Ingredient Data Platform for Label Parsing and Taxonomy Mapping
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Solution Overview
Problem
The complexity of ingredient information on food product labels, including varying formats and unclear terminology, makes it difficult for consumers and manufacturers to compare products and ensure compliance with regulatory requirements, leading to confusion and potential non-compliance.
Innovation Solution
A computing system that automatically captures, processes, and deconstructs label information using a technology stack to assign base attributes and generate master attributes, providing clear and comparable data for consumers and manufacturers, and enabling compliance verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If ingredient information is displayed in detailed format on product labels, then completeness of information is improved, but complexity and difficulty of understanding increases
Solution Approach 1:
The system segments ingredient information into hierarchical levels: full ingredient lists, ingredient categories, and simplified summaries. This allows the same information to be presented in different degrees of detail depending on the user's needs, resolving the contradiction between providing complete information and maintaining simplicity.
Solution Approach 2:
The system changes the parameter of information presentation by offering multiple view modes (detailed, categorical, simplified) for the same ingredient data. This allows users to adjust the level of detail and complexity according to their preferences, maintaining information completeness while controlling presentation complexity.
2Adaptability or versatility
If multiple labeling formats are used across different manufacturers, then adaptability to various product types is improved, but difficulty of comparison between products increases
Solution Approach 1:
The system creates a universal standardized format that can represent multiple labeling formats. By mapping various manufacturer-specific label formats to a common structure with consistent fields and categories, the system enables easy comparison across different products while maintaining adaptability to handle diverse input formats.
Solution Approach 2:
The system introduces an intermediary standardized representation layer between the diverse manufacturer label formats and the user interface. This intermediary format acts as a mediator that normalizes the data from various sources, enabling consistent comparison without requiring changes to the original labeling formats.
3Measurement precision
If comprehensive ingredient analysis is performed, then accuracy of compliance verification is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and normalizing ingredient data during ingestion, organizing it into standardized categories and structures. This preliminary organization enables faster compliance verification later, as the data is already prepared and structured for analysis, reducing the computational burden during actual compliance checking.
Solution Approach 2:
The system segments the comprehensive ingredient analysis into modular processing steps: data extraction, normalization, categorization, and compliance checking. This segmentation allows the system to process only the relevant portions of ingredient data needed for specific compliance requirements, reducing overall processing time while maintaining accuracy.
Data Source
AI summary
A method for parsing information from a plurality of product labels using information technology. The method includes obtaining constituent information with an ingredient data platform from text and graphics found on a portion of a label from a plurality of the product labels and assigning base attributes automatically with the ingredient data platform to each piece of the constituent information on at least one of the product labels. The method includes associating the base attributes assigned by the ingredient data platform with different base attributes in at least one pre-constructed taxonomy data structure handled by the ingredient data platform to establish relationships between the base attributes that were previously assigned with the ingredient data platform and the base attributes from the pre-constructed taxonomy data structure. The method includes assigning a master attribute automatically with the ingredient data platform to a relationship between the base attributes assigned by the ingredient data platform and the associated base attributes in the pre-constructed taxonomy data structure. The method also includes generating at least a portion of a label view containing detail based on the master attribute pertaining to at least one consumer product whose product label lacks information detailed in the portion of the label view.