Enterprise–Patent Matching Through Multi-Source Profiles and LLM Reranking

Abstract

Technology-transfer organizations need effective approaches for matching enterprise technological needs with potentially relevant patents. When a formal technology-demand statement is unavailable, however, enterprise technological demand is only partially observable from organizational records produced for different purposes. This study therefore focuses on enterprise patent, recruitment, and product information as three indirect and differently oriented sources of technological information, and examines their use in enterprise–patent matching without treating these records as verified technological demand. The main purpose of the study is to determine how source composition, matching methods, and review depth affect the retrospective recovery of observed patent assignment or licensing links. To address this purpose, source-specific enterprise profiles are constructed from the three information sources, combined for dense retrieval against a fixed patent corpus, and subsequently reviewed and reranked using a large language model (LLM). The results show that patent-derived profiles provide the strongest retrieval signal under the present representation, while jointly incorporating recruitment and product information provides only limited additional retrieval value. LLM-assisted review can change the ordering of retrieved candidates, particularly at shallower review depth, but the observed differences do not establish a statistically reliable performance advantage over embedding-only retrieval. Scientifically, these findings clarify the respective roles of heterogeneous enterprise information and LLM-assisted reranking in enterprise–patent matching, while also showing that retrieval performance based on organizational traces should not be interpreted directly as measurement of enterprise technological demand. Future research should further examine source-specific contributions, control for shared patent vocabulary, and evaluate the framework using time-aligned, external, and prospective data.