Large Language Model-based R&D Solution Analysis Approach Using Problem-Solution Information of Patents

Patents, i.e., the output of research and development (R&D) activities, are regarded as a concentration of Problem–Solution information. Despite various patent analysis studies aimed at solving problems, large language model (LLM)-based studies are scarce. LLMs, which are effective for natural language processing tasks, such as text summarization and generation, have been applied in numerous fields, including healthcare, finance, and law. By learning the Problem-Solution information of patents as an LLM instead of merely examining existing R&D solutions, one can generate new solutions applicable to a specified problem. Therefore, this study proposes an approach to generate and analyze new R&D solutions using LLMs. Our systematic approach involves 1) collecting numerous patents and constructing a database; 2) extracting Problem-Solution information from the Common Application Form section of patents and constructing a Problem-Solution dataset; 3) fine-tuning an LLM using the problem-solution dataset and generating R&D solutions; and 4) analyzing R&D solutions to present a technology concept portfolio map. This study extends beyond the existing R&D solution exploration, presents a new approach for generating solutions, and suggests technology concepts using LLMs. Therefore, this study contributes to the expansion of the available options and fosters innovation in R&D field.