Composite Score Data Objects for Personalized Health Activity Recommendations
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Solution Overview
Problem
Data analytics systems face challenges in correlating user health data with activity data, leading to fragmented user experiences and ineffective reward mechanisms, as they fail to provide personalized health factor-driven solutions and lack transparency in activity recommendations.
Innovation Solution
The system generates composite score data objects by integrating health score and activity score data objects, using point indicators associated with category and type indicators to calculate activity scores, and performs score-based actions such as rendering activity recommendations on user interfaces, ensuring personalized engagement and clear explanations of health improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data analytics systems process and correlate user health data with activity data, then the system can provide personalized health recommendations, but the computational complexity and data processing requirements increase significantly
Solution Approach 1:
The patent segments the complex data processing into distinct components: activity score generation module that processes activity data separately from health score generation module that processes health data. These segmented modules independently compute their respective scores and then integrate results, reducing overall system complexity while maintaining personalization capability.
Solution Approach 2:
The patent introduces intermediary elements including point indicators that serve as mediators between raw activity data and final activity scores, and category indicators that mediate between different types of health data. These intermediaries simplify the correlation process by providing structured intermediate representations.
2Measurement precision
If the system integrates multiple data sources to generate comprehensive health and activity scores, then the accuracy of personalized recommendations improves, but the data processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-defining category indicators, type indicators, and point indicator mappings before actual data processing occurs. This pre-structuring of the scoring framework allows for faster real-time computation of individual user scores while maintaining comprehensive multi-source data integration.
Solution Approach 2:
The patent utilizes parameter changes by allowing flexible adjustment of point indicators and category weights without requiring complete system reconfiguration. This enables accurate scoring adaptation to different user needs while maintaining efficient processing through a stable underlying framework.
3Loss of information
If the system provides detailed activity recommendations with explanations, then user engagement and transparency improve, but the complexity of generating and managing recommendation data increases
Solution Approach 1:
The patent implements feedback mechanisms where the system generates explanations that connect user activities to health outcomes through the scoring framework. This feedback loop provides transparency by showing users how their activities contribute to their health scores, while the structured nature of the feedback reduces management complexity through standardized explanation templates.
Solution Approach 2:
The patent adds the dimension of interpretability by layering explanatory information over the core scoring mechanism. This creates a two-layer system where the underlying score generation remains computationally simple, while the added explanation layer provides transparency without significantly increasing core processing complexity.
Data Source
AI summary
Methods, apparatuses, systems, computing devices, computing entities, and/or the like are provided. An example method may include retrieving a client profile data object based at least in part on a client identifier indicator, determining a plurality of point indicators based at least in part on the at least one client activity data object, generating an activity score data object associated with the client profile data object, generating a composite score data object, and performing at least one score-based action based at least in part on the composite score data object and the client profile data object.