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Patinformatics: Tasks to tools

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  • Trippe, Anthony J.

Abstract

This article starts with an overview of the field of patinformatics--the science of analyzing patent information to discover relationships and trends. This is followed by a survey of many common analysis tasks in this field, and many of the software tools available to tackle these tasks. The survey is set out under the tasks of list cleanup and grouping of concepts; list generation; co-occurrency matrices and circle graphs; clustering of structured data; clustering of unstructured data; mapping document clusters; adding temporal component to cluster map; citation analysis; subject/action/object functions. The author concludes that patinformatics has developed very rapidly over the last few years, and provides continuing challenges and opportunities in making optimal use of the resources available to achieve reliable and meaningful results. Useful tables summarizing aspects of this survey are included.

Suggested Citation

  • Trippe, Anthony J., 2003. "Patinformatics: Tasks to tools," World Patent Information, Elsevier, vol. 25(3), pages 211-221, September.
    • Handle: RePEc:eee:worpat:v:25:y:2003:i:3:p:211-221
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    Citations

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    Cited by:

    1. Ansgar Moeller & Martin G. Moehrle, 2015. "Completing keyword patent search with semantic patent search: introducing a semiautomatic iterative method for patent near search based on semantic similarities," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 77-96, January.
    2. Jan M. Gerken & Martin G. Moehrle, 2012. "A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 645-670, June.
    3. Scott D. Bass & Lukasz A. Kurgan, 2010. "Discovery of factors influencing patent value based on machine learning in patents in the field of nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 217-241, February.
    4. Martin G. Moehrle & Jan M. Gerken, 2012. "Measuring textual patent similarity on the basis of combined concepts: design decisions and their consequences," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 805-826, June.
    5. Dar-Zen Chen & Wen-Yau Cathy Lin & Mu-Hsuan Huang, 2007. "Using Essential Patent Index and Essential Technological Strength to evaluate industrial technological innovation competitiveness," Scientometrics, Springer;Akadémiai Kiadó, vol. 71(1), pages 101-116, April.
    6. Johannes van Der Pol & Jean-Paul Rameshkoumar, 2021. "A method to reduce false positives in a patent query [Une méthode pour réduire les faux positifs dans une requête brevet]," Working Papers hal-03287970, HAL.
    7. Lothar Walter & Alfred Radauer & Martin G. Moehrle, 2017. "The beauty of brimstone butterfly: novelty of patents identified by near environment analysis based on text mining," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 103-115, April.
    8. Martin G. Moehrle, 2010. "Measures for textual patent similarities: a guided way to select appropriate approaches," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 95-109, October.
    9. Lybbert, Travis J. & Zolas, Nikolas J., 2014. "Getting patents and economic data to speak to each other: An ‘Algorithmic Links with Probabilities’ approach for joint analyses of patenting and economic activity," Research Policy, Elsevier, vol. 43(3), pages 530-542.
    10. Guifeng Liu, 2013. "Visualization of patents and papers in terahertz technology: a comparative study," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1037-1056, March.

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