AI Taste Smell Classification NLP Feature Vectors
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Solution Overview
Problem
Current methods for assessing taste and smell disorders rely on subjective self-report measures that are limited by mood descriptors and interoceptive experiences, which can vary systematically, failing to capture the full range of odors and flavors and may not accurately identify associated health conditions.
Innovation Solution
A computer-implemented method using natural language processing to classify taste and smell perceptions by receiving inputs in multiple languages, generating feature vectors, and training a classification model to categorize individuals based on changes in smell and taste, allowing for the identification of health conditions associated with these disorders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If standardized self-report measures are used to assess taste and smell disorders, then valuable information about subjective states can be obtained, but the sensitivity is limited by mood descriptors and interoceptive experiences that may ignore the effect of a wide range of odors and flavors
Solution Approach 1:
The patent replaces the mechanical system of standardized self-report scales with an artificial intelligence-based natural language processing system. The AI classifier processes free-text descriptions of taste and smell experiences, extracting meaningful information without being constrained by predefined mood descriptors or standardized categories. This substitution enables the system to capture a broader range of odor and flavor effects that traditional scales miss.
2Ease of operation
If standardized self-report scales are used, then assessment can be performed, but the accuracy varies systematically with the type of odor or flavor due to reliance on interoceptive experiences and individual motivation to accurately report
Solution Approach 1:
The patent implements a self-service approach where the AI system automatically processes and analyzes free-text descriptions provided by users without requiring them to navigate complex standardized scales. Users simply describe their taste and smell experiences in natural language, and the AI classifier automatically extracts relevant information, categorizes it, and generates assessments. This eliminates the need for users to have motivation or capacity to accurately map their experiences to standardized descriptors.
3Measurement precision
If natural language processing and AI classification are used to assess taste and smell, then more objective and comprehensive classification can be achieved, but the device complexity increases
Solution Approach 1:
The patent introduces natural language processing as an intermediary layer between user input and the classification model. The NLP component processes free-text descriptions, extracts relevant features, and transforms them into a format suitable for the AI classifier. This intermediary handles the complexity of language understanding, allowing the classification model to focus on pattern recognition and health condition identification, thereby distributing the computational complexity across specialized components.
Data Source
AI summary
Taste and smell classification from multilanguage descriptions can be performed by extracting, by one or more processors using natural language processing, a text including one or more words associated with taste and smell perceptions from an input received from a plurality of users. The input includes multilanguage information regarding at least one of changes in smell and changes in taste perceived by each of the plurality of users. Feature vectors are generated for the text extracted from the input using global vectors, and a distance between the feature vectors and a plurality of reference descriptors associated with taste and smell is calculated for determining a similarity between the text and the reference descriptors and creating a training dataset based on which a classification model is generated for categorizing the plurality of users according to the at least one of changes in smell and changes in taste.