Machine Learning Classifier for Product Tolerability Scoring
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Solution Overview
Problem
Consumers face challenges in selecting products due to the overwhelming number of options and lack of information about how these products will affect their bodies, particularly in terms of tolerability.
Innovation Solution
A system and method utilizing a computing device to receive user complaints, select relevant products, retrieve biological data, generate a classifier using machine-learning models trained on biological extractions and product data, and output a tolerability score for the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If consumers are provided with more product options, then product variety increases, but consumer decision-making becomes more difficult and overwhelming
Solution Approach 1:
The system uses machine learning models to analyze user biological data and provide personalized feedback about product tolerability. The classifier processes biological extractions and returns predictive scores that guide consumers toward suitable products, creating a feedback loop that simplifies decision-making while maintaining product variety.
Solution Approach 2:
The patent introduces an intermediary system (the machine learning classifier) between the consumer and the product selection process. This intermediary analyzes biological data and provides objective tolerability assessments, mediating the complex interaction between product options and consumer needs to reduce decision-making burden.
2Loss of information
If product information is expanded to include biological effects, then product understanding improves, but information complexity and processing requirements increase
Solution Approach 1:
The system extracts only the most relevant biological data features needed for tolerability prediction, rather than processing all possible biological information. The machine learning model identifies and processes key biomarkers and biological extractions that are most predictive of product tolerability, reducing information complexity while maintaining understanding.
Solution Approach 2:
The patent transforms complex biological data into simplified predictive parameters (tolerability scores). The machine learning classifier converts detailed biological extractions into standardized numerical outputs that are easy to interpret, changing the parameter representation from complex biological data to simple predictive scores.
3Reliability
If personalized product recommendations are generated using biological data, then product suitability improves, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary analysis by pre-processing and storing biological data in structured formats (biological extractions) that can be quickly queried. The machine learning model is pre-trained on comprehensive datasets, allowing it to rapidly generate tolerability predictions without requiring extensive processing time during actual product recommendations.
Solution Approach 2:
The patent creates simplified representations (copies) of complex biological data through biological extractions. These extracted features serve as compressed versions of the full biological dataset, enabling fast processing while retaining the essential information needed for accurate tolerability prediction.
Data Source
AI summary
A system for informing product decisions, the system including a computing device configured to receive a conditional complaint relating to a user; select an article of interest intended to correct the conditional complaint; retrieve a biological extraction relating to the user; generate, a classifier, wherein the classifier comprises a machine-learning model trained by training data including a plurality of biological extractions and a plurality of correlated articles of interest, and wherein the classifier is configured to receive the user biological extraction as an input and output a tolerability score as a function of the training data; and display the tolerability score.