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

Tung Tran; Ramakanth Kavuluru

doi:10.1007/978-3-319-71928-3_3

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

Tung Tran, Ramakanth Kavuluru

Internal Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

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 ≈ 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 language	English
Title of host publication	Mining Intelligence and Knowledge Exploration - 5th International Conference, MIKE 2017, Proceedings
Editors	Rajarshi Pal, Ashish Ghosh, Rajendra Prasath
Pages	22-34
Number of pages	13
DOIs	https://doi.org/10.1007/978-3-319-71928-3_3
State	Published - 2017
Event	5th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2017 - Hyderabad, India Duration: Dec 13 2017 → Dec 15 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10682 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2017
Country/Territory	India
City	Hyderabad
Period	12/13/17 → 12/15/17

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)

Access to Document

10.1007/978-3-319-71928-3_3

Cite this

Tran, T., & Kavuluru, R. (2017). Supervised approaches to assign cooperative patent classification (CPC) codes to patents. In R. Pal, A. Ghosh, & R. Prasath (Eds.), Mining Intelligence and Knowledge Exploration - 5th International Conference, MIKE 2017, Proceedings (pp. 22-34). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10682 LNAI). https://doi.org/10.1007/978-3-319-71928-3_3

Tran, Tung ; Kavuluru, Ramakanth. / Supervised approaches to assign cooperative patent classification (CPC) codes to patents. Mining Intelligence and Knowledge Exploration - 5th International Conference, MIKE 2017, Proceedings. editor / Rajarshi Pal ; Ashish Ghosh ; Rajendra Prasath. 2017. pp. 22-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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 ≈ 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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Tran, T & Kavuluru, R 2017, Supervised approaches to assign cooperative patent classification (CPC) codes to patents. in R Pal, A Ghosh & R Prasath (eds), Mining Intelligence and Knowledge Exploration - 5th International Conference, MIKE 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10682 LNAI, pp. 22-34, 5th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2017, Hyderabad, India, 12/13/17. https://doi.org/10.1007/978-3-319-71928-3_3

Supervised approaches to assign cooperative patent classification (CPC) codes to patents. / Tran, Tung; Kavuluru, Ramakanth.
Mining Intelligence and Knowledge Exploration - 5th International Conference, MIKE 2017, Proceedings. ed. / Rajarshi Pal; Ashish Ghosh; Rajendra Prasath. 2017. p. 22-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10682 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N1 - 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.

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N2 - 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.

AB - 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.

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Tran T, Kavuluru R. Supervised approaches to assign cooperative patent classification (CPC) codes to patents. In Pal R, Ghosh A, Prasath R, editors, Mining Intelligence and Knowledge Exploration - 5th International Conference, MIKE 2017, Proceedings. 2017. p. 22-34. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-71928-3_3

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

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