Year: 2025
Type: conference-paper
Abstract: Information retrieval systems for the patent domain have a long and evolving history, serving as effective tools to support patent experts in a variety of daily tasks.They facilitate patent landscape analysis, help in the drafting and evaluation tasks in the patenting process, and enable efficient information extraction to gain practical insights into new technologies and innovations.Moreover, they assist in identifying existing solutions, knowledge gaps, trends, and persistent challenges within specific technological fields, thereby informing strategic decision-making and innovation management.Advances in machine learning and natural language processing allow to further automate such tasks, e.g.paragraph retrieval, question answering (QA) or patent text generation.The exploration of semantic technologies for the intellectual property (IP) industry is still in its early stages, with significant potential yet to be unlocked.Investigating the use of artificial intelligence (AI) 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, and data from various disciplines and technological areas.With the 6th edition of this workshop we will provide a platform for researchers and industry to discuss recent developments for semantic patent retrieval and analysis employing sophisticated methods ranging from patent text mining, domain-specific information retrieval to large language models (LLMs) targeting next generation applications and use cases for the IP and related domains.
Language: English
Subfield: Management of Technology and Innovation
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4412378277