Supervised approaches to assign cooperative patent classification (CPC) codes to patents

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations


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 ≈ 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.

Original languageEnglish
Title of host publicationMining Intelligence and Knowledge Exploration - 5th International Conference, MIKE 2017, Proceedings
EditorsRajarshi Pal, Ashish Ghosh, Rajendra Prasath
Number of pages13
StatePublished - 2017
Event5th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2017 - Hyderabad, India
Duration: Dec 13 2017Dec 15 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10682 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2017

Bibliographical note

Funding Information:
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.

Publisher Copyright:
© 2017, Springer International Publishing AG.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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