=Paper=
{{Paper
|id=Vol-1437/ipamin2015_submission_10
|storemode=property
|title=Patent Technology Competitor Group Analysis Method Based on IPC
|pdfUrl=https://ceur-ws.org/Vol-1437/ipamin2015_paper10.pdf
|volume=Vol-1437
}}
==Patent Technology Competitor Group Analysis Method Based on IPC==
https://ceur-ws.org/Vol-1437/ipamin2015_paper10.pdf
Patent Technology Competitor Group Analysis Method
Based on IPC
Yuan Fu Hongqi Han Lijun Zhu
Information Technology Supporting Information Technology Supporting Information Technology Supporting
Center, Institute of Scientific and Center, Institute of Scientific and Center, Institute of Scientific and
Technical Information of China Technical Information of China Technical Information of China
No. 15 Fuxing Rd,.Haidian Distirct, No. 15 Fuxing Rd,.Haidian Distirct, No. 15 Fuxing Rd,.Haidian Distirct,
Beijing 100038, P.R. China Beijing 100038, P.R. China Beijing 100038, P.R. China
+86 10 5888 2447 +86 10 5888 2447 +86 10 5888 2447
fuyuan2014@istic.ac.cn bithhq@163.com zhulj@istic.ac.cn
ABSTRACT
It is crucial to understand the technical groups of intra-industry
1. INTRODUCTION
Competitiveness is a typical characteristic for industrial
and to master the competition in the field of technology. In order
technology (Yoon, 2008) [1]. Practically, for almost every
to provide valuable information for industry participants and
emerging industry, some kinds of technology will become leading
policymakers, a process model for mining technical competitor
and predominant after developing over a period of time.
groups based on IPC classification number is put forward. Firstly,
Agglomeration is common for an industry. When the industrial
the patent numbers under each IPC are counted for building
technology agglomerates to a certain extent so that it can meet the
feature vectors for competitors. Then, technical similarities
needs of product functions well, the industry will become mature,
between each pairs of competitors are computed. Finally, the
and the industrial technology system is established. On the other
LinLog graph clustering algorithm is carried out to discover three
hand, the technology owner compete reciprocally into different
levels of groups, i.e. institution, province and country. To obtain
technical groups. According to Porter's theory of competitive
patent data for this research, an acquisition system for Chinese
advantage, the real competitors inside an industry are companies
patent data is developed. Experiments on the field of fuel cell is
similar to a company (Lee, 2006) [2]. These similar companies
conducted and the results show the technique is helpful and
constitute a strategic group and become a sub-industry. A
effective.
company has barriers to enter different strategy groups. Therefore
companies which have very similar industrial technology are
Categories and Subject Descriptors likely to be main competitors.
Information extraction from patent documents The clustering method of dividing data into several clusters can
reflect relational schema of the data and the knowledge hidden in
General Terms the data. The method of competitor group analysis of industrial
Experimentation technology is to use appropriate clustering algorithm to divide
competitors into several groups, and thus identify similar
competitors inside an industry competitions and their reciprocal
Keywords influences. The level of technical competitor group analysis can
LinLog; IPC classification number; Technology competitor group
be from different aspects such as countries, provinces, and
institutions. The purpose of the analysis is to understand the
technical groups inside an industry, and to master the competition
in the field of technology from higher levels, and to provide
valuable information for industry participants and policymakers.
Some common clustering algorithms can be used to identify the
competitor group of industrial technology, such as self-organizing
mapping (SOM), K-means (Lee, 2009) [3], factor analysis, etc. In
these models, each competitor is usually expressed as a feature
vector which are measured by several technical characteristics.
Similar objects will be clustered into one group by calculating
distances between them. For example, (Pilkington, 2004)[4] used
UPC number and IPC classification respectively as the technical
features for competitors and used the factor analysis model to
cluster 52 companies in the field of fuel cell into five groups.
Copyright © 2015 for the individual papers by the papers' authors.
Copying permitted for private and academic purposes. Literature studies found that many researchers have used
This volume is published and copyrighted by its editors. visualization methods. The traditional clustering algorithm is
Published at Ceur-ws.org based on the unsupervised learning so people often doubt the
Proceedings of the Second International Workshop on Patent Mining and effectiveness of the analysis results. The visualization method can
its Applications (IPAMIN). May 27–28, 2015, Beijing, China. display abstract data using graph or picture because it combines
the computer technology and human cognitive ability effectively. normalized cut. Normalized cut and edge-repulsive model can
Therefore, the visualization method enhances the user’s produce unbiased results, therefore it is especially suitable for
confidence for the analysis results, so it has been widely accepted normally distributed data. In this paper, LinLog algorithm of
in recent years. Considering the advantages of visualization, the Barnes and Hut hierarchy algorithms is used to draw clustered
proposed method will use graph clustering method to find graphs (Stegmann, 2003) [9]. After the algorithm draw graphics, it
technical competitor groups. also divide nodes into several clusters.
2. RELATED WORK 2.2 IPC
2.1 LinLog graph clustering methods IPC means the international patent classification. IPC is an
LinLog algorithm was first put forward by (Noack, 2007) [5]. The international standard which is used by the patent offices of all
aim of the algorithm is to produce ideal and visual clustering countries or regions in the world. Although some countries or
graphs. Figure 1 shows an example mentioned in Noack's paper regions make its own patent classification system, such as CPC
(Noack, 2005) [6]. In the example, Spring and LinLog algorithm system of USPTO, ECLA system of EPO, they provide the IPC
were employed respectively for graph clustering using the same classification number. Chinese patent classification system also
data. Comparatively, LinLog algorithm clearly divided data into use IPC system. A patent has at least one IPC number, but is not
two large clusters which are connected by two nods, Dan and limited to one IPC classification number. In other words, some
Upton, while Spring algorithm positioned nodes with high degree patents are endowed with two or more IPC classification numbers.
in the center and nodes with low degree near the borders. The first classification number is called the main classification
number when there are multiple patent classification numbers.
According to the characteristics of technical topics of the
invention, the technology fields in IPC system are divided into 8
sections. Each section represents a kind of technology, designated
by one of the capital letters A through H as shown in Table1.
Table 1 section of technology in IPC system
Section Section Title
A HUMAN NECESSITIES
B PERFORMING OPERATIONS; TRANSPORTING
C CHEMISTRY; METALLURGY
D TEXTILES; PAPER
E FIXED CONSTRUCTIONS
MECHANICAL ENGINEERING; LIGHTING;
F
HEATING; WEAPONS;BLASTING
(a) Spring model
G PHYSICS
H ELECTRICITY
The structure of IPC classification system is hierarchical. Sections
are the highest level of hierarchy in the system. Each section is
subdivided into classes which are the second hierarchical level.
Each class comprises one or more subclasses which are the third
hierarchical level. Each subclass is broken down into subdivisions
referred to as “groups”, which are either main groups (the fourth
hierarchical level) or subgroups (lower hierarchical levels
dependent upon the main group level). A complete classification
symbol comprises the combined symbols representing the section,
class, subclass and main group or subgroup, as shown in Figure 2.
Currently, there are approximately 70,000 subdivisions in the
classification system. Figure 3 is a sample of the hierarchical
structure.
(b) LinLog model
Figure 1 Comparison of Spring and Linlog method
The LinLog model does not conform to the traditional aesthetic
standard, it aims to group nodes of closely connected and separate
nodes of partially connected. There are two kinds of LinLog
models: node-repulsion model and edge-repulsion model(Coscia,
2009) [7]. The two models are based on two famous clustering
standards respectively (Li, 2008) [8], namely density of cut and
Figure 2 Hierarchical structure of the IPC classification system
Figure 4 The process model of the graph clustering method
Firstly, selecting a clustering level from three categories:
institutions, provinces and countries. Then, counting the patent
number under each main IPC classification number for each
technology competitor. Then the association matrix is established
between technology competitors and the main IPC classification
Figure 3 A sample of IPC hierarchical structure number (Dibattista, 1994)[10]. Each technology competitor is
expressed as a feature vector whose attributes are IPC
3. METHOD classification numbers. The value of each attribute item is the
An industrial technology field can be divided into several number of patents under the main IPC classification number.
subfields, and each subfield may have smaller technology Finally, calculating the similarity between each pair of
subfields. Technology competitors often have different research technological competitors by using cosine formula(Fruchterman,
background, bases, objectives and priorities. Competitors with 1991)[11]. Let IPC as the number of the IPC main classification
similar technology may be competitors or partners on the market,
number covered by industrial technology, and the patent number
and they are likely to interact with each other. IPC classification
of competitor i under k-th IPC classification number is IPCki .
codes are designated by patent examiner with professional
knowledge. Therefore IPC provide an effective way to know The equation (1) shows how to compute the technological
industrial hot points, and research and development directions of similarity between competitor i and j.
technology competitors. A technology competitor tend to invest IPC
research in several technical subfields, so it is difficult to
determine whether two competitors have similar research IPC IPC
ki kj
technology only from the IPC count statistics. Therefore, a graph sim(i, j ) k 1
clustering method based on main IPC number is put forward to IPC IPC (1)
identify technology competitor groups within an industrial
technology field. Figure 4 shows the process model of this method.
IPCki 2
k 1
IPCkj 2
k 1
In order to obtain good visual graphics, a minimum similarity
threshold (Noack, 2004)[12] should be set. Generally, the threshold
is set to the mean of similarity, yet it can also be determined by
experiments. There will be a connect between two technology
competitors when the similarity between them is higher than the
set threshold. Using technology competitors as nodes, the
connections between them as edges, and the weight of the edges
are the technological similarity values between them, LinLog
graph clustering algorithm will generate visual map. The map will
show the clusters for identifying competitor groups.
4. DATA Table 2 keywords for identifying application category
4.1 Data acquisition category Key words
Nowadays, almost all patent offices of major countries and company company, partnership
regions provide patent databases on their official web sites.
university university, college
People can connect these websites any time and everywhere via
the Internet to obtain the patent data freely. In order to get patent institute research institution,
data quickly, a patent data acquisition system (Laura, 2008) [13] is others committee, association, foundation
developed. The model of the system model is shown in Figure 5.
The acquisition system can fetch HTML web pages which personal
contains the patent description information from the official
website of the state intellectual property office of China
(http://www.sipo.gov.cn/). After the patent information is 5. EXPERIMENTAL RESULTS
collected, it can automatically obtain the items of description and
legal status of patents through the content analysis of web pages
5.1 Research and development institutions
In order to have clear visual map, we choose top 20 research and
and save them into the local databases.
development institutions for graph clustering algorithm. The
result is shown in Figure 6. In the map, the size of nodes
represents the number of granted invention patents, and the color
of nodes shows the group they belong to (Reinhard, 2007) [15].
In the case, the LinLog algorithm identified two technology
competitor groups (shown in Figure 6). The group with red node
color is the first group, including 10. They are: Samsung (177),
Chinese Academy of Sciences(128), Antiq(74), General
Motors(56), Honda(52), Wuhan University of Technology(49),
Shanghai Jiaotong University(38), Sanyo(37), BYD(32), and
Harbin Institute of Technology(26); The group with orange node
color is the second group, including 10 other institutions. They
are: Shanghai Shen-Li High Tech(194), Panasonic(154),
Figure 5 The data acquisition system model Toyota(120), Tsinghua university(72), Nissan(62), Toshiba(48),
In order to test the effectiveness of proposed method, the patent Sunrise Power(26), Hitachi (24), LG(20) and UTC (19). The
acquisition system is run to download patent data in the field of numbers in parentheses after company names means the numbers
fuel cell technology. 6346 patents are collected totally. The of their granted invetion patents. Table 3 shows corresponding
following preprocessing steps and the empirical analysis will English names of Chinese Names in Figure 6.
employ the downloaded patent data.
4.2 Data preprocess
The collected data often have some problems, and it must be
preprocessed before the formal analysis. In the experiment, the
patent data will be preprocessed to meet the analysis requirements,
including identifying the patent categories, countries and
provinces of applicants, and categories of applicants, etc.
If the first applicants are Chinese individuals or organizations, the
addresses of the applicants often contain the information of its
province (Kayal, 1999) [14]. Generally, the first 6 digits of the
address description is the applicant’s postcode, so the province
information can be obtained according to the postcode. If the first
applicants are foreign individuals or organizations, the priority
item and the international publication item in patent descriptions
contain the state information. For example, the priority item of a
patent is "1999.8.27 JP 242132/1999", where JP means that the Figure 6 Clustering result of R&D institutions
applicant is a Japanese.
Table 3 Corresponding English names of Chinese names
For the purpose of the research, applicants are divided into 5 of R&D institutions in Figure 6
categories: company, university, research institute, personal and
the other. The categories are identified by the keywords in the Chinese name English name
applicant names. The corresponding relation of keywords and 清华大学 Tsinghua University
categories are shown in Table 2. If there are more than one
applicants in a patent description, only the first applicant is 新源动力股份有限公司 Sunrise Power
considered. For example, there are two applicants of the patent No. 上海神力科技有限公司 Shanghai Shen-Li High Tech
00112136.7: Nanjing Normal University and Changchun Institute
of Applied Chemistry Chinese Academy of Sciences, the system 松下公司 Panasonic
will designate "university" category to the patent.
日产公司 Nissan 辽宁 Liaoning
丰田公司 Toyota 江苏 Jiangsu
日立公司 Hitachi 天津 Tianjin
东芝公司 Toshiba 山东 Shandong
BTC 公司 BTC
安徽 Anhui
乐金电子电器有限公司 LG
陕西 Shaanxi
上海交通大学 Shanghai Jiaotong University
四川 Sichuan
中国科学院 Chinese Academy of Sciences
三星公司 Samsung 河北 Hebei
三洋公司 Sanyo 北京 Beijing
通用汽车公司 General Motors
广东 Guangdong
胜光科技股份有限公司 Antiq
湖北 Hubei
哈尔滨工业大学 Harbin Institute of Technology
黑龙江 Heilongjiang
比亚迪股份有限公司 BYD
吉林 Jilin
本田株式会社 Honda
重庆 Chongqing
武汉大学 Wuhan University of 湖南 Hunan
Technology
山西 Shanxi
In the province level, two technology competitor groups are
5.2 Provinces identified. The group with red nodes is the first group, including
In the case, totally 22 provinces are extracted in all fuel cell 10 provinces: Shanghai (311), Taiwan (152), Liaoning (127),
patents. The graph clustering result is shown in figure 7. The Jiangsu (41), Tianjin(40), Shandong(23), Shaanxi(13), Anhui
biggest node in the picture is Shanghai, which means the research (19),Sichuan (4) and Hebei(2), The group with orange node color
strength of Shanghai province is the strongest one in China. While represents the second group, including 8 provinces: Beijing(150),
the smallest node is Hebei, which means Hebei province is the Guangdong(93), Hubei(58), Heilongjiang(29), Jilin(18),
weakest one on the research of fuel cell in these provinces. Chongqing (5), Hunan(4) and Shanxi Province (4). Because the
technology similarity value of Zhejiang (16), Fujian (12), Yunnan
(1) and Inner Mongolia (1) is lower than the set threshold, the
clustering result do not include these provinces. Similarly, the
number in parentheses is the number of granted patents of
provinces.
5.3 Countries
In the case, totally 17 countries or regions are extracted in all fuel
cell patents. The graph clustering result is shown in Figure 8.
Obviously, the biggest node in the graph is China, the granted
patent number of which is 1123. While the smallest nodes are
Denmark and Finland. The granted patent number of both country
are 3.
Figure 7 The clustering results of provinces
Table 4 Corresponding English names of Chinese names of
provinces in Figure 7
The Chinese Name The English Name
上海 Shanghai
台湾 Taiwan
Figure 8 The clustering result of countries Figure 9 The clustering figure of unconnected states
Table 5 Corresponding English names of Chinese names of Table 6 Corresponding English names of Chinese names of
R&D institutions in Figure 8 R&D institutions in Figure 9
The Chinese Name The English Name The Chinese Name The English Name
中国 China 中国 China
德国 Germany 德国 Germany
英国 Britain 英国 Britain
法国 France 法国 France
欧洲专利局 EPO 欧洲专利局 EPO
瑞典 Sweden 瑞典 Sweden
荷兰 Netherlands 荷兰 Netherlands
日本 Japan 日本 Japan
美国 the United States 美国 the United States
加拿大 Canada 加拿大 Canada
澳大利亚 Australia 澳大利亚 Australia
芬兰 Finland 芬兰 Finland
In the country level, four technology competitor groups are 挪威 Norway
identified, containing 16 countries and regional organizations.
The group with red node color represents the first group, 意大利 Italy
including seven countries and regional organizations: China 韩国 Korea
(1123), Germany (58), Britain (28), France (16), EPO (10),
Sweden (6), and Netherlands (5). The group of orange node color
represents the second group, including 5 countries: Japan (740),
the United States (292), Canada (33), Australia (4) and Finland 6. CONCLUSION
(3). The third group consists of Korea (202) and Denmark (3) two In the paper, a graph clustering algorithm is used to obtain
countries. The fourth group includes Norway (3) and Italy (1). technology competitor group analysis based on IPC. The
There is an edge between Norway and Italy, but there are no edges proposed method consists of four stages. First, the clustering level
with other nodes (Figure 9), however Figure 8 can't show them is determined. There are three levels for selected, i.e. institute,
because LinLog algorithm has problems to generate clusters with province and country. Second, the numbers of patents are counted
unconnected graphs. The technology similarity of Austria (1) with under each IPC for each object (competitor) in the selected level.
other countries is lower than the threshold, so the clustering figure Third, each object is expressed with a vector, the attributes of
does not include Austria (1). which are IPC classification codes, and the value of each attribute
is corresponding patent count. Fourth, technology similarities are
computed between each pair of competitors. Finally, Linlog
algorithm is used to cluster competitors into groups and display
them in a graph to improve the confidence of analysis results.
Experimental results on fuel cell demonstrate the effectiveness of on Advances in Social Network Analysis and Mining
the proposed method. (ASONAM).
[8] Li, Wanchun., Eades, P., and Nikolov, N. 2008. Using spring
7. ACKNOWLEDGMENTS algorithms to remove node overlapping. C. Proceedings of
This work is partially supported by National Natural Science
the 2005 Asia-Pacific symposium on Information
Foundation of China (Project 71473237), and partially supported
visualization.
by the Key Work Project of Institute of Scientific and Technical
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to the National Natural Science Foundation of China and the guided by co-word clustering. J. Scientometrics. 56, 1, 111-
Ministry of Science and Technology of China for financial 135.
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