Food Categorization Engine Using Text Vectors for Granular Classification
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Solution Overview
Problem
Existing food categorization methods rely on manual entry of broad categories, which are rigid and inefficient for a large number of food items, failing to adapt to changes in categorization needs.
Innovation Solution
A food categorization engine that analyzes text descriptions of food items, extracts features, and uses matrix-vector multiplication to automatically assign categories based on probability, allowing for dynamic and granular categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual entry of broad food categories is used, then the categorization process is simple to implement, but the categorization is rigid and cannot efficiently adapt to changes in food categories
Solution Approach 1:
The patent replaces the manual mechanical categorization process with an automated computational system that uses text analysis and machine learning algorithms to automatically assign food categories based on item descriptions, eliminating the need for manual entry and enabling dynamic adaptation to category changes
Solution Approach 2:
The system changes the parameter of categorization from fixed broad categories to dynamic granular categories by analyzing textual features and using probability-based classification, allowing the categorization structure to adapt flexibly to new foods and changing dietary guidelines
2Productivity
If manual categorization of food items is used, then the system is easy to operate, but it requires significant time and effort for a large number of food items
Solution Approach 1:
The system enables self-service categorization by automatically analyzing food item text descriptions and assigning appropriate categories without human intervention, allowing the system to process large numbers of food items efficiently and eliminating time-consuming manual categorization efforts
Solution Approach 2:
The patent substitutes manual categorization labor with automated text analysis and machine learning processes that can rapidly process and categorize large volumes of food items, dramatically improving productivity and reducing the time required for categorization tasks
3Measurement precision
If broad food categories are used, then the categorization system is simple to maintain, but the granularity and accuracy of food tracking is insufficient
Solution Approach 1:
The system transforms the categorization parameter from coarse broad categories to fine-grained specific categories by extracting and analyzing multiple textual features from food item descriptions, enabling precise classification that accurately reflects the granularity and nutritional characteristics of different foods
Solution Approach 2:
The patent segments the categorization process into multiple independent feature analysis components (textual features, nutritional features, dietary features) that can be evaluated separately and combined to produce accurate granular categorization, managing complexity through modular feature processing
Data Source
AI summary
A method of operating a food categorization engine includes extracting features from each text description of a training food data set to generate a feature set. The feature set is analyzed to determine sets of food categorization features that correlate to one or more food categories, wherein the sets of food categorization features are defined by a plurality of category vectors. Individual words and/or characters of a text description of a food item received from a user are then analyzed in order to generate a numerical vector representative thereof. The numerical vector is multiplied by a matrix defined by the plurality of category vectors. One or more food categories with which to associate the received food item are automatically selected based at least in part on the multiplication, and the data record associated with the food item is amended to include the selected food categories.