A New Proxy for Strategic Patents and
Its Application to Japanese and US Patent Data (*)
Invited Researcher: Henri de BELSUNCE (**)
This paper develops a new proxy for the strategic patents of firms and applies it to the Japanese IIP patent dataset andthe US NBER patent dataset. Specifically, we identify patent clustering à la Rivette and Kline (2000) in the data ex-post.
Initial results - in line with previous findings in the literature – indicate that strategic patenting is an important problem inJapan, even more so than in the US. We acknowledge caveats to our analysis, in particular with respect to the controllingfor patent portfolio sizes. These drawbacks are focal points for further research, but this paper already presents the potentialof a fully-fledged proxy for strategic patenting that could be of great use for regulators and policy makers at the patentoffice.

I

Introduction

Many studies rely on patent data to investigate theeffects of patent scope on innovation (Qian 1 2007,
Motohashi 2 2004, Lerner 3 2009). In these studies
innovation is often proxied for by observable patentstatistics. However, these statistics as measures ofinnovation may be flawed when firms file patents forstrategic reasons.
There exist numerous theoretical papers on how
firms may strategically want to optimise their patentportfolio. The consensus of these papers lies on the pointthat firms may have an incentive to shade their patentapplications in order to achieve other goals than just theprotection of an idea. Possible reasons may include theblocking of the research of competitors, enhancing
royalty income or, more generally, create barriers to entry
for competitors.
An important strategy discussed in the literature isthe creation of patent thickets (Shapiro4, 2001), wherefirms demand many, overlapping patent rights. Whenentering a market in the presence of intellectual property(IP) protection requires either the licensing or invalidatingof those patent rights, these IP rights create barriers tomarket entry. The larger and more obscure a patentportfolio of an incumbent is, the more costly it is forentrants to tackle the issue of dealing with these existing
IP rights. A theoretical foundation that rationalises
excessive patenting as a means to reduce market entry isgiven in Belsunce 5 (2014). In practice, a significant
strategy of firms, which leads to the creation of patent
thickets, is that of patent clustering (Rivette and Kline6,
2000), where firms file for additional related patents ofminor quality around a core patent that protects a valuable

innovation. These additional related patents are often of
low quality and represent a burden on the patent system(Moore7 2005), let alone exacerbate the pressure on thepatent offices. It is a common argument that low quality
patents are inefficient and should be avoided. However,devising policies reducing the use of weak patents is very
complicated since they are difficult to identify ex-ante.
In order to study the problem of weak patents,
identifying them ex-post is a vital step. We contribute tothis task by developing a statistic to identify the additional
related patents (called excess patents) in the vicinity ofvaluable patents in the data. Such a proxy is mandated by
the fact that no clear theoretical prediction exists for the
strategic reaction of firms to patent reforms.
Our approach is purely based on metadata, i.e.
citation patterns, and thus easily implementable for use inpractice. The analysis is composed of (i) devising ameasure of proximity between patents within a firm’s
patent portfolio. The separation of a firm’s patents into
core or related patents is conditional on the two being
related. We use backwards citation overlap as ourmeasure of the proximity. (ii) Among patents that aretechnologically close to each other, we distinguish thecore from related patents by using forward citation
numbers as a proxy for their impact on future innovations.
(iii) We combine the two measures into an overall proxy,
which can be read similar to a probability, that a patent isa core or a related, excess patent. We then look at
averages of these proxies for patents of yearly cohorts tobetter understand the evolution of strategic patenting overtime and across countries.
Our results indicate that excessive patenting has
been constant over time in both the US and Japan.
Furthermore, our proxy indicates that excessive patenting(*) This is a summary of the report published under the 2014FY Industrial Property Research Promotion Project (2014FY-2016FY) Entrusted
by the Japan Patent Office.
(**) Doctoral Research Fellow, Max Planck Institute for Innovation and Competition, at our institute over a period of approximately 5 months
from May 25, 2015 through October 17, 2015, as an Invited Researcher for the Fiscal Year 2015.
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IIP Bulletin 2016 Vol.25

1 ●has been on average higher in Japan than in the US – aresult that coincides with earlier findings by IIP8 (1998).
While these findings are interesting, we emphasisesome important caveats - that have not been thoroughly
tested yet - in the discussion in section VI of the full
paper.
Still, this work and the developed proxy may be ofparticular importance for policy makers and the patentoffice concerned with the large number of patentapplications. The indicator of excess patenting developed
here could help to identify strategic patent applicants andhelp devise policies to prevent such behaviour. In
particular, the proxies developed seem to be adapted forthe analysis of individual firm’s patent portfolios.
Avoiding excess patents may bring a social welfare
improvement as it could foster market entry and thus
competition. Such a rationale may be even more valid ina context with many start-ups and small and medium
enterprises. These companies are particularly sensitive tobarriers to entry created by intellectual property due to theexpertise required in dealing with protection rights. We
look forward to more research in this domain.
This analysis is strongly related to the literature on
devising statistics to measure patent thickets. Graevenitz
et al.9 (2009) are the first to devise a proxy to measure
the density of patent thickets directly.
Two further important indices used to identify patent
thickets have been proposed in the literature. The
fragmentation index developed by Ziedonis10 (2004) andthe blocking index proposed by Siebert and vonGraevenitz11 (2008). In contrast to their approach, ourproxy for strategic patenting does not rely on therelationship of patent portfolios between different
companies, but solely focuses on individual firm’s
patenting activity. The angle of analysis of patent thickets
is thus not the same.
We also note that Clarkson 12 (2005) derives anidentifier of patent thickets, by analysing the citation
density of patent networks.
With respect to Japanese data, two papers that study
the patent thicket problem are particularly notable. Doi
and Zhang13 (2014) use the blocking index of Clarkson14(2005) and Zhang et al.15 (2013) to show that the threatof patent blocking has a positive, significant effect on
patent licensing. Nagaoka and Nishimura16 (2014) use
both complementarity and fragmentation of IP rights asproxies for patent thickets in order to investigate theirimpact on the first-mover-advantage that is understood tobe a key driver of R&D activities. We contribute ananalysis of a newly devised proxy for patent thickets on
Japanese patent data. Furthermore, we compare theresults for Japan with those from the US.

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II Data
For Japan, we use the IIP patent database, the
NISTEP company dataset 17 and the inventor citation
dataset from Artificial Life Laboratory for the analysis.
The first dataset consists of Japanese patent applications
since the late 1960s until 2012. Its specificities aredetailed in Goto et al.18 (2007).
For the United States, we use the NBER patent
database. This dataset contains detailed information on
patents granted in the US between 1976 and 2006. The
specificities of this data are detailed in Hall et al.19 (2001).
Furthermore, company characteristics are given by the
upfront match to firms listed in the COMPUSTAT database.
We make a few data adjustments in our analysis,
mainly in order to ensure comparability between the US
and Japanese datasets. All adjustments with notes on theirimpacts can be found in the full report. We highlight thatthe resulting subsamples are representative of the fullsamples.

III Empirical strategy
1Overview of Identification Strategy
We aim to identify patents in the data, which are
likely to be of a strategic nature. Specifically, we focus on
the type of excessive patenting described in detail by
Rivette and Kline20 (2000), called patent clustering. Thisrefers to strategically patenting weaker, related ideas
around a core invention. We see this as a driver for patent
thickets, when firms file for patents on patentableside-aspects of a core invention since we expect the riskof an IP overlap between the core patent and the related
patent to be high.
We devise a proxy, which identifies these patents.
We take care to clean this proxy of noise in the data, e.g.
truncation and inflation of patent citations – for details see
section IV-3. We investigate the evolution of this statistic
over time and, by applying the same methodology topatent data from the US, compare the results for bothcountries and aim to trace back differences tofundamentals of the patent system.
The identification of these excessive patents is basedon three main components, which are detailed in thefollowing paragraphs. First and given that our focus is on
excessive patenting in vicinity of a core invention, we
assume that the core and related patent are both filed by
the same company21. Companies have the index j in what
follows.
In the second step, we devise alpha, our measure oftechnological similarity of a patent and the other patents inthat firm’s patent portfolio. This measure is based on thecitation overlap of patent i of firm j with all other patents infirm j’s patent portfolio. The simplest example is a patentportfolio consisting of two patents A and B. If patent A has
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3 backwards citations and patent B has 4 backwards
citations, 2 of which are identical to the backwards citationsof A, then the citation overlap percentages are 66% and
50% respectively, indicating a measure of similarity
between the patents.
When there are more than two patents in a firm’s
portfolio, alpha does not measure the similarity between
pairs of patents, but measures the similarity of each patent
with all the other patents together that exist in that firm’s
portfolio at the time of application of the patent inquestion. In the previous example, add a patent C, which
has a single citation that C only shares with patent A.
Then overlap percentages are: 100% for patent A, 50%
for patent B and 100% for patent C.
This percentage gives an abstract measure of thetechnical proximity of a patent to the other patents in theportfolio of firm j at the time of application of the patentin question. The higher this figure is, the closer we expect
the patents to be and thus a possible situation where
excessive patenting may occur is given. However, whilealpha indicates which patents may be part of a group ofcore and related patents, it does not give us information
on which patent is of which type, i.e. which one is a coreor an excessive patent. We do this in the next step.
In the third step, we devise beta, our measure ofquality (or impact) of the patents and use this to separate
the core from excessive patents. This measure ultimately
relies on Trajtenberg22 (1990), who demonstrated a strong
correlation between forward citations of a patent and ameasure of the (social) value of the patents. This measureof patent value is also confirmed by Lanjouw and
Schankerman 23 (1999) as well as by Harhoff et al. 24(1997) and has been used for example by Hall andZiedonis 25 (2001). In our simple previous example ofpatent A and B: If A has 8 forward citations and B has 1
forward citation, then we consider that patent A issignificantly more valuable than patent B.
Finally, we combine alpha and beta into a single
1

proxy, called gamma, as follows:
,

,

1

,

where index i refers to the patent, j to the firm and –jrefers to patents of other firms. Thus, we ignore forward
self-citations when computing beta. Furthermore,
Trajtenberg 26 (1990) finds that the utility of forward
citations as an indicator of patent value is increasing in
the number of citations, i.e. the informational content of acitation increases at the margin. We incorporate thisinsight by squaring the impact of beta. This squared term
enables us to get a clearer separation of high value patents
compared to low value patents.
We encourage the reader to refer to the full report for
further details on the implementation of this algorithm
and the precise definition of the relative measures ofalpha, beta and gamma.
Gammadash, which will be presented only in thissummary, is defined as the ratio of alphadash and(1+betadash2). Alphadash compares the degree of citation
overlap of the patents of firm j with the average citation
overlap of all comparable patent portfolios. Betadash
measures the relative quality in comparison to all patentsof the same cohort.
We will use this proxy for analysis – however, werecommend the reader to take note of the caveats of thisproxy described in the full report.

IV Results
In the following we present parts of the results. We
show the computed measures for gammadash. The left
figures present results for Japan, the right ones refer to the
US. Furthermore, the graphs represents the average over
all technology classes.

Gammadash – Japan/US Comparison

Figure 8: Evolution of average gammadash over time in Japan and the US.
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3 ●Figure 8 summarises much of this work. For reasons
outlined in the full paper, we focus on the timeframe fromapprox. 1980 to 2000, indicated by non-shaded parts ofthe top row of figure 8. Gammadash is a combination of
the relative measures alphadash and betadash, which bothmeasure patents’ similarity and value in comparison totheir peers, i.e. other patents applied for in the same year
and of the same technology class. Combined, we takethese measures as an indicator whether granted patents
may be classified as excessive patents that have been filed
for strategic reasons, e.g. for a patent clustering strategy.
First, we observe that gammadash is constant in thetimeframe of our analysis. This is an interesting result,since it states that, on average and controlling for timetrends, excessive patenting has remained roughly constantover time. This insight lends support to the understandingthat the use of strategic patents has prevailed to a similar
extend over the two decades from 1980 to 2000.
Second, we observe that gammadash is on average
higher in Japan than in the US. This result is particularlyinteresting as it suggests that Japanese firms engage instrategic patenting activity to a higher extend than US firms.
Furthermore, this result coincides with previous findings
by IIP (1998)27. IIP compares the magnitude of patent costs,
which are composed of application and maintenance costsof patents, in relation to operating figures (e.g. total R&D
costs or Sales) in Japan and the US. The report finds thatthe ratio of patent costs as percentage of sales is equal to
0.20% in Japan and 0.10% in the US. Similarly, the ratio of
patent costs as a percentage of R&D costs is significantly
higher in Japan than in the US (4.71% versus 2.69%,
respectively). While his results suggest that Japanese firms
spend additional resources on their IP strategies, our results
is congruent with his and indicates that the high fundallocation of Japanese firms to their patenting activities
could be related to excessive patenting.
While graphs of the average values of alphadash andbetadash would not yield any insights 28 , gammadash
shows variation because it is essentially a matching of thetwo measures of alphadash and betadash. Gammadash isan average taken over all patents, not within firm
portfolios. Hence gammadash is not constant, even underfirm symmetry, because it depends on which value ofalphadash “meets” a value of betadash. And since, alphaand beta are not perfectly correlated, we observe somemeaningful variation.
However, we acknowledge that so far, we do notcontrol for firm size in our analysis – a factor that might
be an important driver of our results. Especially, if we
assume that the US is composed of a high number offirms with patent portfolios of size one29. This is a valid
concern since we use the COMPUSTAT firm identifier
for the US database, which is significantly wider than the
NISTEP firm identifier in Japan30. By definition, alpha(and thus gamma) for patents of size-one-portfolios will●

be zero, since there is no patent owned by the same firm
to measure a citation overlap with. Adding large numbersof single-patent-owners to the averages, will introduce astrong downwards bias of the indicators. This is an
important caveat and we emphasise its relevance for the
results. Controlling for this factor is a natural extension ofthis piece of work and vital to take the proxy further
towards usefulness in practice.

V Discussion
While the newly developed proxy is not yet in its
final version, we believe that this work has many qualities.
First of all, it is a transparent measure of the strategic
patenting behaviour of firms, which is computed at thelevel of the individual patents and may be aggregated toportfolio or even country level. Thereby, the perspectiveof analysis is flexible and adapted for policy makers.
Second, our proxy for excessive patenting iscomputed on metadata only. This means that it relies
solely on patterns in the citation data and does not require
any specific analysis of the individual patent’s contents inthe first place. Computationally, this is considerably lessdemanding and it therefore becomes a feasible tool for thescreening of large patent landscapes and allows to flag
patent of specific interest for further analysis.
We discuss the potential impact of firm portfolios
sizes in the full and thus are cautious to rely overly on thepresent results from the cross-country comparison. Our
results, however, motivate further work on thedevelopment of this proxy.
The potential of this proxy is significant. Being able
to capture the strategic filing nature of individual patents
in a single index would allow to condition broader studies
on the effect of patent rights on innovation on thestrategic behaviour of players in the patent system. Froma patent office perspective, a broader study on thedeterminants of strategic patenting could be developed
using our gamma proxy as the outcome variable. Such a
regression would allow the regulator to anticipate
strategic patenting behaviour by firms and fine-tune thepatent system in accordance.

VI Conclusion
This paper is a first step in devising a new proxy forstrategic patenting, specifically excessive patenting, in thevicinity of a core innovation. Initial results - in line withprevious findings in the literature – indicate that strategic
patenting is an important problem in Japan, even more sothan in the US. We acknowledge caveats to our analysis,in particular with respect to the controlling for patentportfolio sizes. These drawbacks are focal points forfurther research, but this paper already presents thepotential of a fully-fledged proxy for strategic patenting.
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1

Yi Qian, “Do National Patent Laws Stimulate Domestic
Innovation in a Global Patenting Environment? A
Cross-Country Analysis of Pharmaceutical Patent Protection
1978-2002”, The Review of Economics and Statistics, Vol. 89,
No. 3 (Aug., 2007), pp. 436-453
2 Motohashi, Kazuyuki. "Japan’s patent system and business
innovation: reassessing pro-patent policies." Patents, innovationand economic performance: OECD conference proceedings.
OECD Publishing, 2004
3 Josh Lerner, “The Empirical Impact of Intellectual Property
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One Hundred Twenty-First Meeting of the American Economic
Association (May, 2009), pp. 343-348
4 Shapiro, Carl (2001), “Navigating the Patent Thicket: Cross
Licenses, Patent Pools and Standard Setting.” In Innovation
Policy and the Economy, Volume 1, 119–150, MIT press.
5 deBelsunce, Henri „Do More Patents Mean Less
Entry?“ Working paper, 2014. Please ask for the latest version.
An interesting quote from the discussion with the CEO of astart-up company in an industry with important IP rights reveals
that artificially inflated patent portfolios are indeed a problem
when competitors and investors „count patents by thekilo“ (referring to the fact that the length and number of patents
often trumps the intellectual quality of the patents).
6 Rivera, Kevin G. and David Kline (2000). Discovering newvalue in intellectual property. Harvard Business Review, 55.
7 Moore, Kimberly A. (2005), “Worthless Patents.” Technicalreport, George Mason University School of Law Working
Papers Series : 27.
8 Institute of Intellectual Property. (1998), Research and Study on the
Economic Analysis of the Intellectual Property System, 130-131.
9 Graevenitz, Georg v. and Stefan Wagner and Dietmar Harhoff,“How to measure patent thickets – a novel approach”, Economic
Letters, Vol. 111, pages 6-9 (2011)
10 Ziedonis, R. H. (2004). Don't fence me in: Fragmented markets
for technology and the patent acquisition strategies of firms.
Management Science, 50(6), 804-820.
11 Siebert, R., & von Graevenitz, G. (2008). Does Licensing
Resolve Hold Up in the Patent Thicket? (No. 2008-01).
Discussion Papers in Business Administration.
12 Clarkson, G. (2004). Objective identification of patent thickets:
A network analytic approach. Harvard Business School
Doctoral Thesis.
13 Doi, Noriyuki & Xingyuan Zhang (2014): Patent Thickets andLicensing:Empirical Findings from Japanese Listed Companies.
OECD.
14 Footnote 20.
15 From Zhang, Xingyuan, Michael R. Ward and Yoshifumi Nakata.
2013. “The market value of patenting: New findings fromJapanese firm level data.” Unpublished manuscript., based on
source in footnote 21.
16 Nagaoka, Sadao and Yoichiro Nishimura, “Complementarity,
Fragmentation and the Effects of Patent Thickets”, RIETI
Discussion Paper Series 14-E-001 (2014)17 Japanese Name: „Nistep Kigyomei Jisho“18 Goto, Akira and Kazuyuki Motohashi, “Construction of aJapanese Patent Database and a first look at Japanese Patenting
Activities”, Research Policy, Vol. 36, Issue 9 (2007)
19 Hall, B. H., A. B. Jaffe, and M. Trajtenberg (2001). "The NBER
Patent Citation Data File: Lessons, Insights and MethodologicalTools." NBER Working Paper 8498.
20 See footnote 9.
21 For Japanese data, we use the NISTEP company identifier since●

IIP Bulletin 2016 Vol.25

it is more stable over time (i.e. across mergers of applicants,
name changes, etc.) than the applicant number from the IIP
patent database. See the discussion in section VI for thelimitations due to that choice. For the US data, we use theprovided company identifier of the NBER patent database(which originally is drawn from the Compustat database).
22 See footnote 4.
23 Lanjouw, Jean O., and Mark Schankerman. The quality of ideas:measuring innovation with multiple indicators. No. w7345.
National Bureau of Economic Research, 1999.
24 Harhoff, Dietmar, et al. "Citation frequency and the value ofpatented inventions." Review of Economics and statistics 81.3
(1999): 511-515.
25 Hall, Bronwyn H., and Rosemarie Ham Ziedonis. "The patentparadox revisited: an empirical study of patenting in the US
semiconductor industry, 1979-1995." RAND Journal of
Economics (2001): 101-128.
26 See footnote 4.
27 Institute of Intellectual Property. (1998), Research and Study on the
Economic Analysis of the Intellectual Property System, 130-131.
28 By definition of these measures, the average over all patents isdefined equal to 1, i.e. a straight horizontal line.
29 In other words, companies that own only a single patent.
30
See section III for details on the datasets.

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