=Paper=
{{Paper
|id=Vol-1437/ipamin2015_submission_4
|storemode=property
|title=Technology Early Warning Model: a New Approach Based on Patent Data
|pdfUrl=https://ceur-ws.org/Vol-1437/ipamin2015_paper4.pdf
|volume=Vol-1437
}}
==Technology Early Warning Model: a New Approach Based on Patent Data==
https://ceur-ws.org/Vol-1437/ipamin2015_paper4.pdf
Technology Early Warning Model:
a New Approach Based on Patent Data
Ganlu Sun, Ying Guo Fan Yang
Beijing Institute of Technology WIDE-CODE Information Technology Co.,Ltd
5 South Zhongguancun Street Wangjing High-tech Park, Lize Zhong Er Road,
Haidian District Chaoyang District
Beijing, P.R.China Beijing, P.R.China
+8613810027376 (86)(10)64878770
sunganluzz@163.com, guoying_bit@163.com paoever@163.com
ABSTRACT (Congmin She.2003) [2]. This can offer enough time for early
With the development of technology, more and more technical warning subjects to take mearsures before crises happen. It also
issues have been exposed, such as technical disputes, technical can help early warning subjects to reduce significant loss as a
barriers and technical crisis. Thus, it is necessary to warn result.
enterprises about technical deviation and predict future The concept of technology early warning was first put forward by
technology crises. Patent data can contain much information the U.S. military. In this concept, technology early warning refers
about technologies and would be useful in this setting. This to the alertness to a “technical raid” which potential rivals may
paper proposes a technology early warning model based on form in advance to keep military advantage in technology (Boao
patent data. This model helps enterprises analyse the technical Qin.2006) [3]. Yang Cai (1989) [4] redefined it, pointing out that
crisis level and trends from four different perspectives (technical technology early warning is a process from technology forecast
stability, technical monopoly, technical security and technical and relevant factors’ breakthrough to give the alarm to decision
prospects). makers. Yujie Zhang (1999) [5] defined technology early warning
in enterprise as a security alarm of technical deviation and
Keywords technical catch up situations which remind enterprises take
Technology early warning, Patent data, Technical crisis, measures to keep a technical advantage. In this paper, technology
Indicators early warning is regarded as forecast and alert for technical crisis
which would threaten enterprises’ sustainable development, and
lose their technological advantage.
1. INTRODUCTION
With the development of science and technology, many In order to execute technology early warning, technology
enterprises, especially high and new technology enterprises, information needs to be considered. Many enterprises apply for
started to focus on research and development of technology. patents to protect their technology infringment by other actors,
However, as a result of the development of economic and are effective means to protect intellectual property. Recent
globalization, there are many technology disputes between studies have used patent information to study technological
enterprises all over the world every year. Thus, it is necessary for developments, trends and potential (Zhang, 2011; Pilkington et
enterprises to take some measures to avoid these dispute and al., 2009) [6-7] as well as decision making in research and
keep their technology advantage in a fiercely competitive market. development (Thorleuchter et al., 2010) [8]. Information from
We address this in our paper and we aim to provide an effective patent data allows companies to avoid investing in obsolete
method for enterprises in technology early warning. technology (Wang et al., 2012) [9] and it enhances strategic
planning (Abraham and Moitra, 2001) [10]. Patent analysis also
The phrase early warning derives from military planning, offers key information concerning the technology environment
refering to predicting enemy attacks, giving the alarm in a timely (Porter and Cunningham, 2005) [11] and addresses component
manner and preparing the appropriate response to avoid technologies (Trappey et al., 2012) [12]. Patent data is suitable to
significant loss (Jiezhu Pan, 2007) [1]. In other fields, early analyze technology in enterprises and this paper will develop
warning is a forecast method for crisies which could threaten that patent analysis further by introducing perspectives on technology
field’s normal operation, and offer directions for preparation early warning.
Currently, there are few studies which incorporate technology
Copyright © 2015 for the individual papers by the papers' authors. early warning with respect to technical crisis and combine this
Copying permitted for private and academic purposes. with patent data. Therefore, we propose a new method for the
This volume is published and copyrighted by its editors. technology early warning model based on patent data considering
Published at Ceur-ws.org
Proceedings of the Second International Workshop on Patent Mining and forecasting of technical crisis. We aim to contribute to improve
its Applications (IPAMIN). May 27–28, 2015, Beijing, China. technology early warning systems. This paper identifies,
analyzes, and indicates the technology crisis, and then establishes
the technology early warning indicators and a model based on
patent data. We aim to provide a new method for enterprises to which are “health care”, “computer technology”, “management”
solve problems in technical disputes, technical deviation and and “perception”. However, as we can see in Figure 1, “GPRS”
technical catch up situations before they happen. and “early warning score” are the main research domains within
This paper is organized as follows. The literatures about management science. However, this paper will address different
technology early warning are reviewed in Section 2. Model of perspectives in “technology early warning” and combine this
technology early warning based on patent data are proposed in with technical crisis values. To learn more about research in
Section 3. Conclusions and future research directions are management science, we ranked papers’ keywords which appear
provided in Section 4. more than twice – see Table 1. From Table 1 we observe that
much research focuses on early warning methods or systems.
2. LITERATURE REVIEW These studies utilize computer technology (such as data mining
To establish a better technology early warning model, we and AHP) to simulate or forecast alert situations. From these
experimented with information on technology early warning keywords, we also build on the quantifiable methods used by
research status from a broad perspective in a bibliometric many of these papers in our research.
database. This gave us a perspective on the theoretical concepts In tandem with publication data considering technology early
and current trends in the literature of early warning. Our results warning models, we addressed the development of technology
show a wide range of information about the current research early warning in a patent database. Initially, we searched in
fields, previous methods and current critical dialogues of “Web of Science” with a boolean search term- “Topic= (patent)
technology early warning research. AND Topic= (technology early-warning OR technology early
Firstly, we searched in “Web of Science” using a boolean search warn OR tech early-warning OR technology early warning OR
term-“Topic= (technology early-warning OR technology early technical warning)” and generated 8 results, with none related to
warn OR tech early-warning OR technology early warning OR management science. We altered the ‘topic’ term to “patent AND
technical warning)” to study the current research trends and early warning” and generated 17 results, with 5 papers explicitly
indicate the leading field of technology early warning researchs. mentioning patent data in early warning but no reference was
We generated 3436 results which indicated a sufficent quantity made to technology early warning. Of the total, four papers
of research to be worth analysing in a bibliometric database. where relevant for our study.One study, commented on the
After that we downloaded these documents and processed them research literature of early warning mechanisms in China, and
using VantagePoint (a software for data processing), locating 135 presented an international case study of a successful early
items related to management sciences. We can observe that while warning technique using patents (Jianping G, 2011)[13]. Another
there have been many studies ontechnology early warning, there paper discussed the importance of establishing patent early
have been fewer in management sciences and is worth warning systems to forecast potential patent risks and analyzed
considering in the context of enterprise readiness for technical the main reason of patent risk. It proposed three basic functions
crisis. . of a patent risk early warning system. This paper also presented a
basic framework and model of that system (Han, Hongqi; Wang,
We extracted keywords in 3436 papers and selected the top 200
Xuefeng, 2008) [14]. Another paper proposed patent infringement
which were clustered in a visualisation- see Figure 1. From
litigation early warning indicators and a model for
Figure 1 we can see that keywords in previous technology early
pharmaceutical enterprises.
warning research has mainly clustered into four categories
Table 1. Keywords in management rank
Times keywords
18 Early warning system
17 Early warning
10 Technology
7 Technology Innovation
6 Risk management
5 Classification; Crisis; Data mining; Discriminant-analysis; Management; Model
4 China energy; Complex adaptive system; Construction project; Consumption; Early warning management; Emergency;
Indicator system; Information; Real estate; systems; The ability of independent innovation; Trends; Warning system
3 Admissions; AHP; Fuzzy comprehensive evaluation; Governance; Information extraction; Intensive-care; Intrusion detection;
Lessons; Logit; Network; Antecedents; Competition; Data and information quality; Deaths; Design science; Early warning
score; Environment early-warning; Failure; Financial crisis; Neural networks; Project management; Risk assessment;
Sensor-based electronic modified early warning scorecard; Socio-technical information systems design methodology;
Stakeholders; Strategy; The model of early-warning
Factor Map
Keywords (author's) + Keyword... public health
Kenya
Floodvegetation
Factors: 30
% Coverage: 33% (1141)
forecasting
Top links shown
> 0.75 0 (0) Ethiopia
0.50 - 0.75 0 (0)
0.25 - 0.50 0 (0)
EL-NINO
< 0.25 27 (201) malaria
localization
uncertainty
CARE
PCR
pollution
SCIENCE
hazard
Health Technology Assessment PCR
vulnerability
DISCRIMINANT-ANALYSIS
Neural network
Data fusion
IMPACT
early warning score
environment
INFECTION
management
GPRS
perception
Magnitude
Copper Wireless sensor surgery
networks
Figure 1. Top 200 keywords’ cluster
This also presented a three stage early warning indicator system and gradualness (Lixin Xia et al., 2009[17]; Runhua Tan, 2002
(Wang, Ying, 2013) [15]. The final paper considers how the patent [18]
). To forecast technical crisis, we should consider the
early warning mechanism can improve innovation abilities of characteristics. (1)Uncertainty of technical crisisimplies the risks
wind power enterprises. They imply that by establishing patent involved in a period of development. Characteristics of the
early warning mechanisms, common issues experienced by technology, internal and external influences, and limitation of
enterprises in the sector can be resolved through these methods human knowledge could increase uncertainty in these terms.
(Peng Yuanyuan, 2012)[16]. These articles imply that while there (2)Relativity of technical crisis is caused by different
is growing dialogue on the subject, research on technology early organizational conditions. The same technology may have
warning combined with patent data is an underdeveloped area diffierent risks in different organizations as well as in the same
worthy of further study. organization, at a different stage of development. Thus,
technology crisis may be both an opportunity and a challenge for
3. METHODOLOGY an enterprise. (3)Destructiveness of technical crisis implies that
3.1 Research Framework once a crisis begins, it can cause serious disruption for an
In this paper, we regard technology early warning as an alert for enterprise. (4)Imperceptibility of technical crisis implies an event
technical crisis. Technical crisis is a risk that caused by the which cannot be detected and may have significant affect on the
accumulation of negative factors of technologies’, and it will enterprise. This will contribute to the destructive nature of
destroy the quality of a technology if unaddressed. Technology technical crisis. (5)Gradualness of technical crisis implies the
has features of stability, monopoly, security, reliability and accumulative quality of negative factors in the technology and
development potential. However, when technologies are technical environment. This conceptual outline is demonstrated
destroyed, it will turn into a technical crisis for the enterprise in Figure 2.
causing uncertainty, relativity, destructiveness, imperceptibility
Technical Technical crisis
characteristic characteristics
s
Stability Uncertainty
Monopoly Relativity
Destroy Technology
Security early warning
Destructiveness
Reliability Imperceptibility
Prospects Gradualness
Figure 2.Triggers of technical crisis
With a well defined characteristic model of technical crisis, we How will enterprises’ technology develop in the future?
can further develop our analysis towards a technology early
warning model. Judging technical crisis through enterprises’ 3.2 Indicators
data directly is a complicated process, so we reflect on thus by In order to answer these questions and help enterprise
using information gained from the technologies. Therefore, this forecast its technical crisis with quantitive methods, we propose
paper will execute technology early warning research through the four different perspectives (technical stability, technical
patent data which displays information about enterprises monopoly, technical secuity and technical prospects) indicators to
technology development. This paper aims to answer the analyze enterprise technology status. From this we will judge
following research questions with patent data in enterprises: whether an enterprise will encounter technical crisis. From this
we can present an ‘early warning’ index for companies. Based
How do we judge whether an enterprise technology will be
on the rich literature review and consultation with experts, we
affected by internal and external factors?
defined ten indicators to measure these different perspectives.
At what level is an enterprise technology in its field? Indicators are shown in Table 2.
Can enterprises protect their technologies?
Table 2. Indicators and its detailed contents
Perspectives Indicators Operational definition
Technology maturity Enterprise technology’s development stage
Technical R&D staff flow Change of enterprise R&D staff
stability The degree of enterprise technology dependent on
Technical dependence
external factors
Research scope of enterprise technology (technical
Technical breadth
Technical breadth)
monopoly Enterprise technology’s strength in research
Technical strength
(technical depth)
Technical disparity Differences of enterprise technology
Technical security Technical complexity Complexity of enterprise technology system
Technical maintenance Ability to maintain technology
Technical Technical progress Development trend of enterprise technology
prospects Technical environment Development trend of the technical field
First, the perspective of technical stability is used to judge if
enterprise technology can develop stability and to what extent it Table 3. Computational formula of each indicator in first
will be affected by internal and external factors. This insight is perspective
gained from technology maturity, technical staff flow and
technical dependence. Technology maturity reflects technical Indicators Formulas Explaination
stability mainly through technology life cycle. R&D staff flow We define the value of
reflects technical stability through the number of R&D workers We calculate Technology maturity stage which
(can be called ‘technicists’) who apply for a patent in an maturity based on a is the most stable
enterprise. We consider the more technicists are engaged in these Technology
growing model and stage as 5, and the
activites, there is a higher stability coefficient in the enterprise. maturity
divided this into five stage closer to
Technical dependence is a powerful indicator to reflect technical levels. maturity stage have
stability. We argue that if enterprises have a significant higher value.
dependence on another enterprise technology, they are more
prone to technical crisis. The computational formula of each AI: average inventors
indicator is shown in Table 3. in patent applications.
R&D staff TI TI: total inventors to
The second perspective--technical monopoly--is used to judge at AI 100%
flow TPE apply for patents.
what level an enterprise technology is in its field. To achieve TPE: total patents
this, we consider technical breadth and technical strength to enterprise has.
measure technical monopoly. We introduce this indicator to
measure research scope of enterprise compared with its rivals TDE: technology
and we measure technical breadth as the proportion of the dependence. TPF:
enterprise’s technology accounted for in its field. Technical TPF TPE total effective
Technical TDE 100%
strength reflects technical monopoly in the view of an TPF invention patents in
dependence
enterprise’s strength in research and development of a the field. TPE has the
technology. We argue that the value of technical strength has a same meaning with
positive influence on technical monopoly. In Table 4 we detail above.
the computational formula of these indicators.
Table 4. Computational formula of each indicator in second Finally, the perspective of technical prospects is proposed to
perspective estimate the development prospects of an enterprise technology.
We introduce technical progress and technical environment to
Indicators Formulas Explaination
measure this. Technical progress reflects technical prospects
IPC q TB: technical breadth. IPCq: from the variation of patent numbers. We argue that technologies
Technical TB 100% number of IPC in enterprise. will develop well in the future when patent numbers increase
breadth IPC h IPCh: the number of IPC in rapidly. The technical environment reflects technical prospects of
enterprise’s industry. the field and how the enterprise is affected by it. We argue that
TS: technical strength. NAIP: an enterprise technology will have the same potential of
the number of active invention development as the field it inhabits. Computational formula of
Technical TS NAIP ALPE each indicator is shown in Table 6.
patent in an enterprise. ALPE:
strength
average length of patent
enforcement.
Table 6. Computational formula of each indicator in third
perspective
The third perspective--technical security--is used to measure an Indicators Formulas Explaination
enterprise’s capacity to protect its technology from damage.
IAR: increase of
Technical diversity, technical complexity and technical
application rate for
maintenance are indicators we use to measure technical securiy.
invention patents.
Technical diversity refers technology difference between
NPC: number of
enterprise and the field. And we argue that with a lower
NPC NPP patents by
technical diversity value an enterprises technology is more Technical IAR 100%
NPP enterprises in
secure. Also, a higher value in technical complexity and technical progress
current period.
maintenance means a more robust enterprise technology. Table 5
NPP: number of
shows detailed computational formula of these indicators.
patents of
enterprises in prior
Table 5. Computational formula of each indicator in third period.
perspective IRLP: the increase
Indicators Formulas Explaination rate of active
patents. NLPC:
TDI: Technological number of active
diversity. ai and bi NAPC — NAPP
Technical IRAP 100patents of industry
n means the number of environment NAPP in current year.
Technical
disparity
TDI b - a
i 1
i i
2
active invention
patents in an
NLPP: number of
active patents of
enterprise and its field industry in prior
distribution in IPC year.
categories, i=1,···,n.
TC: technical
complexity. NIPCC: 3.3 Process
NAP the number of main
Technical TC 100%
NIPCC IPC categories. Based on indicators shown on 3.2, this method will also address
complexity
NAP:number of active the wieghting and degree of alertness which helps in the analysis
patents in an of technical crisis.
enterprise (1) Weighting
AEG: average AHP (Analytic hierarchical Process) is a powerful decision
TNIP effective age of analysis technique for multi-criteria decision-making, and it can
Technical
AEG 100% technology. TNIP: the decompose problems into a hierarchy of goals, attributes, criteria
main- NAP
total number of and alternatives. Therefore, after the calculation of each indicator
tenance
invention patents in an with patent data in 3.2, we used AHP to weight each indicator.
enterprise. Table 7 is the result of the weighting of each indicator.
Table 7. Summary list of the indicators
Technical Technical Technical Technical Synthetic
Rule level
stability monopoly security propsects weight
Indicators
0.0507 0.1781 0.5539 0.2172
Technology maturityC11 0.6434 0.0326
R&D staff flowC12 0.0738 0.0037
Technology dependence
0.2828 0.0143
C13
Technical
0.1667 0.0297
widthC21
Technical strengthC22 0.8333 0.1484
Technical diversityC31 0.1530 0.0847
Technical complexityC32 1.0548 0.5843
Technical maintenanceC33 0.4010 0.2221
Technical progressC41 0.8333 0.1810
Technical environmentC42 0.1667 0.0362
(2) Setting alert degree
In this model, we calculate the total evaluation score by multi- Wherein T means the degree of enterprise technical crisis, m
objective linear weighting function. Formula of enterprise means the number of perspectives, n means the number of
technical crisis is shown as follows. indicators in each perspective. And Wi means the weight of ith
perspective.Wij is the weight of jth indicator in ith perspective,
Rij is the grade of jth indicator in ith perspective. We outline
m n degree of alertness in Table 8. Enterprises can know their
T R W i 1 ~ m; j 1 ~ n
i 1
Wi
j 1
ij ij
technical crisis level and safety situation by the value of T
contrasted with Table 8.
4. CONCLUSIONS
The objective of this study is to propose a new approach of
Table 8. Alert Degree and its meaning technology early warning with patent data. To this end, we
Degree of analyzed existing literature in the Web of Science to find the
Score Meaning research situation of technology early warning and some studies
technical crisis
combined with patent data. Based on the previous studies of
Safest Technical crisis in enterprise is
technology early warning, we proposed four different
0~1 slight, and it will not affect
（A） perspectives to consider patent and technical crisis characters
enterprise’s interests and actions.
and defined ten indicators to analyze these perspectives. After
Safer Technical crisis is not serious, and that we analysed the weighting of each indicator, and set the
1~2 it will only cause minor damage on degree of alertness to measure the degree of technical crisis
（B） the enterprise. theenterprise faced.
Enterprise technology has a certain This study offers some direction for forcasting enterprise
Safe crisis, and it will affect the technical crisis using patent data. The model for technology early
2~3
（C） enterprise’s interests and actions warning can be used to reduce technical disputes and technical
but will not be fatal. barriers as well as aid further academic research. These models
could be adopted by Enterprises with relevant patent data, to
Technical crisis in an enterprise will
Risky analyze technical crisis and better react to it.
bring cause a signifcant loss. The
3~4
（D） possibility of technical disputes will As with any experimental model there are limitations which will
rise. need to be improved. The most important one is that as the
reason of time and difficult in getting patent data for us, we were
Highly Risky Technical crisis is inevitable, and it
unable to verify the index system and model by empirical study.
4~5 may directly threaten the
（E） So in the future research, we will do an empirical study to prove
enterprise’s survival.
the feasibility and rationality of this new method and make it
better. Because of much knowledge involved in this research, identifying relationships between technologies. Technol.
there are some difficulties caused by abstraction of research Forecast. Soc. Chang. 77 (7), 1037–1050.
contents that make our indicators fuzzy. In the future, another
[9] Wang, Y.L., Huang, S., Wu, Y.C.J. 2012. Information
work for us is to research the method of indicator quantitative.
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