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, question answering (QA) 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.

With the 5th edition of this workshop we will provide a platform for researchers and industry to learn about novel and emerging technologies for semantic patent retrieval and big analytics employing sophisticated methods ranging from patent text mining, domain-specific information retrieval to large language models targeting next generation applications and use cases for the IP and related domains.

R. Krestel, H. Aras, L. Andersson, F. Piroi, A. Hanbury, D. Alderucci, 5th Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech2024), Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (2024), 3021-3024, DOI: 10.1145/3626772.3657986.