@inproceedings{89887d6bc7424d0d84a073ff5c930f2f,
title = "A Method for Calculating Patent Similarity Using Patent Model Tree Based on Neural Network",
abstract = "To make full use of patent information and help companies find similar patent pairs by calculating the similarity of patents, help them deal with the issue of patent infringement detection, patent search, enterprise competition analysis, and patent layout, this paper proposes a method for calculation of patent similarity based on patent text using patent model tree. This method not only simplifies the process of understanding the patent text but also increases the accuracy of calculating the similarity among patents effectively. In this paper, the similarity between patents is calculated based on the patent model tree, and different similarity calculation methods are used according to different properties of tree nodes. Among them, in order to improve the accuracy of the claims node similarity measurement results, the Siamese LSTM network is applied. The experimental results show that the patent similarity calculation method based on text has an outstanding accuracy.",
keywords = "Patent model tree, Patent similarity, Patent text",
author = "Chunyan Ma and Tong Zhao and Hao Li",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018 ; Conference date: 07-07-2018 Through 08-07-2018",
year = "2018",
doi = "10.1007/978-3-030-00563-4_62",
language = "英语",
isbn = "9783030005627",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "633--643",
editor = "Amir Hussain and Bin Luo and Jiangbin Zheng and Xinbo Zhao and Cheng-Lin Liu and Jinchang Ren and Huimin Zhao",
booktitle = "Advances in Brain Inspired Cognitive Systems - 9th International Conference, BICS 2018, Proceedings",
}