@inproceedings{wang-etal-2024-patentformer,
    title = "Patentformer: A Novel Method to Automate the Generation of Patent Applications",
    author = "Wang, Juanyan  and
      Mudhiganti, Sai Krishna Reddy  and
      Sharma, Manali",
    editor = "Dernoncourt, Franck  and
      Preo{\c{t}}iuc-Pietro, Daniel  and
      Shimorina, Anastasia",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2024",
    address = "Miami, Florida, US",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-industry.101/",
    doi = "10.18653/v1/2024.emnlp-industry.101",
    pages = "1361--1380",
    abstract = "In recent years, Large Language Models (LLMs) have demonstrated impressive performances across various NLP tasks. However, their potential for automating the task of writing patent documents remains relatively unexplored. To address this gap, in this work, we propose a novel method, Patentformer, for generating patent specification by fine-tuning the generative models with diverse sources of information, e.g., patent claims, drawing text, and brief descriptions of the drawings. To enhance the generative models' comprehension of the complex task of writing patent specification, we introduce a new task, claim+drawing-to-specification, and release a new dataset. We evaluate our proposed method on thousands of patents from the USPTO and show that our method can generate human-like patent specification in legal writing style. Human evaluations by four patent experts further affirm that our proposed method has the potential to generate correct specification, and the quality of generated specification may sometimes be better than the actual specification."
}