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.

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.

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.

Argumentative Zoning (AZ) is a tool to obtain informative summaries of scientific articles. Using AZ assumes the definition of the main rhetorical structure in scientific articles, which are, then, used for the summary creation. The unavailability of large AZ annotated benchmark datasets is a bottleneck to training AZ-based summarization algorithms. In this work, we present an annotation platform for an AZ that defines four categories (zones), Claim, Method, Result and Conclusion, that are used to label sentences selected from scientific articles. The proposed tool can be used both for collecting benchmark datasets, and to help the researchers to create their own sub-corpora.

Steadily increasing numbers of patent applications per year and large amounts of available patent data necessitate highly efficient and interactive next-generation information retrieval systems in the patent domain. AI and Machine Learning (ML) methods such as Deep Learning (DL) are successfully adopted in many domains, so patent researchers and practitioners start to employ AI-based approaches as well, to support experts in the patenting process or to automate patent analysis and retrieval processes. AI-enhanced Information Retrieval systems can improve patent search and analysis but also require millions of annotated sample data for training the ML models. When working with patent data, particular challenges arise that call for adaption of existing IR and AI methods as well as development of novel approaches suited for the patent domain. The focus of the 3rd edition of this workshop will be on two-way communication between industry and academia from all areas of Information Retrieval, such as Natural Language Processing (NLP), Text and Data Mining (TDM), and Semantic Technologies (ST). We want to bring together novel research results and the latest systems and methods employed by the Intellectual Property (IP) industry.

Information retrieval plays a crucial role in the patent domain. With the success of deep learning (DL) in other domains, patent practitioners and researchers are increasingly developing DL-based approaches to support experts in the patenting process or to automate processes for patent analysis. AI-enhanced information retrieval systems can improve patent search but also require lots of annotated data. When working with patent data, particular challenges arise that call for adaption and novel approaches of general IR and AI methods. with this workshop series we want to establish a two-way communication channel between industry and academia from relevant fields in information retrieval, such as natural language processing (NLP), text and data mining (TDM), and semantic technologies (ST), in order to explore and transfer new knowledge, methods and technologies for the benefit of industrial applications as well as support interdisciplinary research in applied sciences forthe intellectual property (IP) and neighbouring domains.

Patent text is a mixture of legal terms and domain specific terms. In technical English text, a multi-word unit method is often deployed as a word formation strategy in order to expand the working vocabulary, i.e. introducing a new concept without the invention of an entirely new word. In this paper we explore query generation using natural language processing technologies in order to capture domain specific concepts represented as multi-word units. In this paper we examine a range of query generation methods using both linguistic and statistical information. We also propose a new method to identify domain specific terms from other more general phrases. We apply a machine learning approach using domain knowledge and corpus linguistic information in order to learn domain specific terms in relation to phrases' Termhood values. The experiments are conducted on the English part of the CLEF-IP 2013 test collection. The outcome of the experiments shows that the favoured method in terms of PRES and recall is when a language model is used and search terms are extracted with a part-of-speech tagger and a noun phrase chunker. With our proposed methods we improve each evaluation metric significantly compared to the existing state-of-the-art for the CLEP-IP 2013 test collection: for [email protected] by 26% (0.544 from 0.433), for [email protected] by 17% (0.631 from 0.540) and on document MAP by 57% (0.300 from 0.191).

Patent prior art queries are full patent applications which are much longer than standard web search topics. Such queries are composed of hundreds of terms and do not represent a focused information need. One way to make the queries more focused is to select a group of key terms as representatives. Existing works show that such a selection to reduce patent queries is a challenging task mainly because of the presence of ambiguous terms. Given this setup, we present a query modeling approach where we utilize patent-specific characteristics to generate more precise queries. We propose to automatically disambiguate query terms by employing noun phrases that are extracted using the global analysis of the patent collection. We further introduce a method for predicting whether expansion using noun phrases would improve the retrieval effectiveness.