Attribute Tuple Normalization Using Clustering and LLM Canonical Mapping
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Solution Overview
Problem
Online concierge systems face challenges in automatically normalizing attribute tuples for items from multiple sources, leading to inefficiencies and errors in data processing and comparison due to the lack of effective technical solutions for large datasets.
Innovation Solution
Utilizing an unsupervised clustering algorithm and a large language model (LLM) to group and normalize attribute tuples, enabling automatic normalization and mapping to canonical forms, thereby facilitating consistent attribute representation across different sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual human curation is used to normalize attribute tuples, then normalization accuracy can be maintained, but processing time and operational complexity increase significantly for large datasets
Solution Approach 1:
The patent replaces the manual mechanical process of human curation with an automated machine-learning-based normalization system. The system uses clustering algorithms to group similar attribute tuples and language models to determine canonical forms, eliminating the need for manual human intervention while maintaining normalization quality for large-scale datasets.
Solution Approach 2:
The normalization system performs self-service by automatically processing attribute tuples without human intervention. The machine learning models autonomously cluster attribute tuples, identify canonical forms, and generate normalized outputs, enabling the system to serve itself for large-scale normalization tasks that were previously infeasible manually.
2Reliability
If manual normalization processes are applied, then data quality can be controlled, but scalability to large datasets becomes infeasible
Solution Approach 1:
The patent substitutes manual normalization mechanics with automated machine learning systems that can process large volumes of attribute tuples simultaneously. The clustering and language model components enable parallel processing while maintaining data quality through algorithmic consistency, making scalability to large datasets feasible.
Solution Approach 2:
The system changes the operational parameters from manual processing to automated computational processing. By transitioning to machine-based normalization with configurable clustering parameters and language model settings, the system achieves both high throughput and maintained data quality for large-scale operations.
3Productivity
If automated normalization methods are implemented, then processing speed increases, but normalization accuracy and consistency may deteriorate
Solution Approach 1:
The patent replaces simple automated methods with sophisticated machine learning-based normalization systems. The clustering algorithms and language models provide intelligent decision-making capabilities that maintain accuracy while enabling fast automated processing, avoiding the accuracy deterioration associated with basic automation.
Solution Approach 2:
The normalization system incorporates feedback mechanisms where the language model evaluates clustered attribute tuples and adjusts canonical form selection based on learned patterns. This feedback loop ensures that automated processing maintains high accuracy by continuously optimizing normalization decisions based on data characteristics.
Data Source
AI summary
A computer system uses clustering and a large language model (LLM) to normalize attribute tuples for items stored in a database of an online system. The online system collects attribute tuples, each attribute tuple comprising an attribute type and an attribute value for an item. The online system initially clusters the attribute tuples into a first plurality of clusters. The online system generates prompts for input into the LLM, each prompt including a subset of attribute tuples grouped into a respective cluster of the first plurality. Based on the prompts, the LLM generates a second plurality of clusters, each cluster including one or more attribute tuples that have a common attribute type and a common attribute value. The online system maps each attribute tuple to a respective normalized attribute tuple associated with each cluster. The online system rewrites each attribute tuple in the database to a corresponding normalized attribute tuple.