Wellness Recommendation Engine for Cross-Domain Health Interplay
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Solution Overview
Problem
Current health treatment methods focus on improving wellness within a single type (physical, mental, or social) without considering the interplay between these wellness types, leading to ineffective addressing of underlying issues.
Innovation Solution
A system that integrates and analyzes physical, mental, and social wellness data to generate holistic recommendations by determining changes in one wellness type's impact on another, using machine learning to suggest actions and treatments that address the root causes across multiple wellness types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If health treatment focuses on improving wellness within a single type (physical, mental, or social), then the treatment approach is simple and focused, but it fails to address underlying issues and produces ineffective results
Solution Approach 1:
The patent combines multiple wellness types (physical, mental, social) into an integrated assessment system that evaluates their interrelationships. The system merges data from different wellness domains and uses machine learning to analyze how changes in one wellness type impact others, enabling holistic treatment recommendations that address root causes rather than isolated symptoms.
Solution Approach 2:
The system performs multiple functions simultaneously: it assesses multiple wellness types, determines interrelationships between them, generates treatment recommendations, and monitors progress across all wellness domains. This multi-functional approach allows a single system to replace multiple separate assessment and treatment systems.
2Reliability
If health treatment considers the interplay between multiple wellness types, then treatment effectiveness improves, but the complexity of the treatment approach increases
Solution Approach 1:
The patent introduces an intermediary machine learning system that automatically analyzes the complex interrelationships between wellness types. This intermediary processes multiple wellness data streams, determines causal relationships, and generates integrated treatment recommendations, thereby managing complexity without sacrificing the holistic approach's effectiveness.
Solution Approach 2:
The system transforms complex multi-dimensional wellness data into simplified parameters and metrics that can be processed algorithmically. By changing the representation of wellness states into quantifiable parameters with defined relationships, the system makes complex interplay analysis computationally manageable while preserving treatment effectiveness.
Data Source
AI summary
Provided herein are methods and systems for generating a recommendation, comprising receiving a first data of a first wellness type comprising a physical type, a social type, or a mental type; receiving a second data of the first wellness type; receiving a wellness state of a second wellness type comprising the physical type, the social type, or the mental type, wherein the second wellness type is different than the first wellness type; determining a change in the wellness state of the second wellness type based on a change between the first data of the first wellness type and the second data of the first wellness type; and generating, based on the determining, the recommendation.