Wellness Management System Polar Chart Preference Analysis
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Solution Overview
Problem
Current employee wellness systems fail to identify employee preferences and participation statistics effectively, leading to underutilization of preferred activities and overutilization of unpopular ones, resulting in low employee participation in wellness programs.
Innovation Solution
A computer-based wellness management system that aggregates health factors and activity preferences into polar charts to recommend activities tailored to employee groups, ensuring popular activities are scheduled more frequently based on identified preferences and participation statistics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If employee wellness systems monitor and track employee activities, then employee participation data is collected, but employee preferences and participation statistics are not effectively identified
Solution Approach 1:
The system implements feedback mechanisms by continuously collecting employee activity data through monitoring devices, analyzing participation patterns, and using this information to generate personalized wellness recommendations. The system feeds back participation statistics and preference data to improve future activity recommendations, creating a closed-loop system that progressively identifies employee preferences more accurately.
Solution Approach 2:
The patent introduces an intermediary analysis layer between raw activity monitoring data and wellness recommendations. This intermediary component processes monitored activity data through analytical algorithms that identify patterns, preferences, and participation statistics, transforming raw data into actionable insights about employee preferences without requiring direct employee input.
2Productivity
If wellness systems recommend activities based on general guidelines, then implementation is simple, but preferred activities are underutilized while unpopular activities are overutilized
Solution Approach 1:
The system applies local quality by customizing wellness recommendations for different employee segments based on their specific activity preferences and participation patterns. Instead of uniform general guidelines, the system tailors activity recommendations to individual or group characteristics, ensuring that each employee receives recommendations aligned with their preferences, thereby increasing participation in preferred activities.
Solution Approach 2:
The patent implements dynamics by making activity recommendations adaptive and evolving over time. The system continuously updates employee preference profiles based on monitored participation data, dynamically adjusting recommendations to reflect changing employee preferences. This dynamic approach allows the system to optimize participation rates while maintaining flexibility in activity scheduling.
3Quantity of substance
If the system tracks detailed employee activity data, then participation statistics are captured, but the complexity of processing and analyzing preferences increases
Solution Approach 1:
The system merges multiple data processing functions into an integrated analysis platform that simultaneously handles activity monitoring, preference identification, and recommendation generation. By combining these functions into a unified system, the patent reduces overall complexity compared to having separate systems for each function, while still processing detailed employee activity data effectively.
Solution Approach 2:
The patent employs copying by creating simplified analytical models and templates for processing employee preference data. Instead of developing complex custom analysis routines for each employee, the system uses standardized processing templates that can be applied repeatedly, reducing computational complexity while maintaining the ability to analyze detailed activity data across large employee populations.
Data Source
AI summary
A method managing wellness of employees is presented. A computer system receives a group of health factors for activities and group of preferences for activities of the employees. The computer system aggregates the group of health factors into aggregate health factors and the group of preferences into aggregate preferences. The computer system displays the aggregate health factors and the aggregate preferences in a set of polar charts. The computer system then identifies a recommendation for an activity for a portion of the employees based on the set of polar charts.