Enriching Patent Claim Generation with E uropean Patent Dataset (2025)

Abstract: Drafting patent claims is time-intensive, costly, and requires professional skill. Therefore, researchers have investigated large language models (LLMs) to assist inventors in writing claims. However, existing work has largely relied on datasets from the United States Patent and Trademark Office (USPTO). To enlarge research scope regarding various jurisdictions, drafting conventions, and legal standards, we introduce EPD, a European patent dataset. EPD presents rich textual data and structured metadata to support multiple patent-related tasks, including claim generation. This dataset enriches the field in three critical aspects. (1) Jurisdictional diversity: Patents from different offices vary in legal and drafting conventions. EPD fills a critical gap by providing a benchmark of European patents to enable more comprehensive evaluation. (2) Quality improvement: EPD offers high-quality granted patents with finalized and legally approved texts, whereas others consist of patent applications that are unexamined or provisional. Experiments show that LLMs fine-tuned on EPD significantly outperform those trained on previous datasets and even GPT-4o in claim quality and cross-domain generalization. (3) Real-world simulation: We propose a difficult subset of EPD to better reflect real-world challenges. Results reveal that all tested LLMs perform substantially worse on challenging samples, which highlights the need for future research.

## introduction patents grant inventors temporary exclusive rights to prevent competitors from using the invention in return for innovation and its detailed public disclosure @xcite . patent claims define the legal boundaries of an invention, which renders them one of the most critical components of patent 1 https://github.com/scylj1/epd documents @xcite . highquality claims must balance comprehensiveness and enforceability to ensure robust protection while adhering to legal and regulatory standards. the complexity of drafting and revising patent claims makes the process both time-consuming and costly @xcite . we include more detailed background information on patents in appendix a. recent advances in natural language processing (nlp) and large language models (llms) have shown promise in general text generation @xcite and patent-related tasks @xcite . researchers have explored llms to assist inventors and patent professionals in automatically generating well-structured claims. early approaches primarily focused on fine-tuning neural models to generate claims from abstracts @xcite . however, @xcite found that abstract-based claim generation often leads to suboptimal results due to the generic nature of abstracts. consequently, recent work and the increase of context length in llms have enabled description-based claim generation, which can leverage the detailed technical content in patent descriptions to enhance claim completeness and quality @xcite @xcite . although prior studies have made significant progress, they are only based on patents from the united states patent and trademark office (uspto). however, patents from different jurisdictions can vary significantly in linguistic style and legal drafting conventions because of distinct examination practices and legal frameworks. for example, uspto claims emphasize broad coverage, while european claims tend to be more concise and precise, with stricter requirements on clarity and support in the description. these differences not only impact how claims are written and interpreted but also pose challenges for training language models. thus, datasets from different patent offices are essential to comprehensively evaluate llms' performance and robustness across diverse legal and linguistic contexts. this paper introduces epd, a dataset of englishlanguage patents granted by the european patent office (epo) in 2024. compared to existing datasets for patent claim generation, epd offers three major advantages: higher quality: epd contains granted patents directly sourced from the epo to ensure high-quality and legally validated claims. in contrast, as shown in our main contributions are detailed as follows: 1. we present the epd dataset, a collection of 107k european patents with rich textual data and structured metadata to support various patent-related tasks. epd addresses a key gap in the field by enabling cross-jurisdiction evaluation of llms. while our primary focus is on claim generation, we also highlight other potential applications in appendix c. 2. we conduct a comprehensive evaluation of several llms on both the hupd-dcg dataset (from uspto) @xcite and our epd dataset (from epo). results show that llms fine-tuned on epd significantly outperform those trained on hupd-dcg and even gpt-4o across both test sets. it indicates epd's effectiveness in claim generation and cross-domain generalization. 3. we introduce a difficult subset of epd to mimic real-world challenges in claim generation. experimental results reveal a marked performance drop across all tested llms on difficult samples, which highlights the need for future research. ## conclusion we present the epd dataset, a collection of european patents to support various patent-related tasks. compared to existing datasets for patent claim generation, epd offers three major advantages: higher quality, closer to real-world situations, and lower risk of data leakage. epd fills a key gap for cross-jurisdiction evaluation of llms on patent tasks. we conduct extensive experiments on both the hupd-dcg dataset (from uspto) and our epd. results demonstrate that models fine-tuned on epd consistently outperform those trained on hupd-dcg and even gpt-4o across both test sets, which highlights epd's effectiveness in enhancing claim generation quality and cross-domain generalization. additionally, we introduce a difficult subset of epd to simulate real-world claim drafting challenges. our analysis shows that all tested models experience a substantial performance drop on these samples. overall, our findings provide valuable insights into the limitations and opportunities in automated patent claim generation. we hope epd will serve as a foundation for future research toward stronger claim-generation systems.