Taste Profile Prediction Using Genetic Data Analysis
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Solution Overview
Problem
Existing systems for determining user taste preferences are overwhelmed with data and fail to account for changes over time, neglecting genetic variants and individual differences in taste perception.
Innovation Solution
A system and method using a computing device to calculate a user's taste index through a machine learning model that correlates biological extraction data, including genetic information, to predict user taste preferences and detect changes in taste profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing systems analyze individual preferences on a massive scale, then productivity is improved, but measurement precision deteriorates due to data overload and lack of individualization
Solution Approach 1:
The patent segments the analysis into two distinct stages: (1) a fast filtering stage that processes large-scale preference data to identify patterns, and (2) a precision stage that uses genetic data and detailed biological extraction data to accurately determine individual taste preferences. This segmentation allows the system to maintain high productivity in the first stage while achieving high measurement precision in the second stage.
Solution Approach 2:
The patent introduces genetic data and biological extraction data as intermediary elements that bridge the gap between large-scale preference analysis and individualized taste determination. These intermediaries serve as additional data layers that refine the accuracy of taste predictions without compromising the scalability of the overall system.
2Measurement precision
If systems use comprehensive biological extraction data, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent employs a universal machine learning model architecture that can process multiple types of input data (preference data, genetic data, biological extraction data) through the same computational framework. This multi-functional approach allows the system to handle diverse data types without requiring separate complex processing pipelines for each data type, thereby reducing overall system complexity while maintaining high measurement precision.
3Measurement precision
If systems account for genetic variants and individual differences, then measurement precision is improved, but loss of information increases due to data requirements
Solution Approach 1:
The patent extracts and utilizes only the most relevant genetic variants and biological markers that have been scientifically identified as influencing taste preferences. Rather than storing and processing entire genomes or all possible biological data, the system selectively extracts the specific genetic elements and biological parameters that are most predictive of taste preferences, thereby reducing data storage requirements while maintaining high individualization accuracy.
Data Source
AI summary
A system for determining user taste changes using a plurality of biological extraction data and artificial intelligence includes at least a computing device, wherein the computing device is designed and configured to receive, from a user, at least a first element of biological extraction data, calculate at least a first taste index of the user, wherein calculating further comprises training a first machine learning process as a function of training data correlating biological extraction data with taste indices, calculating the at least a first taste index as a function of the first machine learning process and the at least a first element of biological extraction data, generate a taste profile using the first taste index, and determine, using at least a second element of biological extraction data and a second machine learning process, at least a change in user taste profile.