2026 Volume 38 Issue 2 Pages 624-632
This study aims to automatically generate patent maps by estimating technical problems and solutions from a set of patent documents in a specific field. Specifically, our method extracts sentences relevant to technical problems and solutions from patent documents by using RoBERTa. Next, using the extracted sentences as training data, our method estimates terms related to technical problems and solutions by few-shot learning with a large language model (LLM). The estimated terms are clustered based on semantic similarity. Then, a patent map is automatically generated using these clustered terms as axis labels. In the evaluation, the appropriateness of the estimated technical problems and solutions for the patent map is evaluated by calculating the similarity between the axis labels of a manually created patent map and those estimated by the proposed method.
