Alimentary Instruction Generation via Machine Learning Analysis

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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 this data.

Innovation Solution

A system and method that utilizes a diagnostic engine with a machine learning module to identify user conditions and generate alimentary instruction sets by correlating physiological state data with prognostic labels, thereby creating delivery instruction sets for optimal alimentary element delivery.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If existing solutions are used to analyze biological data, then the analysis process is simple, but the ability to account for multivariate complexity and volumes of information is insufficient

Engineering Contradiction:

Improveability to account for multivariate complexityVSAvoidcomplexity of data analysis process

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments the complex biological data analysis into multiple distinct modules: data reception module, data normalization module, data analysis module, and instruction generation module. Each module handles specific aspects of the analysis, allowing the system to manage multivariate complexity through structured division of labor while maintaining overall system coherence.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces intermediate data structures and processing layers that mediate between raw biological data and final alimentary instructions. These intermediaries include normalized data formats, processed feature representations, and structured instruction templates that bridge the gap between complex input data and actionable output.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If comprehensive biological information is collected, then the accuracy of alimentary instructions improves, but the complexity and volume of data to be assessed increases

Engineering Contradiction:

Improveaccuracy of alimentary instructionsVSAvoidvolume of biological data

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent extracts only the most relevant features and parameters from comprehensive biological data through selective data extraction processes. The system identifies and isolates key biomarkers, physiological parameters, and compositional elements that directly impact alimentary recommendations, discarding redundant information while preserving accuracy-critical data.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent transforms raw biological data into standardized parameters and normalized formats that are optimized for analysis. By changing the representation of biological information into consistent, comparable parameters, the system reduces data volume complexity while maintaining the precision needed for accurate alimentary instruction generation.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If manual analysis of biological data is performed, then the system complexity is low, but the productivity and efficiency of generating alimentary instruction sets is reduced

Engineering Contradiction:

Improveefficiency of generating alimentary instruction setsVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements automated self-service mechanisms where the system autonomously receives biological data, normalizes it according to predefined standards, analyzes it through algorithmic processes, and generates alimentary instruction sets without human intervention. This automation dramatically increases productivity while the modular architecture manages the resulting system complexity.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical analysis processes with computational algorithms and automated data processing systems. Machine learning models, statistical analysis routines, and rule-based engines substitute human analysts, enabling high-speed processing of comprehensive biological data and rapid generation of personalized alimentary instructions.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20200321113A1Systems and methods for generating alimentary instruction sets based on vibrant constitutional guidance

Publication Date: 2020.10.08 KPN INNOVATIONS LLC

AI summary

A method for delivery based on an alimentary instruction set includes receiving information related to a biological extraction of a user and generating a diagnostic output. The method also includes identifying, by a machine learning module, a condition of the user and an alimentary element related to the identified condition of the user. The method can also include generating an alimentary instruction set identifying the alimentary element to be delivered to the user and generating a delivery instruction set, said delivery instruction set indicating a delivery performance for the alimentary element.