Automated HS Code Assignment System Using Multi-Attribute Matching
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Solution Overview
Problem
The process of matching objects to assign tariff and duty rates is slow and error-prone, requiring efficient methods to compare and classify items based on various attributes.
Innovation Solution
A Harmonized System (HS) code assignment system that combines supplier quotes with a classification database, using object matching processes, image analysis, and gap analysis to assign HS codes by comparing item attributes such as UPC, vendor stock numbers, materials, and images, and determining similarity percentages to classify new items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual matching processes are used to assign tariff and duty rates, then flexibility in handling complex classification decisions is maintained, but the process becomes slow and error-prone
Solution Approach 1:
The patent introduces an automated object matching system that acts as an intermediary between new items and the classification database. The system uses multiple matching criteria (UPC codes, vendor stock numbers, images, materials, descriptions) and gap analysis to objectively determine HS codes, eliminating manual errors while maintaining classification accuracy through structured comparison protocols.
Solution Approach 2:
The patent replaces the manual mechanical matching process with an automated computer-based system. The system uses image analysis, data comparison algorithms, and automated gap analysis to perform classifications that were previously done manually, significantly increasing speed while maintaining or improving accuracy through consistent application of matching criteria.
2Measurement precision
If multiple matching criteria are used to improve classification accuracy, then assignment precision increases, but system complexity increases
Solution Approach 1:
The patent segments the matching process into distinct criteria: UPC code matching, vendor stock number matching, image analysis, material composition comparison, and description analysis. Each criterion is evaluated separately and contributes to an overall similarity percentage, making the complex system manageable through modular evaluation of individual attributes.
Solution Approach 2:
The patent adds multiple dimensions to the matching process by incorporating various attributes (UPC, stock numbers, images, materials, descriptions) rather than relying on a single criterion. This multi-dimensional approach increases precision by evaluating items from multiple angles, with each dimension contributing to the overall similarity assessment.
3Productivity
If automated matching systems are implemented to increase processing speed, then productivity improves, but error rates may increase
Solution Approach 1:
The patent incorporates gap analysis as a feedback mechanism that compares the new item against the classification database and provides objective similarity percentages. This feedback loop ensures automated decisions are based on quantifiable metrics rather than random errors, maintaining reliability while enabling high-speed processing through algorithmic consistency.
Solution Approach 2:
The patent uses parameter changes by converting qualitative classification decisions into quantitative similarity percentages. By measuring matching criteria numerically (e.g., image similarity scores, material composition matches), the system enables automated processing with measurable accuracy, reducing errors through objective parameter-based decision-making.
Data Source
AI summary
Described in detail herein are systems and methods for HS code assignment. The system includes a data storage system that combines suppliers' quotes of items along with a classification database. The supplier quote can include tariff codes for all import items. The classification database can include all previously classified items. A first set of information associated with a new item is input into the system. The system can attempt to match the new item to a previously classified item based on the first set of information. If the system is unable to match the new item based on the first set of information the system can retrieve a second set of information including, the department/category to which the new item is assigned, along with the materials the new item is made up of and a description of the new item.