Enriching Patent Claim Generation with European Patent Dataset

• URL: http://arxiv.org/abs/2505.12568v1
• Date: Sun, 18 May 2025 23:04:49 GMT
• Title: Enriching Patent Claim Generation with European Patent Dataset
• Authors: Lekang Jiang, Chengzu Li, Stephan Goetz,
• Abstract summary: We introduce EPD, a European patent dataset. EPD presents rich textual data and structured metadata to support patent-related tasks, including claim generation.<n>EPD fills a critical gap by providing a benchmark for European patents to enable more comprehensive evaluation.<n>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.
• Score: 2.0270237738043906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 for 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 of claim generation. Results reveal that all tested LLMs perform substantially worse on these challenging samples, which highlights the need for future research.

Related papers

• Towards Better Evaluation for Generated Patent Claims [0.0]
  We introduce Patent-CE, the first comprehensive benchmark for evaluating patent claims.<n>We also propose PatClaimEval, a novel multi-dimensional evaluation method specifically designed for patent claims.<n>This research provides the groundwork for more accurate evaluations of automated patent claim generation systems.
  arXiv  Detail & Related papers  (2025-05-16T10:27:16Z)
• Can AI Examine Novelty of Patents?: Novelty Evaluation Based on the Correspondence between Patent Claim and Prior Art [5.655276956391884]
  This paper introduces a novel challenge by evaluating the ability of large language models (LLMs) to assess patent novelty.<n>We present the first dataset specifically designed for novelty evaluation, derived from real patent examination cases.<n>Our study reveals that while classification models struggle to effectively assess novelty, generative models make predictions with a reasonable level of accuracy.
  arXiv  Detail & Related papers  (2025-02-10T10:09:29Z)
• Patent-CR: A Dataset for Patent Claim Revision [0.0]
  This paper presents Patent-CR, the first dataset created for the patent claim revision task in English.<n>It includes both initial patent applications rejected by patent examiners and the final granted versions.
  arXiv  Detail & Related papers  (2024-12-03T16:43:42Z)
• PatentEdits: Framing Patent Novelty as Textual Entailment [62.8514393375952]
  We introduce the PatentEdits dataset, which contains 105K examples of successful revisions. We design algorithms to label edits sentence by sentence, then establish how well these edits can be predicted with large language models. We demonstrate that evaluating textual entailment between cited references and draft sentences is especially effective in predicting which inventive claims remained unchanged or are novel in relation to prior art.
  arXiv  Detail & Related papers  (2024-11-20T17:23:40Z)
• Enhancing Legal Case Retrieval via Scaling High-quality Synthetic Query-Candidate Pairs [67.54302101989542]
  Legal case retrieval aims to provide similar cases as references for a given fact description. Existing works mainly focus on case-to-case retrieval using lengthy queries. Data scale is insufficient to satisfy the training requirements of existing data-hungry neural models.
  arXiv  Detail & Related papers  (2024-10-09T06:26:39Z)
• Can Large Language Models Generate High-quality Patent Claims? [0.7535435234862548]
  Large language models (LLMs) have shown exceptional performance across various text generation tasks. This paper constructs a dataset to investigate the performance of current LLMs in patent claim generation.
  arXiv  Detail & Related papers  (2024-06-27T18:07:40Z)
• InternLM-Law: An Open Source Chinese Legal Large Language Model [72.2589401309848]
  InternLM-Law is a specialized LLM tailored for addressing diverse legal queries related to Chinese laws. We meticulously construct a dataset in the Chinese legal domain, encompassing over 1 million queries. InternLM-Law achieves the highest average performance on LawBench, outperforming state-of-the-art models, including GPT-4, on 13 out of 20 subtasks.
  arXiv  Detail & Related papers  (2024-06-21T06:19:03Z)
• Unveiling Black-boxes: Explainable Deep Learning Models for Patent Classification [48.5140223214582]
  State-of-the-art methods for multi-label patent classification rely on deep opaque neural networks (DNNs) We propose a novel deep explainable patent classification framework by introducing layer-wise relevance propagation (LRP) Considering the relevance score, we then generate explanations by visualizing relevant words for the predicted patent class.
  arXiv  Detail & Related papers  (2023-10-31T14:11:37Z)
• Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification [26.85734804493925]
  We propose an integrated framework that comprehensively considers the information on patents for patent classification. We first present an IPC codes correlations learning module to derive their semantic representations. Finally, we combine the contextual information of patent texts that contains the semantics of IPC codes, and assignees' sequential preferences to make predictions.
  arXiv  Detail & Related papers  (2023-08-10T07:02:24Z)
• WiCE: Real-World Entailment for Claims in Wikipedia [63.234352061821625]
  We propose WiCE, a new fine-grained textual entailment dataset built on natural claim and evidence pairs extracted from Wikipedia. In addition to standard claim-level entailment, WiCE provides entailment judgments over sub-sentence units of the claim. We show that real claims in our dataset involve challenging verification and retrieval problems that existing models fail to address.
  arXiv  Detail & Related papers  (2023-03-02T17:45:32Z)
• The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications [8.110699646062384]
  We introduce the Harvard USPTO Patent dataset (HUPD) With more than 4.5 million patent documents, HUPD is two to three times larger than comparable corpora. By providing each application's metadata along with all of its text fields, the dataset enables researchers to perform new sets of NLP tasks.
  arXiv  Detail & Related papers  (2022-07-08T17:57:15Z)
• GERE: Generative Evidence Retrieval for Fact Verification [57.78768817972026]
  We propose GERE, the first system that retrieves evidences in a generative fashion. The experimental results on the FEVER dataset show that GERE achieves significant improvements over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-04-12T03:49:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.