The 4th International Seville Conference on
Future-Oriented Technology Analysis (FTA)
12 & 13 May 2011
Technology Life Cycle Analysis
Modelling based on Patent Documents
]
Lidan Gao,1,2 Alan L Porter,3 Jing Wang,4 Shu Fang,1
Xian Zhang,1 Tingting Ma,5 Wenping Wang,5 Lu Huang5
1 Chengdu Library of the Chinese Academy of Sciences, Chengdu 610041,
2 School of Economics and Management, Southwest Jiaotong University,
Chengdu 610031,
3 School of Public Policy, Georgia Institute of Technology, Atlanta, GA 30332-0345,
4 College of Computer Science & Technology, Huaqiao University, Xiamen,
361021,
5 School of Management and Economic, Beijing Institute of Technology, Beijing
100081
Technology Life Cycle Analysis Modelling based on Patent Documents
Introduction
Research Purpose
•The rapidly changing economic environment and increasingly fierce
competition require companies to be innovative, both in their products and
marketing strategies, if they are to continue to flourish. A successful product
must balance three components: marketing, technology, and user experience
(Norman, 1998). Technology plays a key role among these three components
(Ming et al). To estimate the future development of one technology and make
decisions whether to invest in it or not, one needs to know the stage status of
its technology life cycle (TLC).
Technology Life Cycle Analysis Modelling based on Patent Documents
Introduction
Definition of Technology Life Cycle
•The concept of the technology life cycle (TLC) was developed by Arthur (1981)
to measure technological changes. According to Arthur’s definition, the
characteristic of the emerging stage is a new technology with low competitive
impact and low integration in products or processes. In the growth stage, there
are pacing technologies with high competitive impact that have not yet been
integrated in new products or processes. In the maturity stage, some pacing
technologies turn into key technologies, are integrated into products or
processes, and maintain their high competitive impact. As soon as a
technology loses its competitive impact, it becomes a base technology. It
enters the saturation stage and might be replaced by a new technology.
•Ernst (1997) developed a map to illustrate it (figure 1).
Technology Life Cycle Analysis Modelling based on Patent Documents
Introduction
Definition of Technology Life Cycle
Figure 1. The S-curve concept of technology life cycle
(Ernst H. 1997)
Technology Life Cycle Analysis Modelling based on Patent Documents
Introduction
Research Review
•The major approach to analysing TLC with an S-curve is to observe
technological performance, either over time or in terms of cumulative R&D
expenditures. Usually, patent application activity is tracked as a TLC indicator
for the S-curve analysis (Ernst, 1997; WenYan Zhou, 2005; Chaoming Chu,
2008).
•But using one indicator only to present technological performance would be
problematic. A research team from MIT (Lee et al., 1989) studied the
development trends of Power Transmission technology and Aero-engine
technology by S-curve. The results showed that the S-curve with a single
indicator was not reliable and might lead the research in the wrong direction.
•Accordingly, some multiple indicators are used to measure TLC.
Robert and Alan (1997) have introduced nine indicators that look at
publications of different types during the technology life cycle.
Reinhard et al. (2007) tested seven indicators related to patents. Table1
shows the indicators listed in the two papers.
Technology Life Cycle Analysis Modelling based on Patent Documents
Introduction
Research Review
Table1. Technology life cycle indicators by former researchers
Author
Indicator
Robert J Watts, Number of items in databases such as Science Citation Index
Alan L Porter
Number of items in databases such as Engineering Index
(1997)
Number of items in databases such as U.S. Patents
Number of items in databases such as Newspaper Abstracts Daily
Issues raised in the Business and Popular Press abstracts
Trends over time in number of items
Technological needs noted
Types of topics receiving attention
Spin-off technologies linked
Backward citations
Reinhard
Haupt, Martin Immediacy of patent citations
Forward citations
Kloyer,
Marcus Lange Dependent claims
Priorities
(2007)
Duration of the examination process
Data base requirements
Technology Life Cycle Analysis Modelling based on Patent Documents
Introduction
Research Questions
•Many papers have studied some indicators that would have different
performances based on the changes of technology. And some indicators have
been used separately to measure the TLC stage changing. There is no
research combine the multiple indicators together to measure the TLC stage
changing.
•In this paper, we focus on combining multiple indicators to calculate the life
cycle stages for an object technology and hope that would help decision
makers to estimate the future development trends of the technology.
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
Research Model
•The model we build to calculate the TLC for an object technology includes the
following steps:
first, we focus on devising and assessing patent-based TLC indicators;
then we choose some technologies (training technologies) with
identified life cycle stages;
and finally we compare the indicator features in training technologies
with the indicator values in an object technology (test technology) via the
Nearest Neighbour Classifier, which is widely used in Pattern Recognition,
in order to measure the technology’s life cycle stages. The research
framework is designed as follows.
Technology Life Cycle Analysis Modelling
based on
Patent
Figure 2. Framework
of TLC
analysis Documents
Methodology
Research Model
Figure 2. Research model of TLC analysis
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
Indicators and data source
•We have compiled candidate patent indicators from multiple sources.
Thirteen indicators are selected for TLC assessment.
•We choose the Derwent Innovation Index (DII) as the data source and
VantagePoint (VP) for data cleaning and extraction. Matlab 2010b is used for
implementing the algorithms.
•All the data of indicators are extracted by priority year, except the first
indicator.
•We take nanobiosensor (NBS) as a case study to measure its technology life
cycle status.
Technology Life Cycle Analysis Modelling
basedlifeon
Patent
Documents
Table 2. Technology
cycle
indicators
Methodology
Indicators and data source
Table 2. Technology life cycle indicators
Indicator
Indicator description
No.
Application
Number of patents in DII by application year
1
Priority
Number of patents in DII by priority year
2
Corporate
Number of corporates in DII by priority year
3
Non-corporate
Number of non-corporates in DII by priority year
4
Inventor
Number of inventors in DII by priority year
5
Literature citation
Number of backward citations to literatures in DII by priority year
6
Patent citation
Number of backward citations to patents in DII by priority year
7
IPC
Number of IPCs (4-digit) in DII by priority year
8
IPC top 5
Number of patents of top 5 IPC in DII by priority year
9
IPC top 10
Number of patents of top 10 IPC in DII by priority year
10
MC
Number of MCs in DII by priority year
11
MC top 5
Number of patents of top 5 MCs in DII by priority year
12
MC top 10
Number of patents of top 10 MCs in DII by priority year
13
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
TLC stages of training technologies
•It is better to choose a training technology with four TLC stages.
•Cathode Ray Tube (CRT) has been developed for more than 100 years and
is in the decline stage now (Yeh, 2005; Ding, 1997). But the patent information
in early years is unavailable. So we choose another similar technology, the
Thin Film Transistor Liquid Crystal Display (TFT-LCD), as the second training
technology.
•We then focus on CRT and TFT-LCD technologies and assess their life cycle
stages by the Delphi method and via literature investigation. Table 3 shows the
TLC stages of CRT and TFT-LCD as given by experts and literature.
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
TLC stages of training technologies
Table 3 TLC stages of CRT and TFT-LCD
Stage
Emerging
Growth
Maturity
Decline
Period (year)(CRT)
1897–1929
1930–1972
1973–2000
2001–2020
Period (year)(TFT-LCD)
1976–1990
1991–2007
2008–
–
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
Data
•TFT-LCD : 12596 records in DII.
•CRT: 34469 records in DII.
•NBS: 1493 records in DII.
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
Data Process
•We develop a map for 13 indicators of each training technology.
•Numbers of inventors suggest very interesting changes in different stages.
Figure 3, which presents the emerging and growth stages, shows that the
number of inventors is typically higher than that of all other indicators. The
number of it declines in the mid-maturity stage (figure 4), but slightly increases
in following years. The number of inventors is less than some other indicators,
such as application numbers and priority application numbers in maturity and
decline stages.
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
Data Process
3500
3000
Application
Inventor
IPC-TOP5
MC-TOP10
Priority
Literature citation
IPC-TOP10
Corporate
Patent citation
MC
Non-corporate
IPC
MC-TOP5
2500
2000
1500
1000
500
19
78
19
79
19
80
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
0
Figure 3. Development trends of 13 indicators (TFT-LCD)
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
Data Process
2500
Application
Inventor
IPC-TOP5
MC-TOP10
2000
Priority
Literature citation
IPC-TOP10
Corporate
Patent citation
MC
Non-corporate
IPC
MC-TOP5
1500
1000
500
Figure 4. Development trends of 13 indicators (CRT)
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
1980
1979
1978
1977
1976
1975
1974
1973
1972
0
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
Data Process
•To make clear which indicators are similar with others in development trends,
we employ cross-correlation analysis to measure the similarity among the 13
indicators in four stages. Table 4 provides the results of cross-correlation
analysis (r≥0.9).
•The indicators show different trends in different stages.
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
Data Process
Table 4. Cross-correlation analysis for 13 indicators (r≥0.9)
TLC stage
Group 1
Emerging
5, 6, 7
Group 3
Group 4
Group 6
Maturity
1, 2, 3, 7, 9, 10, 1, 2, 3, 4, 5, 8, 9, 1, 2, 3, 7, 8, 9,
11, 12, 13
10, 11, 12, 13
10, 11, 13
Group 2
Group 5
Growth
6, 7
Decline
1, 2, 7, 9, 10,
12, 13
4
2, 3, 8
4
5
4
8
6
5
11, 12, 13
6, 7
11
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
Data Process
•To process multidimensional data by matrix. The original data are extracted
by VP and imported into excel—13 rows of indicators, 30 columns (years) for
TFT-LCD (from 1978 to 2007), 36 columns (years) for CRT (from 1972 to
2008), and 24 columns (years) for NBS (from 1985 to 2008).
•We propose a normalization method with two steps to preprocess the original
data. We first process the data of training technologies - TFT-LCD and CRT.
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
Data Process
•The first step of preprocess is data smoothing by calculating three year
moving averages. The original data are defined as:
A= [A1, A2].
•Here A1, A2 represent the original data of TFT-LCD and CRT respectively.
Then the smoothed data of TFT-LCD and CRT are defined as:
•
represent the smoothed data of TFT-LCD and CRT respectively.
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
Data Process
•The next step is to divide the smoothed data by their maximums. The
normalized data are defined as:
•
represent the normalized data of TFT-LCD and CRT respectively.
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
Data Process
•We then apply the same normalization steps to the NBS data. The smoothed
data and the final normalized data of NBS are defined as
, respectively,
ˆ
B, B
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
Data Process
•Then the nearest neighbour (NN) classifier is applied to the normalized data
to measure the stage status of NBS.
•The normalized data of TFT-LCD and CRT form the training set
.
There are 30 training points in the TFT-LCD training set, 36 training points in
the CRT training set.
•The normalized data of NBS are considered as test set
are 24 test points in the NBS test set.
•The training points
and test points
are defined as:
. There
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
Data Process
•Since we have the TLC stages of TFL-LCD and CRT, we can form the label
set of training set:
•
represents TLC stages of TFT-LCD and CRT.
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
Data Process
•For a training point
is defined as:
and test point
, the distance between
and
For each test point
, we compute the distance between
and all the
training points and find the nearest training point (Figure 5), that means:
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
Data Process
Figure 5. An example for computing the distance between test
point and training points
Technology Life Cycle Analysis Modelling based on Patent Documents
Methodology
Data Process
•Then the label information of is considered identical as that of
, namely
or
. In order to obtain all label information for NBS, we have to calculate
the minimum distance between each test point and all the training points and
then obtain all the label information of
, that is the TLC stage information of
NBS.
Technology Life Cycle Analysis Modelling based on Patent Documents
Results and policy impact/implications
•Table 5 shows the label results for each test point of NBS. The label
information of the first 12 test points (1985–1996) of NBS can be matched with
that in the emerging stage of TFT-LCD, and the label information of the
second 12 test points (1997–2008) of NBS can be matched with that in the
growth stage of TFT-LCD.
•The TLC stages of NBS are:
Emerging stage ( =1): 1985–1996
Growth stage ( =2): 1997–
Technology Life Cycle Analysis Modelling based on Patent Documents
Results and policy impact/implications
Table 5 TLC stages of NBS
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1
1
1
1
1
1
1
1
1
1
1
1
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2
2
2
2
2
2
2
2
2
2
2
2
Technology Life Cycle Analysis Modelling based on Patent Documents
Results and policy impact/implications
•According to the experts’ opinion, the results are reasonable. Therefore, NBS
is still in its growth stage.
•According to the definition of TLC, in a technology’s growth stage, there are
pacing technologies with high competitive impact that have not yet been
integrated into new products or processes. That means, some product-related
technologies may be commercialized in the future; however, at the moment,
these technologies need more work in order to resolve key problems. The
most successful commercial biosensor technology—surface plasmon
resonance—doesn't have a very good limit of detection (LOD), the
nanoparticle based SPR (or local SPR) can provide excellent LOD. However,
the current fabrication technology is expensive (David et al., 2008). In this
stage, a lot of challenging problems must be overcome, such as enhancement
of gene array and protein array, and some new and promising technologies
are still under research (Gerald et al., 2009).
Technology Life Cycle Analysis Modelling based on Patent Documents
Conclusions
•The study is based on patent documents.
•We adopt 13 indicators that can be quantitated to measure the TLC stages of
an objective technology.
•We introduce the nearest neighbour classifier, which is commonly used in
pattern recognition and some other fields, to process the 13-D data by
calculating the nearest distance among the test point and training points to
find the most similar feature in training points. Therefore, the stage of the
training point with the nearest distance to the test point predicts the stage of
the test point.
•In this study, we take TFT-LCD and CRT as the training technologies and
NBS serves as the test technology. The result shows that NBS is still in its
growth stage. This method can be used not only in NBS but also in other
technology fields.
Technology Life Cycle Analysis Modelling based on Patent Documents
Conclusions
Research Limitations
•First, only two technologies serve as the training technologies to calculate the
similarity feature with the objective technology (test technology). This is due to
the lack of ideal training technologies with four TLC stages. So, this study
resembles a laboratory test. Though the result sounds reasonable, we still
need to find more technologies and obtain more data to validate the method.
•Second, we did not consider the technology type. TFT-LCD and CRT are
categorized as the single-technology type, but NBS is a multi-technology: it
involves nanotechnology and biotechnology. Different types of technologies
may have different developing patterns, especially for those technologies
close to basic science, such as biotechnology. The future research should also
take this into account.
•Third, the classifier we used in this paper is the nearest neighbour classifier.
For future study, we will test some other classifiers, such as nearest feature
line (NFL) and Bayesian classifier, to improve the calculating performance.
Technology Life Cycle Analysis Modelling based on Patent Documents
Acknowledgement
•This research was undertaken at Georgia Tech, drawing on support from the
National Science Foundation (NSF) through the Center for Nanotechnology in
Society (Arizona State University; Award No. 0531194) and the Science of
Science Policy Program—“Measuring and Tracking Research Knowledge
Integration” (Georgia Tech; Award No. 0830207).
The findings and observations contained in this paper are those of the authors
and do not necessarily reflect the views of the National Science Foundation.
•The researchers deeply appreciate the financial support from the Chinese
Academy of Sciences.
•We are further sincerely grateful and dedicate our acknowledgement to the
experts in TFT-LCD, CRT and NBS: Prof. Shouqian Ding, Prof. Linsu Tong,
Prof. Zhihua Gu, Prof. Xurong Xu, Dr. Zhengchun Peng, Dr. Jud Ready; and
two reviewers, Dr. Li Tang and Dr. Jian Wang, for very useful comments.
Technology Life Cycle Analysis Modelling based on Patent Documents
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Thanks!
Lidan Gao, gld@clas.ac.cn
Alan L Porter, alan.porter@isye.gatech.edu
Jing Wang, wroaring@yahoo.com.cn