@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://aclanthology.org/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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Patentformer: A Novel Method to Automate the Generation of Patent Applications
%A Wang, Juanyan
%A Mudhiganti, Sai Krishna Reddy
%A Sharma, Manali
%Y Dernoncourt, Franck
%Y Preoţiuc-Pietro, Daniel
%Y Shimorina, Anastasia
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, US
%F wang-etal-2024-patentformer
%X 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.
%U https://aclanthology.org/2024.emnlp-industry.101
%P 1361-1380
Markdown (Informal)
[Patentformer: A Novel Method to Automate the Generation of Patent Applications](https://aclanthology.org/2024.emnlp-industry.101) (Wang et al., EMNLP 2024)
ACL