Analyzing Trending Technological Areas of Patents

  • Mustafa SofeanEmail author
  • Hidir Aras
  • Ahmad Alrifai
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1062)


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.


Patent analysis Community detection Topic modeling 


  1. 1.
    Yan, B., Luo, J.: Measuring technological distance for patent mapping. J. Assoc. Inf. Sci. Technol. 68, 423–437. Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    Trippe, A.: Guidelines for Preparing Patent Landscape Reports. Patinformatics, LLC, With contributions from WIPO Secretariat (2015)Google Scholar
  7. 7.
    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). Scholar
  8. 8.
    Waltman, L., van Eck, N.J.: A smart local moving algorithm for large-scale modularity-based community detection. Eur. Phys. J. Springer (2013)Google Scholar
  9. 9.
    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). CrossRefGoogle Scholar
  10. 10.
    Buckley, C., Voorhees, E.M.: Retrieval evaluation with incomplete information. In: SIGIR 2004, Sheeld, South Yorkshire, UK, pp. 25–32. ACM (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.FIZ KarlsruheEggenstein-LeopoldshafenGermany

Personalised recommendations