Abstract
Analyzing technological areas of inventions in patent domain is an important stage to discover relationships and trends for decision making. The International Patent Classification (IPC) is used for classifying the patents according to their technological areas. However, these classifications are quite inconsistent in various aspects because of the complexity and they may not be available for all areas of technology specially the emerging areas. This work introduces methods that applied on unstructured patents texts for detecting accurate technological areas to which the invention relates, and identifies semantically meaningful communities/topics for a large collection of patent documents. A hybrid text mining techniques with scalable analytics service that involves natural language processing which built on top of big-data architecture are used to extract the significant technical areas. Community detection approach is applied for efficiently identifying communities/topics by clustering the network graph of technological areas of inventions. A comparison to the standard LDA clustering is presented. Finally, regression analysis methods are applied in order to discover the interesting trends.
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References
Yan, B., Luo, J.: Measuring technological distance for patent mapping. J. Assoc. Inf. Sci. Technol. 68, 423–437. https://doi.org/10.1002/asi.23664
Abbas, A., Zhang, L., Khan, S.U.: A literature review on the state-of-the-art in patent analysis. World Patent Inf. 37, 3–13 (2015)
Ankam, S., Dou, W., Strumsky, D., Zadrozny, W.: Exploring emerging technologies using patent data and patent classification. In: CHI 2012, Austin, Texas, USA. ACM (2012)
Chen, H., Zhang, Y., Zhang, G., Lu, J.: Modeling technological topic changes in patent claims. In: Proceedings of PIC MET 2015, Portland, OR, USA (2015)
Tang, J., Wang, B., Yang, Y., Hu, P., Usadi, A.K.: PatentMiner: topic-driven patent analysis and mining. In: KDD 2012, Beijing, China. ACM (2012)
Trippe, A.: Guidelines for Preparing Patent Landscape Reports. Patinformatics, LLC, With contributions from WIPO Secretariat (2015)
Sofean, M.: Automatic segmentation of big data of patent texts. In: Bellatreche, L., Chakravarthy, S. (eds.) DaWaK 2017. LNCS, vol. 10440, pp. 343–351. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64283-3_25
Waltman, L., van Eck, N.J.: A smart local moving algorithm for large-scale modularity-based community detection. Eur. Phys. J. Springer (2013)
Sofean, M., Aras, H., Alrifai, A.: A workflow-based large-scale patent mining and analytics framework. In: Damaševičius, R., Vasiljevienė, G. (eds.) ICIST 2018. CCIS, vol. 920, pp. 210–223. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99972-2_17
Buckley, C., Voorhees, E.M.: Retrieval evaluation with incomplete information. In: SIGIR 2004, Sheeld, South Yorkshire, UK, pp. 25–32. ACM (2004)
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Sofean, M., Aras, H., Alrifai, A. (2019). Analyzing Trending Technological Areas of Patents. In: Anderst-Kotsis, G., et al. Database and Expert Systems Applications. DEXA 2019. Communications in Computer and Information Science, vol 1062. Springer, Cham. https://doi.org/10.1007/978-3-030-27684-3_18
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DOI: https://doi.org/10.1007/978-3-030-27684-3_18
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