Predictive Wellness Management via Biometric Forecasting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Unhealthy habits are difficult to change due to their subconscious nature and association with social structures, making it challenging for individuals to break negative behaviors and adopt positive ones.
Innovation Solution
A computer-implemented system that uses biometric data to identify and adjust access controls based on individual wellness targets, predicting wellness patterns and providing socially aware guidance to align users with activities and environments that support their goals, thereby influencing behavior change.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If access control is adjusted based on predicted wellness patterns, then wellness goal achievement is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary forecasting of wellness patterns using biometric data before access control decisions are made. This allows the system to proactively identify users who may benefit from restricted or encouraged access to certain locations, enabling preventive wellness interventions rather than reactive measures.
Solution Approach 2:
The patent introduces an intermediary forecasting module that processes biometric data and generates wellness pattern predictions, which then inform access control decisions. This intermediary layer decouples the complex biometric analysis from the simple access control execution, managing system complexity while maintaining reliability.
2Measurement precision
If biometric data is collected and analyzed, then wellness pattern forecasting accuracy is improved, but user privacy concerns increase
Solution Approach 1:
The system applies different processing and analysis methods to different types of biometric data based on their sensitivity and relevance to wellness patterns. Not all biometric data is treated uniformly - the system selectively analyzes data based on local requirements and privacy considerations.
Solution Approach 2:
The patent transforms raw biometric data into aggregated wellness patterns and trends, changing the parameter representation from individual sensitive measurements to generalized wellness indicators. This transformation maintains forecasting accuracy while reducing privacy concerns.
3Productivity
If differential access control is implemented, then behavior change effectiveness is improved, but ease of operation decreases
Solution Approach 1:
The system automatically adjusts access controls based on forecasted wellness patterns without requiring manual intervention from users or administrators. The differential access control is self-managed through automated decision-making algorithms that respond to real-time biometric data.
Solution Approach 2:
Access control policies are made dynamic rather than static, automatically adapting to changing wellness patterns and user needs. The system continuously monitors biometric data and adjusts access permissions in real-time, making the operation seamless despite the complexity of differential control.
Data Source
AI summary
Socially aware guidance and differential access controls within a control zone are determined for users based on individual wellness goals and predicted wellness patterns. Users are socially grouped based on wellness goals and predicted responsiveness to achieving the wellness goals. Biometric data obtained from users from social situations and activities provide insight into individual wellness progress and achievement of individual wellness goals. Predicted wellness patterns further assist users with achieving wellness goals through socially aware guidance and differential access controls.