As the capabilities of Large Language Models (LLMs) continue to advance, the field of patent processing has garnered increased attention within the natural language processing community. However, the majority of research has been concentrated on classification tasks, such as patent categorization and examination, or on short text generation tasks like patent summarization and patent quizzes. In this paper, we introduce a novel and practical task known as Draft2Patent, along with its corresponding D2P benchmark, which challenges LLMs to generate full-length patents averaging 17K tokens based on initial drafts. Patents present a significant challenge to LLMs due to their specialized nature, standardized terminology, and extensive length. We propose a multi-agent framework called AutoPatent which leverages the LLM-based planner agent, writer agents, and examiner agent with PGTree and RRAG to generate lengthy, intricate, and high-quality complete patent documents. The experimental results demonstrate that our AutoPatent framework significantly enhances the ability to generate comprehensive patents across various LLMs. Furthermore, we have discovered that patents generated solely with the AutoPatent framework based on the Qwen2.5-7B model outperform those produced by larger and more powerful LLMs, such as GPT-4o, Qwen2.5-72B, and LLAMA3.1-70B, in both objective metrics and human evaluations. We will make the data and code available upon acceptance at \url{https://github.com/QiYao-Wang/AutoPatent}.

翻译：随着大语言模型（LLM）能力的持续进步，专利处理领域在自然语言处理社区中获得了越来越多的关注。然而，大多数研究集中在分类任务上，例如专利分类与审查，或者集中在短文本生成任务上，如专利摘要和专利问答。在本文中，我们引入了一项新颖且实用的任务，称为Draft2Patent，及其对应的D2P基准。该任务挑战LLM基于初始草稿生成长度平均达17K个标记的全长专利。由于其专业性、标准化术语和巨大篇幅，专利对LLM构成了重大挑战。我们提出了一个名为AutoPatent的多智能体框架，该框架利用基于LLM的规划器智能体、编写器智能体和审查器智能体，结合PGTree和RRAG，生成长篇、复杂且高质量的完整专利文档。实验结果表明，我们的AutoPatent框架显著增强了各种LLM生成全面专利的能力。此外，我们发现，仅基于Qwen2.5-7B模型使用AutoPatent框架生成的专利，在客观指标和人工评估方面，均优于由更大型、更强大的LLM（如GPT-4o、Qwen2.5-72B和LLAMA3.1-70B）生成的专利。我们将在论文被接受后于\url{https://github.com/QiYao-Wang/AutoPatent}提供数据和代码。