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Supervised Approaches to Assign Cooperative Patent Classification (CPC) Codes to Patents

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Mining Intelligence and Knowledge Exploration (MIKE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10682))

Abstract

This paper re-introduces the problem of patent classification with respect to the new Cooperative Patent Classification (CPC) system. CPC has replaced the U.S. Patent Classification (USPC) coding system as the official patent classification system in 2013. We frame patent classification as a multi-label text classification problem in which the prediction for a test document is a set of labels and success is measured based on the micro-F1 measure. We propose a supervised classification system that exploits the hierarchical taxonomy of CPC as well as the citation records of a test patent; we also propose various label ranking and cut-off (calibration) methods as part of the system pipeline. To evaluate the system, we conducted experiments on U.S. patents released in 2010 and 2011 for over 600 labels that correspond to the “subclasses” at the third level in the CPC hierarchy. The best variant of our model achieves \(\approx \)70% in micro-F1 score and the results are statistically significant. To the best of our knowledge, this is the first effort to reinitiate the automated patent classification task under the new CPC coding scheme.

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Notes

  1. 1.

    See Patent No. 9,226,437, granted Jan. 5, 2016. Available at: http://pdfpiw.uspto.gov/.piw?Docid=09226437.

  2. 2.

    According to the Guide to the CPC [24], “unless stated otherwise, the rules and principles are identical” to that of IPC, which can be found in Guide to the IPC [27].

  3. 3.

    These are other older patents that appear in the References Cited section and are sometimes mentioned within the description of a new patent being considered for automated coding.

  4. 4.

    http://patents.tttran.net.

  5. 5.

    Although code assignment in reality is only done at the subgroup level, we can collapse such deeper codes to the subclass level by simply removing code components specific to the deeper nodes. In Table 1, code for resistors (row 3) can be obtained from the more specific subgroup code (row 5) by removing the “3/08” part. Typically, due to extremely high sparsity, code assignments are carried out at a higher level in preliminary studies including our current attempt and other automated PCS efforts.

  6. 6.

    The product of d probabilities is significantly reduced in magnitude especially for large values of d. Here, use of the geometric mean is intended to restore the magnitude of the resulting product while ensuring that it remains in [0, 1]. This eases the interpretability of intermediate probability outputs while guarding against practical concerns such as floating point underflow.

References

  1. Cameron, A.C., Trivedi, P.K.: Regression Analysis of Count Data, vol. 53. Cambridge University Press, Cambridge (2013)

    Book  MATH  Google Scholar 

  2. Don, S., Min, D.: Feature selection for automatic categorization of patent documents. Indian J. Sci. Technol. 9(37), 1–17 (2016). Kindly check and confirm the edit made in Ref. [2]

    Article  Google Scholar 

  3. Eisinger, D., Tsatsaronis, G., Bundschus, M., Wieneke, U., Schroeder, M.: Automated patent categorization and guided patent search using IPC as inspired by MeSH and PubMed. J. Biomed. Semant. 4(S1), 1–23 (2013)

    Google Scholar 

  4. Fall, C.J., Törcsvári, A., Benzineb, K., Karetka, G.: Automated categorization in the international patent classification. ACM SIGIR Forum 37(1), 10–25 (2003)

    Article  Google Scholar 

  5. Gibaja, E., Ventura, S.: A tutorial on multilabel learning. ACM Comput. Surv. (CSUR) 47(3), 52 (2015)

    Article  Google Scholar 

  6. Hsu, C.-W., Chang, C.-C., Lin, C.-J., et al.: A practical guide to support vector classification (2003). https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

  7. Kavuluru, R., Rios, A., Lu, Y.: An empirical evaluation of supervised learning approaches in assigning diagnosis codes to electronic medical records. Artif. Intell. Med. 65(2), 155–166 (2015)

    Article  Google Scholar 

  8. Kim, J.-H., Choi, K.-S.: Patent document categorization based on semantic structural information. Inf. Process. Manag. 43(5), 1200–1215 (2007)

    Article  Google Scholar 

  9. Kotsiantis, S.B., Zaharakis, I., Pintelas, P.: Supervised machine learning: a review of classification techniques (2007)

    Google Scholar 

  10. Li, X., Chen, H., Zhang, Z., Li, J.: Automatic patent classification using citation network information: an experimental study in nanotechnology. In: Proceedings of the 7th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 419–427. ACM (2007)

    Google Scholar 

  11. Liu, D.-R., Shih, M.-J.: Hybrid-patent classification based on patent-network analysis. J. Assoc. Inf. Sci. Technol. 62(2), 246–256 (2011)

    Article  Google Scholar 

  12. Liu, K., Peng, S., Wu, J., Zhai, C., Mamitsuka, H., Zhu, S.: MeSHLabeler: improving the accuracy of large-scale MeSH indexing by integrating diverse evidence. Bioinformatics 31(12), i339–i347 (2015)

    Article  Google Scholar 

  13. Nam, J., Kim, J., Loza Mencía, E., Gurevych, I., Fürnkranz, J.: Large-scale multi-label text classification — revisiting neural networks. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8725, pp. 437–452. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44851-9_28

    Google Scholar 

  14. Richter, G., MacFarlane, A.: The impact of metadata on the accuracy of automated patent classification. World Pat. Inf. 27(1), 13–26 (2005)

    Article  Google Scholar 

  15. Rios, A., Kavuluru, R.: Analyzing the moving parts of a large-scale multi-label text classification pipeline: experiences in indexing biomedical articles. In: 2015 International Conference on Healthcare Informatics (ICHI), pp. 1–7. IEEE (2015)

    Google Scholar 

  16. Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1986)

    MATH  Google Scholar 

  17. Silla, C.N., Freitas, A.A.: A survey of hierarchical classification across different application domains. Data Min. Knowl. Discov. 22(1–2), 31–72 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  18. Simmons, H.J.: Categorizing the useful arts: part, present, and future development of patent classification in the United States. Law Libr. J. 106, 563 (2014)

    Google Scholar 

  19. Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45(4), 427–437 (2009)

    Article  Google Scholar 

  20. Tong, X., Frame, J.D.: Measuring national technological performance with patent claims data. Res. Policy 23(2), 133–141 (1994)

    Article  Google Scholar 

  21. Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer, Boston (2010). https://doi.org/10.1007/978-0-387-09823-4_34

    Google Scholar 

  22. U.S. Patent and Trademark Office: U.S. Patent Statistics Chart. https://www.uspto.gov/web/offices/ac/ido/oeip/taf/us_stat.htm (2016). Accessed 30 Nov 2016

  23. U.S. Patent and Trademark Office and European Patent Office: Cooperative Patent Classification Scheme in Bulk. http://www.cooperativepatentclassification.org/cpcSchemeAndDefinitions/Bulk.html (2015). Accessed 01 Feb 2015

  24. U.S. Patent and Trademark Office and European Patent Office: Guide to the CPC. http://www.cooperativepatentclassification.org/publications/GuideToTheCPC.pdf (2015). Accessed 30 Nov 2016

  25. Wallace, B.C., Small, K., Brodley, C.E., Trikalinos, T.A.: Class imbalance, redux. In: 2011 IEEE 11th International Conference on Data Mining (ICDM), pp. 754–763. IEEE (2011)

    Google Scholar 

  26. Wolter, B.: It takes all kinds to make a world-some thoughts on the use of classification in patent searching. World Pat. Inf. 34(1), 8–18 (2012)

    Article  Google Scholar 

  27. World Intellectual Property Organization: Guide to the IPC. http://www.wipo.int/export/sites/www/classifications/ipc/en/guide/guide_ipc.pdf (2016). Accessed 30 Nov 2016

  28. Zhang, M.-L., Zhou, Z.-H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)

    Article  Google Scholar 

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Acknowledgements

We thank anonymous reviewers for their honest and constructive comments that helped improve our paper’s presentation. Our work is primarily supported by the National Library of Medicine through grant R21LM012274. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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Authors and Affiliations

  1. Department of Computer Science, University of Kentucky, Lexington, KY, USA

    Tung Tran & Ramakanth Kavuluru

  2. Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington, KY, USA

    Ramakanth Kavuluru

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  1. Tung Tran
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  2. Ramakanth Kavuluru
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Correspondence to Ramakanth Kavuluru .

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Editors and Affiliations

  1. Indian Statistical Institute, Kolkata, India

    Ashish Ghosh

  2. Institute for Development and Research in Banking Technology, Hyderabad, India

    Rajarshi Pal

  3. Indian Institute of Information Technology, Sri City, India

    Rajendra Prasath

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Tran, T., Kavuluru, R. (2017). Supervised Approaches to Assign Cooperative Patent Classification (CPC) Codes to Patents. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_3

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  • DOI: https://doi.org/10.1007/978-3-319-71928-3_3

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