Information retrieval systems for the patent domain have a long history. They can support patent experts in a variety of daily tasks: from analyzing the patent landscape to support experts in the patenting process and large-scale information extraction.

Advances in machine learning and natural language processing allow to further automate tasks, such as paragraph retrieval or even patent text generation. Uncovering the potential of semantic technologies for the intellectual property (IP) industry is just getting started.

Investigating the use of artificial intelligence methods for the patent domain is therefore not only of academic interest, but also highly relevant for practitioners. Compared to other domains, high quality, semi-structured, annotated data is available in large volumes (a requirement for supervised machine learning models), making training large models easier. On the other hand, domain-specific challenges arise, such as very technical language or legal requirements for patent documents.

The focus of the 4th edition of this workshop will be on two-way communication between industry and academia from all areas of information retrieval in particular with the Asian community. We want to bring together novel research results and the latest systems and methods employed by practitioners in the field.

R. Krestel, H. Aras, L. Andersson, F. Piroi, A. Hanbury, D. Alderucci, 4th Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech2023), SIGIR ’23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (2023) 3483-3486.