A Method for Calculating Patent Similarity Using Patent Model Tree Based on Neural Network
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.
KeywordsPatent similarity Patent text Patent model tree
The project is supported by HJSW and Research & Development plan of Shaanxi Province (Program No. 2017ZDXM-GY-094, 2015KTZDGY04-01).
- 1.Arts, S., Cassiman, B., Gomez, J.C.: Text Matching to Measure Patent Similarity. Social Science Electronic Publishing, Rochester (2017)Google Scholar
- 2.Yanagihori, K, Tsuda, K.: Verification of patent document similarity of using dictionary data extracted from notification of reasons for refusal. In: IEEE, Computer Software and Applications Conference, pp. 349–354. IEEE Computer Society (2015)Google Scholar
- 4.Ji, X, Gu, X, Dai, F, et al.: Patent collaborative filtering recommendation approach based on patent similarity. In: Eighth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 1699–1703. IEEE (2011)Google Scholar
- 5.Chen, J.-X., Gu, X.-J., Chen, G.-H.: Method of discovering similar patents based on vector space model and characteristics of patent documents. J. Zhejiang Univ. Eng. Sci. 43(10), 1848–1852 (2009)Google Scholar
- 7.Kasravi, K., Risov, M.: Multivariate patent similarity detection, pp. 1–8 (2009)Google Scholar
- 8.Wu, H.C., Chen, H.Y., Lee, K.Y., et al.: A method for assessing patent similarity using direct and indirect citation links. In: IEEE International Conference on Industrial Engineering and Engineering Management. IEEE (2010)Google Scholar
- 9.Yu. S., Su, J., Li, P.: Automatic abstract extraction method based on improved TextRank. In: Computer Science, pp. 240–247 (2016)Google Scholar
- 10.Mihalcea, R., Tarau, P.: TextRank: bringing order into texts. In: Emnlp, pp. 404–411 (2004)Google Scholar
- 11.Mueller, J., Thyagarajan, A.: Siamese recurrent architectures for learning sentence similarity. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 2786–2792. AAAI Press (2016)Google Scholar
- 12.Foland, W., Martin, J.H.: Abstract meaning representation parsing using LSTM recurrent neural networks. In: Meeting of the Association for Computational Linguistics, pp. 463–472 (2017)Google Scholar
- 15.Chen, Z.-Y., Gogoi, A., et al.: Coherent narrow-band light source for miniature endoscopes. IEEE J. Sel. Top. Quantum Electron. (2018), (In press)Google Scholar
- 16.Zhang A., Sun G., et al.: A dynamic neighborhood learning-based gravitational search algorithm. IEEE Trans. Cynernetics (2017). (In press)Google Scholar