Natural Language Processing in Patents: A Survey
<|reference_start|>Natural Language Processing in Patents: A Survey: Patents, encapsulating crucial technical and legal information, present a rich domain for natural language processing (NLP) applications. As NLP technologies evolve, large language models (LLMs) have demonstrated outstanding capabilities in general text processing and generation tasks. However, the application of LLMs in the patent domain remains under-explored and under-developed due to the complexity of patent processing. Understanding the unique characteristics of patent documents and related research in the patent domain becomes essential for researchers to apply these tools effectively. Therefore, this paper aims to equip NLP researchers with the essential knowledge to navigate this complex domain efficiently. We introduce the relevant fundamental aspects of patents to provide solid background information, particularly for readers unfamiliar with the patent system. In addition, we systematically break down the structural and linguistic characteristics unique to patents and map out how NLP can be leveraged for patent analysis and generation. Moreover, we demonstrate the spectrum of text-based patent-related tasks, including nine patent analysis and four patent generation tasks.<|reference_end|>
@article{jiang2024natural,
    title={Natural Language Processing in Patents: A Survey},
    author={Lekang Jiang, Stephan Goetz},
    journal={arXiv preprint arXiv:2403.04105},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04105},
    primaryClass={cs.AI}
}