Abstract
With fiercely increasing competition of intellectual property, the protection of intellectual property (IP) is paid increasing attention from worldwide. Effective patent infringement detection is the foundation of patent protection. Given the big patent data, manual patent infringement detection is inefficient and error prone. The design of automatic infringement detection method faces some challenges including: (1) how to detect patent infringement by novelty and non-obviousness; (2) which parts of a patent are selected against those of its counterpart patents in infringement detection. To solve the above issues, a game theory based patent infringement detection method is proposed. In the method, both the novelty and the non-obviousness are considered when the patentees take actions. The infringement detection is a game process to find the best action. Our method is compared with the state-of-the-art method on some patent data sets. The results show that the dynamic game method outperforms the baseline method in the evaluation measurement. Such method can be applied to patent examination and patent infringement litigation.
Supported by the National Science Foundation of China (grant no. 61801251) and Natural Science Foundation of Inner Mongolia (2018BS06002).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abbas, A., Zhang, L., Khan, S.U.: A literature review on the state-of-the-art in patent analysis. World Pat. Inf. 37, 3–13 (2014)
Cascini, G., Zini, M.: Measuring patent similarity by comparing inventions functional trees. In: Cascini, G. (ed.) CAI 2008. TIFIP, vol. 277, pp. 31–42. Springer, Boston, MA (2008). https://doi.org/10.1007/978-0-387-09697-1_3
Deng, N., Chen, X., Xiong, C.: Semantic similarity calculation of TCM patents in intelligent retrieval based on deep learning. In: Barolli, L., Hellinckx, P., Natwichai, J. (eds.) 3PGCIC 2019. LNNS, vol. 96, pp. 472–481. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-33509-0_44
Gerken, J.M., Moehrle, M.G.: A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis. Scientometrics, 91(3), 645–670 (2012)
Helmers, L., Horn, F., Biegler, F., Oppermann, T., Müller, K.-R.: Automating the search for a patents prior art with a full text similarity search. PLoS ONE 14(3), e0212103 (2019)
Hido, S., et al.: Modeling patent quality: a system for large-scale patentability analysis using text mining. Inf. Media Technol. 7(3), 1180–1191 (2012)
Ji, X., Gu, X., Dai, F., Chen, J., Le, C.: Patent collaborative filtering recommendation approach based on patent similarity. In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), vol. 3, pp. 1699–1703. IEEE (2011)
Kasravi, K., Risov, M.: Multivariate patent similarity detection. In: 2009 42nd Hawaii International Conference on System Sciences, pp. 1–8. IEEE (2009)
Lee, C., Song, B., Park, Y.: How to assess patent infringement risks: a semantic patent claim analysis using dependency relationships. Technol. Anal. Strateg. Manag. 25(1), 23–38 (2013)
Ma, C., Zhao, T., Li, H.: A method for calculating patent similarity using patent model tree based on neural network. In: Ren, J., et al. (eds.) International Conference on Brain Inspired Cognitive Systems, vol. 10989, pp. 633–643. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00563-4_62
Moehrle, M.G., Gerken, J.M.: Measuring textual patent similarity on the basis of combined concepts: design decisions and their consequences. Scientometrics 91(3), 805–826 (2012)
Myerson, R.B.: Game Theory. Harvard University Press, Cambridge (2013)
Park, H., Yoon, J., Kim, K.: Identifying patent infringement using SAO based semantic technological similarities. Scientometrics 90(2), 515–529 (2012)
Rodriguez, A., Kim, B., Turkoz, M., Lee, J.-M., Coh, B.-Y., Jeong, M.K.: New multi-stage similarity measure for calculation of pairwise patent similarity in a patent citation network. Scientometrics 103(2), 565–581 (2015). https://doi.org/10.1007/s11192-015-1531-8
Taduri, S., Lau, G.T., Law, K.H., Kesan, J.P.: Retrieval of patent documents from heterogeneous sources using ontologies and similarity analysis. In: 2011 IEEE Fifth International Conference on Semantic Computing, pp. 538–545. IEEE (2011)
Wang, X., Ren, H., Chen, Y., Liu, Y., Qiao, Y., Huang, Y.: Measuring patent similarity with SAO semantic analysis. Scientometrics 121(1), 1–23 (2019)
Xu, H., Zeng, W., Gui, J., Qu, P., Zhu, X., Wang, L.: Exploring similarity between academic paper and patent based on latent semantic analysis and vector space model. In: 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 801–805. IEEE (2015)
Yanagihori, K., Tsuda, K.: Verification of patent document similarity of using dictionary data extracted from notification of reasons for refusal. In: 2015 IEEE 39th Annual Computer Software and Applications Conference, vol. 3, pp. 349–354. IEEE (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, W., Liu, X., Kong, Y., Yang, Z., Qiao, W. (2020). Game Theory Based Patent Infringement Detection Method. In: Hartmann, S., Küng, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2020. Lecture Notes in Computer Science(), vol 12392. Springer, Cham. https://doi.org/10.1007/978-3-030-59051-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-59051-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59050-5
Online ISBN: 978-3-030-59051-2
eBook Packages: Computer ScienceComputer Science (R0)