Dynamic HTS Code Assignment for International Shipments
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Solution Overview
Problem
The process of determining the accurate cost of importing and exporting items, known as the 'landed cost,' is complex due to varying international regulations and classification requirements, often leading to delays and inaccuracies in customs clearance.
Innovation Solution
A system and method that dynamically classify items using a machine learning algorithm to assign the most confident Harmonized Tariff Schedule (HTS) codes based on item descriptions, improving the efficiency of preparing Declarations and calculating duties and taxes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification and documentation preparation is used for international shipments, then flexibility and adaptability to varying regulations are maintained, but the process becomes time-consuming and prone to errors
Solution Approach 1:
The patent replaces manual mechanical classification processes with an automated machine learning system that uses natural language processing to analyze item descriptions and assign HTS codes. This substitution of human effort with automated intelligent systems directly resolves the contradiction by providing both high accuracy through algorithmic consistency and speed through automated processing.
Solution Approach 2:
The patent introduces an intermediary machine learning classification system between the item description and the HTS code assignment. This intermediary layer processes and interprets varied item descriptions using learned patterns from training data, enabling accurate classification without requiring manual intervention while maintaining adaptability to different description formats and regulatory requirements.
2Reliability
If detailed item information is collected and verified before shipping, then classification accuracy improves, but the preparation process becomes more complex and time-consuming
Solution Approach 1:
The patent enables the classification system to self-improve through continuous learning from feedback. The machine learning model automatically adjusts its classification accuracy based on performance metrics and feedback from customs outcomes, eliminating the need for complex manual verification systems while maintaining high reliability through adaptive intelligence.
Solution Approach 2:
The patent changes the parameter of classification from static rule-based matching to dynamic machine learning predictions. By transitioning from fixed classification criteria to adaptive probabilistic models that continuously learn from data, the system achieves high documentation accuracy without requiring complex manual verification procedures.
3Measurement precision
If multiple classification codes are considered and evaluated, then the confidence in code selection improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-training the machine learning model on extensive historical classification data before actual use. This preliminary training phase enables the model to quickly evaluate multiple potential codes during actual classification with high confidence, resolving the contradiction by shifting computational burden from real-time processing to offline training.
Solution Approach 2:
The patent implements dynamic code selection where the system evaluates multiple potential HTS codes with associated confidence scores and selects the most appropriate code based on learned patterns. This dynamic evaluation process adapts to different item types and descriptions, providing accurate code selection without requiring exhaustive analysis of all possible codes.
Data Source
AI summary
Methods, systems, apparatus, and computer program products are provided. In an example embodiment, a communication comprising form fields is received. A form field corresponding to first item information for a first item is identified. A textual item description for the first item is extracted from the form field. A code schedule is accessed and one or more codes are determined for the first item based on the textual item description and the code schedule. An item database is queried to identify second items that are relevant to the first item and a confidence level for each of the codes is determined based on the relevant second items. If a confidence level corresponding to one of the codes is greater than a configurable confidence level, a first code is selected from the codes. An item database is updated to reflect the first code being assigned to the first item.