Export Code Classification Tree for Accurate HTS Matching
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Solution Overview
Problem
Existing systems face challenges in efficiently determining export codes for products due to the large number of Harmonized Tariff Schedule (HTS) codes and the variability in product descriptions, leading to inaccuracies and inefficiencies in tracking and classifying goods for export.
Innovation Solution
A tree-like data structure model is used to determine export codes, which is periodically refined using web-scraped information and machine learning to classify products based on images and descriptions, allowing for the generation of synthetic codes and adjustments to the model based on regulatory data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a traditional manual classification system is used to determine export codes, then the system complexity is low, but the classification accuracy and efficiency deteriorate due to the large number of HTS codes and variability in product descriptions
Solution Approach 1:
The patent replaces manual classification processes with an automated machine learning-based system. The model processes product images and descriptions automatically to determine export codes, eliminating the need for manual intervention while achieving high classification accuracy among over 9,000 HTS codes.
Solution Approach 2:
The system creates a digital representation of products through image processing and description analysis. By copying product characteristics into the model's data structure, the system can accurately classify products without physical handling or manual examination, improving both accuracy and efficiency.
2Productivity
If the system manually processes each product description and image, then the accuracy can be maintained, but the time required and labor intensity increase significantly
Solution Approach 1:
The machine learning model performs self-service classification by automatically processing product images and descriptions. The system feeds data into the model, which independently analyzes and determines export codes without human intervention, dramatically increasing processing speed and efficiency.
Solution Approach 2:
The system operates continuously to process export code determinations without interruption. The machine learning model can analyze multiple products simultaneously and continuously update its classifications, ensuring uninterrupted productivity and reducing the time required compared to manual processing.
3Reliability
If the export code determination system is highly accurate, then the reliability of export tracking is improved, but the complexity of the classification model increases
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model continuously learns from exported data and adjusts its classifications. This feedback loop improves reliability by refining the model's accuracy over time while managing complexity through iterative improvement rather than requiring overly complex initial designs.
Solution Approach 2:
The model performs preliminary analysis of product images and descriptions before final classification. By pre-processing and preparing data in advance, the system ensures accurate export code determination while managing model complexity through structured preliminary steps rather than complex single-step processing.
Data Source
AI summary
The export of products from a jurisdiction is enabled by choosing export codes from a model. The electronic model comprises a branched tree defining a configuration of the export codes and selection criteria for the codes. The branched tree includes a plurality of branches and leaves. Each of the leaves includes an export code for a product, a probability that the export code for the product is accurate, and a description of the product. The tree is traversed to obtain the codes. The tree is trained to improve the quality of the selection process.