Hitotsubashi University
Institute of Innovation Research

Impact of Patent Scope on subsequent Inventions:Findings from a new Measure
Yoshimi Okada
Yusuke Naito
Sadao Nagaoka

IIR Working Paper WP#17-03

Feb. 2017

Institute of Innovation Research
Hitotsubashi University
Tokyo, Japan
http://www.iir.hit-u.ac.jp

Impact of Patent Scope on subsequent Inventions: Findings from a new Measure†

Yoshimi Okadaa*, Yusuke Naitob, Sadao Nagaokac
a Institute of Innovation Research, Hitotsubashi University, 2-1 Naka, Kunitachi, Tokyo 186-8601, Japanb Institute of Innovation Research, Hitotsubashi University, 2-1 Naka, Kunitachi, Tokyo 186-8601, Japanc Faculty of Economics, Tokyo Keizai University, 1-7-34 Minamicho, Kokubunji, Tokyo 185-0021, Japan
February 2016

Abstract
While patent scope defined by patent claims provides crucial information on the contribution ofunderlying inventions to the state of the art, its existing measures do not seem to appropriately
capture it, especially with respect to the generality of the inventive concept. This study investigates
how significantly the breadth of the first claim can predict the patent’s knowledge impact on
subsequent inventions in complex and discrete technologies using the inverse of the first claim length
as the indicator. There are two major findings. First, this indicator has very significant predictive
power for the knowledge impact of the underlying invention as measured by applicant forward
citations, controlling for two existing indicators of patent scope (the number of patent claims and thenumber of different patent classification codes assigned) in both technology areas. Second, itspredictive power for the incidence of top-ranked patents increases in higher quantiles in the complex
but not the discrete technology area, unlike the other indicators. This is consistent with an economic
model predicting that the knowledge impact of an invention with broad scope has a high variance,
depending on the emergence of complementary inventions that enhance the impact of the initial
invention.
Keywords: patent scope; claim breadth; first claim length; knowledge; complex technology
JEL classification: O34†

This work was supported by JSPS KAKENHI Grant Number 26885031 and 16K13380. We would like tothank Research Assistant Junko Morikawa for checking the samples.
p. 1

1. Introduction

A patent system promotes innovation not only by enhancing the appropriability of inventions but
also by promoting their disclosure and thereby the knowledge available for subsequent inventions.
Surveys among R&D managers and inventors confirm that patent literature is, in fact, one of themost important sources of information suggesting new research projects and contributing to theirimplementation (see Giuri et al. [2007] for the inventor survey in Europe and Walsh and Nagaoka[2009] for surveys in the US and Japan).

In a patent, the very crucial technological information, the contribution of the underlying
invention to the state of the art, is indicated in the independent claim(s), especially in the firstindependent claim1 because the scope of the patent is defined by claims and the first claim generally
conveys the broadest inventive concept that must satisfy the novelty and the inventive steprequirement. As the generality of the inventions will increase with the breadth of the scope of patents,
we can expect that a patent with a broad scope has a large potential knowledge impact: many followon inventions for cumulative improvements, various applications in different technological areas,
and a wide range of combination possibilities with other inventions, in particular, in complex
technology areas. This paper examines how significantly a new measure of the breadth of claim

1Claims that define all the essential components of the invention by itself are called ‘independent claims.’
Claims that refer to one or more previous claims in order to avoid redundant expressions are called‘dependent claims.’ A dependent claim does not by itself define all the components of the invention.
p. 2

scope, the inverse of the first claim length of a patent, can help predict its knowledge impact on
subsequent inventions in complex and discrete technologies. Of the information that has been
directly obtained from patent claims, the existing empirical studies predominantly have used thenumber of claims of a patent to assess the breadth of the scope of the patent right (see, for example,
Lanjouw and Schankerman [2004]). The breadth of independent claims, however, is a distinct
dimension of the patent’s scope, that is, the generality of the inventive concept, which cannot be
captured adequately by simply counting the number of claims. While a patent with many claims
often covers a large number of variations or application areas known at the stage of the patent filing,a patent application with few but broad claims may be filed for an embryonic invention that isfollowed by a large number of subsequent complementary inventions. It is thus highly likely that wemiss substantial information on the patent’s scope if we do not measure the breadth of claim scope
directly. Partially based on this understanding, other studies, since Lerner ‘s (1994) study, have used
the number of different International Patent Classification (IPC) subclasses assigned to each patent
as a proxy for the patent’s scope. It is again highly likely, however, that this measure is very
imprecise because the indicator is not directly derived from the claims and depends on the specificstructure of the patent classification system. The first objective of this study is to demonstrate thatthe inverse of the description length of the first claim of a patent has significant predictive powerwith respect to the knowledge impact of the invention on subsequent inventions, even controlling for
major existing patent scope indicators, including the number of claims and the number of different
p. 3

IPC subclasses, examining a large scale of Japanese patents.
More specifically, we test the inverse of the number of characters involved in the firstindependent claim in a patent as a new proxy for the breadth of the claim scope of the patent inpredicting the knowledge flow to subsequent inventions, based on the following intuition. The
description of an independent claim grows longer as the number of elements limiting the scope of the
exclusive right increases; thus, one can expect that the inverse of the description length of an
independent claim is positively correlated with the breadth of the scope of this claim. Because, in
general, the first independent claim conveys the broadest inventive concept in a patent, we focus on
this claim and use the inverse of the description length of the first independent claim as the measureof the breadth of claim scope, examining whether this indicator significantly predicts applicant
forward citations of the underlying invention.
Moreover, as a combination of complementary inventions is important for innovations in acomplex technology area, it is likely that the knowledge impact of an invention depends heavily on
how extensively the complementary inventions will emerge in this technology area. We thus expectthat the knowledge impact of a patent with a broad claim has a more skewed distribution in thecomplex technology area than in the discrete technology area.2 Given that a patent with a broadclaim is likely to have more technological opportunities for combination with future complementary
inventions, such a patent will appear significantly more frequently among top-ranked patents in2

Cohen, Nelson, and Walsh (2000) discuss how these two areas differ, focusing on appropriability conditions.
p. 4

terms of the scale of its knowledge impact on subsequent inventions. On the other hand, such a
relationship between the breadth of claims and the skewed distribution of the impacts on subsequent
inventions will be less apparent in the discrete technology area, as an invention in such a technology
area is more likely to be exploited as a stand-alone invention and will depend less on combinations
with other inventions. The second objective of this study is to examine whether such a difference inthe dependence of knowledge impact on the breadth of claim scope exists between complex anddiscrete technology areas.
Despite the potential importance of the first claim length as a predictor of the knowledge
impact of underlying inventions to subsequent inventions, to the best of our knowledge, there are no
systematic studies examining this subject. Furthermore, no studies have examined how the effect of
the breadth of claim scope differs in terms of its impact on subsequent inventions, depending on thenature of the innovation process, namely, the difference between complex and discrete technology
areas. This study aims to uncover how well the first claim length predicts the knowledge flow tosubsequent inventions and how such predictive power differs in the complex and discrete technology
areas, depending on the rank of the impact to subsequent inventions.
The rest of the paper is organized as follows. The next section briefly reviews prior literature.
Section 3 offers a discussion of our data construction. Section 4 presents a model of an invention’s
knowledge impact that depends on subsequent complementary inventions in order to clarify why thep. 5quantile regressions are essential for our analysis as well as the potential underlying mechanism of
the observed skewed outcome. Section 5 presents the results and discussion. Section 6 concludes.

2. Prior literature

Although a significant number of empirical studies exist on patent value and its skewness (Scherer
and Harhoff 2000; Harhoff, Scherer, and Vopel 2003; Nagaoka, Motohashi, and Goto 2010;
Squicciarini, Dernis, and Criscuolo 2013), empirically, the scope of a patent is difficult to
operationalize and measure (Harhoff, Scherer, and Vopel 2003). Most of existing empirical studies
use the number of claims and the number of different IPC subclasses to assess the breadth of thescope of a patent right. Lanjouw and Schankerman (2004) use the number of claims in addition to
forward and backward citations and family size as the value indicators of a patent. Lerner (1994)
proposed the number of different subclasses (based on the four-digit code) that a patent examiner
assigned to the patent as a proxy for patent scope, focusing on the technological scope based on theInternational Patent Classification (IPC) scheme, and demonstrated its explanatory power of theforward citations of a patent and the value of start-up firms in the biotechnology industry. Since then,some have followed Lerner’s approach (Shane 2001; Harhoff Scherer, and Vopel 2003; Nerkar andShane 2003) and others furthered this approach, identifying rare combinations of different IPCs
(Fleming 2001; Verhoeven, Bakker, and Veugelers 2016). Novelli (2015) discusses the difference inp. 6dimensions that are measured by the number of claims and by the number of different IPC subclasses.
However, neither the number of claims nor the number of different IPC subclasses directly measure
the generality of the inventive concept, thus, these indicators, even if combined, measure patentscope imperfectly.
Only a very limited number of prior studies use description length of independent claims as ameasure of the breadth of patent scope. Malackowski and Barney (2008) used the average number ofwords per independent claim in an issued US patent as a proxy of the quality of the patentexamination by the United States Patent and trademark Office (USPTO). Their assumption was thatthe constancy of invention quality applied for the patent over the period. They found that the numberof words in independent claims increased through the examination process, from 111.1 on filing to153.2 on issuance (that is, the patent scope narrowed on average). They also found that independentclaims of US patents issued in 2007 had 4.4% more words compared to that of patents issued in 2003,
which was considered as evidence that the quality of the patent examination had not decreased
during that period. Although they examined the length of independent claims as a proxy for thequality of the patent examination procedure, they did not examine its explanatory power as apredictor of the knowledge contribution of the underlying invention to subsequent inventions.
Harhoff (2016) states that the inverse of the length of the first independent claim is used as aproxy measure of patent breadth by analysts at patent authorities. He points out that the average
number of words of European Patent intendant claims became significantly longer over time: 165
p. 7

words in 1990, 175 words in 2008, and 183 words in 2014; and attributed such change to thenarrower scope in the reformed fee structure and the restriction of divisional applications. However,
the relation between the claim length and the social impact of a patent is unknown.
Jansen (2009) examined the relationship between the private patent value and the claims byinvestigating about 2700 European patents in technological fields relevant to the company Phillips.
Contrary to practitioners’ views, he concluded that the description length of independent claims was
not a significant predictor of the patent value, unlike the number of claims, controlling for the family
size. He may have got this result because his estimation was based on the assessment of the meaneffect of the claim length on the value ranking of a relatively small number of patents (2700), mostly
in the complex technology area.
Regarding text data other than claims, Reitzig (2004) examined how the number of words
describing the state of the art and those describing the technical problem predict the likelihood of
opposition, together with procedural indicators (such as accelerated examination request). However,
he did not investigate the first claim length or the difference between complex and discrete
technology areas.
Webster, Jensen, and Palangkaraya (2014) investigated the incidence of changes inindependent claims in 236 European patents (EP) and 82 Japanese patents (JP). They found that
foreign inventors filing patent applications at the European Patent Office (EPO) and the Japanese
Patent Office (JPO) were more likely to receive a grant with claim changes than domestic inventors.
p. 8

The purpose of their investigation was to investigate a potential violation by national patent officesof the obligation of respecting the national treatment principle stipulated in the Trade Related
Aspects on Intellectuals Properties in the World Trade Organization (WTO) agreement. Osenga(2011) focused on the number of words in claims. However, he used the claim length as a proxy of
readability or comprehension of claims and not as a proxy of breadth of patent scope.

3. Data construction

Generally, the first independent claim conveys the broadest inventive concept and we focus on thisclaim. Because the Japanese language does not use spaces between words and thus it is hard to countthe number of words automatically, we use the number of characters instead of the number of words
for the metric of the description length of the first claim in a patent. Hereafter, ‘first claim length’
denotes the number of characters in the first claim. We measure the breadth of the claim scope of apatent by the inverse of the first claim length and referred to it as ‘IFCL.’
We prepared our data sets from Japanese patent databases purchased from Artificial Life
Laboratory, Inc., which covers all text data in patent publications as well as applicant citation data. In
order to identify self- and non-self-citations, we utilized a dictionary of all major Japanese company
names and the connection table for patent application provided by the National Institute of Scienceand Technology Policy (NISTEP) and considered only those patents filed by applicants identified byp. 9

the NISTEP database. Further, we used PATSTAT (2014 autumn, the European Patent Office) toobtain US patent family data.
Considering that the relationship between the IFCL and the impact of the underlying
invention on subsequent inventions may differ significantly between product and process patents, wedeveloped a program that divided patents into categories of ‘product’ and ‘process.’3 We focus on
product patents in the main part of our analysis here, which account for more than 80% of total
patents.4 We find very similar results for process patents, which are reported in Appendix B.
We restrict our assessment to the patents filed between January 1991 and August 2002. The
ending limit (August 2002) was set to eliminate the influence of the patent law change related todisclosure of prior arts on our measures of backward citations as well as to secure a wide window for
applicant forward citation flow. The beginning limit was set because of the availability of the patentclaims text data. For simplicity, we eliminated the divisional applications from our data sets.
We divided the technological fields into six large categories: ‘Chemical (Chem.),’‘Computers & Communications (C & C),’ ‘Drugs and Medical (D & M),’ ‘Electrical & Electronic (E& E),’ ‘Mechanical (Mech.),’ and ‘Others’ (see Appendix Table A.1 for the IPC correspondence

3

The randomly chosen 150 samples showed no errors.

4

The rates of product invention by technology are as follows: Total, 81.4%; Complex, 82.8%; Discrete,
78.3%; Computers & Communications, 85.8%; E & E, 79.1%; Mechanical, 84.5%; Chemical, 65.5%;
Drugs and Medical, 90.9%; and Others, 83.4%.
p. 10

table). ‘C & C,’ ‘E & E,’ and ‘Mech.’ belong to the complex technology area, and ‘Chem.,’ ‘D & M’
and ‘Others,’ the discrete technology area.
We eliminated the patents involving chemical structure formulae, mathematical formulae,
and/or tables in the first claim utilizing ‘tag information’ and its ilk, as the crucial part of the patentright is provided by image data rather than text data in these patents. The amount of eliminated data
was less than 5% in each field except for Chem.5
Figure 1 shows the distribution of the natural logarithm of the IFCL of product patents
aggregated by all fields. Its shape is similar to that of a normal distribution.

[Figure 1 near here]

We use the number of forward applicant citations as an indicator of knowledge impact.
Forward citations have been extensively used in prior literature as indicators of knowledge flow,
although it is well recognized that it is a very noisy measure (Jaffe, Trajtenberg, and Fogarty 2000).
We do not use examiner citations because they are added by examiners ex post when examining thepatent application and they contain many patent applications that the applicants did not know when
they had been engaging in the research of the focal invention. Our data contain applicant citations

5

The rates of the eliminated data are: all, 3.9%; Complex, 1.8%; Discrete, 9.0%; C & C, 0.4%; E & E, 3.6%;
Mech., 1.0%; D & M, 4.8%; and Chem., 25.9%; Others, 1.1%.
p. 11

that are constructed by searching all patents or patent applications cited by the applicants in entire
patent application documents, which include not only the citations of the early patent literature(granted as well as applications) cited as prior art but also citations cited as references to researchtools or methods used to invent or implement the focal inventions. We thus believe that our measureis less noisy and more comprehensive in capturing important knowledge contributions to subsequent
inventions.

4. Analytical framework and estimation model

4.1 Knowledge impact of an invention on subsequent inventions

We consider a statistical model explaining the variation of the knowledge impact of an invention
where the emergence of complementary inventions stochastically enhances the knowledge utility of
the focal initial inventions. This process seems to be much more important in the complex
technology area than in the discrete technology area. We assume that the social value of theinvention as a knowledge source for subsequent inventions ( ) increases multiplicatively with theoccurrence of

6complementary inventions (1)6:

The private value of invention itself is highly skewed and follows a log normal distribution (Scherer andHarhoff 2000). This suggests that both the value and the knowledge impact follow the multiplicative
process.
p. 12

….

Here

(1)

is the stand-alone knowledge impact of the focal invention for subsequent inventions with the

expected value of its logarithmic value

and its variance. Variable

represents themultiplicative contribution of a complementary inventions, which will emerge after the focal
invention. If we regard

as the number of forward citations, it will include citations bycomplementary inventions, but this number is dominated by other citations whenincreases exponentially withis large, as. We assume that the contributions of the complementary inventions

are independently distributed among themselves and the logarithmic value has a common mean
>0 and a common variance. In this case, we have

(2)

(3)

We assume that the breadth of claim scope ( ) affects its impact on subsequent inventions not only
throughbut also throughwhen the opportunity for the emergence of complementary inventions

exists:increases with the breadth of claim scope ( ), considering that an embryonic invention

invites many complementary inventions, which thereby enhances the knowledge value of the original
invention. This means that an invention with broader claims has a high variance in the social value ofthe invention as knowledge for subsequent inventions ( ). For simplicity, let us further assume thep. 13

following:

,

,

0

0

(4)

(5)

Given this, we have the following:|

Denoting the inverse of the standard normal cumulative density function by(6)

for quantile ,the conditional quantile function (which shows the value for quantile for a given patent scope
breadth) is given by|(7)

It follows from equations (6) and (7) that the quantile regression coefficient of the breadth of claim
scope

is larger than that of ordinary least squares (OLS) for upper quantiles. Importantly, if thebreadth of claim scope contributes to the knowledge impact of inventions entirely by increasing thenumber of complementary inventions and has only negligible effect on their stand-alone knowledge
impact on average (≅ 0) and (0), the above equation (6) cannot detect the importance ofthe breadth of claim scope, even if patents with broad claims enhance significantly the subsequentp. 14

inventions as knowledge sources because they disproportionately exist among top impact patents7.
On the other hand, quantile regression can detect this, as shown in equation (7).

4.2 Estimation model

Given the above analytical results, we use both the OLS method as well as the quantile regression
method, which can accommodate the possibility that the impact of a patent with a broad claim scopeon subsequent inventions has a high variance. We assessed how the IFCL can be utilized to predict
the number of forward applicant citations, adding to the predictive power of the combination of allconventionally used major indicator variables, including the number of claims, the number ofdifferent IPC subclasses, the number of inventors, the existence of corresponding US patents,8 theexistence of corresponding patent applications to the EPO, backward applicant citations (self andnon-self), backward examiner citations, and the existence of divisional applications based on the
focal patent as well as technology by priority year dummies.
Specifically, we used the following model whereis thequantile or mean. We choose thefollowing three quantiles (.70, .90 and .99) for the presentations of our estimations, given that close

7

The patents in the top 10% quantile account for more than 80% of the aggregate value (Scherer and Harhoff
2000).

8

The US adopted a pre-grant publication system for patent applications filed on and after November 29, 2000.
Before this legal reform, a patent application document was published only when a patent was granted.
Hence, we used the existence of corresponding US patents instead of US patent applications.
p. 15

to a half of the patents do not have forward citations9 and we focus especially on the predictive
power of the IFCL on top impact patents.
_ _ _
_

|

)

_ _

_ _

_

_ _
1

1_

1

_ _
_ _

_

0
_ _

_

_ _
_

1_

_
_

_

_
,

0_

_
_

_
_

_

(8)

The definitions of the variables and descriptive statistics are shown in Table 1 for productpatents; descriptive statistics for process patents are presented in the Appendix Table B1.

[Table 1 near here]

9

The values of 50, 70, .90 and .99 quantile of ln_N_F_Citation for product patents are as follows,
respectively: complex, 0.00, 0.69, 1.61, 2.89; discrete, 0.00, 0.69, 1.61, 3.04; for process patents: complex,
0.00, 0.69, 1.61, 3.00; discrete, 0.69, 1.10, 1.79, 3.14.
p. 16

5. Results and discussion

Table 2 shows the summary results for complex and discrete technology areas in the case of productpatents. According to the results of the OLS estimation, the signs of the coefficients for the IFCL
(indicated as_) are positive and statistically highly significant in the two technology areas.

The IFCL, therefore, has a significant explanatory power for predicting the applicant forward
citations, even controlling for the existing major indicators. Its explanatory power in the complex
technology area, however, is only a quarter of that in the discrete technology area (0.014 vs. 0.051).
Conversely, the estimated coefficients for the number of claims and the number of IPC subclasses
are similar (0.073 (complex) vs. 0.071 (discrete) and 0.067 (complex) vs. 0.064 (discrete)) andstatistically significant in both areas. Although the explanatory power of the IFCL is smaller thanthat of the number of claims and the number of different IPC subclasses, it is still comparable even if
we consider the size of the standard deviations of the IFCL, the number of claims, and the number ofdifferent IPC subclasses (0.51 (0.61), 0.82 (0.76) and 0.43 (0.45), in the complex (discrete)technology areas, respectively).

[Table 2 near here]

p. 17

The results of the quantile regressions reveal that the predictive power of the IFCL increases
significantly in higher quantiles in the complex technology area (the highest (0.16) in the 0.99
quantile; and negligible (0.005) in the 0.70 quantile), whereas it is stable for all quantiles in thediscrete technology area (0.060 in the 0.99 quantile; and 0.062 in the 0.70 quantile). As a result, the
IFCL predicts the 0.99 quantile far more in the complex technology area than in the discrete
technology area, but much less in the 0.70 quantile. Figure 2 compares the coefficient value for the
IFCL relative to that of the number of different IPC subclasses and Figure 3 compares the coefficientvalue for the IFCL relative to that of the number of claims. Given that the number of different IPC
subclasses and the predictive power of the number of claims are relatively stable across all quantiles
in both technology areas, the IFCL predicts the 0.99 quantile more in complex technology than indiscrete technology, but much less in the 0.70 quantile in complex technology than in discrete
technology.
Table B.2, Figure B.1, and Figure B.2 in the Appendix show that very similar findings (that is,
the rise of the relative predictive power of the IFCL in higher quantiles in the complex technology
area and its relative stability in the discrete technology area) hold for process patents too.

[Figure 2 near here][Figure 3 near here]p. 18

Figure 4 shows the relative values of the coefficients estimated by 99% quantile regression
against those estimated by the OLS method for each explanatory variable for product patents. The
value for the IFCL in the complex technology area is outstanding, more than 12, whereas others areless than four; the values are less than two for the number of IPC subclasses and the number ofclaims. Thus, the IFCL very significantly predicts top ranked patents in terms of applicant forward
citations in the complex technology area. Figure B.3 in the Appendix shows the corresponding
values for process patents with similar results.
The different effect of the breadth of claim scope between the two technology areas isconsistent with the prediction from the analytical framework introduced in Section 4.1 and supports
the following interpretation. In the complex technology area, the impact of a pioneering patent with abroad claim on subsequent inventions depends on the emergence of many complementary inventions
due to the necessity of combining many technologies to produce a product; hence, the impact of apatent with a broad claim in the complex technology area is more uncertain and tends to have a highvariance. In contrast, the value of a patent in the discrete technology area depends much more on
standalone value, and thus, the impact of a patent with a broad claim on subsequent inventions ismore certain.
The IFCL therefore detects a very important and hitherto unrecognized characteristic ofpatent scope in the complex technology area, as articulated by our analytical framework explained in
Section 4.1: patents in the complex technology area can display a higher variance of impact on
p. 19

subsequent inventions due to the varied enhanced impact depending on complementary inventions.
The IFCL is therefore a unique proxy for the breadth of claim scope in that it effectively explains thedifference between complex and discrete technology areas in terms of the mechanism of thecontribution of a high impact patent on subsequent inventions.

[Figure 4 near here]

6. Conclusions

While patent scope defined by patent claims provides crucial information on the contribution ofunderlying inventions to the state of the art, its existing measures (the number of claims and thenumber of different IPC subclasses) do not seem to appropriately capture it, especially with respect
to the generality of the inventive concept. This study proposed a new measure of the patent scope,
the IFCL (inverse of the first claim length), and assessed the significance of this new measure inpredicting the knowledge impact of the invention. The results validate that this new measuresignificantly predicts the number of applicant forward citations, controlling for the conventional
proxies for patent scope and other major bibliographic indicators. The explanatory power of the
IFCL is indeed comparable with that of the number of claims and the number of different IPC
subclasses. Moreover, whereas its predictive power is stable across the 70% to 99% quantiles in thep. 20

discrete technology area, it becomes increasingly more significant for top-ranked patents in thecomplex technology area, unlike the other measures of patent scope. These findings are consistent
with an economic model predicting that the impact of a pioneering patent with few limitations on its
claim on subsequent inventions is more uncertain in the complex technology area where such impact
depends significantly on the emergence of complementary inventions that enhance the impact of theinitial focal invention. Unlike the conventional two proxies of patent scope, this new IFCL proxy
therefore provides important hitherto unexploited information on a patent scope’s crucial
characteristic, which is a source of the variation of the skewness of its impact on subsequent
inventions (especially high-impact patents).
Our findings open up a number of follow up research possibilities. One immediate question is
how significantly the IFCL can contribute to accounting for the highly skewed distribution of private
patent values, and another question is how the IFCL could be used as a measure of the contributionsof patent examinations. We are currently addressing these questions. One important policy
implication will be that, whereas ex-post value indicators, such as forward citations or the renewal ofpatent maintenance, need a relatively long time to measure, the IFCL provides one useful ex-ante
information for a policy maker in identifying potentially high-spillover inventions both in thecomplex and discrete technology areas, which may be useful in monitoring the performance of R&D
projects funded by the government in the future.

p. 21

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Verhoeven, D., J., Bakker, and R. Veugelers. 2016. Measuring Technological Novelty with PatentBased Indicators. Research Policy 45 (3): 707-723.
Walsh, J. P., and S. Nagaoka. 2009. “How ‘Open’ Is Innovation in the US and Japan? Evidence fromthe RIETI-Georgia Tech Inventor Survey.” Research Institute of Economy, Trade and Industry
Discussion Paper 09-E-022.
Webster E., P. H. Jensen, and A. Palangkaraya. 2014. “Patent examination outcomes and the national
treatment principle.” The RAND Journal of Economics 45 (2): 449–469.

p. 24

Table 1. Descriptive statistics for the data (product patents)
Variables

ComplexDiscrete

N=601, 435

N=231,841

Definition

Mean

Std. Dev.

Mean

Std. Dev

0.553

0.733

0.605

0.775

-5.78

0.506

-5.51

0.614

1.18

0.818

1.05

0.763

0.308

0.434

0.335

0.450

0.586

0.603

0.745

0.601

0.09330.291

0.0681

0.252

0.143

0.350

0.04570.209

0.008160.08990.00100

0.0993

0.272

0.489

0.345

0.580

0.143

0.354

0.180

0.391

1.28

0.638

1.15

0.675

0.101

0.268

0.124

0.300

logarithm of “the number of
ln_N_F_Citation
forward citations + 1”
logarithm of the inverse of firstln_IFCL
claim- length
ln_N_claims

logarithm of the number of claims
logarithm of the number of IPC

ln_N_IPC_subclasssubclass
logarithm of the number of
ln_N_inventors
inventors
1 if both the corresponding US
US1EP1_dmy

patent and the corresponding EP
application exist; otherwise, 0
1 if there is a corresponding US

US1EP0_dmy

patent but no corresponding EP
application; otherwise, 0
1 if there is no corresponding US

US0EP1_dmy

patent and there is an EP patentapplication; otherwise; 0logarithm of “the number of

ln_N_Bnonself_Citn
backward non-self-citations + 1”
logarithm of “the number of
ln_N_Bself_Citn
backward self-citations + 1”
logarithm of “the number of
ln_N_Bttl_ExCitn“examiner citations + 1”
logarithm of “the number ofbackward citations cited both by
ln_N_Bttl_CoCitn
the applicant and the patentexaminer + 1”

p. 25

1 when subsequent divisional
subAppln_dmy

0.04060.197

0.04140.199

0.06130.240

0.07050.256

applications exist; otherwise; 0
1 if the patent is jointly held; 0 if
joint_dmy
the patent holder is single
earliest priority year offiling_year_dmy
application
technology_dmy

6 Categories

p. 26

Table 2. Summary results of the regression (product patents)
Dependent variable: ln_N_F_Citation
Complex (N = 601,435)
Discrete (N = 231,841)
.99
.90
.70
.99
.90
OLS
quantilequantilequantilequantilequantile.70
quantile.0135***.158*** .0385*** .00511** .0511*** .0599*** .0768***(.00192)(.0113)(.00444)(.00214)(.00283)(.0143)(.00624)ln_N_claims.0725***.156***.125*** .0674*** .0712*** .146***.117***(.00133)(.00757)(.003)(.00152)(.00229)(.0119)(.00516)ln_N_IPC_subclass .0666***.141***.110*** .0683*** .0644*** .135***.105***(.00225)(.0131)(.00508)(.00281)(.0038)(.0200)(.00869)ln_N_inventors.0711***.173***.133*** .0711*** .0597*** .117***.100***(.00166)(.00951)(.00383)(.00205)(.0027)(.0148)(.00624)
US1EP1_dmy.201***.486***.363***.298***.295*** .536***.478***(.00388)(.0205)(.00849)(.00509)(.00814)(.0372)(.0172)
US1EP0_dmy.115***.270***.209***.161***.155*** .293***.291***(.00293)(.017)(.0067)(.00552)(.00859)(.0338)(.0213)
US0EP1_dmy.0853***.138***.143***.131***.119*** .188***.197***(.0111)(.0532)(.0228)(.0235)(.0182)(.0516)(.0382)ln_N_Bnonself_Citn.144***.412***.257***.174***.133*** .292***.222***(.00257)(.0128)(.00535)(.00395)(.00358)(.022)(.00808)ln_N_Bself_Citn.0896***.354***.172***.102*** .0764*** .151***.118***(.0037)(.0201)(.00713)(.00480)(.00499)(.0294)(.0105)ln_N_Bttl_ExCitn.0687***.196***.129*** .0608*** .0584*** .161***.110***(.00156)(.00915)(.00363)(.00169)(.00263)(.0143)(.00599)
ln_N_Bttl_CoCitn -.0538*** -.244*** -.126*** -.0713*** -.0187*** -.160*** -.0459***(.00441)(.0267)(.00976)(.00648)(.00669)(.0350)(.0147)
subAppln_dmy.338***.752***.576***.466***.347*** .840***.610***(.00623)(.0248)(.0126)(.00886)(.0100)(.0477)(.0219)joint_dmy-.0272*** -.0746*** -.0599*** -.0302***-.00422-.0563* -.000349(.00393)(.0245)(.0091)(.00434)(.00625)(.0307)(.0149)filing_year
yes
yes
yes
yes
yes
yes
yes
tech
yes
yes
yes
yes
yes
yes
yes
filing_year × tech
yes
yes
yes
yes
yes
yes
yes

.0615***(.00345).0724***(.00276).0689***(.00445).0568***(.00325).414***(.0107).200***(.0143).211***(.0353).170***(.00474).0908***(.00571).0538***(.00322)-.0162*(.00843).452***(.0150)-.0106(.00651)
yes
yes
yes

Independent
variables

OLS

ln_IFCL

R-squared
.0701
.0985
Adjusted R-Squared
.0700
.0983
Note. Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.10

p. 27

Number of patents

70,00060,00050,00040,00030,00020,00010,000
0
-8.00

-7.00

-6.00

-5.00

-4.00

ln(IFCL)
Figure 1. Distribution of the natural logarithm of IFCL
Note: The figure shows the distribution of the natural logarithm of the inverse of thefirst claim length (IFCL) of product patents aggregated by all technological fields.

p. 28

120.0%100.0%
80.0%
60.0%
40.0%
20.0%
0.0%
.99
.97
.95
.90
.80
.70
quantile quantile quantile quantile quantile quantile
Complex

OLS

Discrete

Figure 2. Relative value of the coefficient for the IFCL against that of the number of
IPC subclasses (product patents)
Note: The figure shows the values of the following ratio, coefficient for the
IFCL/coefficient for the number of different IPC subclasses, of product patents
estimated by quantile regression where the quantiles are 0.99, 0.97, 0.95, 0.90, 0.80, and0.70 as well as those estimated by the OLS method.

p. 29

120.0%100.0%
80.0%
60.0%
40.0%
20.0%
0.0%
.99
.97
.95
.90
.80
.70
quantile quantile quantile quantile quantile quantile
Complex

OLS

Discrete

Figure 3. Relative value of the coefficient for the IFCL against that of the number ofclaims (product patents)
Note: The figure shows the values of the following ratio, coefficient for the
IFCL/coefficient for the number of claims, of product patents estimated by quantile
regression where the quantiles are 0.99, 0.97, 0.95, 0.90, 0.80, and 0.70 as well as thoseestimated by the OLS method.

p. 30

ln_IFCL
ln_N_Claims
ln_N_IPC
ln_N_Inventors
US1EP1_dmy
US1EP0_dmy
US0EP1_dmy
ln_N_Bnonself_Citn
ln_N_Bself_Citn
ln_N_Bttl_ExCitn
subAppln_dmy
0.00

5.00
Discrete

10.00

15.00

Complex

Figure 4. The relative values of the coefficients estimated by 99% quantile regression
against those estimated by the OLS method (product patents)

p. 31

Appendix A
Table A.1 Technology sector classification
Technology sector
Computers & Communications
Complex

Electrical & Electronic

technology

IPC
G04-G12, H03-H04
G01-G03, H01-H02,H05
B21-B32(excludes B31)-B44,

Mechanical
B60-B68, F01-F04,F15-F17
A01N, C01-C11(excludes C06),
ChemicalC21-C30
Drugs & Medical

A61-A63, C12-C14

DiscreteA01(excludes A01N), A21- A47,

technology

B01-B09, B31, B81,B82, C06,
Others
D01-D21, E01-E21, F21-F42,
G21

p. 32

Appendix B
120.0%
100.0%
80.0%
60.0%
40.0%
20.0%
0.0%
.99
.97
.95
.90
.80
.70
quantile quantile quantile quantile quantile quantile
Complex

OLS

Discrete

Figure B.1 Relative value of the coefficient for IFCL against that of the number of IPC
subclasses (process patents)
Note: The figure shows the values of the following ratio, coefficient for the
IFCL/coefficient for the number of IPC subclasses, of the process patents estimated byquantile regression where the quantiles are 0.99, 0.97, 0.95, 0.90, 0.80, and 0.70 as wellas those estimated by the OLS method.

100.0%
90.0%
80.0%
70.0%
60.0%
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
.99
.97
.95
.90
.80
.70
quantile quantile quantile quantile quantile quantile
Complex

OLS

Discrete

Figure B.2 Relative value of the coefficient for the IFCL against that of the number ofclaims (process patents)
Note: The figure shows the values of the following ratio, coefficient for the
IFCL/coefficient for the number of claims, of the process patents estimated by quantile
regression where the quantiles are 0.99, 0.97, 0.95, 0.90, 0.80, and 0.70 as well as thoseestimated by the OLS method.

p. 33

ln_IFCL
ln_N_claims
ln_N_IPC_subclass
ln_N_inventors
US1EP1_dummy
US1EP0_dummy
US0EP1_dummy
ln_N_Bnonself_Citn
ln_N_Bself_Citn
ln_N_Bttl_ExCitn
subAppln_dmy
0.00

5.00
Discrete

10.00

15.00

Complex

Figure B.3. The relative value of coefficients estimated by 0.99 quantile regression
against that estimated by the OLS method (process patents)

p. 34

Table B.1. Descriptive statistics for the data of process patents that are used inregression analysis reported in Table B.2
ComplexDiscrete(N= 125,721)
Variables

Mean(N= 64,333)

Std. Dev.

Mean

Std. Dev.

ln_N_F_Citation

0.570

0.751

0.660

0.805

ln_IFCL

-5.67

0.511

-5.35

0.540

ln_N_claims

1.24

0.847

1.05

0.768

ln_N_IPC_subclass

0.307

0.430

0.369

0.459

ln_N_inventors

0.662

0.603

0.904

0.563

US1EP1_dmy

0.08560.280

0.0913

0.288

US1EP0_dmy

0.133

0.340

0.04270.202

US0EP1_dmy

0.00798

0.08900.01270.112

ln_N_Bnonself_Citn

0.310

0.535

0.497

0.654

ln_N_Bself_Citn

0.176

0.410

0.255

0.468

ln_N_Bttl_ExCitn

1.35

0.616

1.25

0.648

ln_N_Bttl_CoCitn

0.111

0.285

0.178

0.356

subAppln_dmy

0.04430.206

0.03080.173

joint_dmy

0.05650.231

0.0724

0.259

p. 35

Table B.2. Summary results of the regression (process patents)
Dependent variable: ln_N_F_Citation
Independent
variables
ln_IFCL

Complex (N = 125,721)
.99
.90
OLS
quantilequantile.00746*.111***.0183**.70
quantile.00154Discrete (N = 64,333)
.99
.90
.70
OLS
quantilequantilequantile.0432***.148*** .0639*** .0515***ln_N_claims(.00419).0696***(.0242).163***(.00927).118***(.00331).0557***(.00589).0710***(.0206).167***(.0124) (.00701).115*** .0751***ln_N_IPC_subclass(.00291).0588***(.0156).116***(.00613).100***(.00271).0538***(.00451).0579***(.0158).205***(.00940) (.00587).0990*** .0521***ln_N_inventor(.00503).0805***(.0258).165***(.0112).149***(.00511).0643***(.00711).0672***(.0256).0787***(.0154) (.00922).134*** .0762***

US1EP1_dmy(.00362).211***(.0205).533***(.00805).386***(.00363).326***(.00565).233***(.0213).522***(.0125).382***(.00703).347***

US1EP0_dmy(.00914).128***(.0339).269***(.0185).219***(.0115).208***(.0132).153***(.0449).533***(.0240).298***(.0159).220***

US0EP1_dmy(.00684).122***(.0371).134**(.0149).298***(.0121).127**(.0179).138***(.0491).406***(.0425).322***(.0188).196***

ln_N_Bnonself_Citn(.0268).137***(.0576).362***(.0691).253***(.0509).169***(.0331).125***(.0574).262***(.0497).210***(.0416).193***

ln_N_Bself_Citn(.00513).0962***(.0271).398***(.0107).218***(.00886).102***(.0059).0637***(.0207).174***(.0115) (.00796).131*** .0855***

ln_N_Bttl_ExCitn(.00717).0577***(.0355).160***(.0154).109***(.0104).0448***(.00801).0533***(.0224).120***(.0147)(.0122).0884*** .0623***

ln_N_Bttl_CoCitn(.00358)-.0284***(.0207)-.151***(.00781)(.00289)-.111*** -.0469***(.00537)(.0193)-.0179 -.0851***(.0115)-.0392*(.00637)-.0100subAppln_dmy(.00918).348***(.0425).888***(.0200).608***(.0146).448***(.0109).411***(.0330).840***(.0233).651***(.0161).504***

joint_dmy(.0135)-.0384***(.0412)-.227***(.0295)(.0145)-.0953*** -.0332***(.0236).00923(.0511)-.103***(.0571)-.0239(.0289)-.00320

filing_year(.00884)yes(.0426)
yes(.0213)yes(.00679)
yes(.0121)
Yes(.0328)yes(.0317)yes(.0192)yestechfiling_year × tech

yes
yes

yes
yes

yes
yes

yes
yes

Yes
Yes

yes
yes

yes
yes

yes
yes

R-squared
Adjusted R-Squared

.067
.0667

.062
.0613

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.10

p. 36