Text Matching Architecture for Consumable Item Databases
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Solution Overview
Problem
Current health and fitness tracking applications face inefficiencies in food consumption logging due to the complexity of matching short, structured text with items from a large set of short, unstructured text data, particularly in restaurant food matching, where lexical and grammatical structure impact accuracy and real-time performance is crucial.
Innovation Solution
A multistage architecture using machine learning techniques, specifically an iterative Markov Decision Process (MDP) for query generation and a Convolutional Neural Network (CNN) for re-ranking, to efficiently match user-entered consumable item descriptions with database entries, optimizing for scalability and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text matching methods are used to match user-entered food descriptions with database entries, then the system is simple to implement, but the matching accuracy and precision are insufficient due to the complexity of short text matching and lexical variations
Solution Approach 1:
The patent segments the text matching process into two distinct stages: (1) an initial filtering stage that retrieves candidate items based on basic text similarity, and (2) a re-ranking stage that applies machine learning models to precisely rank candidates. This segmentation allows the system to achieve high matching accuracy through the ML models while keeping the overall system manageable by separating complex processing from simple retrieval.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between the user's text input and the database entries. These ML models act as mediators that learn the complex relationships and patterns in food descriptions, transforming raw text inputs into accurate matches by capturing lexical, grammatical, and semantic variations that traditional methods miss.
2Reliability
If a large database of consumable items is queried to ensure comprehensive matching, then the coverage and recall are improved, but the query time and processing delay increase
Solution Approach 1:
The patent divides the database querying process into two phases: first, a rapid initial query retrieves a subset of candidate items that potentially match the user input; second, a more time-consuming machine learning re-ranking process is applied only to these candidates. This segmentation ensures comprehensive coverage by searching the entire database initially, while minimizing processing time by applying heavy computation only to a filtered subset.
Solution Approach 2:
The patent applies partial action by performing exhaustive database searching only to the extent necessary for initial candidate retrieval, then using machine learning to achieve the bulk of the matching accuracy work on a smaller set. This avoids applying the full computational power of ML models to the entire database, thus reducing overall processing time while maintaining high recall.
3Measurement precision
If machine learning techniques are applied to improve text matching accuracy, then the precision and recall are enhanced, but the computational overhead and processing time increase
Solution Approach 1:
The patent segments the computational workload by applying machine learning techniques only to a filtered subset of candidate items rather than the entire database. The initial filtering stage uses lightweight methods to reduce the candidate set, and then ML models are applied only to these reduced candidates, significantly reducing computational overhead while maintaining high precision.
Solution Approach 2:
The patent uses partial action by applying machine learning re-ranking only to the extent necessary to differentiate among top candidates. The system performs sufficient ML processing to achieve high matching precision but avoids excessive computation by limiting the re-ranking to a manageable number of candidates identified in the initial filtering stage.
4Measurement precision
If the system processes and analyzes detailed food descriptions in real-time, then the matching accuracy is improved, but the processing speed and responsiveness decrease
Solution Approach 1:
The patent segments the processing pipeline into a fast initial retrieval phase that provides immediate responses with basic matching, followed by a more detailed ML-based re-ranking phase that enhances accuracy. This segmentation allows the system to maintain real-time responsiveness through the fast initial phase while improving accuracy through the subsequent ML processing of top candidates.
Solution Approach 2:
The patent applies partial processing by performing detailed analysis only on the most promising candidates identified in the initial filtering stage. Rather than analyzing all database entries in detail, the system applies comprehensive ML analysis only to a small subset of top candidates, thus maintaining processing speed while achieving high matching accuracy where it matters most.
Data Source
AI summary
System and method for associating user-entered consumable item description to an entry in a consumable item database. In one embodiment, formally structured restaurant menu item is matched to a large database of food items that has been constructed via crowd-sourcing. A novel, practical, and scalable machine learning solution architecture, consisting of two major steps is utilized. First a query generation approach is applied, based on a Markov Decision Process algorithm, to reduce the time complexity of searching for matching candidates. That is then followed by a re-ranking step, using deep learning techniques, to ensure matching quality goals are met.