〈日本知財学会誌〉Vol. 8 No. 2―2012 : 56 67-〈Original Paper〉

Identifying Knowledge Structure ofPatent and Innovation Research
Prof. Ichiro Sakata (The University of Tokyo)
Project Researcher Hajime Sasaki (The University of Tokyo)
Lecturer Yuya Kajikawa (The University of Tokyo)
The global intellectual property system is currently being revolutionized by changes in innovation and the quantitative
expansion in patenting. There is an expectation of applying the results of academic patents studies to the reformationof the intellectual property system. On the other hand, the rapid quantitative expansion in academic knowledge is posing difficulties for the application of conventional techniques to extract required knowledge or apply a suitable synthesis. This article has the purpose of effective implementation of large volumes of knowledge for policy formation andexamines the methodology for identifying academic growth fronts, important research domains or comprehension of theacademic landscape. We conducted a panoramic analysis of intellectual property rights (IPR) researches in the work
published from 1956 through citation network analysis. As a result, 3,833 academic papers were extracted as the largest connected component of the network of IPR. We specified five main clusters and twenty-five sub-clusters in the
network. Through a time series analysis, we identified young or recently growing research fields. Our findings provethat the citation network analysis allows us to accurately and comprehensively detect papers discussing important issues in the area of intellectual property rights and innovation.

■ KEYWORDS

I.

Intellectual Property System, Citation Network Analysis, Academic Landscapescience linkage (Bonaccorsi and Toma, 2007, Moto-INTRODUCTION

hashi and Yun, 2007, Kanda et al., 2008), the increasingly complicated relationship between products, pat-

The intellectual property system centering on pat-ents and scientific knowledge (Miyazaki and Islam,ents when viewed globally is undergoing a far-reaching

2007, Shibata et al., 2010), the development of open in-

revolution.

Quantitative problems include the in-novation (Chesbrough, 2003, Kirschbaum, 2005, Pyka-

creasing difficulty of reducing backlogs and maintaining

lainen, 2007, van de Vrande, 2009) and globalization ofpatent quality due to the increase in patent applications

knowledge circulation (Smith and Sharif, 2007, Sasaki

including applications by non-residents.

These de-et al., 2010). The basis of the patent system is beingvelopments are occurring against a background of therocked by structural change in the basic assumptions

internationalization of innovative activities, the accel-

which are based on the above premises.

There is aneration of innovation and intensifying competition inincreasing recognition that the current intellectual

strategic areas such as sustainable energy technologies

property system which has formed the basis of current

or next-generation IT.

patent law may no longer be necessarily capable ofConspicuous qualitative prob-

lems include changes in innovation such as increasing

promoting innovation.

* 本論文は，日本知財学会誌編集委員会による複数の匿名レフェリーの査読を経たものである．

tion, empirical knowledge, case studies and brief sur-56

In the current fact-changing and complicated situa-

©

2012

IPAJ

ISSN 1349-421X
All rights reserved.

Identifying Knowledge Structure of Patent and Innovation Research

Fig. 1

Number of papers including “patent” or“intellectual property”in the title or abstract

ondary battery (Shibata et al., 2009) and translational
research in cancer and cardiovascular medicine (Joneset al., 2011).
The aim of this paper is to offer a meta structure ofacademic knowledge (academic landscape) on patentand innovation research to assist effective policy discussion for intellectual property system reform.

With

citation-based approach, this paper analyzes the academic landscape of patent and innovation research tounderstand the current structure and trend of research,and to detect major sub-research fields and core pavey are not sufficient for the knowledge base of intel-

pers within it.

lectual property system reform.

methodology.

Policy makers haveto increasingly rely on accumulated academic knowledge which includes quantitative evidences, detailed
findings and in-depth legislative debate.

II.

In the next section, we present our

METHODOLOGY

But grasping

the current status of academic research on the intel-

An analysis of citation information in academic pa-

lectual property rights has become a rudimentary task

pers was initiated by Garfield (1955) and currently rep-

for policy makers because of the growing body of publi-

resents a useful tool for extracting important papers or

cations as shown in Fig. 1.

hot topics from a large volume of bibliographic infor-

Generally, there are two

approaches to obtaining comprehensive perspective on
academic research.

mation (Börner et al., 2003).

A paper is used as a

One straightforward manner isnode and when a citation relationship between papers

the expert-based approach, which utilize the implicit

is formed by tracking a linkage between nodes, it isknowledge of domain experts. The other is the com-possible to construct a citation network.

puter-based approach, which analyzes explicit knowl-considered that the authors of such papers cite anotheredge such as journal paper and letters (Kajikawa andpaper in the recognition of some relationship to theTakeda, 2008).

To meet policy makers’ need a com-contents of their own paper, such a network can be said

puter-based approach can be used to complement theto demonstrate a linkage between respective con-

When it isexpert-based approach because it is compatible withtent.

the volume of information (Börner et al., 2003 ; Boy-

specified research domains undergoing sharp growth in

To give a more precise example, Small (2006)

ack et al., 2005).

A citation-based approach, which isrecent years such as research conducted in relation tocomputer-based, operates on the assumption that cit-

carbon nanotubes or infectious diseases such as SARS

ing and cited papers have similar research topics.

By

by using a bibliographic database to create a mapping

analyzing this citation network, we can comprehendor segmentation (clustering) using citation information

the structure of a research domain constituting larger

and, in particular, by identifying the average age of pa-

volume of papers than we can read (Kajikawa et al.,pers associated with large numbers of citations.

2007).

In previous works, a citation-based approachbata et al. (2007) proposes a method for rapid detection

has been applied to emerging academic research fields

of emergent research fronts and papers associated withsuch as water resource management (Thelwall et al.,a high possibility of future citation by using a compli-

2006), biomass and bio-fuel (Kajikawa and Takeda,

cated network analysis and by modeling citation behav-

2008), organic LEDs (Kajikawa and Takeda, 2009), sec-

ior. Kajikawa et al. (2007) and Hashimoto et al. (2009)

Shi-

57

〈日本知財学会誌〉 Vol.8 No.2―2012

enable visualization of sustainability and innovation re-Fig. 2Analysis Steps in Citation Network Analysis

search fronts by using citation network analysis andnatural language processing in order to create a landscape of related concepts.

Shibata et al. (2011) identi-

fied important papers from citation relationships in relation to solar cells which are attracting attention
around the world.

Most previous studies discussed

non-weighted, non-directed network.
This paper uses such network analysis as a tool tocreate a landscape of intellectual property rights andinnovation research which has undergone conspicuous

method is a graphic method based on spring models

recent development.

More specifically, papers con-and assumes an attractive force between node having

taining the words “patent” or “intellectual property” inmutual links or, if that is not the case, a repulsive force,the title, keywords or abstract were extracted from a

and applies a coordinate calculation to each node.

database (Social Science Citation Index (SSCI) andother words, cluster groups having dense citation rela-(Arts & Humanities Citation Index (AHCI)). As a re-tionships are in proximity to each other and papers

sult, 9,458 papers from 1956 to 2008 were extracted

without citation relationships are arranged separately.

In

Next, these

The relative position of cluster groups is visualized bydata were used to construct a network using respec-

using the same color to display of citation relationships

tive papers as nodes and citation relationships asassociated with the same cluster.

links.

visual distance can be used to comprehend the level ofhaving 13,053 links between the papers.

In this manner, the largest document group

In this manner, the(largest connected component) connecting citation re-

the citation relationship between clusters.

lationships were 3,833 papers.

Then thirdly, cluster-tion of the average age of the cluster can be performed

ing was conducted by applying a Newman methodfrom the date of publication of papers contained in each(Newman, 2004) to those results.

cluster.

This method is a

A calcula-

A low average age demonstrates that themethod of creating cohesive clustering so that the ratio

cluster is a research front and that a large number ofof links contained in the cluster is sufficiently in-papers have been published in recent years relative tocreased in relation to the ratio of link between clusters,

the past.

that is to say, so that both the inter-cluster and intra-

analysis.

cluster link density contrasts are increased.

In thismanner, clustering enables the identification of docu-

Fig. 2 illustrates the steps in the above

III.

RESULTSA.

Characteristics and Cluster Structure ofment groups having dense citation relationships, inother words, document groups recognized by researchers and having close content relationships.

Intellectual Property Domains

Afterclustering the network, we analyzed the characteristics

Initially, the journal classification of the Web of Sci-of each cluster by titles and abstracts of papers that areence was used to analyze all papers with respect to thefrequently cited by other papers in the cluster, and also

associated academic field.

major journals, in which the papers in the cluster werevarious fields including law, business, information sci-published.

ence, economics and engineering as shown in Table

ally.

Fourthly, the results were recreated visu-

A Large Graph Layout (Adai et al., 2004) wasused as an algorithm for visualizing the network. The
58

1.

Papers were present from

Although the highest number of papers was fromlaw, it can be seen that no academic field was particu-

Identifying Knowledge Structure of Patent and Innovation Research

Table 1

AREAS OF STUDY

gate the brief structure of a research domain and to

Academic Field# of Papers

consider the frequently cited papers in the cluster as arepresentative of the same.

As a result, top five clus-

1.

Law

3,002

2.

Information Science & Library
Science

1,851

3.

Business

1,619

4.

Economics

1,372

5.

Management

1,010

6.

Computer Science

798

2002.61) and C5 is the oldest one (average year of pub-

7.

Planning & Development491

lication, 1982.01).

8.

Engineering

459

year between C4 and C5 is 20 years.

9.

Multidisciplinary Sciences342

C1 : Patent Innovation & Econometrics

Social Sciences

304

10.

ters are named as Patent Innovation & Econometrics(C1), Institution & Legal System (C2), Technology
Management and Patent (C3), Academic Activity &Patent (C4) and Patent Meta-Information (C5). C4 isthe youngest group (average year of publication,
The gap of average publication

The principal journals in the largest cluster are influential journals into systems research such as Research Policy and Scientometrics.

In these clusters,Thus, we can consider that the re-there are many papers discussing the usefulness of us-

search on patent and innovation research is apparently

ing patent information as an indicator of corporate in-multi-disciplinary field.

novation or as an economic indicator, or papers dis-larly prominent.

Next, clustering method was used to categorize thecussing the economic value of patents themselves.

largest connected component with 3,822 papers into 54

These can be said to be a research group constituting aclusters.

portion of innovation research 1 .

We regarded papers not citing other papers

in the component as digressional.

3,184 papers arecontained in just the top 5 clusters having a particularly large number of papers and account for 83% of thelargest connected component.

Thus, it can be deter-This cluster istermed “patent innovation and econometrics”.
C2 : Institution & Legal System

The principal journals in the second large cluster are
the Texas Law Review and Stanford Law Review.

The

mined that verification of these five clusters is suffi-

paper groups associated with this cluster include manycient for identification of a broad landscape related topapers discussing recent trends in the law and the le-intellectual property fields.

gal system or trends in intellectual property law in-

Consideration of the papers contained in the fivecluding patent and copyright law.

Key topics include

clusters shows that although specific content charac-

nature and function of patent system, law and policy ofteristics can be extracted, there remains a high level ofintellectual property licensing, pharmaceutical patentvariety.

We characterized each cluster by the titles

and information security and law.

Furthermore, theand abstracts that are frequently cited by other papers

field is also characterized by the large volume of thein the same cluster.

individual papers. This cluster is termed the “institu-

We also use the information ofmajor journals in which the papers in the cluster weretion and legal system”.

published.

C3 : Technology Management & PatentThis does not mean that all papers in thecluster study the same topics as covered in these frequently cited papers.

This cluster is related to the patent system or patent

In fact, each paper studies its

management in the intellectual property creation cycle

own topics, and each paper has its own unique fo-

for the creation, protection and exploitation by compa-

cus.

nies or universities.

However, as a first approach, it is reasonable totreat these inter-cited papers as a cluster to investi-

The principal journals are Re-

search Policy and RAND J ECON.

This field is mainly59

〈日本知財学会誌〉 Vol.8 No.2―2012

related to discuss regarding optimization of length oflectual property.

rights or the technical scope of patents, the exploita-

ed to open innovation, globalization and patents.

tion of licenses and the corporate management of intel-

termed “research related to technological management

Table 2

It is

CLUSTER STRUCTURE
Cluster name

C1 Patent Innovation & Econometrics# of papers

Ave. Year

1120

2001.65

PATENT STATISTICS AS ECONOMIC INDICATORS─A SURVEY

Hub papers

C1-1 Patent as Indicator

315

2002.14PATENT STATISTICS AS ECONOMIC INDICATORS─A SURVEY

C1-2 Patent Value

290

2002.09

A PENNY FOR YOUR QUOTES─PATENT CITATIONS AND THE VALUE OFINNOVATIONS

C1-3 Science Linkage

246

2000.64

THE INCREASING LINKAGE BETWEEN US TECHNOLOGY AND PUBLICSCIENCE

C1-4 Strategy & Technology

117

2000.66

TRADE IN IDEAS─PATENTING AND PRODUCTIVITY IN THE OECD

C1-5 Market Value & Patent48

2001.92

MARKET VALUE, R-AND-D, AND PATENTS

C2 Institution & Legal System

1999.80

ON THE COMPLEX ECONOMICS OF PATENT SCOPE

C2-1 IP Law Policy (Patent)

344

1999.40

ON THE COMPLEX ECONOMICS OF PATENT SCOPE

C2-2 IP Law Policy (Copyright)

287

2001.52

A P R O P E R T Y R I G H T I N S E L F - E X P R E S S I O N ─E Q U A L I T Y A N D
INDIVIDUALISM IN THE NATURAL LAW OF INTELLECTUAL PROPERTY

902

C2-3 Information Security & IP Law

135

2000.48LEGAL HYBRIDS BETWEEN THE PATENT AND COPYRIGHT PARADIGMS

C2-4 Pharmaceutical Patent

38

1992.97

PLANTS, POVERTY, AND PHARMACEUTICAL PATENTS

C2-5 IP for IT

28

1991.57

CREATING A NEW KIND OF INTELLECTUAL PROPERTY─APPLYING THE
LESSONS OF THE CHIP LAW TO COMPUTER-PROGRAMS

1982.50INNOVATION, IMITATION, AND INTELLECTUAL PROPERTY-RIGHTS

C3 Technology Management & Patent

641

C3-1 IP in Global Economics

151

2001.77

INNOVATION, IMITATION, AND INTELLECTUAL PROPERTY-RIGHTS

C3-2 Optimal Patent Design145

2000.46

OPTIMAL PATENT LENGTH AND BREADTH

C3-3 IP Piracy Matter

139

2001.02

THE INTERTEMPORAL, CONSEQUENCES OF UNAUTHORIZEDREPRODUCTION OF INTELLECTUAL PROPERTY

C3-4 Patent Races

57

2000.54

A MODEL OF GROWTH THROUGH CREATIVE DESTRUCTION

C3-5 Protection & Exploitation

47

1997.21IMITATION COSTS AND PATENTS─AN EMPIRICAL-STUDY

2002.61

THE GROWTH OF PATENTING AND LICENSING BY US UNIVERSITIES :AN ASSESSMENT OF THE EFFECTS OF THE BAYH-DOLE ACT OF 1980

C4 Academic Activities & Patent

287

C4-1 Univ. Technology & Licensing

69

2004.22

THE GROWTH OF PATENTING AND LICENSING BY US UNIVERSITIES :AN ASSESSMENT OF THE EFFECTS OF THE BAYH-DOLE ACT OF 1980

C4-2 Life-Science & Patent54

1999.31

UNIVERSITIES AND THE MARKER FOR INTELLECTUAL PROPERTY IN
THE LIFE SCIENCES

C4-3 Effect by R&D of Univ.

34

2003.62

PUTTING PATENTS IN CONTEXT : EXPLORING KNOWLEDGE TRANSFERFROM MIT

C4-4 Technology Transfer30

2003.30TECHNOLOGY TRANSFER AND PUBLIC POLICY : A REVIEW OFRESEARCH AND THEORY

C4-5 Patents & Commons22

2005.39

WITHHOLDING RESEARCH RESULTS IN ACADEMIC LIFE SCIENCE─EVIDENCE FROM A NATIONAL SURVEY OF FACULTY

C5 Patent Meta-information

60

This field also contains papers relat-

1982.01

ONLINE PATENT SEARCHING─THE REALITIES

C5-1 IP History & Culture234
33

1984.64

UNITED-STATES PATENT OFFICE RECORDS AS SOURCES FOR THE
HISTORY OF INVENTION AND TECHNOLOGICAL PROPERTY

C5-2 Patent Searching32

1987.72

ONLINE PATENT SEARCHING─THE REALITIES

C5-3 Leterature (Chemical)

31

1979.45CHEMICAL-ABSTRACTS AS A PATENT REFERENCE TOOL

C5-4 Patent Documentation

27

1968.81PATENT CITATION INDEXING AND NOTIONS OF NOVELTY SIMILARITY
AND RELEVANCE

C5-5 European Patent25

1987.92

LEGAL MONOPOLY IN LIBERAL ENGLAND─THE PATENT CONTROVERSYIN THE MID-19TH CENTURY

Identifying Knowledge Structure of Patent and Innovation Researchand patent”.

COMPUT SC or NACHR DOK. Although there was a

C4 : Academic Activities and Patent

relatively active publication period from 1975 to 1980,

This cluster includes many papers regarding the re-this may be due to the fact that changes were made tolationship of intellectual property and academic activi-

the system to enable full-text searching after 1976

ties at universities.

Key topics include effect by uni-

whereas records prior to 1975 can only be searchedversity R&D, technology transfer and licensing fromwith respect to date, patent number or patent classifi-

universities, commercialization of university developed

cation.

technologies, university related life science patent andent meta-information.”scientific commons.

This field is termed “research related to pat-

The principal journals are Re-

search Policy and Scientometrics.

When the same method was used to further catego-

The oldest publica-tion date for a paper is 1984 and it can be seen that pa-

rize these five clusters into five sub-clusters, a land-pers have been published after the enactment of thescape was created in the form of a layered struc-

Bayh-Dole Act (1980). Thus, overall it can be seen to

ture.

be a relatively new field and all of the sub-clusters

intellectual property field was created by verifying the

A landscape of the academic field related to thehave an average publication year of approximately

total of 30 clusters comprising 5 clusters and 25 sub-2000. It is termed “academic activities and patent.”clusters.

C5 : Patent Meta-Information

characteristics of the principal 5 clusters.

This field contains many papers discussing electronic libraries of patent publications, patent journal infor-

Table 2 shows the layered structure andHub papers

are the most frequently cited papers within a sub-cluster.

mation, online search systems and economic indicators
acquired from patent information using these sources.

The principal journals are J CHEM INFORM

Fig. 3

B.

Visualization

Fig. 3 is a schematic map of the overall clusters.

Academic landscape of patent and innovation research61

〈日本知財学会誌〉 Vol.8 No.2―2012

The horizontal and vertical dimensions of the figure do

Fig. 4

Time series analysis

not have any meaning. Since the figure is created using a spring model, strongly linked clusters are disposed in proximity not only by document unit but also
by cluster unit.
As shown by the figures, C1 (Patent Innovation &Econometrics) and C3 (Technology Management andPatent) are in the center of the citation relationship.

On the other hand, there is a valley between

the academic fields of C1 (Patent Innovation & Econometrics) and C3 (Technology Management and Patent),
and C2 (Institution & Legal System) and C5 (PatentMeta-Information).

C4 (Academic Activities and Pat-ent) is positioned slightly separated in the overall arrangement.

In contrast, C1 (Patent Innovation &Econometrics) and C5 (Patent Meta-Information) areadjacent to each other and can be viewed as researchareas which mutually interact.

In reality, the patentjournal information in C5 is essential for C1 research.
The relationship between the research areas is clear
from the visualization and a number of valleys are evident between the research domains.

These valleys

demonstrate insufficient mutual recognition or exchange. In particular, there is a necessity for fusion ofing scientific commons, the merits of patenting re-legal system research centering of research into thesearch results at universities.

legal system and econometric approaches or informa-

papers is still small, this cluster is categorized as ation science.

“young cluster.” In the same manner, C4-1 : Univ.

Since the number ofTechnology & Licensing (average year of publicaC.

Time Series Analysis

The application of time series analysis enabled separation of the sub-clusters into young, emerging do-tion : 2004.2) and C4-3 : Effect by R&D (average yearof publication 2003.6) are young clusters.
A representative example of the document groupmains, domains undergoing rapid growth, domains

undergoing rapid growth in recent years is C1-

characterized by stable research and domains with

1 : Patent as Indicator (average year of publication

stagnation of research.

2002.1).

A representative research ex-

Mid 1990s, although there were less thanseven publications per annum, by 2004, this had grown

ample is shown in Fig. 4.
The most recent cluster of the 5 principal clusters

to more than 20 publications.

Other domains under-and 25 sub-clusters is C4-5 : Patents & Commons.

going similar growth are C1-2 : Patent value (average

The earliest publication of a paper classified into thisyear of publication 2002.1) and C3-1 : IP in Global

sub-cluster was in 1997.

Economics (average year of publication 2001.8).

The average year of publi-Allcation in this cluster is 2005.4. This sub-cluster is aof these sub-clusters have an average year of publica-

document group in which debate is conducted regard-tion which is later than 2000. This fact demonstrates

62

Identifying Knowledge Structure of Patent and Innovation Researchthat in these domains the amount of academic knowl-

C-1 and 117 journal names from research papers inedge in the 7 years from 2001 is approximately equal

C-2.

to the previous 45 years.

effectively for comprehensively extracting research

Domains characterized by

Therefore, using journal names does not work

stable research when taking into account trends inpapers of our interest.

time series development on an annual basis are C1-

tered research topics with similar contents by using ci-

3 : Science Linkage (average year of publication

tation information. We would like to demonstrate that

To resolve the issue, we clus-

2000.6), C1-4 : Strategy and technology (average year

such approach allows for an accurate and comprehen-of publication 2000.7), C2-1 : IP law policy in patents

sive extraction of knowledge, even in the domain of in-(average year of publication 1999.4), C2-2 : IP law pol-tellectual property rights and innovations.

icy in copyrights (average year of publication 2001.5)

Designing and reforming intellectual property rights

C2-3 : Information security and IP Law (average year

have generated various kind of discussion.

of publication 2000.5), C3-2 : Optimal patent designof popular issues include patent scope and length, bal-

Examples(average year of publication 2000.5) and C3-3 : IP pi-

ancing patent protection and use of patent rights, pat-racy matter (average year of publication 2001.0). Do-

ent value, and patent quality.

mains characterized by stagnation of research corre-

quality, discussion focused on legal stability recently.

spond to all the C5 sub-clusters.

The average year of

Many discussions have been carried out on patentpublication is extremely old in comparison with otherscope and length in two steadily growing sub-clusters

With respect to patentdomains and in particular, the average year of publica-of intellectual property studies : C1-2 (Patent Value)

tion for C5-4 : Patent Documentation is 1968.

and C3-2 (Optimal Patent Design).

IV.

Topics include“complex economics of patent scope,” “optimal patent

DISCUSSION

length and breadth,” “patent rights and cost of imitation,” and “patent scope and innovation in the software

Our analyses indicate that patent research can be di-Industry.”

Research papers that discuss balancing

vided into five clusters and 25 sub-clusters by con-patent protection and use of patent rights are includedtent.

in C3-2 (Optimal Patent Design) and C3-5 (Protection

We counted the number of research papers andTopics include “optimal patentcalculated the average publication year of research pa-

and Exploitation).

pers by cluster and by year.

Values indicate the accu-

with compulsory licensing,” “imitation cost and pat-

mulation of knowledge and the growth rate of patentents,” “limiting patentee’s market power without re-

We also created an aca-

ducing innovation incentives,” and “future develop-

demic landscape depicting the distance between clus-

ment and effects of pharmaceutical drugs in developing

ters by content.

countries.” Research papers discussing patent value

research by academic field.

In general, although we search for research papers

are included in C1-2 (Patent Value) and C1-5 (Marketof our interest by query and journal name, this search

Value and Patent), and are almost non-existent in othermethod may not be effective in case the query is notclusters.

present in the author keywords or in the abstract of re-ent backward citations.

Patent values are typically measured by pat-

Searches become even more difficult

On the other hand, we cannot easily identify re-when different academic fields use different words tosearch papers directly addressing patent quality issues,

imply the same meaning.

Moreover, a sub-cluster

which become increasingly important topics recently.

with similar contents may also contain research papers

With respect to legal quality issues, we searched in C2from a number of different journals.

In fact, we dis-(Institution and Legal System) by focusing on fre-covered 104 journal names from research papers inquently cited papers and discovered only small number

search papers.

63

〈日本知財学会誌〉 Vol.8 No.2―2012

of papers.

This discovery explains why the topic did

not form a sub-cluster despite being an important issue.

tect patents at low cost.
As for Objective 2, simply looking at the cluster
names allows us to identify many research papers on

The aforementioned results revealed that we can ac-

industry-academia-government collaboration in C4-1

curately and comprehensively search for journal papers〜C4-4 and C1-3.

of our interest by using a specific keyword listed in a

into sub-clusters by key topics such as licensing, life-

domain, rather than methods using query or journalscience, effect by R&D, technology transfer, and sci-

names.

ence linkage. Among those sub-clusters, C4-1, C4-3,

Next, we will examine whether our search method
is effective in identifying papers related to new sub-

These clusters are further split

and C4-4 contain various recently published papers.

Based on the commonly known relationship be-jects that are becoming increasingly popular in intel-

tween innovative drug development and universities,

lectual property reform issues.

Compared to existing

we also searched in C2-4 (Pharmaceutical patent) for

subjects that have been studied for years, new subjects

research papers on university-industry research col-have attracted relatively small number of studies andlaboration and found many.

Topics include “commer-

have not established a knowledge domain. Therefore,cialization of university-developed biomedical technol-

extraction of papers on new subjects is considered rel-ogies,” and “optimal control over intellectual property

atively difficult in general.

rights by faculties.” Our investigation found that a ma-

Since policy discussions

and academic activities influence each other, it is un-

jority of research papers with highest citation frequen-

derstood that there is a correlation between their tem-cy are included in these sub-clusters.

poral quantitative variations.

Therefore, information

effective prior art investigation to take place in re-on the average publication year may lead us to thesponse to the growth of university-industry research

In order for anright direction in finding related research papers as

collaboration, the development of a seamless searchwell as sub-cluster names.

system is underway.

Among various existing

The seamless search system al-

policy agendas, we would like to discuss “Intellectuallows simultaneous searching for patents, academic pa-

Property Plan 2010” by the Japanese government aspers and other non-patent publications, and ultimately

an example.

identifies connections between patents and academic

Devised by the Cabinet Office, this planis the highest-ranked policy agenda for intellectual

studies or other publications by content.

Research

property comprising three strategies.

Among them,papers discussing patent search exist in C5 (Patent

Strategy 3, “How to Promote Intellectual Property

Meta-information) and the average publication year isacross Industries,” corresponds to our analyses on re-very old.

search papers.

ies on seamless search in intellectual property have

The other two strategies cover con-tents and international standards.

Strategy 3 lists

four objectives :
1.
2.
3.
4.

64

This elucidates the fact that academic stud-stagnated despite the recent advancement of text mining and link mining approaches.

The concept of open

Promoting intellectual property in small andinnovation forms a part of Objective 3 and is included

medium ventures and regional communities.

in C4-1, C4-4, and C4-5 (Patent & Commons) based
The topics include “market

Achieving the best industry-academia-govern-on sub-cluster names.

ment collaboration in the world.

economy and scientific commons” and “the dynamics

Improving infrastructure to support open inno-of commercialization of scientific knowledge.” C4-5

vations and accelerate overall innovations.

has the latest average publication year, but includes

Setting an international intellectual property

only 22 papers.

system that allows us to efficiently file and pro-

lective action show also in C1 when examining fre-Research papers on commons or col-

Identifying Knowledge Structure of Patent and Innovation Researchquently cited papers.

Overall, our findings prove that an academic land-

Many research papers on International Intellectual

scape allows us to accurately and comprehensively de-Property System exist in C3-1(IP in Global Econom-

tect papers discussing institutional issues in the areaics). Topics include an empirical study that examinesof intellectual property rights and innovations.

how differences in the level of patent protection affecttrading and investments.

C3-1 has the latest average

publication year among sub-clusters in C3.

V.

CONCLUSION

After in-vestigating other sub-clusters, it was found that C1-1

The results of the academic landscape demonstrates

also contains some research papers on internationalthat in the domain of Intellectual Property Rights andtechnology diffusion.

Innovation, overall there are 9,458 papers and that

These research papers, howev-

er, do not discuss intellectual property system or cus-

3,833 important papers exist even when examining

toms.

only the largest connected component.

Instead, they analyze the characteristics andchanges of innovation pattern.

Approximate-ly, half of those papers were published after 2000 andOn the other hand, we are unable to search for re-

are therefore considered to be research papers reflect-search papers on small and medium ventures or re-ing the recent changes in the innovation environ-gional communities by sub-cluster name or averagement.

publication year. On investigating journal papers withters and 25 sub-clusters of knowledge domain.

highest citation frequency, it was found that many pa-

also specified hub papers as frequently cited papers

pers on regional business and innovation environments

within each sub-cluster.

In the component, we identified 5 major clusWe

Topics included “geographic

Then, we searched for research papers discussing

localization of innovation”, “regional network andcentral issues by using the following information : re-were contained in C1-1.

knowledge spillover.” These papers discuss the char-search area names provided in clusters and sub-clus-

acteristic and evolvement of innovations, thus forming

ters, the average publication years of research papers

the background of intellectual property system. How-in clusters and sub-clusters, and frequently cited pa-

ever, few research papers discuss intellectual property

pers within sub-clusters.

system specifically.

method detected a set of related research papers more

Sub-clusters discussing recent institutional issues
are either growing in size or are considered new.

By

As a result, our search

accurately and more thoroughly than the search method using query and journal names.

Our search meth-using cluster names and average publication years, we

od also worked somewhat effectively for finding re-are able to accurately and comprehensively search forsearch papers discussing new issues, which are

papers that deal with even new issues if they discuss

relatively difficult to find.

We were able to retrieve ain depth issues in the intellectual property system aslarge number of research papers across the board inwell as in technology management. Our research also

the following areas : Scope and length, balancing pro-found that a similar search method is not effective in

tection and use of rights, patent value, and industry-

identifying research papers on innovation activity anal-

academia-government collaboration.

yses in C1-1, which uses patent as indicators.

On the otherWe be-

hand, we faced difficulty finding papers providing infor-

lieve this is because authors in this academic field tendmation on institutional issues such as intellectual prop-

to cite papers discussing specified institutional issues

erty in small and medium ventures and regional com-and technology management topics rather than re-munities, and legal quality of patents because suchsearch papers analyzing innovation trends and charac-

topics have not yet structured or split into sub-clus-

teristics.

ters.
65

〈日本知財学会誌〉 Vol.8 No.2―2012

As shown in Table 1, Patent and Innovation Research is a multi-disciplinary academic field where anumber of different journals discuss similar topics using different notations.

Such nature, therefore, pre-vents us from identifying related research papers by
keyword and journal names.

567

Our search method has

successfully demonstrated that by analyzing frequently

8cited papers, major journals of each cluster, and average publication year, it is possible retrieve a set of re-9

lated research papers without missing an importantone.

This also suggests that our research method

helps minimize the risk of policy assessments being
influenced by specific marginal academic opinions.

10It

has been demonstrated that the limitations on imple-

11

mentation of knowledge caused by the recent “knowledge explosion” can be overcome to a certain extent.

12

In order to complement the issue of difficulty in
identifying citation analysis as sub-clusters, we willconsider using full-text information together with our
research method in the future to extract research papers by content.
ACKOWLEDGMENT
1 We are grateful to anonymous referees for their valuablecomments, owing to which our manuscript was greatly
improved. We wish to express our sincere gratitude to
Dr. Dominique Guellec, Senior Economist of OECD, Professor Toshiya Watanabe, the University of Tokyo and Mr.
Takashi Suzuki, Chairman, Nippon Export and Investment
Insurance.
2 This paper was supported by a grant from the NEDO.
The views contained here reflect those of the authors.
NOTE
1 Research conducted by Hashimoto et al. (2009) using
the same database showed that one of the sub-clusters
of“Innovation Fundamentals”which is the largest research domain in innovation studies is“intellectual property rights”and contains 701 documents.
REFERENCES
1 A.T. Adai, S.V. Date, S. Wieland, and E.M. Marcotte“LGL : Creating a map of protein function with an algorithm for visualizing very large biological networks,”J Mol
Biol , 340, pp. 179–190, 2004.
2 A. Bonaccorsi and G. Toma,“Institutional complementarity and inventive performance in nano science and technology,”Research Policy 36(6), pp. 813-831, 2007.
3 K. Börner, C. Chen, and K.W. Boyack,“Visualizing knowledge domains,”Annu Rev Info Sci Technol 37, pp. 179–
255, 2003.
4 K.W. Boyack, R. Klavans and K. Börner,“Mapping the
backbone of science,”Scientmetrics 64, pp. 351-374,
66

13
14

15

16
17

18

19

20

21

22

23
24

2005.
H. Chesbrough,“The era of open innovation,”Sloan Management Review , 44(3), pp. 35-41, 2003.
E. Garfield,“Citation indexes for science : a new dimension in documentation through association of ideas,”Science 122, pp. 108-111, 1955.
M. Hashimoto, I. Sakata, Y. Kajikawa, Y. Takeda, and K.
Matsushima, “Academic landscape of innovation research and innovation policy by network Science,”Hitotsubashi Business Review 56(4), pp.194-211, 2009.
DS. Jones, A. Cambrosio and A. Mogoutov,“Detection
and characterization of translational research in cancer
and cardiovascular medicine,”Journal of Translational
Medicine 9 : 57, 2011.
Y. Kajikawa, J. Ohno, Y. Takeda, K. Matsushima and H.
Komiyama,“Creating an academic landscape of sustainability science : an analysis of the citation network,”Sustainability Science 2, pp. 221-231, 2007.
Y. Kajikawa and Y. Takeda,“Strucure of research on biomass and bio-fuels : a citation-based approach,”Technological Forecasting and Social Change 75, pp. 13491359, 2008.
Y. Kajikawa and Y. Takeda,“Citation network analysis oforganic LEDs,”Technological Forecasting and Social
Change 76, pp. 1115-1123, 2009.
Y. Kanda, H. Ebihara, M. Igami, and T. Kuwahara,“Scienceand technology indicators –data updated in 2008,”National Institute of Science and Technology Policy web
page, 2008.
R. Kirschbaum,“Open innovation in practice,”ResearchTechnology Management , 48(4), pp. 24-28, 2005.
K. Miyazaki and N. Islam,“Nanotechnology systems of innovation - An analysis of industry and academia research
activities,”Technovation 27, pp. 661-675, 2007.
K. Motohashi and X. Yun,“China’s innovation system reform and growing industry and science linkage,”Research Policy 36(8), pp. 1251-1260, 2007.
M.E.J. Newman,“Fast Algorithm for detecting community
structure in networks,”Phys Rev , E69, 066133, 2004.
T. Pykalainen,“Model for profiting from software innovations in the new era in computing,”Technovation 27,
pp. 179-193, 2007.
H. Sasaki, I. Sakata, Y. Kajikawa and K. Fujisue,“Detecting the valley of international academic collaboration in
renewable energy,”IEEE International Conference on Industrial Engineering and Engineering Management (IEEM
2010 ), in Macau (Dec. 7-10, 2010).
N. Shibata., Y. Kajikawa, and K. Matsushima,“Topologicalanalysis of citation networks to discover the future core
papers,”Journal of the American Society for Information
Science and Technology 58, pp. 872-882, 2007.
N. Shibata, Y. Kajikawa and I. Sakata,“Extracting thecommercialization gap between science and technology case study of solar cell-,”Technological Forecasting andSocial Change 77, pp. 1147-1155, 2010.
N. Shibata, Y, Kajikawa, Y. Takeda, I. Sakata, and K.
Matsu­shima,“Detecting emerging research fronts in regenerative medicine by the citation network analysis ofscience publications,”Technological Forecasting and Social Change 78, pp. 274-282, 2011.
N. Shibata, Y. Kajikawa and I. Sakata,“Assessing the gap
between science and technology : case study of secondary batteries,”Journal of Intellectual Property Association of Japan 6 (3), pp. 5-12, 2009.
H. Small,“Tracking and predicting growth areas in science,”Scientometrics 68(3), pp. 595-610, 2006.
R. Smith and n. Sharif,“Understanding and acquiring
technology assets for global competition,”Technovation

Identifying Knowledge Structure of Patent and Innovation Research

25

27, pp. 643-649, 2007.
T. Thelwall, K. Vannm and R. Fairclough, “Web issue
analysis : an integrated water resource managementcase study,”J Am Soc Info Sci Technol 57, pp. 13031314, 2006.

26 V. van de Vrande, J.P.J. de Jong, W. Vanhaverbeke and M.
de Rochemont,“Open innovation in SMEs : Trends, motives and management challenges,”Technovation 29,
pp. 423-437, 2009.

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