PatentQ&A: Proposal of Patent Q&A Neural Search System Using Transformer Model

Yoonmin Lee, Taewook Hwang, Sangkeun Jung, Hyein Seo, Yoonhyung Roh

http://doi.org/10.5626/JOK.2023.50.4.306

Recent neural network search has enabled semantic search beyond search based on statistical methods, and finds accurate search results even with typos. This paper proposes a neural network-based patentQ&A search system that provides the closest answer to the user"s question intention when a general public without patent expertise searches for patent information using general terms. A patent dataset was constructed using patent customer consultation data posted on the Korean Intellectual Property Office website. Patent-KoBERT (Triplet) and Patent-KoBERT (CrossEntropy) were fine-tuned as patent datasets were used to extract similar questions to questions entered by the user and re-rank them. As a result of the experiment, values of Mean Reciprocal Rank (MRR) and Mean Average Precision (MAP) were 0.96, confirming that answers most similar to the intention of the user input were well selected.