AI Alimentary Instruction Set Generation for Multivariate Biological Data

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Solution Overview

Problem

The complexity of biological data from personal constitutions poses a challenge in generating effective alimentary instruction sets, as existing solutions fail to adequately analyze and account for the multivariate complexity of the data involved.

Innovation Solution

A system utilizing artificial intelligence that includes a server configured to receive training data, perform machine-learning algorithms, and generate alimentary instruction sets by recording biological extractions, producing comprehensive and physical performance instruction sets, and selecting optimal handlers to execute these instructions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If existing solutions are used to analyze biological data, then the process is simpler, but the analysis effectiveness and ability to account for multivariate complexity is insufficient

Engineering Contradiction:

Improveanalysis effectivenessVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical data analysis methods with machine learning algorithms and artificial intelligence systems. The diagnostic engine uses trained machine learning models to automatically analyze biological extraction data, physiological state data, and prognostic labels, substituting manual or rule-based analysis with intelligent computational systems that can handle multivariate complexity effectively

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces a comprehensive instruction set as an intermediary between the diagnostic output and the final alimentary instruction set. This intermediary layer processes and integrates multiple data sources including biological extractions, physiological states, and prognostic information, enabling systematic handling of complex multivariate data before generating final recommendations

Inventive Principle:

Principle #24Intermediary (Mediator)

2Loss of information

If comprehensive biological data is collected, then the information completeness is improved, but the difficulty of analysis and data processing increases

Engineering Contradiction:

Improveinformation completenessVSAvoiddata analysis difficulty

Core Design Contradiction:

Loss of informationVSDifficulty of detecting and measuring

Solution Approach 1:

The patent employs machine learning algorithms to automatically process and analyze comprehensive biological data. The diagnostic engine uses trained models to interpret biological extractions, physiological state data, and prognostic labels, replacing manual analysis methods with intelligent systems that can efficiently handle large volumes of complex data without losing information

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent segments the comprehensive data analysis process into distinct modules: data collection, machine learning processing, diagnostic output generation, and instruction set creation. This segmentation allows each component to handle specific aspects of data processing, making the overall complex analysis task more manageable and systematic

Inventive Principle:

Principle #1Segmentation

3Productivity

If manual analysis methods are used, then the system complexity is lower, but the productivity and efficiency of generating instruction sets is reduced

Engineering Contradiction:

Improveinstruction set generation efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces manual analysis and instruction generation with automated machine learning systems. The diagnostic engine automatically processes biological data and generates diagnostic outputs, which are then transformed into comprehensive and alimentary instruction sets through automated processes, significantly improving productivity compared to manual methods

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent implements self-service capabilities where the machine learning models automatically analyze data and generate instruction sets without requiring manual intervention at each step. The system serves itself by using trained algorithms to process data, generate diagnostics, and create personalized instruction sets autonomously

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS11222727B2Systems and methods for generating alimentary instruction sets based on vibrant constitutional guidance

Publication Date: 2022.01.11 KPN INNOVATIONS LLC

AI summary

A method for generating an alimentary instruction set identifying a list of supplements, comprising receiving information related to a biological extraction and physiological state of a user and generating a diagnostic output based upon the information related to the biological extraction and physiological state of the user. The generating comprises identifying a condition of the user as a function of the information related to the biological extraction and physiological state of the user and a first training set. Further, the generating includes identifying a supplement related to the identified condition of the user as a function of the identified condition of the user and a second training set. Further, the method includes generating, by an alimentary instruction set generator operating on a computing device, a supplement plan as a function of the diagnostic output, said supplement plan including the supplement related to the identified condition of the user.