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Automated Patent Classification

  • Karim Benzineb
  • Jacques Guyot
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.

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

Further Reading

  1. 2.
    WIPO’s website page dedicated to international patent classifications (IPC, Nice, Locarno, Vienna): http://www.wipo.int/classifications/en/. Accessed 23 Dec 2010
  2. 3.
    EPO’s website page dedicated to ECLA: http://test.espacenet.com/ep/en/helpv3/ecla.html. Accessed 23 Dec 2010
  3. 4.
    World Patent Information, Elsevier: an International Journal for Industrial Property Documentation, Information, Classification and Statistics (Quarterly) Google Scholar
  1. 5.
    Berry MW, Castellanos M (eds) (2007) Survey of text mining: clustering, classification, and retrieval. Springer, Berlin Google Scholar
  2. 6.
    Fall CJ, Törcsvári A, Benzineb K, Karetka G (2003) Automated categorization in the international patent classification. SIGIR Forum 37(1) Google Scholar
  3. 7.
    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 Google Scholar
  4. 8.
    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

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.SIMPLE SHIFTPlan-les-OuatesSwitzerland

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