Cardiovascular Nourishment Program Generation System
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Solution Overview
Problem
Current nutrition program generation systems do not account for individual cardiovascular characteristics, leading to inefficiencies and dissatisfaction due to a lack of uniformity.
Innovation Solution
A system that receives cardiovascular samples to generate cardiovascular parameters, profiles, and health scores, identifies nutrition elements using machine-learning models, and creates personalized nourishment programs incorporating cardiovascular health scores and atherosclerosis indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If general nutrition program generation systems are used without cardiovascular considerations, then the system is simpler to implement, but the program effectiveness for cardiovascular health is reduced
Solution Approach 1:
The system changes the parameters considered in nutrition program generation by incorporating cardiovascular-specific parameters (cardiovascular health score, atherosclerosis indicator, inflammation marker) alongside traditional nutritional parameters. This allows the system to generate more effective cardiovascular-focused programs without excessive complexity by selectively adding relevant parameters.
Solution Approach 2:
The patent introduces an intermediary processing layer that analyzes cardiovascular samples and generates cardiovascular profiles, which then inform the nutrition program generation. This intermediary step bridges the gap between general nutrition systems and cardiovascular health needs without requiring complete system redesign.
2Adaptability or versatility
If personalized cardiovascular profiles are generated using machine learning models, then user satisfaction and program uniformity improve, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary analysis by generating cardiovascular profiles and calculating health scores before final program generation. Machine learning models pre-process cardiovascular sample data to create standardized profiles that can be quickly referenced during program generation, reducing real-time processing requirements.
Solution Approach 2:
The patent applies machine learning selectively to specific cardiovascular parameters rather than processing all possible data points. By focusing computational resources on key indicators (atherosclerosis, inflammation, cardiovascular health score), the system achieves effective personalization without excessive processing time.
3Measurement precision
If comprehensive cardiovascular analysis is performed including multiple biomarkers, then measurement precision of cardiovascular status improves, but cost and complexity of sample analysis increase
Solution Approach 1:
The system extracts and focuses on specific critical biomarkers (atherosclerosis indicator, inflammation marker, cardiovascular health score) from comprehensive cardiovascular samples. By selectively analyzing only the most relevant parameters for nutrition program generation, the system achieves sufficient measurement precision without requiring analysis of all possible cardiovascular markers.
Data Source
AI summary
In an aspect, a system for generating a cardiovascular disease nourishment program includes a computing device configured to receive a cardiovascular sample relating to a user, generate a cardiovascular parameter as a function of the cardiovascular disease sample, determine a cardiovascular profile as a function of the a cardiovascular parameter wherein the cardiovascular profile includes a numerical cardiovascular health score correlated to the cardiovascular parameter and an atherosclerosis indicator correlated to the cardiovascular parameter, identify a nutrition element as a function of the cardiovascular profile, wherein identifying comprises obtaining a nutrient composition correlated to a nutrition element, determining a nourishment score as a function of the effect of the nutrition element on the cardiovascular profile, and identifying a nutrition element as a function of the nourishment score and nutrition element machine-learning model, and generate a cardiovascular disease nourishment program as a function of the nourishment score and the cardiovascular profile.