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Playing the ‘Name Game’ to identify academic patents in Germany

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Abstract

Identifying academic inventors is crucial for reliable assessments of academic patenting and for understanding patent-based university-to-industry technology transfer. It requires solving the “who is who” problem at the individual inventor level. This article describes data collection and matching techniques applied to identify academic inventors in Germany. To manage the large dataset, we adjust a matching technique applied in prior research by comparing the inventor and professor names in the first step after cleaning. We also suggest a new approach for determining the similarity score. To evaluate our methodology we apply it to the EP-INV-PatStat database and compare its results to alternative approaches. For our German data, results are less sensitive to the choice of name comparison algorithm than to the specific filtering criteria employed. Restricting the search to EPO applications or identifying inventors by professor title underestimates academic patenting in Germany.

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Notes

  1. Improving data and methods to deal with these challenges was the objective of the project “Academic Patenting in Europe – Inventor Database”, which was supported by the European Science Foundation and provided the context for the present study. Results from this project have been published in Lissoni (2013).

  2. The country of residence is identified by using the country code information provided by PatStat.

  3. The standardized name id is provided by PatStat (doc_std_name_id) and allocates a standardized name to applicants and inventors with exactly the same or very similar names. However, the limited quality of the id does not allow us to use it as unique inventor id.

  4. Raffo and Lhuillery (2009) examined patents by professors at Ecole Polytechnique Fédérale de Lausanne (EPFL).

  5. Using European patent data is advantageous because more complete address information is available in PatStat. Addresses are provided for 94 % of the inventors listed on EPO patents but only for 0.2 % of German patent inventors.

  6. For a more detailed description of the cleaning stage please refer to Appendix 1.

  7. To calculate the Jaccard similarity coefficient, we used the Microsoft Fuzzy Lookup add-in for Microsoft Excel (downloadable at: http://www.microsoft.com/en-us/download/details.aspx?id=15011, last accessed on February 8, 2014).

  8. The matching procedure allows us to consider misspelled names and consequently reduce false negative matches (error type I). The algorithm, as discussed before, has the following disadvantage: the similarity score comprises PROFLIST names included in the names stored in DE-INV and vice versa. This results in false positive matches (error type II). To reduce the false positives, we delete matches wherein the length of the professor first name and surname differs from the inventor first and second names by more than two characters.

  9. We used a slightly modified procedure for the simple-string name comparison. Instead of comparing the full name-strings we first separated them into their components. Afterwards, we re-sampled the portions with first name and surname combinations. These name combinations were used for the simple-string name comparison.

  10. The term “name group” denotes the inventors matched with a professor in the first step.

  11. The identified combinations of criteria generated values of three. Therefore, we conservatively assumed that true positive matches would produce at least values greater than four.

  12. Project on academic patenting in Europe (APE-INV) funded by the European Science Foundation (ESF).

  13. We also disregarded professors from German Federal Armed Forces universities and professors affiliated with the Charité medical center.

  14. We could have taken these steps at the beginning of matching; however, we included these professors in another sample. The results are available upon request.

  15. In contrast, we did not find statistically significant differences between the groups regarding forward or backward citations.

  16. The precision rate is computed by dividing the number of true positive matches by the sum of true positive and false positive matches. The recall rate is computed by dividing the number of true positive matches by the sum of true positive and false negative matches (Lissoni et al. 2010).

  17. Cleaning was based on SQL scripts developed by Julio Raffo (http://wiki.epfl.ch/patstat/cleaning). We adapted the scripts and determined the order for execution. We executed the different steps in the following order.

    1. (1)

      Replacement of umlauts

    2. (2)

      Elimination of invalid characters

    3. (3)

      Replacement of accented letters

    4. (4)

      Conversion in upper case

    5. (5)

      Removal of title information and saving in a separate variable

    6. (6)

      Removal of double spaces as well as spaces at the beginning or end

    7. (7)

      Removal of commas

    8. (8)

      Replacement of abbreviations

    9. (9)

      Removal of letters not included in the Latin alphabet

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Acknowledgments

The authors are grateful for comments received from the participants at the Name Game Workshops in Brussels and Leuven. Financial support by the European Science Foundation (project ESF-APE-INV) is gratefully acknowledged.

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Authors and Affiliations

  1. TUM School of Management, Technische Universität München, Munich, Germany

    Anja Schoen

  2. Institute of Economics, University of Kassel, Kassel, Germany

    Dominik Heinisch & Guido Buenstorf

  3. International Centre for Higher Education Research (INCHER-Kassel), University of Kassel, Kassel, Germany

    Dominik Heinisch & Guido Buenstorf

Authors
  1. Anja Schoen
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  2. Dominik Heinisch
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  3. Guido Buenstorf
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Corresponding author

Correspondence to Anja Schoen.

Appendices

Appendix 1

Cleaning

The cleaning stage was aimed at reducing noise for the relevant data without losing relevant information in subsequent stages (Raffo and Lhuillery 2009). The number of false positive matches should be as low as possible. Raffo and Lhuillery (2009) tested the impact of different cleaning algorithms on the matching results using the “simple-string match” algorithm as a standard. The results showed that, separately, each cleaning algorithm produces a small improvement. However, together, the precision rate was increased 7 % and the recall rate by 64 %.Footnote 16

Based on these results, the cleaning stage in this project comprises nine steps and was applied to DE-INV and PROFLIST. In addition to capitalization and comma deletion, these nine steps include eliminating title information in the name field and saving it in a separate file.Footnote 17 Replacing “umlauts” (ä, ö, ü) was especially important for Germany. Moreover, we separated the address field into street, postal code, and country information as well as extracted initials for names. Where either the postal code or the city was specified, we supplemented the missing information.

Appendix 2

See Table 10.

Table 10 Identified criteria combinations inventor–inventor filtering
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Schoen, A., Heinisch, D. & Buenstorf, G. Playing the ‘Name Game’ to identify academic patents in Germany. Scientometrics 101, 527–545 (2014). https://doi.org/10.1007/s11192-014-1400-x

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