7 papers · 1 filter
Benchmarking Patent Drafting from Inventor-Style Disclosures
Lekang Jiang, Wenjun Sun, Stephan Goetz
While recent large language models (LLMs) have achieved promising results on individual patent drafting tasks, they fundamentally fail to investigate the core challenge of real-wor…
When Reasoning Hurts Legal Drafting: The Verbalization Bottleneck in Patent Claim Generation
Lekang Jiang, Wenjun Sun, Stephan Goetz
Patent claim drafting is a challenging legal drafting task that requires technical expertise, precise linguistic control, strict adherence to formal conventions, and the preservati…
Reasoning for Hierarchical Text Classification: The Case of Patents
Lekang Jiang, Wenjun Sun, Stephan Goetz
Hierarchical text classification (HTC) assigns documents to multiple levels of a pre-defined taxonomy. Automated patent subject classification represents one of the hardest HTC sce…
Patent-CR: A Dataset for Patent Claim Revision
Lekang Jiang, Pascal A Scherz, Stephan Goetz
This paper presents Patent-CR, the first dataset created for the patent claim revision task in English. It includes both initial patent applications rejected by patent examiners an…
Can Large Language Models Generate High-quality Patent Claims?
Lekang Jiang, Caiqi Zhang, Pascal A Scherz +1
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…
Enriching Patent Claim Generation with European Patent Dataset
Lekang Jiang, Chengzu Li, Stephan Goetz
Drafting patent claims is time-intensive, costly, and requires professional skill. Therefore, researchers have investigated large language models (LLMs) to assist inventors in writ…