer that bridges raw biometric data and AI-generated content. This intermediary layer processes and contextualizes user responses, preparing structured information that the AI model can effectively process. The intermediary acts as a buffer between complex data inputs and content generation, simplifying the overall system architecture.
3Speed
If real-time biometric data processing is implemented, then responsiveness to user needs is improved, but processing time and computational resources worsen
Solution Approach 1:
The patent implements preliminary action by pre-processing biometric data thresholds and AI model parameters before actual wellness sessions. User profiles and response patterns are analyzed in advance to establish baseline parameters, allowing the system to respond more quickly during actual use without performing complex computations in real-time. The heavy lifting is done beforehand, leaving only lighter processing for live responses.
Data Source
AI summary
Exemplary embodiments include a computer-implemented method of training a neural network and/or a large language model for automatically collecting, analyzing, and transmitting data to induce a mental state and change a behavior in a mammal.