Risk Factor Engine for Personalized Health Score Calculation
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Solution Overview
Problem
Existing health tracking services are inconvenient for users, as they require extensive manual input of meal data and provide generalized information that is not tailored to individual health needs, lacking personalization and confidentiality in interactions with healthcare professionals.
Innovation Solution
A method involving a risk factor engine that processes user health data to calculate a personalized health score, providing tailored health information, recommendations, and alerts based on individual health risks, incorporating physical, social, and family health data, and allowing for flexible protocol authoring and execution by a computing device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a user manually tracks all foods consumed throughout the day to access calorie information, then the service can provide calorie breakdown, but the service becomes inconvenient and time-consuming for users
Solution Approach 1:
The system automatically tracks food consumption using sensors and devices without requiring manual user input. The food consumption trend detection device autonomously monitors what the user eats and processes this information to generate health scores and predictions, eliminating the need for users to manually log every meal while maintaining accurate tracking.
Solution Approach 2:
The patent replaces the mechanical system of manual food logging with automated sensor-based detection. Instead of users physically writing down or selecting foods from lists, the system uses optical, weight, or other sensing mechanisms to automatically detect and track food consumption, substituting human effort with automated detection technology.
2Adaptability or versatility
If web services provide generalized health information to all users, then the service is easy to implement, but the information is not tailored to individual health needs and risks
Solution Approach 1:
The system pre-calculates and stores health scores and health predictions for different users based on their baseline characteristics before specific health events or consultations occur. This preliminary processing of user data creates personalized profiles that enable tailored health information delivery without requiring complex real-time calculations during user interactions.
Solution Approach 2:
The patent introduces a health score and health prediction as intermediary metrics that bridge raw user data and personalized health recommendations. These intermediaries translate complex multi-factor user profiles into actionable, personalized health information, simplifying the system architecture while enabling individualized care.
3Loss of information
If meal tracking services return only the number of calories consumed, then the service is simple to operate, but additional useful health information is not provided to users
Solution Approach 1:
The system merges multiple data sources including food consumption data, user health profiles, and predictive algorithms into a unified health score and health prediction output. Instead of providing separate calorie counts and health information, the system combines these elements into integrated personalized health assessments that deliver comprehensive information in a cohesive format.
4Reliability
If practitioners provide health information through posted answers on web services, then the service is easy to access, but confidentiality between user and practitioner is not maintained
Solution Approach 1:
The system extracts and processes sensitive health information through automated algorithms rather than requiring direct practitioner-user communication for routine assessments. By taking out the computational analysis function from human practitioners and assigning it to automated systems, the patent maintains confidentiality while preserving accessibility, as users receive personalized information without exposing their data in public forum settings.
Data Source
AI summary
A method for processing risk factors for a user is disclosed. The method may include receiving protocol data for creating a risk factor engine by an application stored and executed at a computing device. The risk factor engine may be stored in memory and executable by a processor to process a first set of stored user health data for a user. The protocol data may include a rule and one or more health attribute values. The rule and the one or more health attribute values may each include a computer programming expression editable by an administrator. The method may further include determining a user health score through execution of the risk factor engine by the processor and based on the user health data.