Special issue on applications of Generative AI and Large Language Models in the patent domain

Special issue on applications of Generative AI and Large Language Models in the patent domain

Journal article

1 volume.

Generative AI and Large Language Models (LLMs) are significantly influencing a wide range of domains. These models have made it possible for a vast array of researchers, professionals, and the general public to undertake an impressive scope of knowledge-based tasks. By efficiently identifying pertinent information and generating content in a clear and accessible format, LLMs significantly reduces cognitive effort and fosters creativity. As a result, LLMs are presently being applied in many diverse domains. Within these, the patent domain is a key area where LLMs could potentially transform how inventors invent and how patent professionals practice going forward. With a forward-looking perspective, this Special Issue (SI) aims to assemble a collection of high-quality papers centered on the applications and impacts of Generative AI and LLMs in the patent domain. This Special Issue aims to explore the applications of Generative AI and Large Language Models in the patent domain. Topics of interest include, but are not limited to, the following: • AI-assisted inventions and patent drafting • Prior art search based on LLMs and vector-based algorithms • Human-in-the-loop approaches in the design and application of LLMs • Retrieval-Augmented Generation (RAG) for patent text generation • Use of LLMs for foundational NLP tasks, such as classification, summarization, question answering, named entity recognition, machine translation, etc. • Use of LLMs for industrial applications, such as technology landscaping, identifying licensing opportunities, invention disclosure analysis, technology forecasting, analyzing SEPs (Standard-Essential Patents), etc. • Developments of multilingual LLMs in the patent domain • Developments of multimodal LLMs for processing patent drawings and text • Patent claim analysis and proofreading • Datasets for training and evaluating domain-specific models • Use of LLMs for generating synthetic data to reduce annotation effort • Adoptions of Generative AI and LLMs towards improving IP search, analysis, and synthesis • Automatic generation of patent abstracts and summaries • IP (e.g., patent, trademark, copyright) digital content patrols and protections adopting Generative AI and LLM technologies

Crossref

World Patent Information ; 82, September, 2025

World Patent Information

Oxford [England] : Elsevier Ltd., 2025.

English

https://www.sciencedirect.com/science/article/abs/pii/S0172219023000108

External Online Journals > World Patent Information