Algorithmic Wine Generation via Chemical Profile Machine Learning
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Solution Overview
Problem
Conventional methods for recommending consumable items like wine are subjective and qualitative, relying on expert tasting, which is inherently variable and limited to physically existing wines, lacking precision and objectivity.
Innovation Solution
A platform utilizing machine learning processes, including chemical profiling, Gaussian Mixture Models, deep learning, and dynamic time warping, to analyze and standardize chemical data from consumables, enabling algorithmic wine generation and personalized recommendations based on user taste profiles and chemical attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert tasting is used to evaluate wine characteristics, then qualitative assessment is achieved, but objectivity and precision are compromised
Solution Approach 1:
The patent replaces the mechanical system of human expert tasting with a machine learning system that processes chemical profile data. The ML model objectively analyzes chemical compositions and generates wine recommendations without human subjectivity, thereby improving measurement precision while maintaining automation.
Solution Approach 2:
The patent introduces chemical profile data as an intermediary between the wine and the evaluation system. Instead of direct human tasting, the ML model analyzes chemical compositions (intermediary data) to make objective assessments, resolving the contradiction between precision and automation.
2Measurement precision
If chemical profiling with machine learning is implemented, then objectivity and precision are improved, but system complexity increases
Solution Approach 1:
The patent segments the wine evaluation system into distinct functional modules: chemical profiling module, machine learning model module, and recommendation generation module. This segmentation manages complexity by breaking down the complex system into manageable, independent components that can be developed and maintained separately.
Solution Approach 2:
The patent uses chemical profile data as a copy or representation of the wine's physical properties. Instead of directly analyzing complex wine characteristics, the system works with standardized chemical composition data (a simplified copy), reducing system complexity while maintaining recommendation accuracy.
3Productivity
If algorithmic wine generation is pursued, then productivity is enhanced, but manufacturing precision requirements increase
Solution Approach 1:
The patent applies preliminary action by using machine learning to predict optimal chemical compositions and blending ratios before actual wine production. The system analyzes historical chemical data and generates target profiles, allowing producers to prepare precise formulations in advance, thereby enhancing productivity while maintaining manufacturing precision.
Solution Approach 2:
The patent utilizes parameter changes by adjusting chemical composition parameters (acidity, sugar content, alcohol level) based on ML model predictions. The system identifies optimal parameter ranges for different wine styles, enabling precise control during production while streamlining the overall manufacturing process.
Data Source
AI summary
Embodiments of the present invention relate to a platform for controlling production and distribution of consumable items based on machine learning processes derived from chemical profiles of the consumable items.