Wellness Program Matching via Computational Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional wellness platforms lack effective mechanisms for matching clients with suitable coaches, providing oversight during coaching programs, and incorporating feedback, leading to inefficient user onboarding, skewed statistics, and difficulty in evaluating coach and client performance.
Innovation Solution
A system and method that utilize computational models to match clients with coaches by processing historical data sets and corpus of communications, providing recommendations for improving engagement, and incorporating feedback to enhance coaching programs, while allowing coaches to create and edit wellness programs through a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional platforms use basic search and keyword-based matching, then the system complexity is reduced, but the matching precision between clients and coaches deteriorates
Solution Approach 1:
The system implements feedback mechanisms where clients provide ratings and comments on coach performance, and coaches receive feedback on client outcomes. This feedback loop enables continuous refinement of matching algorithms, improving precision over time while keeping the system manageable through iterative optimization rather than complex one-time designs.
Solution Approach 2:
The patent replaces manual search and keyword-based matching with automated machine learning algorithms that process multiple data dimensions including client goals, coach specialties, historical performance data, and communication patterns. This substitution of mechanical search processes with intelligent algorithms achieves superior matching precision without proportionally increasing system complexity.
2Reliability
If conventional platforms lack feedback mechanisms, then the system operation is simplified, but the reliability of performance evaluation deteriorates
Solution Approach 1:
The system automatically collects and processes feedback from multiple sources including client ratings, coach evaluations, and outcome measurements. This multi-source feedback mechanism ensures reliable performance evaluation by aggregating data across different dimensions while maintaining operational simplicity through automated data collection and processing routines.
Solution Approach 2:
The platform enables self-service feedback collection where clients and coaches automatically provide evaluations and outcomes are automatically measured and recorded. This self-service approach ensures reliable performance data without requiring complex manual intervention, maintaining ease of operation while improving evaluation reliability.
3Productivity
If conventional platforms use disparate third-party tools, then the ease of manufacture is improved, but the productivity of the overall system deteriorates
Solution Approach 1:
The patent merges previously disparate third-party tools into a unified integrated platform that handles client onboarding, coach matching, program delivery, feedback collection, and analytics in a single cohesive system. This consolidation eliminates data silos and integration friction, improving overall system productivity while managing complexity through modular architecture design.
Solution Approach 2:
The system creates a universal platform that performs multiple functions including client matching, program coordination, performance tracking, and analytics within a single integrated system. This multi-functionality eliminates the need for multiple separate tools, improving productivity by reducing the coordination overhead between disparate systems while managing complexity through standardized interfaces and data formats.
4Measurement precision
If conventional platforms lack scientific background guidance, then the ease of operation is improved, but the measurement precision of course relevance deteriorates
Solution Approach 1:
The system introduces computational models and algorithms as intermediaries between client inputs and course recommendations. These intermediary models process client goals, preferences, and historical data to generate scientifically-informed recommendations, improving course relevance precision while maintaining ease of operation by automating the complex matching logic in the background.
Solution Approach 2:
The patent replaces simple keyword-based search with intelligent computational models that understand client needs and match them with appropriate courses based on scientific and expert knowledge. This substitution maintains ease of operation through automated processing while dramatically improving the precision of course relevance through sophisticated algorithms.
Data Source
AI summary
Systems and methods for matching a client and a coach are provided. A request for a wellness program including a set of attributes is received. A plurality of coaching profiles is obtained, each associated with a corresponding coach and includes a corresponding one or more wellness programs and a first corresponding data set associated with performance of the corresponding coach. Responsive to the request, a plurality of wellness programs is obtained, each associated with one or more corresponding coaches and includes one or more attributes improved by the respective wellness program and a second corresponding data set associated with performance of the respective wellness program. The coaching profiles, the wellness programs, and the set of attributes are processed, producing a respective result for each computational model that is collectively considered, producing a set of at least one coaching profile and wellness program that is communicated to a remote device.