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Completing keyword patent search with semantic patent search: introducing a semiautomatic iterative method for patent near search based on semantic similarities

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Abstract

Patent search is a substantial basis for many operational questions and scientometric evaluations. We consider it as a sequence of distinct stages. The “patent wide search” involves a definition of system boundaries by means of classifications and a keyword search producing a patent set with a high recall level (see Schmitz in Patentinformetrie: Analyse und Verdichtung von technischen Schutzrechtsinformationen, DGI, Frankfurt (Main), 2010 with an overview of searchable patent meta data). In this set of patents a “patent near search” takes place, producing a patent set with high(er) precision. Hence, the question arises how the researcher has to operate within this patent set to efficiently identify patents that contain paraphrased descriptions of the sought inventive elements in contextual information and whether this produces different results compared to a conventional search. We present a semiautomatic iterative method for the identification of such patents, based on semantic similarity. In order to test our method we generate an initial dataset in the course of a patent wide search. This dataset is then analyzed by means of the semiautomatic iterative method as well as by an alternative method emulating the conventional process of keyword refinement. It thus becomes obvious that both methods have their particular “raison d’être”, and that the semiautomatic iterative method seems to be able to support a conventional patent search very effectively.

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Notes

  1. See Appendix “Identified reference documents” with a list of the ten reference documents.

  2. To prove the robustness of our method, we compared its results with a relaxed approach (see Appendix “Robustness check”).

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Acknowledgments

The authors wish to thank Dr. Lothar Walter and Dr. Jan M. Gerken for their critical comments and constructive ideas, particularly regarding the keyword search emulating method. We also would like to acknowledge the contributions of two anonymous reviewers, whose suggestions helped enhancing this paper’s readability and general quality.

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Authors and Affiliations

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

    Ansgar Moeller & Martin G. Moehrle

Authors
  1. Ansgar Moeller
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  2. Martin G. Moehrle
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Corresponding author

Correspondence to Ansgar Moeller.

Appendix

Appendix

Identified reference documents

Table 9.

Table 9 Reference documents identified by means of the semi-automatic iterative method and/or the keyword search emulating method
Full size table

Robustness check

Analogous to experimental design we create an additional procedure. We interpret the ranking of patents in our semi-automatic iterative method as treatment for an “experimental group” and define a second procedure without treatment for a “control group” (see Mitchell and Jolley 2012 for the design of experiments). In other words, the second procedure represents a relaxation of our method, neglecting the ranking of the dataset created by similarity measurement. For this purpose pure coincidence was chosen as a comparative value for the qualitative evaluation of the presented method. This is based on the assumption that as many patents are randomly reviewed as were marked during the examination of the dataset by means of the presented method. In the course of the semiautomatic iterative method, the review of the semantically most similar documents produced an average quantity of 63 marked documents. If the same quantity were reviewed randomly, the resulting average recall would amount to 63 % (see Table 10). The precision of this purely coincidental approach can be calculated by means of the given formula but would merely represent the share of relevant documents in proportion to the total number of documents (in this case 36 %). Thus, the semiautomatic iterative method presented here proves to be robust, as it is unrestrictedly superior to the random selection of patents in terms of recall and precision.

Table 10 Comparison between the results of the semi-automatic iterative method and the comparative value coincidence regarding the indicators precision and recall
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Moeller, A., Moehrle, M.G. Completing keyword patent search with semantic patent search: introducing a semiautomatic iterative method for patent near search based on semantic similarities. Scientometrics 102, 77–96 (2015). https://doi.org/10.1007/s11192-014-1446-9

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