  Patent landscaping is a method used for searching related patents during a
research and development (R&D) project. To avoid the risk of patent
infringement and to follow current trends in technology, patent landscaping is
a crucial task required during the early stages of an R&D project. As the
process of patent landscaping requires advanced resources and can be tedious,
the demand for automated patent landscaping has been gradually increasing.
However, a shortage of well-defined benchmark datasets and comparable models
makes it difficult to find related research studies. In this paper, we propose
an automated patent landscaping model based on deep learning. To analyze the
text of patents, the proposed model uses a modified transformer structure. To
analyze the metadata of patents, we propose a graph embedding method that uses
a diffusion graph called Diff2Vec. Furthermore, we introduce four benchmark
datasets for comparing related research studies in patent landscaping. The
datasets are produced by querying Google BigQuery, based on a search formula
from a Korean patent attorney. The obtained results indicate that the proposed
model and datasets can attain state-of-the-art performance, as compared with
current patent landscaping models.
