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Patent citation indicators: One size fits all?

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Abstract

The number of citations that a patent receives is considered an important indicator of the quality and impact of the patent. However, a variety of methods and data sources can be used to calculate this measure. This paper evaluates similarities between citation indicators that differ in terms of (a) the patent office where the focal patent application is filed; (b) whether citations from offices other than that of the application office are considered; and (c) whether the presence of patent families is taken into account. We analyze the correlations between these different indicators and the overlap between patents identified as highly cited by the various measures. Our findings reveal that the citation indicators obtained differ substantially. Favoring one way of calculating a citation indicator over another has non-trivial consequences and, hence, should be given explicit consideration. Correcting for patent families, especially when using a broader definition (INPADOC), provides the most uniform results.

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Notes

  1. Often referred to as patent citations, forward citations or patent citation count. We will use these terms throughout this paper.

  2. Up to 88 % of applications score a non-zero citation count on at least one of the citation indicators we computed.

  3. This paper needs to be cited when the NBER database is used.

  4. We show this later in Table 4.

  5. The ‘duty of candor’ rule requires that applicant and inventors involved in a patent application must disclose all known information which may adversely affect the probability of obtaining a granted patent.

  6. INPADOC is an abbreviation for INternational PAtent DOCumentation, the patent data collected but not generated by the EPO (2014). It is also used to denote the extended patent family in the EPO PATSTAT databases.

  7. DOCDB is the EPO master documentation database (Martínez 2011). It is also used to denote the examiner’s technology-based patent family in the EPO PATSTAT databases.

  8. Albrecht et al. (2010) define the DOCDB patent family as patent applications that have an equal ‘priority picture’: this can, under certain circumstances, include the priority application itself. Additionally, this family is corrected to include applications that have the same technical content but have been excluded due to a ‘discrepancy in the priority picture’ Albrecht et al. (2010: 283).

  9. This statement holds for the vast majority of patent applications in the EPO PATSTAT database; there is a small minority of patents (0.09 % of DOCDB patent families) that do not fulfill this criterion due to discrepancies in their priority picture. However, these families do not affect the analyses presented later in this paper.

  10. These imply changes in publication types; patent duplicates that occur before and after 2001; and applications that are not available before 2001 but partly available thereafter.

  11. These are added to the database to maintain logical links and do not actually represent any patent applications.

  12. An example of this pertains to the well-known issue that EPO references other patents by referring to the references of their PCT equivalents via a non-patent reference in PATSTAT. This has been noted in Harhoff et al. (2003) and Neuhäusler et al. (2011).

  13. In the case of applications filed through the PCT, other applications that followed this route were taken.

  14. The exact figures are: 21 % for EPO applications, 12 % for USPTO applications and 37 % for PCT applications.

  15. The size of the groups of highly cited patents identified by the 5 SD outlier criterion varies between 765 and 35,145 depending on the source office and indicator specification.

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Acknowledgments

Lin Zhang acknowledges the support of the National Natural Science Foundation of China Grant No. 71103064.

Author information

Authors and Affiliations

  1. Department of Managerial Economics, Strategy and Innovation, KU Leuven, Naamsestraat 69, 3000, Louvain, Belgium

    Jurriën Bakker, Dennis Verhoeven, Lin Zhang & Bart Van Looy

  2. North China University of Water Conservancy and Electric Power, No. 36, Beihuan Road, Zhengzhou, 450045, Henan, China

    Lin Zhang

Authors
  1. Jurriën Bakker
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  2. Dennis Verhoeven
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  3. Lin Zhang
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  4. Bart Van Looy
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Corresponding author

Correspondence to Jurriën Bakker.

Appendices

Appendix 1: Correlation between indicators from the same office

See Tables 11, 12 and 13.

Table 11 Correlation of indicators of patents filed at the EPO
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Table 12 Correlation of indicators of patents filed at the USPTO
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Table 13 Correlation of indicators of patents filed at the PCT
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Appendix 2: Correlation between indicators from different offices

See Tables 14, 15 and 16.

Table 14 Correlation coefficients of indicators pertaining to patents filed both at the EPO (columns) and the USPTO (rows)
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Table 15 Correlation coefficients of indicators pertaining to patents filed both at the EPO (columns) and the PCT (rows)
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Table 16 Correlation coefficients of indicators pertaining to patents filed both at the USPTO (columns) and the PCT (rows)
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Appendix 3: Variable cluster method

This appendix explains the cluster algorithm that was used to cluster indicators. This method is an implementation of the VARCLUS procedure in the SAS® software package (SAS Institute 2008). What follows are excerpts from the SAS manual (SAS Institute 2008: 7461–7463) explaining the logic of the underlying procedure. Our specific settings are detailed in italics. Options not related to our analysis have been omitted.

‘The VARCLUS procedure divides a set of numeric variables into disjoint or hierarchical clusters. Associated with each cluster is a linear combination of the variables in the cluster. The linear combination used here consists of the first principal component. (…) The first principal component is a weighted average of the variables that explains as much variance as possible.

(…)

The VARCLUS procedure tries to maximize the variance that is explained by the cluster components, summed over all the clusters. The cluster components are oblique, not orthogonal, even when the cluster components are first principal components. In an ordinary principal component analysis, all components are computed from the same variables, and the first principal component is orthogonal to the second principal component and to every other principal component. In the VARCLUS procedure, each cluster component is computed from a different set of variables than all the other cluster components. The first principal component of one cluster might be correlated with the first principal component of another cluster. Hence, the VARCLUS algorithm is a type of oblique component analysis.

We use the correlation matrices as input for the principal component analysis used in the VARCLUS procedure (…)

The VARCLUS algorithm is both divisive and iterative. By default, the VARCLUS procedure begins with all variables in a single cluster. It then repeats the following steps:

  1. 1.

    A cluster is chosen for splitting. Depending on (…) the largest eigenvalue associated with the second principal component (…)

  2. 2.

    The chosen cluster is split into two clusters by finding the first two principal components, performing an orthoblique rotation (raw quartimax rotation on the eigenvectors; Harris and Kaiser 1964), and assigning each variable to the rotated component with which it has the higher squared correlation.

  3. 3.

    Variables are iteratively reassigned to clusters to try to maximize the variance accounted for by the cluster components.

(…)VARCLUS stops splitting when every cluster has only one eigenvalue greater than one, thus satisfying the most popular criterion for determining the sufficiency of a single underlying dimension.’

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Bakker, J., Verhoeven, D., Zhang, L. et al. Patent citation indicators: One size fits all?. Scientometrics 106, 187–211 (2016). https://doi.org/10.1007/s11192-015-1786-0

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