Kohei Tsuda, Frank J. Rinaldo, Victor V. Kryssanov, Ruck Thawonmas
Faculty of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan
Keywords: Patent, Inventorship networks, Power law.
Abstract: Technological and research strategies are becoming more significant as they create future value in the
market. The core of these strategies is the creation of patents, which help eliminate or contain competition.
Companies seek to learn the research strategies of their competitors. At the same time, all companies try to
hide their own strategies but they generally cannot, because patents have to be filed and therefore exposed at
a patent office and even made globally (e.g. via the WWW) accessible on-line. Part of the technological
strategy of a company can be determined by observing the patents it files, their timing and their authors.
There have been many studies about patents reported in the literature, with most of them focusing on the
connectivities existing in co-citation, co-patent networks. In the presented work, the focus is on the
inventors. Given the patent files of a company, one could possibly predict the company’s current and future
research and production strategies. Furthermore, if the inventors are known, the human resources of the
corresponding companies could naturally be scrutinized. The latter would allow to estimate the mechanism
in the process of patent creation at a specific company. A novel approach to analyze the
professional activities of company inventors is proposed and applied to determine the inventive strategy of
Japanese manufacturing companies. The presented results can be used to optimize knowledge and recourse
management within a company.
Technological strategies are becoming more
significant as they create future value in the market
(Probert and Shehabuddeen, 1999; Burgelman et al.,
2003). The core of these strategies is the creation of
patents that helps eliminate or contain competition.
Companies also need to learn the patent strategies of
their competitors (Rivette and Kline, 2000).
At the same time, all companies need to hide
their own technological strategies but cannot,
because patents have to be published at a patent
office as quickly as possible. As a result, part of the
technological strategy of a company can be
determined by observing the patents it files, their
timing and their authors.
There have been many studies reported in the
literature about patent/authorship networks. Most of
them deal with the structure of the authorship and
focus on the patent system and its surroundings.
There are studies that explore the grouping of
patents through a bibliographic coupling analysis
(Huang et al., 2003) and the linking of science and
technology as revealed with bibliographic references
(Verbeek et al., 2002). A recent study suggests that
there are two mechanisms of patent diffusion:
geographic localization of knowledge flows and
concentration of knowledge flows within company
boundaries (Singh, 2005). Many works described
knowledge diffusion and flow by using complex
network theory (Chen and Hicks, 2004); target areas
included industries (Meyer, 2001), universities (Jaffe
and Trajtenberg, 1996), and global enterprises
(Tijssen, 2001).
Much of the current research is therefore about
patent citations, yet as a rule it targets multinational
corporations (Verspagen, 2000; Bhattacharya and
Meyer, 2003). Researchers tend to focus on the
structure of patent co-citation and generally on
patent networks (Mogee and Kolar, 1999). These
studies analyze knowledge diffusion through patent
citation, but there are few studies that attempt to
utilize these analyses to improve the efficiency of
patent creation process. There is little information
available on the management mechanism to create
patents effectively and on inventor networks.
Tsuda K., J. Rinaldo F., V. Kryssanov V. and Thawonmas R. (2006).
In Proceedings of the International Conference on e-Business, pages 289-293
DOI: 10.5220/0001427102890293
The purpose of this work is to explore the
mechanisms responsible for the creation of patents at
different manufacturing companies. A model of
these mechanisms is proposed and used to analyze
the professional activities of company inventors
using patent authorship data.
Figure 1 provides a conceptual view used in the
presented study. The patent data is taken from the
Japanese Patent Authorship Network; it includes
7,396 inventors filing 9,349 patent applications. In
Section 2, characteristics of the patents, the
inventors, and their social network are discussed. In
Section 3, a sketch of the system-theoretic analytic
framework used in this study is given, and Section 4
presents a case study. Finally, Section 5 discusses
characteristics of the patent creation process and
Section 6 gives concluding remarks and outlines
directions for future research.
Patents are created by inventors, and their authorship
is similar to that of academic papers. One might
assume that the academic authorship analysis might
be used to investigate the process of patent creation
(Liu et al., 2005).
The above analogy does not however work.
Patents as a rule have a strategic (and financial)
significance, while academic papers do not, and the
academic citation studies are not readily applicable
for inventor network.
Inventors are usually researchers at companies
with R&D sections. While there are many individual
inventors in the US, in Japan almost all inventors
(and their patents) are affiliated with companies.
What is an inventorship network? It is the list of
researchers and their inventions, and their
corroborative efforts. It reflects the professional
relationships that exist between the authors in the
process of working on a patent. Invention activities
assume communication and exchange of ideas, and
if there are co-authors (co-inventors), there is
usually a strong connection between them as
indicated by the patent (authorship) network. If such
collaborating individuals are co-authors on more
than one patent, the corresponding professional
connection is even stronger.
Who would be considered as the most important
person in a company’s R&D? Probably the person
who has the greatest number of patents; this may
however not necessarily be so. In Japan, it has been
the case for a researcher’s superiors to be included
as a patent author. This could distort who might be
the most important person as a high level manager
could have his name on many patents with very little
contribution. People having a number of registered
patents are probably important, but the truly
essential person is the one who is the most “truly”
influential to other researchers.
A complete (whatever it would mean in the given
context) theory of social interactions requires a
theoretical understanding of statistical regularities
observed in or simply associated with the social
system under examination. The most often cited
(and overwhelmingly best studied) approach to the
study of various networks having a social origin is
Zipf’s “principle of least effort” (cumulative
advantage, preferential attachment, etc), yet
sometimes presented as simply the power law. The
latter law states that the frequency of a link (contact,
citation, authorship, etc.) decays as a power function
of its rank (Newman, 2005). It should be noted
however, that despite the apparent relevance and
proven universality of this law, it can be obtained
from a variety of mechanisms (Mitzenmacher, 2003)
and by itself, it does not provide insights about the
organization of the social system, the system’s
dynamics and evolution.
In an attempt to establish a theoretical basis for
the investigation of inventorship networks, the
presented study exploits the analytic framework
originally proposed by the authors of Reference
Figure 1: The concept of the study: focusing o
communication networks of inventors.
(Kryssanov et al., 2005). The framework deals with
the observed behavior of a complex system (e.g.
social, economic, biological, etc.), and it allows for
the evaluation of the internal, “hidden” structure and
dynamics of the system, based on estimated
parameters of the observed stochastic process (i.e.
the system’s behavior as registered).
Specifically, to explore the mechanisms of patent
generation, the following model characterizing the
dynamics and structure of the social network in
focus has been used (also see Kryssanov et al.,
where parameters
0>b , 0>
, and )(kP is the
probability mass function of the occurrence (count)
of the network’s observed state change.
It should be emphasized that the well-studied
form of the power law
kbkP (Pareto 1
distribution) is a particular case of the more general
distribution (1) when the investigated system is
homogeneous (i.e. when
). In the latter case,
an “equivalent” of the Zipf’s law for the rank
statistics can be written as
1/1/11 +
= lbrlbf
f is the relative occurrence frequency of the
r-th popular unit. While the rank-frequency form is,
perhaps, most often used in the studies of patent and
article authorship- networks, we will employ the
general form (1) to explore the dynamics and
structure of inventorship networks in Japanese
manufacturing companies. The observed property
(as represented by the stochastic variable
k ) is, in
this case, the number of patents filed by an
individual. It will thus be assumed that filing
(registering) a patent indicates a change in the
internal dynamics of the corresponding social
Data used in the following analysis is a sample
representing the number of patents applications filed
for 7,396 inventors (totally 9,349 applications) using
specific IPC (International Patent Classification at
WIPO - World Intellectual Property Organization)
categories about micromachining-techniques (Table
1) in Japan during 1988-2003. In this section, an
analysis of the specific sub-set (the IPC categories)
of the data is done, and its results are used to detect
the structure of the patent creation process.
Figure 2 shows the results of modeling the patent
authorship with 2-component and 3-component
models from the previous section (i.e. for M = 2 and
3, respectively). A traditional model would suggest
only 1 component represented by a single straight
line in the double-logarithmic chart, which is
obviously not the case for this data. In the analysis,
the 2-component model appears to be a better
Figure 2: Results of the modeling of the Japanese
companies’ patent network.
/1 is an estimate of the
average number of patents per author;
bvE /)( =
Section Class Subclass Description
B 81 All
H 02 N
Table 1: IPC categories used for sampling.
choice, based on the parsimony principle ("the best
is the simplest model"), since the AIC difference –
Akaike’s Information Criterion assesses the relative
Kullback-Leibler distance between the fitted model
and the unknown true mechanism, which actually
generated the observed data (Akaike, 1983) – is not
too large (models may be considered equivalent or at
least close when
2AIC Δ (Sakamoto et al.,
1986)). Both models, however, suggest that there are
two subsystems, "static" (~20%) and "dynamic"
(~80%), in the social network investigated. The
static system appears to be due to basic study
activities in the R&D (or “pure” research) section of
a company, while the dynamic system due to
innovation activities in the mass production process
(incremental improvements, etc.).
It is thus proposed that the static system is based
on (or is made up of) “basic” or “pure” research.
This would be research without a very specific
(focused) goal. The dynamic system, on the other
hand, could be based on the Japanese idea of
incremental improvements. This is research (in a
very specific area and, usually, with a very specific
goal) to improve an already manufactured product or
product design. Figure 3 provides a conceptual
image of the two systems to create patents.
Almost all patents discussed above were created by
collaboration of inventors. Regarding the patent
categories used in this study, Japanese companies
have competitive power in the field of
micromachining technique, but do not have it in the
IT and the biotechnology areas. In addition, as it
follows from the previous analysis, almost all of the
patents are created in the dynamic system. In other
words, it appears plausible that, in Japanese firms,
R&D departments (“pure research”) do not play a
major role in the creation of patents.
The previous studies about effective team work
suggested that the decisions of team members are
important for the success of a project (Bixby, 1987),
and referred to factors needed for creative teams, but
did not mention the team dynamics.
Japanese manufactures are involved in training
processes for innovation, especially focusing on the
importance of “Kaizen” (Imai, 1987; Lillrank and
Kano, 1990). It is perhaps this culture that caused
the observed dynamics in the inventorship networks.
Possible reasons for why the contribution of the
innovative activities in the patent creation process is
more significant are as follows. If every patent
application is valuable, the product development
processes prevails over the basic study activities. If,
however, the product development has the same
impact (priority, etc.) as the basic study activities,
then one patent in basic studies is more “powerful”
(in terms of the patent authorship, as revealed
through the social connections and count of filed
patents) than one in product development. Otherwise,
the product development processes may naturally
result in more patents than the basic study activities.
It has been found through interviews that the
presented study findings confirm the general
intuition of the respective companies’ managers
about the structure and the dynamics of the inventive
The analysis in the previous section has shown that
there are two possible mechanisms of patent creation
in Japanese manufacturing companies. The less
(observationally) influential mechanism tends to be
focused on genuinely new products and new ideas.
Traditionally the later has a higher risk (e.g. due to
low return on investment), as large jumps in
technology may not be financially justifiable. In
contrast, the more influential mechanism is
associated with activities focused on current
products and ideas with the goal of enhancing or
improving on them. This type of patent creation
activities obviously has a better financial
justification, as there is more knowledge about the
Figure 3: The concept of the patent creation.
current product and its profitability. Hence, the
cost/reward ratio for the incremental patent research
makes it easier to justify.
Currently, the process to create patents is often
highly inefficient and very costly in Japan; the
emphasis on process innovation should be shifted to
product innovation in Japanese firms since, in fact,
R&D budgets have been recently increasing without
producing economic results (OECD, 2001). If
‘process’ innovation is driven by the dynamic
mechanism, the companies need to change the
management system to reduce dynamics in the
inventor networks.
In a future study, we will focus on the details of
inventorship networks, particularly on the inventors
and their (apparent) areas of expertise. This more
detailed analysis might suggest ways for a company
to increase its synergy between inventors to more
quickly develop patents.
Japanese working culture is changing
dramatically, and recently it is not enough to focus
solely on the organization of a company to grasp its
potential. The importance of individual inventors
and their connectivity should further be analyzed
because they are the source of the patents that
largely determine technological strategies.
There are also plans to conduct a similar analysis
of other countries’ patent data (e.g. the US and the
EU) to determine if the patent creation mechanisms
within a company environment differ from country
to country.
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