Data Matching System Using Client Tables for Catalog Variations

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Solution Overview

Problem

Existing data processing systems face challenges in accurately matching data elements with local variations and human errors, leading to incorrect matches due to evolving product catalogs and different representations of items, requiring continuous training of data entry personnel.

Innovation Solution

A data matching system that classifies clients into learning and post-learning phases, using a two-step process to build and utilize client matching tables, which searches the master catalog for input requests based on similarity and confidence scores to ensure precise matching.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If data entry personnel are trained to use local product identifiers, then local data entry accuracy is improved, but the system cannot adapt when the catalog evolves and creates mismatches

Engineering Contradiction:

Improvedata entry accuracyVSAvoidcatalog evolution adaptability

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The system performs self-learning by automatically analyzing input requests and master catalog data to build and update client matching tables without requiring retraining of personnel. The system serves itself by autonomously adapting to catalog changes through the learning phase and post-learning phase mechanisms.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary actions by building client matching tables in advance during the learning phase, so that when catalog changes occur, the matching tables are already prepared to handle the new variations. This preliminary preparation eliminates the need for reactive retraining.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If constant training of data entry personnel is implemented, then matching accuracy is improved, but time loss and operational disruption increase

Engineering Contradiction:

Improvematching accuracyVSAvoidtraining time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces the mechanical system of human training with an automated computational system. Instead of training personnel through repeated instruction, the system uses algorithms to automatically learn and adapt matching rules, substituting human cognitive processes with machine learning processes.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

Inventive Principle:

Principle #25Self-service

3Stability of the object's composition

If a single master catalog is maintained, then data consistency is improved, but the system cannot accommodate local variations in data representation

Engineering Contradiction:

Improvecatalog consistencyVSAvoidlocal variation accommodation

Core Design Contradiction:

Stability of the object's compositionVSAdaptability or versatility

Solution Approach 1:

The system segments the matching logic into two layers: a single master catalog that maintains global consistency, and client-specific matching tables that handle local variations. This segmentation allows the master catalog to remain stable while accommodating diverse local representations through separate matching tables for each client.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system applies local quality by allowing each client to have customized matching tables tailored to their specific data representation variations, while the master catalog maintains uniform global standards. Each client's matching table has specialized properties to handle their local needs.

Inventive Principle:

Principle #3Local quality

4Productivity

If automated matching systems are implemented, then productivity is improved, but matching precision decreases due to inability to handle local variations

Engineering Contradiction:

Improvedata processing speedVSAvoidmatching accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system segments the matching process into automated global matching (master catalog) and semi-automated local matching (client matching tables). This segmentation enables high-speed automated processing for standard cases while maintaining precision for local variations through the learning phase that adapts to client-specific patterns.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentEP3704596B1Complex catalog match system

Publication Date: 2025.01.15 COOPERVISION INT LTD

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AI summary

Systems and techniques are provided for a data matching system having a plurality of clients and a master catalog. In a learning phase of a client, the data matching system builds a client matching table for the client and matches the input request to a specific entry in a particular set of entries in the master catalog. In a post-learning phase of the client, the data matching system uses the client matching table to match the input request to a specific entry in a particular set of entries in the master catalog. In a specific implementation, the data matching system uses a two- step match to build the client matching table. In a first step, a plurality of a set of entries in the master catalog is selected for the input request. In a second step, a particular set of entries is selected using a confidence score.