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.

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.

Collaborative Tagging is a powerful method to create folksonomies that can be used to grasp/filter user preferences or enhance web search. Recent research has shown that depending on the number of users and the quality of user-provided tags powerful community-driven semantics or "ontologies" can emerge - as it was evident analyzing user data from social web applications such as del.icio.us or Flickr. Unfortunately, most web pages do not contain tags and, thus, no vocabulary that describes the information provided. A common problem in web page annotation is to motivate users for constant participation, i.e. tagging. In this paper we describe our approach of a binary verification game that embeds collaborative tagging into on-line games in order to produce domain specific folksonomies.

This paper presents SmartKom Mobile, the mobile version of the SmartKom system. SmartKom Mobile brings together highly advanced user interaction and mobile computing in a novel way and allows for ubiquitous access to multi-domain information. SmartKom Mobile is device-independent and realizes multi-modal interaction in cars and on mobile devices such as PDAs. With its siblings, SmartKom Home and SmartKom Public, it provides intelligent user interfaces for an extremely broad range of scenarios and environments.