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Automated Patent Classification

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Part of the The Information Retrieval Series book series (INRE,volume 29)

Abstract

Patent classifications are built to set up some order in the growing number and diversity of inventions, and to facilitate patent information searches. The need to automate classification tasks appeared when the growth in the number of patent applications and of classification categories accelerated in a dramatic way. Automated patent classification systems use various elements of patents’ content, which they sort out to find the information most typical of each category. Several algorithms from the field of Artificial Intelligence may be used to perform this task, each of them having its own strengths and weaknesses. Their accuracy is generally evaluated by statistical means. Automated patent classification systems may be used for various purposes, from keeping a classification well organized and consistent, to facilitating some specialized tasks such as prior art search. However, many challenges remain in the years to come to build systems which are more accurate and allow classifying documents in more languages.

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Notes

  1. 1.

    http://en.wikipedia.org/wiki/Zipf’s_law. Accessed 23 Dec 2010.

  2. 2.

    http://en.wikipedia.org/wiki/Precision_and_recall. Accessed 23 Dec 2010.

Abbreviations

AI:

Artificial Intelligence

APC:

Automated Patent Classification

ECLA:

European Patent Classification

EPO:

European Patent Office

IPC:

International Patent Classification

kNN:

k Nearest Neighbors

MCD:

Master Classification Database

NN:

Neural Network

PCT:

Patent Cooperation Treaty

SVM:

Support Vector Machine

WIPO:

World Intellectual Property Organization

References

  1. Sebastiani F (2002) Machine learning in automated text categorization. ACM Comput Surv 34(1):1–47

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  2. Berry MW, Castellanos M (eds) (2007) Survey of text mining: clustering, classification, and retrieval. Springer, Berlin

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  3. Fall CJ, Törcsvári A, Benzineb K, Karetka G (2003) Automated categorization in the international patent classification. SIGIR Forum 37(1)

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  4. Fall CJ, Benzineb K, Guyot J, Törcsvéri A, Fiévet P (2003) Computer-assisted categorization of patent documents in the international patent classification. In: Proceedings of the international chemical information conference (ICIC’03), Nîmes, France, Oct 2003

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  5. Proceedings of the CLEF-IP 2010 (classification task), to be published in 2011. The related web site is here: http://www.ir-facility.org/research/evaluation/clef-ip-10. Accessed 23 Dec 2010

Further Reading

  1. WIPO’s website page dedicated to international patent classifications (IPC, Nice, Locarno, Vienna): http://www.wipo.int/classifications/en/. Accessed 23 Dec 2010

  2. EPO’s website page dedicated to ECLA: http://test.espacenet.com/ep/en/helpv3/ecla.html. Accessed 23 Dec 2010

  3. World Patent Information, Elsevier: an International Journal for Industrial Property Documentation, Information, Classification and Statistics (Quarterly)

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Correspondence to Karim Benzineb .

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Benzineb, K., Guyot, J. (2011). Automated Patent Classification. In: Lupu, M., Mayer, K., Tait, J., Trippe, A. (eds) Current Challenges in Patent Information Retrieval. The Information Retrieval Series, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19231-9_12

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  • DOI: https://doi.org/10.1007/978-3-642-19231-9_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19230-2

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