AutoPatent: A Multi-Agent Framework for Automatic Patent Generation

TL;DR

AutoPatent introduces a multi-agent framework that significantly improves the generation of full-length, high-quality patents using LLMs, outperforming larger models in both objective and human evaluations.

Contribution

The paper presents a novel multi-agent framework for patent generation, addressing the challenge of creating lengthy, specialized patent documents with LLMs.

Findings

AutoPatent enhances patent generation quality across various LLMs.

Patents generated by AutoPatent outperform larger models like GPT-4o in evaluations.

The framework demonstrates the feasibility of high-quality patent synthesis with smaller models.

Abstract

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}.