Presentation on theme: "Technology Life Cycle Analysis Modelling based on Patent Documents"— Presentation transcript:
1Technology Life Cycle Analysis Modelling based on Patent Documents The 4th International Seville Conference on Future-Oriented Technology Analysis (FTA) 12 & 13 May 2011Technology 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 Huang51 Chengdu Library of the Chinese Academy of Sciences, Chengdu ,2 School of Economics and Management, Southwest Jiaotong University, Chengdu ,3 School of Public Policy, Georgia Institute of Technology, Atlanta, GA ,4 College of Computer Science & Technology, Huaqiao University, Xiamen, ,5 School of Management and Economic, Beijing Institute of Technology, Beijing
2Introduction Research Purpose Technology Life Cycle Analysis Modelling based on Patent DocumentsIntroductionResearch PurposeThe 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).
3Introduction Definition of Technology Life Cycle Technology Life Cycle Analysis Modelling based on Patent DocumentsIntroductionDefinition of Technology Life CycleThe 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).
4Figure 1. The S-curve concept of technology life cycle Technology Life Cycle Analysis Modelling based on Patent DocumentsIntroductionDefinition of Technology Life CycleFigure 1. The S-curve concept of technology life cycle(Ernst H. 1997)
5Introduction Research Review Technology Life Cycle Analysis Modelling based on Patent DocumentsIntroductionResearch ReviewThe 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.
6Introduction Research Review Technology Life Cycle Analysis Modelling based on Patent DocumentsIntroductionResearch ReviewTable1. Technology life cycle indicators by former researchersAuthorIndicatorRobert J Watts,Alan L Porter(1997)Number of items in databases such as Science Citation IndexNumber of items in databases such as Engineering IndexNumber of items in databases such as U.S. PatentsNumber of items in databases such as Newspaper Abstracts DailyIssues raised in the Business and Popular Press abstractsTrends over time in number of itemsTechnological needs notedTypes of topics receiving attentionSpin-off technologies linkedReinhard Haupt, Martin Kloyer,Marcus Lange(2007)Backward citationsImmediacy of patent citationsForward citationsDependent claimsPrioritiesDuration of the examination processData base requirements
7Introduction Research Questions Technology Life Cycle Analysis Modelling based on Patent DocumentsIntroductionResearch QuestionsMany 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.
8Methodology Research Model Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyResearch ModelThe 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.
9Methodology Research Model Figure 2. Research model of TLC analysis Figure 2. Framework of TLC analysisFigure 2. Framework of TLC analysisTechnology Life Cycle Analysis Modelling based on Patent DocumentsFigure 2. Framework of TLC analysisMethodologyResearch ModelFigure 2. Research model of TLC analysis
10Methodology Indicators and data source Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyIndicators and data sourceWe 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.
11Methodology Indicators and data source Table 2. Technology life cycle indicatorsTechnology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyIndicators and data sourceTable 2. Technology life cycle indicatorsIndicatorIndicator descriptionNo.ApplicationNumber of patents in DII by application year1PriorityNumber of patents in DII by priority year2CorporateNumber of corporates in DII by priority year3Non-corporateNumber of non-corporates in DII by priority year4InventorNumber of inventors in DII by priority year5Literature citationNumber of backward citations to literatures in DII by priority year6Patent citationNumber of backward citations to patents in DII by priority year7IPCNumber of IPCs (4-digit) in DII by priority year8IPC top 5Number of patents of top 5 IPC in DII by priority year9IPC top 10Number of patents of top 10 IPC in DII by priority year10MCNumber of MCs in DII by priority year11MC top 5Number of patents of top 5 MCs in DII by priority year12MC top 10Number of patents of top 10 MCs in DII by priority year13
12Methodology TLC stages of training technologies Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyTLC stages of training technologiesIt 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.
13Table 3 TLC stages of CRT and TFT-LCD Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyTLC stages of training technologiesTable 3 TLC stages of CRT and TFT-LCDStageEmergingGrowthMaturityDeclinePeriod (year)(CRT)1897–19291930–19721973–20002001–2020Period (year)(TFT-LCD)1976–19901991–20072008––
14Methodology Data TFT-LCD : 12596 records in DII. Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyDataTFT-LCD : records in DII.CRT: records in DII.NBS: 1493 records in DII.
15Methodology Data Process Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyData ProcessWe 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.
16Figure 3. Development trends of 13 indicators (TFT-LCD) Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyData ProcessFigure 3. Development trends of 13 indicators (TFT-LCD)
17Figure 4. Development trends of 13 indicators (CRT) Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyData ProcessFigure 4. Development trends of 13 indicators (CRT)
18Methodology Data Process Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyData ProcessTo 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.
20Methodology Data Process Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyData ProcessTo 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.
21Methodology Data Process Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyData ProcessThe 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.
22Methodology Data Process Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyData ProcessThe 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.
23Methodology Data Process Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyData ProcessWe then apply the same normalization steps to the NBS data. The smoothed data and the final normalized data of NBS are defined as , respectively,
24Methodology Data Process Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyData ProcessThen 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 There are 24 test points in the NBS test set.The training points and test points are defined as:
25Methodology Data Process Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyData ProcessSince 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.
26Methodology Data Process Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyData ProcessFor a training point and test point , the distance between and is defined as:For each test point , we compute the distance between and all the training points and find the nearest training point (Figure 5), that means:
27Methodology Data Process Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyData ProcessFigure 5. An example for computing the distance between test point and training points
28Methodology Data Process Technology Life Cycle Analysis Modelling based on Patent DocumentsMethodologyData ProcessThen 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.
29Results and policy impact/implications Technology Life Cycle Analysis Modelling based on Patent DocumentsResults and policy impact/implicationsTable 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): 1985–1996Growth stage ( =2): 1997–
30Results and policy impact/implications Technology Life Cycle Analysis Modelling based on Patent DocumentsResults and policy impact/implicationsTable 5 TLC stages of NBS19851986198719881989199019911992199319941995199611997199819992000200120022003200420052006200720082
31Results and policy impact/implications Technology Life Cycle Analysis Modelling based on Patent DocumentsResults and policy impact/implicationsAccording 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).
32Conclusions The study is based on patent documents. Technology Life Cycle Analysis Modelling based on Patent DocumentsConclusionsThe 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.
33Conclusions Research Limitations Technology Life Cycle Analysis Modelling based on Patent DocumentsConclusionsResearch LimitationsFirst, 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.
34Technology Life Cycle Analysis Modelling based on Patent Documents AcknowledgementThis 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 ) and the Science of Science Policy Program—“Measuring and Tracking Research Knowledge Integration” (Georgia Tech; Award No ). 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.
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37Thanks! Lidan Gao, firstname.lastname@example.org Alan L Porter,Jing Wang,