Ingredient List NLP Mapping for Automated Item and Quantity Selection
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Solution Overview
Problem
Conventional online systems struggle to create orders from lists containing generic item descriptors, as they often rely on user interaction to map unstructured text to structured data, leading to inefficiencies in item selection and quantity determination.
Innovation Solution
An online system that maps generic item descriptors to specific items by leveraging an item catalog, using trained models and rules to determine item quantities that satisfy the generic descriptors, allowing automated order creation without manual user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional online systems rely on user interaction to map unstructured text to structured data, then item selection accuracy is improved, but order creation efficiency deteriorates
Solution Approach 1:
The system performs self-service by automatically mapping generic item descriptors to specific items using trained machine learning models and rules, eliminating the need for user interaction in the mapping process while maintaining accurate item selection
Solution Approach 2:
The system performs preliminary action by pre-training machine learning models and establishing rules that enable automatic mapping of generic descriptors to specific items, allowing the system to be ready for automated order creation before actual use
2Measurement precision
If manual user input is required for item selection, then mapping accuracy is improved, but user interaction complexity increases
Solution Approach 1:
The system performs self-service by automatically determining item quantities and selecting specific items using trained models and rules, completely eliminating user interaction complexity while maintaining mapping accuracy through automated intelligent processing
3Extent of automation
If automated mapping from generic descriptors to specific items is implemented, then order creation automation is improved, but handling of quantity variations deteriorates
Solution Approach 1:
The system handles parameter changes by using trained machine learning models that can adapt to different quantity specifications and item catalog variations, allowing automated mapping while reliably adjusting to quantity differences between generic descriptors and specific items
Solution Approach 2:
The system uses feedback mechanisms where the trained models learn from the relationship between generic item descriptors, their quantities, and corresponding specific items in the catalog, continuously improving the reliability of quantity matching through iterative training
Data Source
AI summary
An online system receives a list of ingredients and corresponding quantities of each ingredient. Based on an item catalog of specific items offered by a source, the online system retrieves items offered by the source matching the ingredients and selects an item for an ingredient. Because the source may not offer an item in the same quantity specified by the list of items, the online system also maps a quantity of an ingredient in the list to a quantity of the selected item in a unit in which the source offers the corresponding item. The online system may convert a quantity of an ingredient to a quantity of an item through application of one or more rules or through application of one or more trained models to the quantity of the ingredient.