Neural Network HS Code Classification With Reduced Manual Errors
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Solution Overview
Problem
Existing methods for allocating Harmonized System (HS) customs tariff numbers are prone to human error and require extensive manual intervention, leading to inefficiencies and inaccuracies in the import and export processes.
Innovation Solution
A supervised learning-based method using artificial neural networks trained with five clusters to classify items and allocate unique identification numbers, minimizing user intervention and reducing errors through a systematic, arborescent data processing approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection methods are used for HS code allocation, then human judgment and flexibility are maintained, but human error and processing time increase
Solution Approach 1:
The patent replaces the mechanical manual inspection system with an artificial neural network-based automated classification system. The neural network is trained on historical customs data and commodity information to automatically allocate HS codes, eliminating human manual inspection while maintaining or improving accuracy through consistent algorithmic processing of commodity descriptions and characteristics.
Solution Approach 2:
The patent creates a digital model (neural network) that replicates and learns from human experts' classification patterns and decision-making processes. By training the network on extensive historical data representing human judgment, the system copies and automates expert knowledge, enabling fast automated classification that mirrors human accuracy without the time cost of manual inspection.
2Adaptability or versatility
If extensive manual intervention is used in HS code allocation, then complex judgment can be applied, but error susceptibility and processing load increase
Solution Approach 1:
The patent replaces manual intervention with an automated neural network system that consistently applies learned classification rules without human error. The system handles complex commodity classification by processing multiple parameters simultaneously (commodity description, material composition, intended use, etc.) through automated algorithmic decision-making, eliminating susceptibility to human fatigue and inconsistency.
Solution Approach 2:
The patent implements a feedback mechanism where the neural network is continuously trained on historical customs data and classification outcomes. The system learns from past decisions and adjusts its classification algorithms to improve accuracy over time, incorporating feedback from customs authorities and traders to refine its understanding of complex classification scenarios and reduce errors in handling versatile commodity types.
3Productivity
If automated systems are implemented for HS code allocation, then processing speed increases, but accuracy and precision may decrease
Solution Approach 1:
The patent implements an automated neural network system that processes commodity information through multiple layers of artificial neurons, enabling rapid classification while maintaining precision. The system achieves both speed and accuracy by parallel processing of multiple commodity parameters simultaneously, eliminating the sequential nature of manual inspection while preserving decision-making quality through learned patterns from extensive training data.
Solution Approach 2:
The patent performs preliminary training of the neural network on extensive historical customs data and commodity information before deployment. This preliminary action prepares the system with pre-learned classification patterns and decision rules, enabling it to make precise automated determinations without requiring real-time human intervention. The pre-trained model rapidly processes new commodities by applying previously learned knowledge, ensuring both speed and precision in HS code allocation.
Data Source
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AI summary
The invention disclosed hereby generally concerns a method for allocating specific/unique numbers for any suggested item according to, but not limited to, the Harmonized System (HS) which is the internationally valid generalized commodity nomenclature standards, and a system implementing said method for designating said specific/unique numbers to a suggested item or merchandise that is, a machine-learning oriented approach capable of handling given texts in a conceptual manner to produce accurate and precise prediction(s).