Automated Classification Accuracy Estimation for Electronic Catalogs
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Solution Overview
Problem
Existing methods for calculating the accuracy of classifications in electronic catalogs rely on human auditors to evaluate correctness but do not update inaccurate classifications, leading to inefficiencies and the need for separate audits.
Innovation Solution
The system allocates product groupings to trained and crowd-sourced classification agents, who evaluate and update classifications, with update metrics used to calculate before and after accuracy estimates, eliminating the need for human auditors to evaluate correctness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human auditors are used to evaluate classification correctness, then measurement precision of classification accuracy is improved, but productivity is worsened due to manual evaluation processes
Solution Approach 1:
The patent replaces the mechanical human auditor evaluation system with an automated computer-based system that uses machine learning models and algorithms to evaluate classification accuracy. The system automatically compares product attributes against classification criteria, eliminating manual intervention while maintaining measurement precision through systematic automated assessment procedures.
Solution Approach 2:
The system enables self-service evaluation where the classification system automatically assesses its own accuracy without requiring external human auditors. The automated system performs self-validation by comparing classifications against stored criteria and product data, allowing continuous self-monitoring of classification accuracy.
2Measurement precision
If human auditors evaluate classifications but do not update them, then measurement precision is improved through independent verification, but device complexity increases due to separation of evaluation and update functions
Solution Approach 1:
The patent merges the previously separate evaluation and update functions into a single integrated automated system. The same computer system that evaluates classification accuracy also performs updates when inaccuracies are detected, eliminating the need for separate human auditor roles and simplifying the overall system structure while maintaining verification precision.
Solution Approach 2:
The automated classification system is designed to perform multiple functions: it evaluates classification accuracy, identifies inaccuracies, and updates classifications when needed. This multi-functional approach consolidates what were previously separate tasks into a single universal system, reducing complexity while maintaining measurement precision.
3Loss of time
If periodic sampling is used for accuracy estimation, then loss of time is reduced compared to full audits, but measurement precision deteriorates due to sample limitations
Solution Approach 1:
The system implements continuous feedback loops where classification accuracy is constantly monitored and evaluated. The automated system provides ongoing feedback on classification performance, allowing for real-time adjustments and improvements. This continuous feedback mechanism enables precise accuracy measurement without requiring periodic full audits, as the system continuously validates and refines classifications.
Data Source
AI summary
A technology for determining accuracy estimates for classifications used in an electronic catalog. In one example, classifications for product groupings included in an electronic catalog may be updated as a result of the classifications inaccurately representing products included in the product groupings. The electronic catalog of products may be grouped into a plurality of product groupings using classifications. Classifications of product groupings that inaccurately represent products included in the product grouping may be updated with suggested classifications. Update metrics for updates made to the grouping classifications may be collected and the update metrics may be used to calculate an accuracy estimate for the classifications used in the electronic catalog.