Profile-Cluster Nutrient Scoring for Personalized Nutrition
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Solution Overview
Problem
Existing meal preparation methods fail to optimize for a range of phenotypic factors, leading to suboptimal nutritional outcomes.
Innovation Solution
An apparatus and method that utilizes a computing device with a processor and memory to classify user data into profile clusters, assign cohort labels, and score nutrients based on user-specific physiological and genetic data, enabling personalized nutritional recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If personalized nutrient scoring based on user profiles is implemented, then nutritional optimization is improved, but device complexity increases
Solution Approach 1:
The system segments users into distinct profile clusters based on physiological and genetic characteristics. This segmentation allows the complex task of personalized nutrition optimization to be divided into manageable segments, where each cluster receives tailored nutrient scoring without requiring the entire system to handle all possible personalization scenarios simultaneously, thus improving reliability while controlling complexity.
Solution Approach 2:
The system changes parameters by transitioning from generic nutrient recommendations to personalized nutrient scoring based on multiple user parameters including physiological data, genetic information, and dietary preferences. This parameter transformation enables precise nutritional optimization by adjusting nutrient scores according to individual user profiles, directly addressing the reliability improvement while the automated parameter management keeps device complexity manageable.
2Measurement precision
If multiple user data parameters are collected and analyzed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and organizing user data into structured profiles during onboarding, and by pre-defining profile clusters and classification criteria. This preliminary preparation ensures that when nutrient scoring is performed, the system can quickly match users to appropriate clusters without time-consuming analysis, thus maintaining high measurement precision while reducing processing time.
Solution Approach 2:
The system uses copying by creating standardized profile cluster templates that can be reused across multiple users. Instead of performing full analysis for each individual user, the system copies and applies pre-established cluster characteristics to matched users, significantly reducing processing time while maintaining classification accuracy through the standardized copying process.
Data Source
AI summary
In an aspect, an apparatus for scoring a nutrient is presented. An apparatus may include at least a processor and a memory communicatively connected to the at least a processor. A memory contains instructions configuring at least a processor to receive user data from a user. At least a processor classifies a user to a profile cluster as a function of user data. At least a processor assigns the user one or more cohort labels as a function of the user data. At least a processor receives edible data. At least a processor extracts, from edible data, at least a nutrient. At least a processor scores at least a nutrient as a function of a profile cluster of a user.