A two-stage deep learning-based system for patent citation recommendation.

The increasing number of patents leads patent applicants and examiners to spend more time and cost on searching and citing prior patents. Deep learning has exhibited outstanding performance in the recommendation of movies, music, products, and paper citation. However, the application of deep learning in patent citation recommendation has not been addressed well. Despite many attempts to apply deep learning models to the patent domain, there is little attention to the patent citation recommendation. Since patent citation is determined according to a complex technological context beyond simply finding semantically similar preceding documents, it is necessary to understand the context in which the citation occurs. Therefore, we propose a dataset named as a PatentNet to capture technological citation context based on textual information, meta data and examiner citation information for about 110,000 patents. Also, this paper proposes a strong benchmark model considering the similarity of patent text as well as technological citation context using cooperative patent classification (CPC) code. The proposed model exploits a two-stage structure of selecting based on textual information and pre-trained CPC embedding values and re-ranking candidates using a trained deep learning model with examiner citation information. The proposed model achieved improved performance with an MRR of 0.2506 on the benchmarking dataset, outperforming the existing methods. The results obtained show that learning about the descriptive citation context, rather than simple text similarity, has an important influence on citation recommendation. The proposed model and dataset can help researchers to understand technological citation context and assist patent examiners or applicants to find prior patents to cite effectively. [ABSTRACT FROM AUTHOR]