Lewensohn D & Gold R - working paper prepared for VALGEN annual meeting Jan 2011, Banff
Patent landscaping
1. Introduction
Scholars, policy makers, higher education and business managers that employ patent data to
measure the productivity and impact of innovation of an entity (e.g. a firm, industry or country)
need to be able to do so in a reliable, systematic and standardized manner. For decades, patent
data have been analyzed by researchers in economics, management, law and sociology to
measure knowledge accumulation and knowledge flow (Scherer 1965; Scherer 1965;
Schmookler 1966; Griliches 1990; Jaffe 1996; Jaffe 1999; Jaffe 2001) and technological change
(Basberg 1987; Archibugi and Planta 1996). In addition, patent data has been applied to assess
national innovation performance (Trajtenberg 1990; Gassler 1996; Henderson 2005) and to
forecast technology (M.E. Mogee 1994; Ernst 1997; B.P. Abraham 2001; Daim 2006; Daim
2006). Another example, where patent data plays an important role is in the study of patent
thicket phenomena in areas such as biomedical research (Jensen and Murray 2005; Bergman
2007; Huys 2009) and agriculture (Mittal 2006; Glenna 2009).
Alongside scholarly utilization of patent data, practitioners in business and management have
turned to patent data as support in decisions regarding technology planning and business strategy
(Rivette 2000; Breitzman 2002) and in search of technological opportunities (Raffo 2009).
The increase of patent applications in the last decade has expanded the pool of patent data
available for analysis (van Zeebroeck 2009). Knowledge intensive sectors such as ICT
(information communication technology), nanotechnology and molecular biotechnology are
examples of areas that have seen extensive patenting of various applications in the last two
decades (Harris 2007; Hayman 2009; Pham 2009; Phukan 2010; Quan 2010).
Although numerous articles have been published based on various sorts of patent data (i.e. patent
count, patent claims, patent renewal data, opposition and litigation data, patent citations,
inventor, applicant), the literature does not seem to provide a coherent framework for patent data
analysis in terms of what methods to apply and for what purposes. In fact, the literature lacks a
standardized definition for methods that use patent data.
One of the few attempts to provide some order to the abundant “patent data literature” is an
article by Lai et al. (2007). The authors set out to classify research papers that make use of patent
data between 1980 and 2003. However, the authors of that same paper does not label the actual
method used for the analysis of patent data. Their classification is solely based on the topic of the
reviewed research papers such as “indicators of technological and innovative activities”. The
possible relationship between certain methods such as “patent count” (e.g. number of patent
1
applications or patents in a certain domain) and purpose such as “measuring a country’s
innovation activity” is not covered in the article by Lai et al. (2007). In addition, limitations,
pointed out by Lai et al. (2007) include the somewhat narrow focus on technology management
literature, the time limit of articles (1980-2003) and the lack of practitioner literature or empirical
evaluations of different methods.
We argue that it is critical to be able to assess the value of using patent data with regards to
business decision-making, research and innovation policy evaluations and other purposes. In this
respect, there are several aspects that need to be taken into consideration. Firstly, a literature
review should investigate both the purpose of the research and the method applied. Secondly, an
empirical investigation of the resources allocated (e.g. cost, time, need for special computer
systems, and expertise in terms of for example patent agents) for different methods of analysis of
patent data are necessary to be able to inform interested stakeholders. Thirdly, an actual
comparison of different methods (e.g. a numerical based method versus using patent agents
versus using researchers in science and technology) applied to the same dataset would be
valuable. These three steps would guide us to a practical and easily interpretable framework,
which could be adopted by stakeholders in government, industry and academia, to support
decision-making ranging from freedom-to-operate analysis (including a few patent claims) to
statistical explorations of large patent data sets for longitudinal studies on the innovation activity
in a country.
The objective of this paper is to cover the first step by reviewing the literature that applies patent
data as a basis for analyses of phenomena in research, innovation and business. The methodology
has similarities to the one presented by (Lai 2007). However, we try to address some of the
limitations of their article pointed out above. Because of a lack of a unified definition for
methods that use patent data, we suggest the use of the term patent landscaping for methods that
use patent data to investigate the distribution and relationships of exclusive rights in a given
domain. However, when referring to articles in the dataset we will use the term the scholars of
those particular publications use (e.g. patent analysis, patent landscape, intellectual property
landscape, patent bibliometrics etc).
This paper is structured as follows. In section 2 (method), we outline the method used for the
literature review including data collection and limitations of the method. In section 3 (definition),
we point to definitions other scholars and practitioners have used for patent landscaping and try
to explain our rational for providing our definition. In section 4 (results), we present our findings
from the literature review with regards to general observations and what objectives and methods
scholars use in our dataset. In section 5 (conclusion), we explore possible future research
questions to be addressed with regards to patent landscaping.
2
2. Method
a. Data collection
We based our review method on the reviews applied in for example medicine in which
systematic processing of the literature are used to identify and classify results of all major studies
in a certain field (Higgins 2009). The search terms were selected in discussions with academic
scholars in the field of law and sociology. To capture the multidisciplinary use of patent
landscaping methods we did not limit our search to a specific field or sector. In addition, in order
to make sure we covered the field of patent landscaping research we adopted the following
related keywords: patent landscape, intellectual property landscape, intellectual property
mapping, patent analysis, patent data analysis, freedom to operate, patent mapping, patent trends,
patent scope, patent search, patent ownership, patent indicators, patent statistics, patent citations
and patent citation analysis. According to table 1 below, we applied our search terms in the title
or topic field or the field corresponding to them in the database Scopus. The different search
terms generated a certain number of articles. The search was conducted for articles published
between 2000 and 2010.
Table 1 The fifteen search terms listed below were applied in the search of the Scopus database. The search was
limited to articles published between the years 2000 and 2010. The data was downloaded in Nov 2010.
Literature source
Search terms
Field
Scopus
patent landscape
Scopus
intellectual
property landscape
intellectual
property mapping
patent analysis
Topic field (article title,
abstract and key words)
Topic field (article title,
abstract and key words)
Topic field (article title,
abstract and key words)
Topic field (article title,
abstract and key words)
Topic field (article title,
abstract and key words)
Topic field (article title,
abstract and key words)
Topic field (article title,
abstract and key words)
Topic field (article title,
abstract and key words)
Topic field (article title,
abstract and key words)
Topic field (article title,
abstract and key words)
Topic field (article title,
abstract and key words)
Topic field (article title,
Scopus
Scopus
Scopus
Scopus
patent data
analysis
freedom to operate
Scopus
patent mapping
Scopus
patent trends
Scopus
patent scope
Scopus
patent search
Scopus
patent ownership
Scopus
patent indicators
3
Total no of
articles
retrieved
60
Years
15
2000-2010 (Nov 2010)
19
2000-2010 (Nov 2010)
209
2000-2010 (Nov 2010)
5
2000-2010 (Nov 2010)
49
2000-2010 (Nov 2010)
16
2000-2010 (Nov 2010)
28
2000-2010 (Nov 2010)
25
2000-2010 (Nov 2010)
109
2000-2010 (Nov 2010)
16
2000-2010 (Nov 2010)
36
2000-2010 (Nov 2010)
2000-2010 (Nov 2010)
Scopus
patent statistics
Scopus
patent citations
Scopus
patent citation
analysis
abstract and key words)
Topic field (article title,
abstract and key words)
Topic field (article title,
abstract and key words)
Topic field (article title,
abstract and key words)
76
2000-2010 (Nov 2010)
78
2000-2010 (Nov 2010)
24
2000-2010 (Nov 2010)
The overall data collection involved different steps outlined in figure 1. The first step was to
retrieve all publications generated based on the fifteen keywords referred to above. This initial
step generated 801 results. After that, duplications and results where the author could not be
found were eliminated gave 636 articles. In addition, articles were tagged with the keyword that
generated it. Some articles were tagged based on more than one keyword, thus occurring twice.
We tagged those articles that had multiple keywords, but we only saved one copy in order to
avoid duplications. Then we read through the abstracts of the remaining publications to assess
whether they dealt with the search terms we had applied. When we were unsure we downloaded
and read the full publication. We eliminated those search results that we considered irrelevant
and where we could not retrieve the full text automatically (due to lack of access or due to cost
attached to the article). In terms of the assessment of “article relevance” in our sample, we
decided to include articles that either explored a general method for patent landscaping (B.P.
Abraham 2001) or exemplified the use of such methods by a practical case (Barrett 2005; Alencar
2007). Articles that we would eliminate from the sample were those written in another language
(e.g. Albuquerque, 2001; Diáz-Párez; de Moya-Anegon, 2008) and those that dealt with pure
patent law issues such as patent law reform (Schacht 2008). Also articles that dealt with
transactions of intellectual property rights such as licensing or patent value were left out in our
sample (Reitzig 2003). In those cases where a method for patent landscaping was not explicitly
stated in the article, but referred to another article or document, we looked that document up
(Bergman 2007). The final sample generated consisted of 204 articles (subject to changes).
These remaining articles were analyzed based on the parameters displayed in table 2 below.
4
Search db (e.g. Scopus) using
different key words
Save results (N=801)
Check for duplications and for
results, where author could not be
found
Save results (N=636)
Mark key words (A1-A15) Some
articles will cover more than one
keyword
Go through abstracts (electronically).
Save interesting articles and
eliminate irrelevant articles from list.
Save interesting articles (N=204,
subject to change)
Print relevant articles.
Read and analyse articles
Classify articles by objective (why)
and method (how)
Figure 1 Steps involved in search strategy
5
Table 2 The articles in the sample were analyzed based on the parameters outlined in the table.
Field
Objective:
code (B, P, L)
Method:
code
(M1, M2,
M3)
Patent
database
Search basis
(keywords,
IPC codes
etc)
Outcome of
search
First
author:
academic
First author:
practitioner
Affiliation of
first author
The fields of the articles were of scientific or technological nature (e.g. medicine,
nanotechnology, electronics or biotechnology) or had a business-related focus (e.g. research,
innovation and management). The objectives were classified as B, P or L. B stands for business
and included articles using patent data to understand for example competition between players in
a certain market or to detect the extent of patenting activity in a specific domain. P stands for
policy and deal with issues of regulation of research and innovation policy as well as trends of an
industry or a certain technological field. Also, P-tagged articles cover topics that employ patent
data to e.g. characterize patterns of collaborations between inventors in different sectors or
regions. The objective with code L covers articles with legal questions, such as freedom-tooperate and patentability. It is important to point out that some articles in the sample have more
than one objective (e.g. assess trends in a certain technology domain and compare patent activity
of leaders in the field), which may or may not require double coding (e.g. P and B, or P and L).
The same is true for the methodological classification. The methods M1, M2 and M3 capture
different methodological approaches and uses patent data elements to a greater or lesser extent.
Articles tagged with M1 use patent data counts, which can be number of patents, number and
name of assignees, number and name of inventors, number of citations in a certain domain or in
relation to a certain technology. Methods that fall under M2 are often a bit more “sophisticated”
in that they apply for example IPC (International Patent Classification) codes and patent claims
to assess the scope of patents. M3 methods make use of patent data such as patent citations to
analyze relationships between patents and their contents. Besides citation data, inventor
information (name and nationality) and IPC codes, can also be used to map relationships for
different objectives described above. It is important to point out that the method used here for the
overall literature review is exploratory. Furthermore we will not attempt to analyze any possible
correlations between the objectives (B, P and L) and the methods (M1, M2 and M3). In the
figure below the codes and a short explanation of them are outlined.
6
Code
P
Explanation
Investigate trends to understand a phenomena,
which involve:
 regulation of an industry, growth of a
technology, industry, sector
 allocation of resources
 collaboration between different inventors
or entities
 research and innovation policy
Investigate competition, acquisition, freedom to
operate issues
Has to do with patent scope, patentability (also
freedom to operate)
Methods focused on patent count, number of
patents, number of assignees, quantitative in
nature,
Methods focused on patent content/scope "using
more intelligence", patents connected to
technology, IPC analysis
Methods focused on the relationship between
patents (patent content, inventors, IPC classes etc
etc)
B
L
M1
M2
M3
Limitations with method
We recognize that there are some limitations with our methodology. First of all, we limited our
search to one database (i.e. Scopus). We based this decision on an earlier test search of four
different sources (i.e. Scopus, Pubmed and Compendex and Nature.com). When applying the
search terms “patent landscape” or “intellectual property landscape” in the topic field, title or
abstract of these sources, we realized a definitive overlap between the sources. Thus our
conclusion was to use the database which gave us the best results in terms of coverage. Secondly,
we chose to look at articles published both in the peer-reviewed and non-peer reviewed
literature. We did this since we wanted to capture evidence of use of the patent landscaping tools
developed by practitioners. In addition, our search covered the period between the year 2000 and
2010. Specifically we were interested to see how patent landscaping methods have been applied
in a relatively current time frame. Also, since the study by Lai et al. (2007) covered a time period
from 1980-2003, we were curious to apply our search between 2000 and 2010. In the sample,
there were several articles found that lacked a detailed explanation of the method used for patent
landscaping (Phukan 2010), which we decided to include to give a complete view of the
literature in the field.
3. Definition of keywords
When it comes to the term patent landscaping and related keywords it is clear that the literature
does not provide a single standardized definition. Rather, through the literature review, new
7
related patent landscaping terms such as patent intelligence, patentometrics and patent
bibliometrics emerge. (Lo 2007) use the latter term for “when patent data is used for
bibliometrics methods including citation analysis”. From the results of our literature search and
also looking at other sources such as some Patent Offices (i.e. EPO, WIPO, JPO), it appears that
the original keywords applied in the search are defined in many different ways. For example,
(Palazzoli 2009; Palazzoli 2010) recognizes that “patents provide an opportunity to construct an
IP and a technological development strategy”. He defines patent landscapes as the results of
patent database searches. Palazzoli further characterizes the use of patent landscapes into two
separate levels, where the first level focuses on the bibliographic patent information such as
publication number and date, priority data, applicants and inventors. The second level makes use
of information in the claims and in the description or specification of the invention itself.
According to Palazzoli (2009), a patent landscape generated through the first level, can facilitate
identification of leading players in a certain sector or market and track patent application trends
over time, while a patent landscape of the second level can provide insights on how to
circumvent competitors’ claims and thereby pave way for the development of a technology
without risking to infringe competitors’ patents. (Suh 2006) elaborate on the term patent map in
the following way “In patent documents, structured items mean they are uniform in semantics
and in format across patents such as patent number, filing date, or investors. On the other hand,
the unstructured ones represent free texts that are quite different in length and content for each
patent such as claims, abstracts, or descriptions of the invention. The visualized analysis results
of the former items are called patent graphs and those of the later are called patent maps,
although loosely patent maps may refer to both cases…” In a similar way (Yang 2010) defines
patent landscape analysis as “a state-of-the-art [patent] search that provides graphic
representations of information from search results”. The European Patent Office states that
patent mapping “is essentially the visualisation of the results of statistical analyses and text
mining processes applied to patent documents.” The World Intellectual Property Organisation
(WIPO, 2003) defines a patent map in the following way: “A patent map is the visualized
expression of total patent analysis results to understand complex patent information easily and
effectively.”
Of the examples above, only Palazzoli relates the term patent landscape to a practical use. The
other examples here give a very generic definition for patent landscaping/patent landscape or
related keyword. In the literature sample, very few thorough definitions of the keywords are
given. It appears a definition that incorporates both a conceptual and methodological dimension
is needed. In order to arrive at such a definition more empirical evidence is needed as suggested
in the introduction. However, the literature review conducted here gives some clues to a
definition for patent landscaping. In line with the brief explanation given in section 3, we define
patent landscaping as a set of methods that use patent data to investigate the distribution and the
relationships of exclusive rights in a given domain. In this way, we allow for different types of
patent data such as inventors, patents, IPC codes etc to be used to investigate the distribution and
the relationships of monopoly rights in a certain technology domain, in a company, in a sector, in
8
a region or in a country. This definition is broad enough to give way to a framework based on
both quantitative and qualitative empery. Moreover, the levels presented above by Palazzoli
(2009) as well as the different attempts by other scholars to define patent landscaping, whether
conceptual or more technical (e.g. visualization, use of semantics, statistics etc) can be included
in our definition. Because the use of patent landscaping methods to a large extent is
multidisciplinary, it is essential to allow for economical, legal and technical dimensions in the
definition.
4. Results
a. General observations
Since our data set contains publications of many different fields, we tried to base our core
analysis on the objectives and the methods applied in the articles. Before going into the details of
those two parameters, a few general observations about the data can be made. Firstly, the top 6
journals in our dataset include Scientometrics, World Patent Information, Technovation and
Research Policy (see figure 2). Secondly, the development of number of journals based on the
fifteen keywords used to retrieve our data is illustrated in figure 3 (to be inserted). It appears that
there has been a slight increase/decrease with regards to number of articles over the decade
investigated. In addition, it would be interesting to follow the different journals over the time,
clustered based on the different keywords. Thirdly, the authors’ background were divided into
whether they had an academic (A) or practice affiliation (P). In figure 4, it is clear that the
majority of the articles in our sample are written by academic scholars. In order to assess any
author related trends, it could be interesting to further subcategorize the articles to reflect the
composition of author backgrounds in relation to technological/business field, objective and
methods used in articles. Fourthly, the majority of the databases used for patent landscaping in
our data set are publically available databases such as the databases available with the USPTO
(United States Patent and Trademark Office) and the EPO (European Patent Office). In figure 5
an overview of all patent databases used in the articles are given categorized as “primary” and
“secondary” databases. Primary patent databases are those developed by different PTOs (patent
and trademark offices), while secondary patent databases contain data specifically collected by
for research purposes or for commercial purposes (e.g. NBER, Innography, Delphion). The
secondary databases build on the primary ones and often have “customer-friendly” technology
such as visualization features.
b. Objectives and methods for patent landscaping
As outlined briefly in the method section above, the objectives were grouped according to
whether the article emphasized a policy, business or legal related issue. It is important to point
out that what we mean by policy here is more of a general term for topics that deal with policy or
strategy related to R&D and innovation management in the public and the private sector. The
results from the analysis of these articles show that articles given the objective code P dominate
9
the sample, while articles that deal with legal related issues are in minority. In figure 5, the
number of times the codes P, B and L are occurring are listed. To a certain extent this reflects the
“number of P-related, B-related and L-related articles”. However, because several articles in the
sample are characterized by more than one code, the sum of the numbers in figure 3 is naturally
larger than the overall sample. The same is true for the methods categorized as M1, M2 and M3.
M1 is the most frequent occurring method code (see figure 4).
When it comes to investigating the details of the objectives and methods of the sample, the
articles in our dataset analyze patent data from the perspective of scientific or technological
domains, firms, industries or countries. The different tags or codes for the objectives and
methods presented above may be useful for all of these perspectives. For example, the method
tag M1 (i.e. methods to conduct patent counts) can be applied to many different fields or entities.
A simple patent count can be used for business (B) or policy (P) reasons. Actually, a patent
search, preceding a patent count is often the starting point for all of the objectives including the
L-tagged articles. The articles that “stand out” in the sample are those tagged with L and focus
on examining the extent of patent protection a certain technology has compared to other similar
patent technologies or non-patented technologies (Kowalski 2002; Mouttet 2007; Hayman 2009;
Schmees 2009).
The P tagged articles are the broadest in terms of what topics or objectives are presented. For
example many articles use historical patent data to answer a question, ranging from for example
the study of the contribution of the respective role of GB corporate and independent innovators
since 1950 (Spear 2006) to analyzing research trends in a specific scientific domain (Sekar 2008)
Other articles are concerned about answering questions in relation to regulatory changes in an
industry (Pilkington 2002).
B-tagged articles share objectives that include the comparison of patent portfolios of two
different companies (Lee 2006), the investigation of factors that influence patenting in companies
of a certain size in a certain domain (Olsson 2000) or the examination of innovation strategies
between innovative firms in a certain industry (Storto 2007). Since the same method (e.g. M1)
can be applied in articles of different objectives, it can sometimes be difficult to label articles
that are more “business” oriented versus those that are more policy focused. A simplification of
the quite extensive material covered here (ca 300 articles) would be to suggest the B-tagged
articles are trying to answer questions on “what goes on in a company”, while P-tagged articles
are trying to answer questions on “what goes on in an industry, between industries, in a country
or between countries” and L-tagged articles answers question regarding patent scope.
Certain articles cover all objectives and serve as a basis for policy, business and legal discussions
(Jensen and Murray 2005; Bergman 2007; Huys 2009; Chi-Ham 2010).
The method codes are slightly easier to distinguish compared to the objective codes. As
described above and unnecessary to repeat in detail, the M1, M2 and M3 represent methods that
10
use patent data at different “levels of sophistication”. Here, we have not gone into the details of
the tools and exact steps taken in the data set. It could, however, be valuable to empirically test
and compare a group of methods that would fall under each category (M1, M2 and M3). Also, in
our relatively simple categorization we have not included methods that use algorithms that
extract information from patent documents beyond patent data such as patent number, assignee
and inventor name. There are methods that are able to extract text found in the patent title,
abstract, specification or claims (Storto 2007; Suh 2009). Also, there are databases that allow for
search of molecular and chemical structures, which requires yet other approaches for analysis
(Rhodes J 2007). An extensive investigation of the different databases that can be used for patent
search and analysis could give more information on the breadth and development of the field of
patent landscaping (beyond the scope of this article).
5. Conclusion
The objective with this article was to review the literature published in the field of patent
landscaping in the last decade. Clearly the topic of patent landscaping is broad and has not been
fully defined, analyzed and translated into an understandable framework that can be used by
scholars, business managers and policy makers alike. This article is a first step to bring some
order to the topic of patent landscaping. By categorizing the literature into objectives and
methods, we pave way for more in-depth analysis of the field. For example, future research
might wish to attempt to analyze any possible correlations between the objectives (B, P and L)
and the methods (M1, M2 and M3). In addition, the results obtained through this literature search
only gives us a hint into the practical use and perspectives of patent landscaping and leaves us
with central questions with regards to stakeholders’ practical experiences and demands for patent
landscaping, whether they come from academia, industry or government. In order to bring this
field forward it might be relevant to conduct empirical studies that address the following
questions: 1) What tools of patent landscaping are used by different stakeholders (both patent
specialists and non-patent specialists) in practice? 2) What issues are stakeholders trying to solve
or decide on based on patent landscaping? and 3) Who should develop conceptual and
methodological standards for patent landscaping in the future?
As discussed in this article, patent data – a vast and ever increasing information pool rich in
technical and legal terminology - offer analysis opportunities in both science and technology. At
the same time, it is important to make sure patent data is used in a reliable and representative
manner. Therefore, patent landscaping that considers the multifaceted needs of its current and
potential users would bring this fragmented field en route towards a solid and easy-to-apply
framework.
11
Figures will be updated.
Journal
No of articles
World Patent Information
Expert Opinion on Therapeutic Patents
Scientometrics
Technological Forecasting and Social Change
Nanotechnology Law and Business
Research Policy
Technovation
Journal of Nanoparticle Research
Nature Biotechnology
25
16
15
11
7
7
7
6
6
Figure 2 Distribution of articles per journal (top 6)
INSERT FIGURE ON JOURNAL DEVELOPMENT OVER TIME
Number of
Number of articles in
articles in sample sample where first
where first author
author can be
can be identified
identified as a
as an academic (A)
practitioner (P)
43
15
Figure 3 The distribution of articles in dataset, where first author can be identified as academic (A) or
practitioner (P)
12
Primary patent databases Secondary patent databases
USPTO
WIPS
EPO
SPRU
JPO
PatentScope
CIPRO
Thomson Derwent
Spanish PTO
Prous Integrity database
Gazette of India (Indian PTO)Google Patents
UK Patents and Design Journal
FreePatentsOnline
Micropat
Technology Information Forecasting and Assessment Council for Indian Patent
Aureka ThemeScape Software
FOCUST-J
Derwent Innovation Index
WEBPAT
EPOLINE
STN International
Derwent Patent Index
Figure 4 The patent databases used in the articles. The majority of the authors have used the USPTO
as a basis for their patent landscaping methods.
Objectives
Business
Policy
Legal
Number of times occuring in dataset
34
54
29
Figure 5 The number of times articles with the different objective codes appear in the sample.
Methods Number of times occuring in dataset
M1
48
M2
28
M3
21
Figure 6 The number of times articles with different method codes appear in the sample.
13
References
Alencar, M. S. M., Porter, A.L., Antunes, A.M.S. (2007). "Nanopatenting patterns in relation to product
life cycle." Technological Forecasting & Social Change 74: 1661-1680.
Archibugi, D. and M. Planta (1996). "Measuring technological change through patents and innovation
surveys." Technovation 16(9): 451-468.
B.P. Abraham, a. S. D. M. (2001). "Innovation assessment through
patent analysis." Technovation 21: 245-252.
Barrett, S. (2005). "Patent analysis identifies trends in fuel cell R&D." Fuel Cells Bulletin.
Basberg, B. L. (1987). "Patents and the measurement of technological change: A survey of the
literature." Research Policy 16(2-4): 131-141.
Bergman, K. (2007). "The global stem cell patent landscape: implications for efficient technology transfer
and commercial development." Nature Biotechnology 25(4): 419-424.
Breitzman, A., Thomas, P (2002). "Using Patent Citation Analysis to Target/Value M&A Candidates
" Research-Technology Management 45(5): 28-36.
Chi-Ham, C. (2010). "The intellectual property landscape for gene suppression technologies in plants."
Nature Biotechnology 28(1): 32-36.
Chi-Ham, C. (2010). "The intellectual property landscape for gene suppression technologies in plants."
Nature Biotechnology 28(1): 32-36.
Daim, T. (2006). "Forecasting emerging technologies: Use of bibliometrics and patent analysis."
Technological Forecasting & Social Change(73): 981-1012.
Daim, T. (2006). "Forecasting emerging technologies: Use of bibliometrics and patent analysis.
." Technological Forecasting & Social Change 73: 981-1012.
Ernst, H. (1997). "The Use of Patent Data for Technological
Forecasting: The Diffusion of CNC-Technology
in the Machine Tool Industry." Small Business Economics 9: 361-381.
Gassler, H., Frohlich J., Koposa A. (1996). "Selective Information on the National System of Innovation as
an Important Input for the Technology Management of Firms." World Patent Information 18(4).
Glenna, L. a. C., D (2009). "Agribusiness concentration, inte;;ectual property, and the prospects for rural
economic benefits from the emerging biofuel economy " Southern Rural Sociology 24(2): 111-129.
Griliches, Z. (1990). "Patent Statistics as Economic Indicators: A Survey." Journal of Economic Literature
28(December): 1661-707.
14
Harris, D. L., and Bawa, R. (2007). "The carbon nanotube patent landscape in nanomedicine: An expert
opinion.
." Expert Opinion on Therapeutic Patents 17(9): 1165-1174.
Hayman, M. (2009). "The Emerging Product and Patent
Landscape for Nanosilver-Containing
Medical Devices." Nanotech. L. & Bus.
Henderson, R., Jaffe, Adam and Trajtenberg, Manuel (2005). "Patent Citations and the Geography of
Knowledge Spillovers: A Reassessment: Comment." The American Economic Review 95(1): 461-464.
Higgins, J. a. G., S (2009). Cochrane Handbook for Systematic Reviews of Interventions Version 5.0.2
[updated September 2009].
. G. S. Higgins JPT.
Huys, I. (2009). "Legal uncertainty in the area of genetic diagnostic testing." Nature Biotechnology
27(10): 903-909.
Jaffe, A. B., Trajtenberg, Manuel. (1996). "Flows of Knowledge from Universities and Federal Labs:
Modeling the Flowof Patent Citations Over Time and Across Institutional and Geographic Boundaries."
Proceedings of the National Academy of Sciences of the United States of America 93(23): 12671-12677.
Jaffe, A. B. a. L., Josh (1999). "Privatizing R&D: Patent Policy and the Commercialization of National
Laboratory Technologies." NBER Working Paper No. W7064.
Jaffe, A. L., J (2001). "Reinventing Public R&D: Patent Policy and the Commercialization of National
Laboratory Technologies." The RAND Journal of Economics 32(1): 167-198.
Jensen, K. and F. Murray (2005). "Intellectual Property Landscape of the Human Genome." Science
310(5746): 239-240.
Kowalski, S. P. E., R.V.; Kryder, R.D.; Reichman, J. (2002). "Transgenic crops, biotechnology and
ownership rights: what
scientists need to know. ." Plant Journal 31: 407-421.
Lai, K.-K., Lin, Mei-Lan, Chang, Shann-Bin , Hsu Chin-Fu (2007). The Study of Taxonomy and Evolutional
Trends of Relevant Literatures
on Patent Analysis. PICMET 2007 Proceedings, 5-9 August, Portland, Oregon - USA © 2007 PICMET.
Lee, C. K. a. O., R. (2006). An Analysis of the Liquid Crystal Cell Patents of LG
and Samsung Filed at the USPTO. 2006 IEEE International Conference on Management of Innovation and
Technology.
Lo, S. C. (2007). "Patent analysis of genetic engineering research in Japan, Korea and Taiwan."
Scientometrics 70(1): 183-200.
M.E. Mogee, a. R. G. K. (1994). " International patent analysis as a tool for corporate technology analysis
and planning
15
" Technology
Analysis & Strategic Management 6: 485-503.
Mittal, R. a. S., G (2006). "Patenting scenario in agriculture: Indian
perspective." CURRENT SCIENCE 90(3).
Mouttet, B. (2007). "The Patent Landscape for Electron Emitting Nanomaterials." Nanotech. L. & Bus.
Olsson, H. a. M., Douglas (2000). "2000 Factors influencing patenting in small computer software
producing companies " Technovation 20: 563-576.
Palazzoli, F., Testu, F. X., Merly, F., Bigot, Y. (2009). "Transposon tools: Worldwide landscape of
intellectual property and technological developments." Genetica 138(3): 285-299.
Palazzoli, F., Testu, F. X., Merly, F., Bigot, Y. (2010). "Transposon tools: Worldwide landscape of
intellectual property and technological developments." Genetica 138(3): 285-299.
Pham, C. a. T., J (2009). "Commercialization Potential of Silver Nanomaterials." Nanotech. L. & Bus.
Phukan, S., Babu, V. S., Kannoji, A., Hariharan, R., Balaji, V. N. (2010). "GSK3β: Role in therapeutic
landscape and development of modulators
" British Journal of Pharmacology 160(1): 1-19.
Pilkington, A., Dyerson, Romano, Tissier, Omid (2002). "The electric vehicle:
Patent data as indicators of technological development." World Patent Information 24: 5-12.
Quan, L.-d., Thiele, Geoffrey M , Tian Jun & Wang Dong (2010). "The development of novel therapies
for rheumatoid arthritis." Expert Opinion on Therapeutic Patents.
Raffo, J. a. L. (2009). "How to play the “Names Game”: Patent retrieval comparing different heuristics."
Research Policy 38: 1617-1627.
Reitzig, M. (2003). "What determines patent value?
Insights from the semiconductor industry." Research Policy 32: 13-26.
Rhodes J, B., S, Kreulen J, Chen Y, Ordonez P (2007). Mining Patents Using Molecular Similarity Search.
Pacific Symposium on Biocomputing 12:304-315(2007).
Rivette, K. G. a. K., David, Ed. (2000). Rembrandts in the Attic Unlocking the Hidden Value of. Patents.
Boston, Harvard Business School Press.
Schacht, W. H. (2008). Patent Reform:
Issues in the Biomedical and Software Industries.
Scherer, F. M. (1965). "Firm Size, Market Structure, Opportunity, and the Output of Patented
Inventions." The American Economic Review 55(5): 1097-1125.
16
Scherer, F. M. (1965). "Firm Size, Market Structure, Opportunity, and the Output of Patented
Inventions." American Economic Review(December): 1097-1125.
Schmees, N. a. W., H (2009). "2009 Recent patent trends in the field of progesterone receptor agonists
and modulators " Expert Opinion on Therapeutic Patents.
Schmookler, J., Ed. (1966). Invention and Economic Growth. Cambridge, Harvard University Press.
Sekar, S. a. C., M (2008). "Phycobiliproteins as a commodity: trends in applied
research, patents and commercialization." J Appl Phycol 20: 113-136.
Spear, B. (2006). "orld Patent Information GB Innovation since 1950 and the role of the independent
inventor: an analysis of completed term patents " World Patent Information 28: 140-146.
Storto, l. (2007). Dynamics of Innovation Strategies in the Optical Memories Industry: An Analysis Based
on Patent Indicators PICMET 2007 Proceedings, 5-9 August, Portland, Oregon - USA © 2007 PICMET.
Suh, J. H. a. P., Sang Chan (2006). "A New Visualization Method for Patent Map: Application to
Ubiquitous Computing Technology." ADMA 2006, LNAI 4093: 566 - 573.
Suh, J. H. a. P., Sang Chan (2009). "Service-oriented Technology Roadmap (SoTRM) using patent map
for R&D strategy of service industry." Expert Systems with Applications 36: 6754-6772.
Trajtenberg, M. (1990). "A penny for your quotes: patent citations and the value of innovations." RAND
Journal of Economics 21(1).
van Zeebroeck, N., B. van Pottelsberghe de la Potterie and D. Guellec (2009). "Claiming more: the
increased voluminosity of patent applications and its determinants." Research Policy 38(6).
WIPO (2003). Patent Map with Exercises W. W. I. P. Organization).
Yang, Y. Y. A., L.; Yang, C.B.; Klose, T.; Pavlek, S. (2010). "Enhancing patent landscape analysis with
visualization output." World Patent Information 32(1): 203-220.
17