Microbiome Prediction via Questionnaire and Machine Learning

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Solution Overview

Problem

Current methods for assessing gut microbiome status are invasive, time-consuming, and costly, requiring biological samples, and do not provide individualized recommendations for maintaining or improving microbiome health effectively.

Innovation Solution

The implementation of Artificial Intelligence-based Machine Learning methods that use questionnaire responses to assess and predict an individual's gut microbiome status, providing personalized dietary and nutrition recommendations without the need for biological samples.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If biological sampling methods (fecal or plasma samples) are used to assess microbiome status, then measurement precision is improved, but ease of operation and user acceptance deteriorate due to invasive procedures and complex processing requirements

Engineering Contradiction:

Improvemicrobiome status assessment accuracyVSAvoiduser acceptance of sampling method

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The patent creates a virtual copy of the biological sample assessment by using questionnaire responses to predict microbiome status. Instead of requiring actual fecal or plasma samples, the system captures user responses about diet, lifestyle, and health characteristics, then uses machine learning models to generate predictions that mirror what would be obtained from biological sampling, thereby eliminating the need for invasive procedures while maintaining assessment capability

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent replaces the mechanical/biological sampling system with an information-based system. Questionnaire responses about user characteristics substitute for physical sample collection, and computational algorithms replace the laboratory processing steps (DNA extraction, sequencing, bioinformatics analysis), transforming a physically invasive process into a non-invasive digital assessment

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If next generation sequencing and complex bioinformatics analyses are used to process biological samples, then measurement precision is improved, but loss of time and device complexity increase significantly

Engineering Contradiction:

Improvemicrobiome composition detection accuracyVSAvoidsample processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary data collection through questionnaires that capture user characteristics (diet, lifestyle, health status) before any analysis is needed. The machine learning models are pre-trained on existing microbiome datasets, so when a user responds to the questionnaire, the system can immediately generate predictions without requiring time-consuming sample collection, DNA extraction, sequencing, or bioinformatics processing steps

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates a computational model that replicates the functionality of next generation sequencing and bioinformatics analysis. Instead of actually performing these complex laboratory procedures, the patent uses machine learning algorithms trained on sequencing data to predict microbiome status from questionnaire responses, providing a time-efficient copy of the analytical process

Inventive Principle:

Principle #26Copying

3Measurement precision

If next generation sequencing and complex bioinformatics processing are used, then measurement precision is improved, but device complexity and processing requirements increase

Engineering Contradiction:

Improvemicrobe presence and abundance detectionVSAvoidlaboratory processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces complex laboratory equipment and procedures (next generation sequencing machines, DNA extraction apparatus, bioinformatics computing infrastructure) with a simplified information-based system. Questionnaire interfaces and machine learning software substitute for laboratory instruments, transforming a complex wet-lab process into a straightforward digital assessment that can be performed without specialized equipment

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system creates a virtual replica of the microbiome analysis process using computational models. Machine learning algorithms trained on sequencing data replicate the detection and identification functions of next generation sequencing without requiring the actual sequencing equipment or complex bioinformatics pipelines, providing a simplified copy of the analytical capability

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20240006051A1Systems and methods to predict an individuals microbiome status and provide personalized recommendations to maintain or improve the microbiome status

Publication Date: 2024.01.04 SOCIETE DES PRODUITS NESTLE SA

AI summary

The present invention relates to systems and methods for predicting individual microbiome status and for providing personalized recommendations to maintain or improve the microbiome status. In several embodiments of the invention, the individual microbiome features are clustered based on their responses to a questionnaire. In several embodiments, the methods are implemented by a computer system. In several embodiments of the invention, personalized recommendations and dietary advice are given to the individual to maintain or improve said individual's microbiome status.