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Out of sight: patents that have never been cited

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Abstract

Patent citations have become an acceptable proxy for inventions’ quality. Our study offers the first systematic exploration of uncited patents. Analyzing data on all US patents issued between 1976 and 2008, we examine the ratio of uncited patents out of all patents granted each year. We find a robust pattern, consistent across technological fields, whereby the percentage of uncited patents declined between 1976 and the mid-1990s, but has been significantly increasing since then. We discuss policy implications of these findings and suggest that the ratio of uncited patents can serve as a complementary measure for evaluating the patent system.

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Notes

  1. For pioneering studies see, e.g., Schmookler, 1966; Griliches, 1984; Trajtenberg, 1990. For more recent studies see, e.g., Lanjouw and Schankerman, 2001; Allison et al. 2004; Érdi et al. 2013; Shur-Ofry, 2017; Ashtor, 2019; Pedraza-Farina and Whelan, 2020.

  2. In the analysis below we use the term “quality” or “patent quality” in a broad and expansive manner, to refer not only to a patent’s technological quality but also to aspects such as its impact on subsequent technologies, or its value. We discuss the evidence on the connection quality and a large number of forward citations infra.

  3. See, e.g., Arts and Veugelers, 2015:1218 (“[P]atents receiving a disproportionately large number of citations can be considered as breakthroughs while patent receiving no citations as failures”).

  4. For a discussion of the general disregard of negative information in the innovation system, see Shur-Ofry, 2016.

  5. The role of the patent system as a major vehicle for promoting innovation traces back to the U.S. Constitution—See U.S. Const. Art. I, Sec. 8, Cl. 8. (empowering Congress to “promote the Progress of Science and useful Arts, by securing for limited Times to Authors and Inventors the exclusive Rights to their respective Writings and Discoveries”).

  6. Infra. As explained herein, for the purpose of our study we broadly define this field to include patents in information and communications technology (ICT), fin-tech and med-tech.

  7. Infra, tables 1 and 2, and accompanying text. As we explain below, the associations between cited/uncited status and additional two factors (number of inventors and “degree of similarity”) are weak.

  8. Infra. Notably, even after controlling for these characteristics, the “U” shaped pattern we find continues to hold, which implies that the increase in the ratio of uncited patents cannot be attributed to any of the aforesaid factors.

  9. See the USPTO Manual of Patent Examining Procedure (MPEP) (9th. edition, last revised January 2018), available at https://mpep.uspto.gov/RDMS/MPEP/current#/current/d0e18.html.

  10. U.S. Patent Act, 35 USCS § 101 (“Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title”), and § 103 (“A patent may not be obtained..[…]..if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which the claimed invention pertains”) (emphases added).

  11. See "Duty to Disclose Information Material to Patentability” 37 C.F.R. § 1.56 (“Each individual associated with the filing and prosecution of a patent application has a duty of candor and good faith in dealing with the Office, which includes a duty to disclose to the Office all information known to that individual to be material to patentability..[…]..”).

  12. Fleming (2001) and Arts and Veugelers (2015) found a positive association between patent citations and a high level of recombinations in the patent’s backward citations and subclasses.

  13. Two prominent studies that focused on other negative aspects embedded in patent data are Moore (2005), who examined patents that were not renewed by their owners, and Cotropia and Schwartz (2020), who explored abandoned patent applications that did not mature into issued patents.

  14. http://www.patentsview.org/download/. A small number of patents (two percent) did not have complete data needed for the analysis; hence, they were not included in our data. This small amount of missing data is typical in such analyses and does not affect our results.

  15. For an overview of the US Patent Classification System (USPC), see http://www.uspto.gov/patents/resources/classification/overview.pdf. For identifying subclasses relevant for pharmaceutical drug patents (our first category) we used the aforesaid classification system and included in this group USPC Classes 424 and 514. For identifying subclasses relevant for med-tech, fin-tech, and information and communications technology patents (our second group) we relied on the list of subclasses previously compiled by Branstetter et al. (2019), and by Gandal and Cohen (2019), which includes ICT/Information Security (USPC), Fin-Tech (IPC) and Med-Tech (IPC). Our third category is a residual group and includes patents that are not included in the other two groups (for example, certain mechanical patents). Notably, because inventions can be classified into more than a single subclass our categories are not completely exclusive.

  16. See Table 3 in Appendix 1, infra. The Table as well as other Tables in the Appendix demonstrate that the additional two factors (numbers of inventors and backwards similarity) do not have a substantial association with “uncited” status.

  17. See https://www.uspto.gov/patents-maintaining-patent/maintain-your-patent. The fees are due for patents issued after 1980, and are payable in three intervals during the life of the patent, occurring after 3–4 years, 7–8 years and 11–12 years from issuance.

  18. https://uspto.data.commerce.gov/dataset/Patent-Maintenance-Fee-Events-1981-Present-/95ij-9exb. Note that this data is available only with respect to patents issued after 1982.

  19. A partial correlation between two variables is the correlation between them after removing the effects of all other variables. See Table 5.

  20. See 35 U.S.C. 122; https://www.uspto.gov/web/offices/pac/mpep/s1120.html. As detailed therein, there are certain nuances in the calculation of the 18 months period, yet these are immaterial for the purpose of this study.

  21. Additionally, our results are robust when comparing “rarely cited patents”, namely patents that obtained either zero citations or a single citation, to patents that receive more than one citation.

  22. The associations between uncited patents and the two other factors-backwards similarity and number of inventors-are relatively small or negative.

  23. Obviously, with respect to younger patents issued after 1996 the gap between uncited after “10 years” and uncited after “all years” narrows.

  24. See United State Patent and Trademark Office, Patent Quality, https://www.uspto.gov/patent/patent-quality (stating that “[t]o ensure we continue to United State Patent and Trademark Office, Patent Quality issue high-quality patents that will fuel innovation well into the future, the Office of the Deputy Commissioner for Patent Quality, along with our partners across the Patents organization, promotes and supports the continuous improvement of patent products, processes and services through collaboration with internal and external stakeholders of the intellectual property community.”) United State Patent and Trademark Office, Office of the Deputy Commissioner for Patent Quality https://www.uspto.gov/about-us/organizational-offices/office-commissioner-patents/office-deputy-commissioner-patent-19 (“The Deputy Commissioner for Patent Quality is responsible for optimizing the quality of patent products, processes and services to build a culture of process improvement and enhanced patent quality “).

  25. For example, Fleming (2001) and Arts and Veugelers (2015) found a positive association between patent citations and a high level of recombinations in the patent’s backward citations and subclasses.

  26. For the patents included in each category, see supra, note 21 and accompanying text. As explained therein, our definition of software-related patents includes patents in the fields of information and communications technology, fin-tech and med-tech.

  27. One should note, however, that the legal requirement is to consider patent applications against all relevant prior art, namely patents and NPL. Therefore, this hypothesis certainly warrants further validation.

  28. Note, however, that according to Glänzel and Meyer (2003: 422), 98.5% of US patents have not been cited in scientific literature.

  29. Cf. Noorden (2017) who discusses uncited science and suggests that certain domains may be less “cumulative” than others.

  30. Two prominent decisions are Bilski v. Kappos, 561 U.S. 593 (2010) (holding that patents can be rejected on subject-matter eligibility grounds and denying a patent over a method of risk hedging); Alice Corp. Pty. v. CLS Bank Int'l, 134 S. Ct. 2347 (2014) (holding that implementing a merely abstract idea on a computer is unpatentable).

  31. Cf. Chien (2018: 90–92) (reviewing the recent case law that elevated the patentability standards that apply to software, and suggesting that their long-term impact on patent quality is yet to be evaluated).

  32. Leahy-Smith America Invents Act, 125 Stat. 284.

  33. Cf. Cotropia & Schwartz (2020)(discussing patent applications that were abandoned for various reasons, and demonstrating that “abandoned applications” still receive significant citations from patent examiners).

  34. E.g., the data concerning patent assignments recorded with the USPTO: Patent Assignment Dataset, https://www.uspto.gov/learning-and-resources/electronic-data-products/patent-assignment-dataset.

  35. See, e.g., the Stanford NPE Litigation Database, which tracks patent litigation initiated by non-practicing entities and patent assertion entities (often referred to as “patent trolls”)—https://law.stanford.edu/projects/stanford-npe-litigation-database/#slsnav-brief-dataset-methodology.

  36. The variable CITED includes self-citations unless noted otherwise.

  37. Since we have a constant in the regression, we cannot include a dummy variable for 1976. Otherwise, there would be perfect multicollinearity.

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Acknowledgements

We are grateful to the two anonymous referees for extremely helpful suggestions and comments. For valuable comments and input we also thank Ronen Avraham, Eran Bareket, Miriam Marcowitz-Bitton, Reuven Cohen, Rebecca Eisenberg, Roger Ford, Jeanne Fromer, Richard Gruner, Sharon Hausdoff, Dmitry Karshedt, Paul Kaye, Mark Lemley, Michael Livermore, David Schwartz, Ofer Tur-Sinai, Saurabh Vishnubhkat, and the participants of the European Law and Economics Association Conference (2019), the Data Science and Law Conference at Bar-Ilan University (2019), the WIPO Conference on Emerging Technologies (2019), and the Intellectual Property Scholars Conference at Stanford University (2020, Online). We are also grateful Andrew Toole for assistance in obtaining relevant data. Neil Gandal is very grateful for a grant from the Foerder Institute for Economic Research.

Author information

Authors and Affiliations

  1. Berglas School of Economics, Tel Aviv University, Dr George Wise St. 40, Tel Aviv-Yafo, Israel

    Neil Gandal & Michael Crystal

  2. Hebrew University of Jerusalem Law Faculty, Jerusalem, Israel

    Michal Shur-Ofry & Royee Shilony

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  1. Neil Gandal
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  2. Michal Shur-Ofry
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Correspondence to Neil Gandal.

Appendices

Appendix: Formal econometric (regression) analysis

Variables

The dependent variable is “Cited”, where Cited equals one if the patent receives one or more citations in the first ten years following its issuance. If the patent receives no citations during the first ten years following its issuance, Cited equals zero.Footnote 36

Since the dependent variable is a binary variable, we run a Logistic regression. The same qualitative results are obtained using a Probit regression.

The independent variables in the regressions are number of backward citations, number of subclasses, number of claims, number of inventors and backwards similarity.

Because all independent variable except for backwards similarity are highly skewed, we enter these variables in logarithms. The independent variables included in the regression are.

l_back_cites–the natural logarithm of the number of citations the patent made to preexisting patents.

l_subclasses–the natural logarithm of the number of subclasses listed on the patent.

l_claims–the natural logarithm of the number of claims.

l_inventors–the natural logarithm of the number of inventors on the patent.

b_similarity–the backward similarity as defined above.

Finally, we include dummy variables for the grant year. These are the primary variables of interest. We include data from 1976 to 2008; therefore we have dummy variables for each year from 1977 to 2008.Footnote 37

Regression analysis

The estimation equation is as follows, where the subscript “j” refers to each patent. For compactness we do not list the dummy variables for year.

$${\text{Cited}}_{j} = \beta_{0} + \beta_{1} *{\text{l}}\_{\text{back}}\_{\text{cites}}_{j} + \beta_{2} *{\text{l}}\_{\text{inventors}}_{j} *{\text{l}}\_{\text{subclasses}}_{j} + \beta_{3} *{\text{l}}\_{\text{claims}}_{j} + \beta_{4} *{\text{b}}\_{\text{similarity}}_{j}$$

The results of the logistic regression are shown in Tables 3 and 4.

Table 3 Regression Results
Full size table
Table 4 Coefficients on the yearly dummy (binary) variables
Full size table

All of the estimated parameters are highly significant (*** means significant at the 99% level of confidence.) The estimated coefficients on the yearly dummy (binary) variables from the regression in equation I are shown in Table 4 below.

Thus after controlling for patent characteristics Table 4 shows that the pattern is exactly as in the raw data. From 1976 to 1996, a higher percentage of the patents are being cited over time. This is because the estimated coefficients on the yearly dummy variables increase (essentially monotonically) over that period. From 1996 through 2008, a lower percentage of the patents are cited over time and this decline is also essentially monotonic.

Lack of citations and patent renewals

The partial correlations between whether a patent was cited at least once and the explanatory variables are shown in Table 5. The first explanatory variable refers to the expiry of the patent due to non-payment of maintenance fees.

Table 5 Partial Correlations between the variable “Cited” and the explanatory variables (grant year 1982 or later)
Full size table

We further conducted formal regression analysis in which the variable “nonpayment of maintenance fees” was added to the regressions as an explanatory variable, to denote patents that expired due to non-payment of maintenance fees. In this part of the analysis, we included patents from 1982 through 2008, since the information on patent renewals is only available beginning in 1982. Our regressions results in Table 6 show that the estimated coefficient on the variable “nonpayment of maintenance fees” is negative and statistically significant (at 99 percent level of confidence). This means that, other things being equal, uncited patents were less likely to be renewed. We further found that after controlling for patent traits including the non-payment of renewal fees the “U-shape” pattern continues to hold. The latter results are available from authors upon request.

Table 6 Regression Results –with the variable “nonpayment of patent maintenance fees included.)
Full size table

All these result hold regardless of whether we include or exclude self-citations.

Robustness analysis: citations to published patent applications

See Tables 7 and 8

Table 7 Regression Results taking into account citations to published patent applications since 2002
Full size table
Table 8 Coefficients on the yearly dummy (binary) variables
Full size table

Robustness analysis: uncited versus once-cited patents

See Tables 9 and 10

Table 9 Regression Results for Uncited Patents versus Once-Cited Patents (“***” means significant at the 99% level of confidence.)
Full size table
Table 10 Coefficients on the yearly dummy (binary) variables
Full size table

Additional robustness analyses

We also ran the following robustness regressions:

  • We excluded self-citations for all regressions, that is, citations to patents with the same patent holder or the same inventor.

  • We included dummy variables for the eight IPC classes.

  • We included dummy variables for software and pharmaceutical patents

  • We used a linear rather than log linear functional form for the explanatory variables

The results are qualitatively unchanged, except that in the case where we exclude self-citations, the coefficient associated with the number of inventors is negative and significant, rather than positive and significant. The temporal graph of uncited patents is still U-shaped. These results are available from the authors upon request.

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Gandal, N., Shur-Ofry, M., Crystal, M. et al. Out of sight: patents that have never been cited. Scientometrics 126, 2903–2929 (2021). https://doi.org/10.1007/s11192-020-03849-z

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  • DOI: https://doi.org/10.1007/s11192-020-03849-z

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