Multilingual patent translation dataset optimization system and method based on large language model

By optimizing a multilingual patent translation dataset based on a large language model, the efficiency and accuracy issues of paragraph alignment and semantic alignment in multilingual patent documents were resolved, achieving efficient and low-cost translation optimization and improving the quality of patent document processing.

CN122174846APending Publication Date: 2026-06-09BEIJING AUGUST MELON TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING AUGUST MELON TECHNOLOGY CO LTD
Filing Date
2026-02-05
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing technologies struggle to efficiently and cost-effectively handle paragraph and semantic alignment in multilingual patent documents, resulting in low translation accuracy and inefficient traditional processing methods.

Method used

A multilingual patent translation dataset optimization system based on a large language model is adopted, including data processing, iterative repair and optimization modules, and optimized dataset output module. The system performs paragraph matching, semantic similarity calculation and text repair through a large language model, supports multi-GPU parallel computing and concurrent API calls, and implements a hierarchical repair strategy and recursive binary search method.

Benefits of technology

It significantly improves the efficiency and quality of patent document processing, ensures the accuracy of paragraph and semantic alignment in multilingual patent documents, and reduces processing costs.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application provides a multilingual patent translation dataset optimization system and method based on a large language model. The system includes a data processing module for obtaining an initial translation dataset of multilingual patent documents, performing quality evaluation on each original text-initial translation pair in the initial translation dataset to obtain a current quality score of each pair; a data filtering module for filtering out original text-initial translation pairs with a current quality score lower than a quality threshold as a to-be-optimized data block according to the quality threshold; an iterative repair optimization module for performing an iterative repair optimization loop on the to-be-optimized data block until its quality score reaches the quality threshold or meets an iteration termination condition; and an optimized dataset output module for outputting data blocks meeting the quality threshold and their corresponding optimized translations as an optimized translation dataset. The application supports multi-GPU parallel computing and concurrent API calling to significantly improve the efficiency and quality of patent document processing.
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