time & dates

ART79.pdf

Technology life cycle analysis method based on patent documents Lidan Gao a b,, Alan L. Porter c, Jing Wang d, Shu Fang a, Xian Zhang a, Tingting Ma e, Wenping Wang e, Lu Huang e

Received 14 may 2011 Received in revised form 22 june 2012 Accepted 23 august 2012 Available online 28 november 2012 To estimate the future development of one technology

The dominant approach to analysing TLC uses the S-curve to observe patent applications over time.

and make their R&d strategy accordingly. 2012 Elsevier Inc. All rights reserved. Keywords: Technology life cycle Patent Indicator Cathode ray tube Thin film transistor liquid crystal display Nano-biosensor 1. Introduction The rapidly changing economic environment

Within the Future-oriented technology analysis (FTA), technology forecasting traces back to the 1950's 4. One of its half dozen

or so basic techniques, dating from that time at least, is trend analysis. This includes both historical time series analyses

It follows that such projection becomes increasingly precarious as the future horizon is extended beyond a few years.

Technological forecasting & Social Change 80 (2013) 398 407 Corresponding author at: Chengdu Library of the Chinese Academy of Sciences, Chengdu 610041, PR China.

In the maturity stage, some pacing technologies turn into key technologies, are integrated into products or processes, and maintain their high competitive impact.

either over time or in terms of cumulative R&d expenditures. But using one indicator only to present technological performance would be problematic.

/Technological forecasting & Social Change 80 (2013) 398 407 (test technology) via the nearest neighbour classifier,

Thirteen indicators are selected for TLC assessment (Table 2). All the data of the indicators are extracted by priority year (the first filing date year for a patent application

three kinds of dates are included in the DII database: application year, priority year, and basic year.

The basic year has no legal meaning, but only represents the year in which DII obtained the patent documents.

Currently, most of TLC related literatures are based on application year 15,17 20. But the priority year presents the first time an invention has been disclosed.

So in this paper, we choose the other two indicators to measure the development of technology:

we count the number of patents Table 1 Technology life cycle indicators by former researchers. Author Indicator Robert J Watts, Alan L Porter 14 Number of items in databases such as Science Citation Index number of items in databases such as Engineering

Index 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 Reinhard Haupt, Martin Kloyer

, Marcus Lange 15 Backward citations Immediacy of patent citations Forward citations Dependent claims Priorities Duration of the examination process Data base requirements Fig. 2

. Framework of TLC analysis. 400 L Gao et al.//Technological forecasting & Social Change 80 (2013) 398 407 in DII by application year for the Application indicator and count the number of patents in DII by priority year for the Priority indicator

. 2. 1. 2. Assignee Some business software, such as Patentex and Webpat, has adopted assignee numbers to develop an S-curve.

We count the respective numbers for each of these two indicators in DII by priority year. 2. 1. 3. Inventor This indicator indicates the amount of human resources invested in R&d of one particular technology.

We count the number of unique individual inventors of each year by priority year. 2. 1. 4. Citation Two major types of cited references are given in a patent:

We count the number of literature citations and the number of patent citations in DII by priority year. 2. 1. 5. IPC (four-digit) The International Patent Classification (IPC) system,

We count the number of IPCS 4-digit) in DII by priority year for the IPC indicator;

count the number of patents among the top 5 IPCS in DII by priority year for the IPC top 5 indicator;

and count the number of patents among the top 10 IPCS in DII by priority year for the IPC top 10 indicator. 2. 1. 6. MCS The Derwent manual code (MC

We count the number of MCS in DII by priority year for the MC indicator;

count the number of patents among the top 5 MCS in DII by priority year for the MC top 5 indicator;

and count the number of patents among the top 10 MCS in DII by priority year for the MC top 10 indicator.

Indicator Indicator description 1 Application Number of patents in DII by application year 2 Priority Number of patents in DII by priority year 3 Corporate Number

of corporates in DII by priority year 4 Non-corporate Number of non-corporates in DII by priority year 5 Inventor Number of inventors in DII by priority year

6 Literature citation Number of backward citations to literatures in DII by priority year 7 Patent citation Number of backward citations to patents in DII by priority year

8 IPC Number of IPCS (4-digit) in DII by priority year 9 IPC top 5 Number of patents of top 5 IPCS in DII by priority

year 10 IPC top 10 Number of patents of top 10 IPCS in DII by priority year 11 MC Number of Manual Codes (MCS) in DII by priority year 12 MC top 5 Number of patents of top 5 MCS

in DII by priority year 13 MC top 10 Number of patents of top 10 MCS in DII by priority year 401 L. Gao et al./

/Technological forecasting & Social Change 80 (2013) 398 407 2. 2. TLC stages of CRT and TFT-LCD It is better to choose a training technology with four TLC stages.

From the literature, we find that the Cathode Ray Tube (CRT) has been developed for more than 100 years

and is now in the decline stage 27,28. But the patent information in the early years is unavailable (patent data in DII covers 1963 to the present.

So we choose another similar technology, the Thin Film Transistor Liquid crystal display (TFT-LCD), as the second training technology.

but slightly increases in the following years. The number of inventors is less than some other indicators, such as application numbers and priority application numbers in the maturity and decline stages.

and imported into MS 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. 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 402 L. Gao et al.//Technological forecasting & Social Change 80 (2013) 398 407 We propose a normalisation method with two steps to pre-process the original data.

The first step is data smoothing by calculating three-year moving averages. The original data are defined as A A1;

A2: ð1þ Here A1, A2 represent the original data of TFT-LCD and CRT respectively.

A2 h i ð2þ 0 500 1000 1500 2000 2500 3000 3500 1978 1979 1980 1981 1982 1983 1984 1985 1986

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

2007 2008 2009 Application Priority Corporate Non-corporate Inventor Literaturecitation Patentcitation IPC IP-CTOP10 MC MC-TOP5 IP-CTOP5 MC

0 500 1000 1500 2000 2500 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

2006 2007 2008 2009 Application Priority Corporate Non-corporate Inventor Literaturecitation Patentcitation IPC IPC-TOP5 IPC-TOP10 MC MC-TOP5

/Technological forecasting & Social Change 80 (2013) 398 407 A1 i; j ð Þ A1 i;

/Technological forecasting & Social Change 80 (2013) 398 407 For each test point bk, we compute the distance between bk

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.

Therefore, NBS is still in its growth stage (1997 to the present. And 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.

Therefore, the fabrication technology is one of the pacing technologies of NBS. In this stage, a lot of challenging problems must be overcome, such as enhancement of gene array and protein array,

Technology managers might informtheir NBS R&d investments by analysing patent application data from 1997 to the present to identify hot research topics or technological gaps.

it is a good time to invest in NBS to pursue potentialmarkets. 4. Conclusions How might technology life cycle analysis based on patents contribute to FTA?

/Technological forecasting & Social Change 80 (2013) 398 407 definitive projections. Indeed, explicit analyses of what factors and forces are apt to alter projected developmental trends are worthwhile note Ted Gordon's Trend Impact analysis (TIA) especially 34.

This means that there are pacing technologies with high competitive impact that have not yet been integrated into new products or processes.

, 2 10 years in the future) to provide a more robust sense of likely developmental trajectory than does single variable trend projection.

Online at, http://www. nngroup. com/reports/life cycle of tech. html 1998. Table 5 TLC stages of NBS. 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 lb 1 1

1 1 1 1 1 1 1 1 1 1 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

2007 2008 lb 2 2 2 2 2 2 2 2 2 2 2 2 So the TLC stages estimated for NBS are:

Emerging stage (lb=1: 1985 1996. Growth stage (lb=2: 1997 present. 406 L. Gao et al./

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Policy 6 (1/2/3)( 2010) 36 45. Lidan Gao is an Associate professor of Chengdu Library of The Chinese Academy of Sciences.

including Tech Mining (Wiley, 2005) and Forecasting and Management of Technology (Wiley, 2011. Jing Wang is an Associate professor of Huaqiao University.

/Technological forecasting & Social Change 80 (2013) 398 407

ART8.pdf

Evolutionary theory of technological change: State-of-the-art and new approaches Tessaleno C. Devezas Technological forecasting and Innovation theory Working group, University of Beira Interior, Covilha, Portugal Received 13 may 2004;

accepted 6 october 2004 Abstract It is well known the fact that the world of technology is full of biological metaphors,

as for instance, evolution, mutation, selection, life cycle, survival of the fittest, etc. One of the most powerful technological forecasting tools, the logistic equation, has its origin in the biological realm

Recognition of this fact in last decades is leading firmly to a new scientific paradigm, a complex bio-socioeconomics, with the convergence of different fields of science toward

Technological forecasting & Social Change 72 (2005) 1137 1152 promising approaches under way. The fourth part with conclusions closes the article,

D 2005 Elsevier Inc. All rights reserved. Keywords: Technology evolution; Technological change; Complex systems; Universal Darwinism 1. Introductory thoughts The main objective of this seminar concerns the exploitation of the powerful new capabilities provided by the Information technology Era to advance Future-oriented technology analysis (TFA), both product and process.

Among these new capabilities the TFA Methods Working group has identified recently 1 three main converging areas of development:

much in accordance with the perspective envisioned by Harold Linstone in 1999 2, following his optimistic view of a strong, confident technology-driven scenario,

and tools that have grown explosively in recent years related to the biosciences, bioinformatics and evolutionary approaches. Among the needs for TFA envisioned by the TFA Methods Working group we find the questioning about the validity of the analogy between technological evolution and biological evolution (Ref. 1, pp. 299:

and we can even trace an at least three-decade long debate on this issue. What makes the difference now are exactly the powerful new capabilities provided by the Information technology Era

and the manifold convergence of information and molecular technologies that are contributing enormously to new insights in simulation methods and evolutionary programming.

In the previously cited 30-year anniversary issue of TF and SC (1999) Bowonder et al. 3 have reviewed briefly this topic,

mainly focusing some of the lessons learned from evolutionary theory involved in anticipating changes in evolutionary trajectories,

The present paper intends to present the state-of-the-art on this debate and to address some important considerations necessary to answer the question above.

whether it T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1138 can ever be achieved.

and Fig. 1. Technological forecasting in perspective presented by Linstone in the 30-year anniversary issue of TF and SC (1999).

T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1139 quality control. Peter Corning 5 has pointed out that complexity in nature

became in last years the most common figures permeating social and natural sciences as well. The metaphorical language provides the means for understanding

and are listed as points in the paragraphs below in a quasi-logical sequence. Needless to say that these points are interrelated strongly

the amount of practical work using simulation methods is still a dwarf T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1140 one.

some important modeling attempts were undertaken along with the last decades and I think that some of the above mentioned points are hindering the development of working computational algorithms to simulate technological evolution.

Recognition of this fact in last decades is leading firmly to a new scientific paradigm, a complex bio-socioeconomics

and it was only with considerable delay that more mathematical (algorithmically based) arguments and models were advanced.

The mathematical tools that began to be employed in economics (as well as in technological forecasting) starting in the 1970s had been developed by mathematical biologists in the 1920s

and 1930s and were known widely. The widespread availability of computers (and of computer literacy) has contributed undoubtedly for the rapid diffusion of the usage of such mathematical tools,

but the matter-of-fact is that we still observe the same obstacle that has caused this delay:

Formalization of evolutionary thinking in biology in algorithmic terms began in 1930 when R. A. Fisher 6 published his opus bthe Genetical Theory of Natural selection,

By the same epoch, and not necessarily motivated by evolutionary concepts, the bio-mathematicians Vito Volterra and Alfred Lotka popularized a set of differential equations to describe the growth of population levels,

T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1141 Yet in 1925 the American biologist and demographer Raymond Pearl 8 in his seminal book

It was not until the 1970s that the Volterra Lotka equations have found numerous applications in the world of business

More recently, Devezas and Corredine 12 proposed a generalized diffusion-learning model to explain the succession of long waves in the techno-economic world,

T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1142 3. 2. To point 2:

Q This statement, written a century ago, epitomizes one of the greatest mysteries of evolution still challenging scientists the emergence of novelty.

On the Internet, a Google search yields the following results (April 2004) 2, 800,000 hits for globalization, 6, 600,000 for complexity,

if we restrict our analysis to dtechnological innovationt (our present context). I want to advance the following arguments favoring an evolutionary approach to define innovation

but by shifting the context and timing of their expression within the developmental sequence of an organism.

if we substitute the words dgenetic underpinningst by building blocks T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1143 (following John Holland's 14 original

#Evolution of organisms is the conjunction of two facts: the selective amplification of genotypes based on the differential reproductive success conveyed by their phenotypes through chance events at the level of genotypes.

dgenotypest by any sequence of building blocks, ddifferential reproductive successt by differential adoption in a market and dphenotypet by technical expression. $ My final argument favoring an evolutionary definition of innovation regards the aspect mentioned above of how strongly evolutionary

That is, how do technological units (whatever they may be) carry their information forward through time? 3 Are technological innovations indeed teleological or Lamarckian in nature or not?

when we compare these contributions with texts published in the 1980s, as for instance the very often cited books of Nelson and Winter 16 and Basalla 17.

There is still little in the way of formal theorizing and model building, and we can say that a lot of work remains to be done to make evolution a viable strategy and school of thought in the study of technology.

T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1144 Such a bridge could be offered by a better-developed danthropology of technique,

T in the way paved by the German philosopher of technology Hans Sachsse 18 almost three decades ago.

because nature owns the basic structure (then a fundamental law) of over shortcuts to reach easily the goals immediately ahead. 5 Technology is a recent human achievement that flourished conceptually in the 18th century,

demotion and rise of evolutionary concepts in economics It is well known the fact that the social sciences after experiencing an initial thrust from evolutionary concepts at the turn of 19th to 20th centuries have insisted historically in ignoring Darwinian ideas.

& Social Change 72 (2005) 1137 1152 1145 never correctly realized that Darwin in his second

and by liberally using the concept of inherited habits gave birth to the most controversial scientific debacle that lasted for over a century.

But during the last two decades we have seen a growing interest in evolutionary ideas among economists.

and for more than fifteen years there has been the djournal for Evolutionary Economicst (Springer), devoted particularly to this topic.

This upswing in evolutionary economics was in great part due to the renewed interest in the discussion on long waves in economics during the last two decades

as envisioned by Richard Dawkins 21 in his Universal Darwinism in 1983. Hodgson 20 stated that Darwinism provides a compelling ontology

asserting that our knowledge consists, at every moment, of those hypotheses that have shown their fitness by surviving so far in their struggle for existence,

beginning with Donald Campbell 23 in the T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1146 1960s (who coined the term Evolutionary Epistemology to characterize Popper

's epistemology) and conducting to some conceptual breakthroughs like Richard Dawkins'24 memes in the 1970s and more recently Daniel Dennet's 25 Darwin's Dangerous Idea (the idea that all the fruits of evolution,

after a lapse of almost a half century after the initial thrust commented on in point 1. Basically he suggested that Darwinism contained a general theory of the evolution of all complex systems,

To finalize the present discussion on point 4 I would like to add some other further aspects equally not yet considered as well:

T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1147 the coevolutionary complexity of managing two inheritance systems (the vertical, genetic,

since the 1950s, is btop-downq in character (so called because it views the system from above, as a whole).

and their dynamics (behavior over time) is defined via the change of their organization (or dstatet) as described by the system's differential equations.

Its origin remounts to the 1970s with the emergence of gaming simulation. Theoretically and methodologically this approach makes possible the construction of models from the level of processes that are immediately and empirically observable, namely the local interactions of single units (agents) governed by local rules.

T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1148 Although a consistent ETTC still not exists

The formal mathematical models developed in the past two decades and most often used are (mentioning only some important publications for each approach):

or evolutionary computation, were invented by John Holland 14 in the 1960s and were developed by Holland and his students at the University of Michigan in the 1970s.

In technology and science GAS have been used as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems,

In the present stage of our knowledge no one can be sure which method is suited best for purposes of simulating technological evolution and/or for developing useful tools for technological forecasting.

& Social Change 72 (2005) 1137 1152 1149 sophisticated simulations due to the simplicity of its basic assumptions and limitations that must be imposed in the rules governing interactions between agents.

or more generally speaking to genetic programming (a refinement of GAS developed in 1987 by John Koza 38),

They claim to have reproduced din silicot 15 previously patented inventions in the field of electronics (6 of them patented after January 2000)

besides the improvements in the computational methods, is to incorporate in the simulations some of the general evolutionary principles that were outlined in the present paper,

& Social Change 72 (2005) 1137 1152 1150 recent proposal of this author with George Modelski for a seminar on Globalization as Evolutionary Process 40 to be held in the spring of 2005 in Paris,

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and Co, London, 1904.14 J. Holland, Emergence: From Chaos To order, Perseus Books, Cambridge, Ma, 1998.15 J. Ziman (Ed.),Technological innovation as an Evolutionary Process, Cambridge university Press, 2003.16 R. R. Nelson, S g. Winter

, An Evolutionary theory of Economic Change, Beknap of Harvard university Press, Boston, 1982.17 G. Baslalla, The Evolution of Technology, Cambridge university Press, 1988.18 H. Sachsse, Anthropologie der Technik

Econ. 12 (2002) 259 281.21 R. Dawkins, Universal Darwinism, in: D. S. Bendall (Ed.),Evolution from Molecules to Men, Cambridge university Press, 1983, pp. 403 425.22 K. Popper, Objective Knowledge:

An Evolutionary approach, Oxford university Press, Oxford, 1972.23 D. T. Campbell, Blind variation and selective retention in creative thought as in other knowledge processes, Psychol.

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a review of recent books, Networks 4 (13)( 2003) 174 180.30 A l. Barabasi, E. Bonabeau, Scale-free networks, Sci.

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, S. Solomon, Inevitably reborn: the reawakening of extinct innovations, Technol. Forecast. Soc. Change 71 (2004) 881 896.35 G. Silverberg, B. Verspagen, A percolation model of innovation in complex technology spaces, J. Econ.

Dyn. Control 29 (2005) 225 244.36 Z. Michalewicz, D. B. Vogel, How to Solve it:

Modern Heuristics, Springer, Berlin, 2002.37 D. Goldberg, The design of innovations: lessons from the genetics, lessons from the real world, Technol.

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2003 February) 52 59.40 T. Devezas, G. Modelski, Globalization as Evolutionary Process: Modeling, Simulating and Forecasting Social Change, a Proposal of a Seminar to the Calouste Gulbenkian Foundation,

to be held in Laxenburg (IIASA), April 2006 (http://www. tfit-wg. ubi. pt/globalization). Tessaleno Devezas is Associate professor with Habilitation in the Faculty of engineering, University of Beira Interior (UBI), P-6200-001 Covilha,

T. C. Devezas/Technological forecasting & Social Change 72 (2005) 1137 1152 1152

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