# Can Large Language Models Generate High-quality Patent Claims?

arXiv: https://arxiv.org/abs/2406.19465 · HTML: https://mlsanity.com/paper/2406.19465/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 51 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [The Benefit of Multitask Representation Learning](https://mlsanity.com/paper/1505.06279/) (2015)
- [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://mlsanity.com/paper/1906.03741/) (2019)
- [SaulLM-7B: A pioneering Large Language Model for Law](https://mlsanity.com/paper/2403.03883/) (2024)
- [LUQ: Long-text Uncertainty Quantification for LLMs](https://mlsanity.com/paper/2403.20279/) (2024)
- [Patent Claim Generation by Fine-Tuning OpenAI GPT-2](https://mlsanity.com/paper/1907.02052/) (2019)
- [The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications](https://mlsanity.com/paper/2207.04043/) (2022)
- [Natural Language Processing in the Patent Domain: A Survey](https://mlsanity.com/paper/2403.04105/) (2024)
- [Do We Need Language-Specific Fact-Checking Models? The Case of Chinese](https://mlsanity.com/paper/2401.15498/) (2024)
- [PatFig: Generating Short and Long Captions for Patent Figures](https://mlsanity.com/paper/2309.08379/) (2023)
- [Creating a silver standard for patent simplification](https://mlsanity.com/paper/2310.15689/) (2023)

## Built on by (most cited first, 19 of 19)

- [LoGU: Long-form Generation with Uncertainty Expressions](https://mlsanity.com/paper/2410.14309/) (2024)
- [AutoPatent: A Multi-Agent Framework for Automatic Patent Generation](https://mlsanity.com/paper/2412.09796/) (2024)
- [Towards Better Evaluation for Generated Patent Claims](https://mlsanity.com/paper/2505.11095/) (2025)
- [Enriching Patent Claim Generation with European Patent Dataset](https://mlsanity.com/paper/2505.12568/) (2025)
- [Patent-CR: A Dataset for Patent Claim Revision](https://mlsanity.com/paper/2412.02549/) (2024)
- [Agent Ideate: A Framework for Product Idea Generation from Patents Using Agentic AI](https://mlsanity.com/paper/2507.01717/) (2025)
- [MK2 at PBIG Competition: A Prompt Generation Solution](https://mlsanity.com/paper/2507.08335/) (2025)
- [AutoSpec: An Agentic Framework for Automatically Drafting Patent Specification](https://mlsanity.com/paper/2509.19640/) (2025)
- [PATENTWRITER: A Benchmarking Study for Patent Drafting with LLMs](https://mlsanity.com/paper/2507.22387/) (2025)
- [PatentVision: A multimodal method for drafting patent applications](https://mlsanity.com/paper/2510.09762/) (2025)
- [ToC: Tree-of-Claims Search with Multi-Agent Language Models](https://mlsanity.com/paper/2511.16972/) (2025)
- [Pointer-Augmented Autoregressive Generation of Patent Claims with Joint Topology and Content Decoding](https://mlsanity.com/paper/2607.24040/) (2026)
- [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)
- [Patentformer: A demonstration of AI-assisted automated patent drafting](https://mlsanity.com/paper/2510.09752/) (2025)
- [Self-Knowledge Retrieval Augmented Generation Framework for Patent Matching](https://mlsanity.com/paper/2608.11030/) (2026)
- [Benchmarking Patent Drafting from Inventor-Style Disclosures](https://mlsanity.com/paper/2608.21249/) (2026)
- [BLANC: Discovering Patent White Space via Changes in Normalized Pointwise Mutual Information Between Multi-View Clusters](https://mlsanity.com/paper/2608.26685/) (2026)
- [Vibe Patenting: Evaluating LLM Judges for Professional Patent-Drafting Agents](https://mlsanity.com/paper/2609.13422/) (2026)
- [LogicTree-RAG: Logic Tree-guided Retrieval-Augmented Generation for Long-form Patent Drafting](https://mlsanity.com/paper/2609.30943/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have shown exceptional performance across various text generation tasks but remain under-explored in the patent domain, which offers highly structured and precise language. This paper constructs a dataset to investigate the performance of current LLMs in patent claim generation. Our results demonstrate that generating claims based on patent descriptions outperforms previous research relying on abstracts. Interestingly, current patent-specific LLMs perform much worse than state-of-the-art general LLMs, highlighting the necessity for future research on in-domain LLMs. We also find that LLMs can produce high-quality first independent claims, but their performances markedly decrease for subsequent dependent claims. Moreover, fine-tuning can enhance the completeness of inventions' features, conceptual clarity, and feature linkage. Among the tested LLMs, GPT-4 demonstrates the best performance in comprehensive human evaluations by patent experts, with better feature coverage, conceptual clarity, and technical coherence. Despite these capabilities, comprehensive revision and modification are still necessary to pass rigorous patent scrutiny and ensure legal robustness.