Metabolic Dysfunction Nourishment Program Generation

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Solution Overview

Problem

Current edible suggestion systems do not account for an individual's metabolic system, leading to inefficiencies and dissatisfaction due to the lack of personalized nutrition plans.

Innovation Solution

A system and method using machine-learning models to generate a metabolic dysfunction nourishment program by obtaining metabolic components, identifying metabolic panels, determining edible items based on nourishment compositions, and creating a nourishment program tailored to individual metabolic needs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a generic edible suggestion system is used, then the system complexity is low, but the nutrition plan effectiveness is poor

Engineering Contradiction:

Improvenutrition plan effectivenessVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system segments the nutrition planning process into multiple specialized machine-learning models: a metabolic panel identification model that analyzes metabolic components, an edible determination model that selects appropriate foods, and a nourishment program generation model that creates personalized plans. This segmentation allows each model to specialize in a specific aspect of metabolic analysis, improving overall effectiveness while managing complexity through modular architecture.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically changes parameters based on individual metabolic profiles by analyzing multiple metabolic components (glucose, insulin, HbA1c, lipid panel, kidney function, liver function, thyroid function) and adjusting nutritional recommendations accordingly. This parameter-driven approach enables personalized nutrition plans that adapt to each user's specific metabolic state rather than applying generic recommendations.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If personalized nutrition plans are created using machine-learning models, then user satisfaction is improved, but the computational resources required increase

Engineering Contradiction:

Improvepersonalization capabilityVSAvoidcomputational energy

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary actions by pre-processing and analyzing all metabolic components upfront to generate a comprehensive metabolic profile. The metabolic panel identification model pre-identifies relevant metabolic markers and their relationships, which are then reused by subsequent models for edible determination and program generation. This preliminary analysis reduces redundant computations in later stages, lowering overall computational energy requirements.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The machine-learning models operate autonomously to generate personalized nutrition plans without requiring continuous human intervention. Once trained, the models self-service by automatically processing metabolic data, determining appropriate edibles, and generating nourishment programs based on established algorithms and nutritional databases, minimizing the need for external computational resources.

Inventive Principle:

Principle #25Self-service

3Measurement precision

If multiple machine-learning models are used to analyze metabolic components, then the accuracy of nourishment recommendations is improved, but the processing time is increased

Engineering Contradiction:

Improvemetabolic analysis accuracyVSAvoidprogram generation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system merges the functionality of multiple machine-learning models into an integrated nourishment program generation system. The metabolic panel identification model, edible determination model, and program generation model work together as a unified pipeline, sharing intermediate results and coordinating their operations. This merging allows the system to maintain high measurement precision through comprehensive metabolic analysis while reducing processing time through optimized data flow and result reuse across models.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS11688507B2Systems and methods for generating a metabolic dysfunction nourishment program

Publication Date: 2023.06.27 KPN INNOVATIONS LLC

AI summary

A system and method for generating a metabolic dysfunction nourishment program comprises a computing device configured to obtain a metabolic component as a function of a user metabolic system, identify a metabolic panel as a function of the metabolic component, wherein identifying further comprises receiving a status grading, ascertaining a metabolic functional goal, and identifying the metabolic panel as a function of the status grading, metabolic functional goal, and metabolic component using a metabolic machine-learning model, determine an edible as a function of the metabolic panel, wherein determining further comprises receiving a nourishment composition from an edible directory, producing a nourishment demand as a function of the metabolic panel, and determining the edible as a function of the nourishment composition and nourishment demand using an edible machine-learning model, and generate a nourishment program as a function of the edible and a metabolic outcome using a nourishment machine-learning model.