Method and system for automatically classifying medical instrument patents based on deep learning

By constructing a BERT-TextCNN model and using generative data augmentation technology, the problem of mapping medical device patent classification to regulatory categories was solved, achieving efficient and accurate automatic classification and improving the efficiency of regulatory review and industry analysis.

CN122309743APending Publication Date: 2026-06-30SHENYANG PHARMA UNIV
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Patent Information

Application Number
CN202610316191.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-16
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing patent classification systems cannot directly map medical device patents to industry regulatory categories. Traditional methods are time-consuming, labor-intensive, and have limited classification accuracy. Models such as BERT struggle to capture highly discriminative local keywords in medical devices and lack labeled data for regulatory categories.

Method used

We constructed a high-quality automatically labeled dataset, designed a BERT-TextCNN model that integrates global and local features, performed label calibration by combining a large language model with retrieval enhancement generation techniques, generated synthetic data for categories with insufficient sample numbers, and constructed a BERT-TextCNN fusion model for classification.

Benefits of technology

It achieves a precise mapping from patent technology classification to industry regulatory standards, improves classification accuracy and robustness, solves the problem of lack of labeled data in vertical fields, and alleviates the problem of long-tail category identification.

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Abstract

The invention belongs to the technical field of data processing and artificial intelligence, and particularly relates to a medical instrument patent automatic classification method and system based on deep learning, and the method comprises the following steps: obtaining the text data of medical instrument patents, and carrying out the preprocessing operation; introducing a medical instrument classification standard as an external knowledge base, and performing label calibration on the preprocessed text data to form a standard data set; for the category with insufficient sample quantity in the standard data set, generating synthetic data based on the patent text by utilizing a generative model; constructing a deep learning classification model fusing the global semantic features and the local semantic features; and training a deep learning classification model by using the enhanced data set, inputting a to-be-classified medical instrument patent text into the trained model for classification prediction, and outputting a corresponding medical instrument classification standard category. The problems that an existing patent classification system is not matched with an industry supervision standard, and the classification accuracy is low are solved.
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