TY  - JOUR
A1  - Krestel, Ralf
A1  - Chikkamath, Renukswamy
A1  - Hewel, Christoph
A1  - Risch, Julian
T1  - A survey on deep learning for patent analysis
JF  - World patent information
N2  - Patent document collections are an immense source of knowledge for research and innovation communities worldwide. The rapid growth of the number of patent documents poses an enormous challenge for retrieving and analyzing information from this source in an effective manner. Based on deep learning methods for natural language processing, novel approaches have been developed in the field of patent analysis. The goal of these approaches is to reduce costs by automating tasks that previously only domain experts could solve. In this article, we provide a comprehensive survey of the application of deep learning for patent analysis. We summarize the state-of-the-art techniques and describe how they are applied to various tasks in the patent domain. In a detailed discussion, we categorize 40 papers based on the dataset, the representation, and the deep learning architecture that were used, as well as the patent analysis task that was targeted. With our survey, we aim to foster future research at the intersection of patent analysis and deep learning and we conclude by listing promising paths for future work.
KW  - deep learning
KW  - patent analysis
KW  - text mining
KW  - natural language processing
Y1  - 2021
U6  - https://doi.org/10.1016/j.wpi.2021.102035
SN  - 0172-2190
SN  - 1874-690X
VL  - 65
PB  - Elsevier
CY  - Amsterdam
ER  -