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A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis

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Abstract

Given that in terms of technology novel inventions are crucial factors for companies; this article contributes to the identification of inventions of high novelty in patent data. As companies are confronted with an information overflow, and having patents reviewed by experts is a time-consuming task, we introduce a new approach to the identification of inventions of high novelty: a specific form of semantic patent analysis. Subsequent to the introduction of the concept of novelty in patents, the classical method of semantic patent analysis will be adapted to support novelty measurement. By means of a case study from the automotive industry, we corroborate that semantic patent analysis is able to outperform available methods for the identification of inventions of high novelty. Accordingly, semantic patent information possesses the potential to enhance technology monitoring while reducing both costs and uncertainty in the identification of inventions of high novelty.

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Notes

  1. Although we will focus on SAO-structures in this article, it is noteworthy to show an alternative: Word n-grams. Word n-grams can be extracted with or without regard to syntactical classes and functions. Extracting n-grams regardless to syntactical functions cause loss of syntactical information. Nevertheless, n-grams still have semantic information. n-grams take the co-occurrence of words into account and hence, highlight a relationship between these words on the content level, as they show that n words co-occur close together in a patent.

  2. For further information on Invention Machine and the Knowledgist™ see invention-machine.com.

  3. For detailed information about SUBARU: http://www.subaru-global.com/.

  4. For detailed information about the FVA: http://www.fva-net.de/. The FVA can be seen as the leading innovation network in the field of drive train technology in Germany. The FVA enhance the collaboration between industry and science in the field of drive train technology.

  5. In the algorithm we took into account, that several patents may have the same level of novelty. In such cases we assume that analysts read patents stepwise. Every novelty value has to be considered as one step. Hence, if an analyst reads one patent with a novelty equal 0.2, he also reads all other patents with a novelty of 0.2 independent of the relevance of the first patent he has read with a novelty equal 0.2. In some of these cases it makes no sense to report a precision value on a specific level of recall.

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Acknowledgments

The cited case study was produced in the course of a joint project with the Forschungsvereinigung Antriebstechnik (FVA). We wish to thank the FVA and all industrial members for their contributions and their support. Furthermore, we would like to thank Dipl.-Ing. (FH) Jens Potthast for extensive programming efforts on the PatVisor®, Dr. Lothar Walter for commenting an earlier version of this paper and two anonymous reviewers for their constructive and helpful comments.

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  1. Institute of Project Management and Innovation (IPMI), University of Bremen, Wilhelm-Herbst-Str.12, 28359, Bremen, Germany

    Jan M. Gerken & Martin G. Moehrle

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  1. Jan M. Gerken
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Correspondence to Jan M. Gerken.

Appendix A: algorithm for the calculation of precision

Appendix A: algorithm for the calculation of precision

See Fig. 3.

Fig. 3
figure 3

Flow chart of the algorithm for the calculation of precision

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Gerken, J.M., Moehrle, M.G. A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis. Scientometrics 91, 645–670 (2012). https://doi.org/10.1007/s11192-012-0635-7

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