N-Dimensional Well-Being Profile System for Personalized Intervention
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Solution Overview
Problem
Conventional wearable devices are insufficient for tracking an individual's well-being over time, as they only monitor single health metrics and do not account for the multi-dimensional nature of well-being, which varies by individual and environment.
Innovation Solution
A method is introduced to create n-dimensional well-being profiles specific to users and their environments, refining these profiles based on implicit and explicit responses to intervention activities, and using collaborative filtering to recommend personalized interventions, with data stored and updated in a hierarchical tree structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional wearable devices track single health metrics, then device complexity is reduced and ease of operation is improved, but well-being tracking precision and comprehensiveness deteriorate
Solution Approach 1:
The patent segments well-being tracking into multiple independent dimensions (physical, emotional, social, environmental) with separate sensors and processing pipelines for each dimension, allowing comprehensive tracking while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The patent transitions from single-metric tracking to multi-dimensional well-being assessment by adding temporal, environmental, and contextual dimensions to traditional health metrics, enabling comprehensive well-being evaluation through n-dimensional profiling
2Loss of information
If single health metrics are monitored, then measurement focus is sharpened and data processing is simplified, but well-being assessment completeness deteriorates
Solution Approach 1:
The patent merges data from multiple sensors measuring different physical, emotional, and environmental parameters into a unified well-being profile, combining heterogeneous data sources to provide comprehensive well-being assessment while integrating processing through standardized frameworks
3Adaptability or versatility
If generic well-being tracking is implemented, then system adaptability is reduced and implementation is simplified, but personalization effectiveness deteriorates
Solution Approach 1:
The patent implements local quality by creating personalized well-being profiles tailored to each user's specific characteristics, environment, and goals, with customized sensor configurations, weightings, and intervention recommendations rather than applying uniform tracking to all users
4Measurement precision
If multi-dimensional well-being profiles are created and refined, then well-being tracking precision and personalization are improved, but computational requirements and system complexity increase
Solution Approach 1:
The patent performs preliminary action by pre-processing sensor data, pre-computing baseline well-being profiles, and pre-identifying intervention opportunities before full analysis is required, reducing real-time computational burden while maintaining measurement precision
Data Source
AI summary
Based on the recognition that an individual's well-being is not a function of a single factor or variable but is instead a multi-dimensional function of a number of factors or variables where interventions (i.e., activities that people perform to help with their well-being) affect each individual differently, a system recommends interventions that best influence well-being across multiple factors by creating an n-dimensional profile specific to each individual. Upon completion of the interventions, the profile is refined to develop n-dimensional sub-profiles for each individual. This refining process is informed using the effectiveness of each intervention as it relates to each individual and environmentally specific well-being factors using implicit and explicit signals responsive to the interventions. The recommendation of future well-being interventions is based on well-being factors in the refined n-dimensional well-being profile. The n-dimensional profile enables tracking of the user's well-being journey in n-dimensional space as the user completes interventions.