NLP Entity Resolution for Customs Record Categorization
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Solution Overview
Problem
Existing data analysis systems face challenges in efficiently categorizing and analyzing large datasets, especially those with varied languages and inconsistent terminology, making it difficult to accurately classify and analyze customs transaction records for international shipments.
Innovation Solution
The implementation of natural language processing (NLP) in a self-learning environment, using statistical language understanding techniques and similarity-matching algorithms, to facilitate language-agnostic processing of free text fields in customs records, enabling prediction and categorization of shipments into Harmonized Tariff Schedule (HTS) categories without preconfigured analysis rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data analysis systems are used to categorize customs records, then the system structure is simple, but the system cannot handle varied languages and inconsistent terminology effectively, leading to poor categorization accuracy
Solution Approach 1:
The patent introduces natural language processing algorithms as an intermediary layer between the raw customs records and the categorization system. These NLP algorithms process and normalize the text data, handling language variations and inconsistent terminology before the data reaches the categorization logic, thereby improving accuracy without requiring complex rule-based systems
Solution Approach 2:
The system employs self-learning capabilities where the NLP algorithms automatically adapt to different languages and terminology patterns encountered in the data. The system learns from the data itself rather than requiring pre-configured analysis rules for each language or terminology variation, enabling it to handle diverse customs records autonomously
2Adaptability or versatility
If preconfigured analysis rules are used for categorization, then the system is easy to operate, but it cannot adapt to language variations and inconsistent terminology across different datasets
Solution Approach 1:
The patent implements dynamic adaptability where the NLP algorithms continuously learn and adjust to new languages and terminology patterns encountered in the data stream. The system evolves its categorization capabilities based on the actual data it processes, rather than relying on static preconfigured rules, enabling it to handle emerging language variations and terminology inconsistencies
Solution Approach 2:
The NLP-based categorization system serves multiple functions: it handles text normalization, language translation, terminology standardization, and categorization simultaneously. This multi-functional approach allows a single system to adapt to various languages and terminology styles without requiring separate configuration for each language or domain
3Measurement precision
If keyword matching is used for searching customs records, then the search process is simple, but it produces inaccurate results due to language differences and terminology variations
Solution Approach 1:
The patent replaces the mechanical keyword matching approach with natural language processing algorithms that understand language semantics and variations. The NLP system transforms and normalizes search queries and record texts into a common linguistic representation, enabling accurate matching across different languages and terminology styles without sacrificing search efficiency
Data Source
AI summary
An apparatus includes a data access circuit that interprets data records, each having a number of data fields, a record parsing circuit that determines a number of n-grams from terms of each of the data records and maps the number of n-grams to a corresponding number of mathematical vectors, and a record association circuit that determines whether a similarity value between a first mathematical vector for the first data record and a second mathematical vector for the second data record is greater than a threshold similarity value, and associates the first and second data records in response to the similarity value exceeding the threshold similarity value. An example apparatus includes a reporting circuit that provides a catalog entity identifier, associates each of the first term and the second term to the catalog entity identifier, and provides a summary of activity for an entity.