Hitotsubashi University
Institute of Innovation Research

Claim Length as a Value Predictor of a Patent

Yoshimi Okada
Yusuke Naito
Sadao Nagaoka

IIR Working Paper WP#16-04

Revised in May 2016 2016

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

Claim length as a value predictor of a patent
May 2016
Yoshimi Okadaa*, Yusuke Naitoa, Sadao Nagaokab
aHitotsubashi University, Institute of Innovation Research, 2-1 Naka, Kunitachi, Tokyo 186-8601, Japanb

Tokyo Keizai University, 1-7-34 Minamicho, Kokubunji, Tokyo 185-0021, Japan

Abstract
The claim of a patent defines the scope of patent right and provides crucial information on patent value. However,most empirical research uses only the number of claims as an indicator of patent value. We show that the breadth ofa claim of Japanese patents, measured by the inverse of claim length, has significant explanatory power for patentvalue measured by applicant forward citations. Indeed, the explanatory power of claim breadth is comparable withthat of the number of cla ims. The pred ictive power of cl aim breadth is st able for all quantiles in the discretetechnology area, while it is far more significant for top-ranked patents in the complex technology area.

Keywords: Claim length, patent value, quantile regression, discrete technology, complex technology, applicant
forward citation
JEL classification: O34

p. 1

1. Introduction
The scope of patent rights is described by “claims,” which should therefore provide crucial information on patentvalue. However, the e xisting empirical research predominantly uses only t he number of clai ms to assess pat ent
value. For e xample, Lanjouw and Schankerman (2004) use the number of cl aims in ad dition to forward andbackward citations and family size as value indicators.
The higher the number of elements limiting the scope of the patent right, the longer the claim. We expectthat the claim length is negatively correlated with the breadth of the patent’s scope, and therefore, the patent value.
Moreover, the predictive power of claim length is likely to differ between the complex technology and discrete
technology areas,1 since the value of a pioneer patent with a broad claim is likely to depend heavily to what extent
complementary patents will emerge in t he complex technology area (but not in the discrete technology area).
However, except for Jansen (2009), to the best of our knowledge, there are no systematic studies on claim lengthwith regard to patent value. Jansen (2009) examined the relationship between patent value and claims byinvestigating around 2700 European patents. He concluded that the length of ind ependent claims2 was not asignificant predictor of patent value, contrary to practitioners’ views.
This study aims to uncover how the length of an independent claim predicts patent value and how suchpredictive power differs between the complex technology and discrete technology areas.
2. Data construction
Generally, the first independent claim conveys the broadest inventive concept. We therefore focused on this claim.
Because the Japanese language does not use spaces between words and it is hard to count the number of words, weused the number of characters instead of the number of words for the metric of claim length. Hereinafter, “claimlength” denotes the number of characters in the first claim. We measured the breadth of the claim by the inverse ofclaim length and referred to it as “claim breadth.”
We prepared our data sets from Japanese patent databases purchased from Artificial Life Laboratory, Inc.,
which covers all text data of patent publications as w ell as applicant citation data. In order to identify self- andnon-self-citations, we utilized the dictionary of al l major Japanese company names and the connection table forpatent application provided by the National Institute of Science and Technology Policy (NISTEP) and considered
only those patents filed by the applicants identified by the N ISTEP database. Further, we used PATSTAT (2014
Autumn, the European Patent Office) to obtain US patent family data.
Since the relationship between claim length and patent value may differ significantly between productand process patents, we developed a program to divide patents into categories of “product” and “process.”3 Since
product inventions accounted for more than 80% of patents,4 we focused on product patents.
We restricted our assessment to patents filed between January 1991 and August 2002. The end limit was
set to eliminate the influence of patent law change relating to disclosure of prior arts. The beginning limit was set
1Cohen et al. (2000), for example, show how appropriability conditions differ between these two areas.
Claims that define all the essential components of the invention by itself are called “independent claims.”
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%.
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because of the availability of 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 & El ectronic (E & E ),” “Mechanical(Mech.),” and “Others” (see the appendix Table A.1 forthe International Patent Classification (IPC)

correspondence 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 formulae, mathematical formulae, and/or tables in the firstclaim, utilizing “tag information” and its ilk; the crucial part of the patent right 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 claim breadth of product patents aggregated by
all fields. Its shape is similar to that of a normal distribution.

70,000frequency

60,00050,00040,00030,00020,00010,0000-8.00

-7.00

-6.00

-5.00

-4.00

ln(claim breadth)Figure 1 Distribution of the natural logarithm of claim breadth (-ln(claim length)) of product patents
aggregated by all fields

3. Estimation model
We used ordinary least squares (OLS) as well as quantile regressions, which can accommodate the possibility of thevalue of a patent with broad claims having high variance. We used the number of forw ard citations as a valueindicator. We assessed how claim breadth can be utilized to predict the number of forward c itations, adding to thepredictive power of the combination of all conventionally used major indicator variables, including the number ofclaims. Specifically, we used the following model, where5is the x quantile or mean.

The rates of the e liminated data are: all, 3.9%; Complex, 1.8%; Discrete, 9.0%; C & C, 0. 4%; Mech., 1.0%;
Others, 1.1%; E & E, 3.6%; D & M, 4.8%; and Chem., 25.9%.
p. 3

____ _|
1_ ___1_1_ ___+,___0_ _1___ __0___ _____

effective_filing_year_dummies × technology_dummies + ε(1)The meanings of the variables are shown in Table 1.
Table 1 Descriptive statistics for the data
Variable

Meaning

Complex: N=601,474
Mean

Std. Dev.

Discrete: N=231,858
Mean

Std. Dev

logarithm of “the number of forward citations + 1”

0.553

0.733

0.605

0.775

logarithm of the inverse of claim length

-5.78

0.506

-5.51

0.614

ln_N_claims

logarithm of the number of claims

1.180.818

1.050.763

ln_N_inventors

logarithm of the number of inventors

0.586

0.603

0.745

0.601

0.09330.291

0.0681

0.252

0.143

0.350

0.046

0.209

0.008160.09000.00995

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

0.04060.197

0.04140.199

ln_Num_Forward_Citation

ln_claim_breadth

1 if both the corresponding US patent and theUS1EP1_dummycorresponding EP application exist; otherwise, 01 if there is a corresponding US patent but noUS1EP0_dummycorresponding EP application; otherwise, 0
1 if there is no corresponding US patent and there

US0EP1_dummy

is an EP patent application; otherwise; 0logarithm of “the number of backward

ln_N_Bnonself_Citn

non-self-citations + 1”
logarithm of “the number of backward

ln_N_Bself_Citn

self-citations + 1”
logarithm of “the number of “examiner citations +ln_N_Bttl_ExCitn

1”logarithm of “the number of backward citations

ln_N_Bttl_CoCitn

cited both by the applicant and the patent examiner+ 1”
1 when subsequent divisional applications exist;

subseq_appln_dummyotherwise; 0p. 4

1 if the patent is jointly held; 0 if the patent holder
co_ownship_dummy

is single

effective_filing_year_dummy
technology_dummy

0.06130.240

0.07050.256

earliest priority year of application
6 Categories

4. Results and discussion
Table 2 shows the summary results for complex and discrete technology areas.
Table 2 Summary results of the regression
Dependent variable: ln_Num_Forward_Citation
complex
.99

.90

.70

.99

.90

.70

quantilequantilequantilequantilequantilequantile.0146***.154***.0406***.00623***.0544***.0694***.0815***.0621***(.00192)(.0111)(.00437)(.00197)(.00283)(.0150)(.00598)(.00308).0741***.165***.128***.0648***.0738***.155***.123***.0727***(.00133)(.00739)(.00296)(.00144)(.00229)(.0123)(.00503)(.00257).0710***.169***.132***.0664***.0605***.118***.105***.0552***(.00166)(.00914)(.00375)(.00197)(.00270)(.0152)(.00614)(.00305).201***.499***.363***.305***.300***.529***.478***.421***(.00388)(.0233)(.00933)(.00482)(.00814)(.0431)(.0162)(.0100).115***.266***.205***.167***.160***.270***.298***.204***(.00293)(.0169)(.00655)(.00578)(.00859)(.0359)(.0204)(.0131).0878***.137***.152***.135***.123***.252***.188***.211***(.0111)(.0177)(.0238)(.0232)(.0182)(.0737)(.0435)(.0321).145***.419***.263***.177***.136***.298***.224***.175***(.00257)(.0121)(.00553)(.00407)(.00357)(.0212)(.00762)(.0045).0896***.348***.172***.101***.0781***.130***.117***.0916***(.00370)(.0178)(.00737)(.00485)(.00499)(.0274)(.0103)(.00534).0721***.200***.134***.0593***.0616***.161***.115***.0555***(.00156)(.00908)(.00357)(.00153)(.00263)(.0143)(.00588)(.00302)-.0549***-.240***-.134***-.0748***-.0208***-.160***-.0531***-.0178**(.00442)(.0250)(.00964)(.00645)(.00669)(.0357)(.0135)(.00786).340***.751***.577***.467***.345***.853***.611***.452***(.00623)(.0293)(.0132)(.0094)(.0101)(.0521)(.0208)(.0128)VARIABLES

OLS

ln_claim_breadth

ln_N_claims

ln_N_inventors

US1EP1_dummy

US1EP0_dummy

US0EP1_dummy

ln_N_Bnonself_Citn

ln_N_Bself_Citn

ln_N_Bttl_ExCitn

ln_N_Bttl_CoCitn

subAppln_dummy

discreteOLS

p. 5

-.0270***-.0588**-.0637***-.0292***-.00460-.0534.00222-.0105*(.00393)(.0293)(.00883)(.00412)(.00625)(.0381)(.0133)(.00598)

eff_filing_year

yes

yes

yes

yes

yes

yes

yes

yes

tech

yes

yes

yes

yes

yes

yes

yes

yes

eff_filing_year × tech

yes

yes

yes

yesyes

yes

yes

yes

co_ownship_dummy

R-squared

.0686

.0972

adjusted R-Squared.0685.0970

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

According to the results of the OLS estimation, the signs of the coefficients for claim breadth are positiveand statistically highly significant in the two areas. Thus, claim breadth has a significant explanatory power forpatent value, controlling for the e xisting indicators.6 Its explanat ory power in t he complex technology area,
however, is only one-third of that in the discrete technology area (0.015 vs. 0.054). Conversely, the values of thecoefficients for number of cla ims are si milar (0.074) and statistically significant in both areas. Although theexplanatory power of clai m breadth is smaller than that of the number of clai ms, it is co mparable even if weconsider the size of the standard deviations of c laim breadth and number of clai ms (0.51 (0.61) and 0.82 (0.76)
respectively in the complex (discrete) technology area).
The results of the quantile regressions reveal that the predictive power of claim breadth rises significantlywith quantiles in the complex technology area (the highest (0.15) in the 0.99 quantile; negligible (0.006) for the0.70 quantile), while it is stable for all quantiles in the discrete technology area. Conversely, the predictive power ofthe number of claims is relatively stable across all quantiles in both areas. Figure 2 compares the explanatory powerof claim breadth relative to that of number of claims. It increases from 10% to 93% in the complex technology area
(0.70 quantile to 0 .99 quantile) and declines from around 85% to 45% in the discrete technology area. In thecomplex technology area, the value of a patent with a broad claim has high variance; the broader the inventive
concept, the more frequently the patent will be ranked in the top 1% of c ommonly cited patents. The varianceremains relatively constant in the discrete technology area.

6Jansen (2009) could not discover the predictive power of claim length possibly because his estimation was basedon the mean effect of the indicators on the ranking of the value of a relatively small size of patents (2700), mostly
in the complex technology area.
p. 6

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 2 Relative value of the coefficient for claim breadth against that of number of claims
Note: The figure shows the values of (coefficient for claim breadth /coefficient for number of claims) estimated byquantile regression where the quantile is 0.99, 0.97, 0.95, 0.90, 0.80, and 0.70 as well as those estimated by OLS.
The differential effect of claim breadth between the two areas is c onsistent with the followinginterpretation. Since many patented technologies are combined to produce a product in the complex technology
area, the value of a pioneering patent with a broad claim depends on the existence of many complementary
technologies in this area. Thus, the value of a patent with a broad claim tends to have high variance in the complex
technology area, since the availability of complementary technologies is uncertain, and such uncertainty rises withthe scope of the patent claim. However, the value of a patent in the discrete technology area d epends on thestandalone value only; thus, a patent with a broad claim is not as uncertain.
5. Conclusions
The empirical results clearly show the considerable predictive power of claim breadth, which is measured by theinverse of claim length, for patent value. It is statistically significant in predicting the number of applicant forward
citations, controlling for the major bibliographic indicators. Moreover, its pred ictive power is stable across allquantiles in the discrete technology area and rises s ignificantly with quantiles in the complex technology area.
These findings suggest that the value of a pioneering patent with few limitations on its claim is more uncertain in
the complex technology area than in th e discrete technology area, since the former depends on the existe nce ofcomplementary technologies.
Acknowledgment
This work was supported by JSPS KAKENHI Grant Number 26885031. We would like to thank Research AssistantJunko Morikawa for checking the samples.

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References
Cohen, W.M., Nelson, R.R., Walsh, J.P., 2000. Protecting their intellectual assets: Appropriability conditions andwhy the US manufacturing firms patent (or not). NBER working paper 7552.
Jansen, W., 2009. Examining the relation between patent value and patent claims. Eindhoven: Eindhoven
University of Technology.
Lanjouw, J.O., Schankerman, M.A., 2004. Patent quality and research productivity: Measuring innovation withmultiple indicators. Econ. J.114, 441–465.
NISTEP, 2015, “Company name dictionary.”
URL: http://www.nistep.go.jp/research/scisip/data-and-information-infrastructure (accessed 17.04.15).
NISTEP, 2015, “Connection table to other databases.”
URL: http://www.nistep.go.jp/research/scisip/data-and-information-infrastructure (accessed 17.04.15).

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Appendix
Table A.1 Technology sector classification

technology sector

ChemicalIPC

A01N, C01-C11(excludesC06),C21-C30

Computers & Communications G04-G12, H03-H04

Drugs & Medical

Electrical & Electronic

Mechanical

OthersTechnology
Nonorganic chemistry,
Fertilizer, Organic chemistry,
Pesticides, Organic moleculecompounds, Dyes, Petroleum,
Metallurgy, Coating metals
Clock, Controlling, Computer,
Display, Information Storage,
Instruments, Electronics circuit,
Communication tech.

A61-A63, C12-C14

Health and Amusement, Drugs,
Biotechnology, Beer,Fermentation, Genetic
Engineering

G01-G03, H01-H02,H05

Measurement, Optics,
Photography, Electronics
components, Semiconductor

B21-B32(excludes B31)-B44,
B60-B68, F01-F04,F15-F17

Machine tools, Metal working,Casting, Grinding, Layered
product, Printing, Transporting,Packing, Lifting, Engine, Pump,
Engineering elements

A01(excludes A01N), A21A47, B01-B09, B31, B81,B82,
C06, D01-D21, E01-E21,
F21-F42, G21

Agriculture, Food stuffs,
Personal and Domestic Articles,
Separating, Mixing, Textile,
Paper, Construction, Mining,
Drilling, Lighting, Steamgeneration, Heating, Weapons,Blasting, Nuclear physics

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