# AutoPatent: A Multi-Agent Framework for Automatic Patent Generation

arXiv: https://arxiv.org/abs/2412.09796 · HTML: https://mlsanity.com/paper/2412.09796/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 15 of 15)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [From Persona to Personalization: A Survey on Role-Playing Language Agents](https://mlsanity.com/paper/2404.18231/) (2024)
- [LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs](https://mlsanity.com/paper/2408.07055/) (2024)
- [Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects](https://mlsanity.com/paper/2401.03428/) (2024)
- [Agents' Room: Narrative Generation through Multi-step Collaboration](https://mlsanity.com/paper/2410.02603/) (2024)
- [AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents](https://mlsanity.com/paper/2408.08089/) (2024)
- [Natural Language Processing in the Patent Domain: A Survey](https://mlsanity.com/paper/2403.04105/) (2024)
- [Can Large Language Models Generate High-quality Patent Claims?](https://mlsanity.com/paper/2406.19465/) (2024)
- [LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation](https://mlsanity.com/paper/2407.16252/) (2024)
- [Pap2Pat: Benchmarking Outline-Guided Long-Text Patent Generation with Patent-Paper Pairs](https://mlsanity.com/paper/2410.07009/) (2024)
- [IPEval: A Bilingual Intellectual Property Agency Consultation Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2406.12386/) (2024)
- [Educational-Psychological Dialogue Robot Based on Multi-Agent Collaboration](https://mlsanity.com/paper/2412.03847/) (2024)

## Built on by (most cited first, 16 of 16)

- [OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking](https://mlsanity.com/paper/2501.09751/) (2025)
- [Tiered Agentic Oversight: A Hierarchical Multi-Agent System for Healthcare Safety](https://mlsanity.com/paper/2506.12482/) (2025)
- [Can LLMs Automate Fact-Checking Article Writing?](https://mlsanity.com/paper/2503.17684/) (2025)
- [WikiAutoGen: Towards Multi-Modal Wikipedia-Style Article Generation](https://mlsanity.com/paper/2503.19065/) (2025)
- [Enriching Patent Claim Generation with European Patent Dataset](https://mlsanity.com/paper/2505.12568/) (2025)
- [LLM Agents in Law: Taxonomy, Applications, and Challenges](https://mlsanity.com/paper/2601.06216/) (2026)
- [PILOT-Bench: A Benchmark for Legal Reasoning in the Patent Domain with IRAC-Aligned Classification Tasks](https://mlsanity.com/paper/2601.04758/) (2026)
- [FlowPlan-G2P: A Structured Generation Framework for Transforming Scientific Papers into Patent Descriptions](https://mlsanity.com/paper/2601.02589/) (2026)
- [When Reasoning Hurts Legal Drafting: The Verbalization Bottleneck in Patent Claim Generation](https://mlsanity.com/paper/2607.10480/) (2026)
- [The Wisdom of Agent Crowds: A Human-AI Interaction Innovation Ignition Framework](https://mlsanity.com/paper/2505.06947/) (2025)
- [Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test?](https://mlsanity.com/paper/2507.10576/) (2025)
- [ConvergeWriter: Data-Driven Bottom-Up Article Construction](https://mlsanity.com/paper/2509.12811/) (2025)
- [PatRe: A Full-Stage Office Action and Rebuttal Generation Benchmark for Patent Examination](https://mlsanity.com/paper/2605.03571/) (2026)
- [SurveyAgent-HKA: A multi-agent framework for scientific survey generation with LLMs and human knowledge augmentation](https://mlsanity.com/paper/2609.05938/) (2026)
- [LogicTree-RAG: Logic Tree-guided Retrieval-Augmented Generation for Long-form Patent Drafting](https://mlsanity.com/paper/2609.30943/) (2026)
- [Grounded Revision vs. Prior Injection: Probing Retrieval-Augmented Patent Claim Amendment](https://mlsanity.com/paper/2609.36550/) (2026)

## Abstract (arXiv)

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 urlhttps://github.com/QiYao-Wang/AutoPatent.