A Human-AI collaborative framework for public policy analysis: Fostering dual-chain synergy via LLM-augmented TRIZ

A Human-AI collaborative framework for public policy analysis: Fostering dual-chain synergy via LLM-augmented TRIZ

Journal article

1 volume.

Patent documents are expected to disclose technical information and delineate the scope of exclusive rights. However, the extent to which their structure facilitates the subsequent identification and use of patent information remains insufficiently understood. This study examines how two structural features of patent documents—the length of independent claims and the length of technical specifications—are associated with a citation-based indicator related to patent information use. Using 765,290 Chinese patents granted between 2005 and 2012, we use non-self forward citations as an empirical proxy for the subsequent visibility and use of patent documents. We interpret this measure cautiously: forward citations do not directly measure patent information quality, but they provide an observable signal that a patent has been identified and used in later patenting activity. The results show an inverted U-shaped association between both independent claim length and technical specification length, on the one hand, and the citation-based indicator, on the other. The number of dependent claims strengthens the inverted U-shaped association for independent claim length, whereas backward citations flatten the association for technical specification length. These findings suggest that both overly concise and overly lengthy patent documents may be less conducive to subsequent information use. The study contributes to research on patent information by providing large-scale empirical evidence on how document structure relates to patent information usability, while also highlighting the need for greater methodological transparency in patent-text extraction and analysis.

Crossref

World Patent Information ; 86, September, 2026

World Patent Information

Oxford [England] : Elsevier Ltd., 2026.

English

https://www.sciencedirect.com/science/article/abs/pii/S0172219023000108

External Online Journals > World Patent Information