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Multi-model Collaboration and Prompt-driven Patent Classification Methods

Published: 24 October 2024 Publication History

Abstract

Patent classification plays a crucial role in intellectual property management. With the increasing number of patents and the refinement of classification systems, traditional classification methods find it difficult to meet current needs. This paper proposes a multi-model collaboration and prompt-driven patent classification method. By optimizing the structure of prompt tokens, employing a multi-model collaboration strategy, and utilizing the pre-trained knowledge of Large Language Models (LLMs), this method classifies patents to the IPC subclass level. Verified by multiple sets of comparative experiments, this method achieves notable results in small sample photovoltaic patent classification, offering advantages in reducing the requirement for annotated data and training costs, thus providing a feasible approach for patent text classification.

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CAIBDA '24: Proceedings of the 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms
June 2024
1206 pages
ISBN:9798400710247
DOI:10.1145/3690407
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2024

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Author Tags

  1. Large language models
  2. Natural language processing
  3. Patent Classification
  4. Prompt engineering
  5. Single-label

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