Technological Forecasting and Social Change

Technological Forecasting & Social Change 76 (2009) 754–768

Contents lists available at ScienceDirect

Technological Forecasting & Social Change

Monitoring the organic structure of technology based on the patentdevelopment paths

Changwoo Choi, Yongtae Park⁎Seoul National University, Seoul, Republic of Korea

a r t i c l e i n f o

⁎ Corresponding author. Department of Industrial En742, Republic of Korea. Tel.: +82 2 880 8358; fax: +8

E-mail address: [email protected] (Y. Park

0040-1625/$ – see front matter © 2008 Elsevier Inc.doi:10.1016/j.techfore.2008.10.007

a b s t r a c t

Article history:Received 29 August 2008Received in revised form 31 October 2008Accepted 31 October 2008

As the strategic importance of understanding changes in technology for successful business ofmost firms increases, the ability to analyze and monitor the current stage and history oftechnology is reckoned as a critical asset both for gaining competitive advantage andidentifying promising niches. Patent citation networks have been widely used for systematicand empirical analysis of technology development. Understanding of technology's detailedchanges in large patent citation networks, however, is difficult to achieve because of thesenetworks' large and complex structures. To overcome this problem, we suggest an algorithmthat identifies patent development paths from a large patent citation network by evaluating theweight of citations between patents. We then apply this algorithm to flash memory patents inan empirical study. Our algorithm is a new methodology that can be used to analyze thedynamic and complex structure of individual technologies.

© 2008 Elsevier Inc. All rights reserved.

Keywords:Technology monitoringTechnology development pathPatent citation network, Systematic approach

1. Introduction

Technology is one of the most important elements for providing companies with remarkable revenue in the currentcompetitive environment. Even when a company dominates a competitive market based on its technological advantage, thecompany should continue technology development activity to create dominant products or services by identifying, adopting, andleading the changes in technology in the competitive field. Hence, companies operating in competitive environments demandingnew product development, process improvement, and technology-enhanced services must obtain and organize information onemerging technologies [1].

Firms may conduct research and development (R&D) activities and invest in technology, but it is not easy for firms to orienttheir strategies to this technological environment and use them to their own benefit [2]. This is why investing based on forecasts ofpromisingmarkets, preparing products, and selecting emerging technologies for those products is very risky. In this process, one ofthe critical factors for firms to establish technological strategies is to identify and understand the technological developmenttrends. The increasing importance and benefits of technology have led to a wide range of applications and studies examining themanagement of technological forecasting. The objective of these applications and studies is to track developments in a particulararea that may serve to forecast technology [3], set up research-funding priorities [4], investigate product development history [5],monitor technology trends [6], integrate technology management processes [7], identify technological opportunities [1], andvisualize technological information [8].

Previous approaches to analyzing and forecasting technological development, however, have several limitations. First, theseapproaches cannot provide systematic information about the technology development process based on objective technologicaldata. Objective information is a critical factor in successful technological forecasting. It is, however, very difficult to explain the

gineering, School of Engineering, Seoul National University, San 56-1, Shillim-Dong, Kwanak-Gu, Seoul 151-2 2 889 8560.).

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http://dx.doi.org/10.1016/j.techfore.2008.10.007

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Table 1Technological forecasting techniques [15].

Technique Characteristics Description

Consensusmethod

Subjective procedure Panel of experts debate the subject face-to-face. It is an intuitive procedure

Delphimethod

Subjective procedure Panel of experts answer several rounds of questionnaires, but they usually do not meet eachother face-to-face. It is

Structuralmodels

Quantitative procedure Attempting to develop a mathematical or analytic model for accomplishment of the forecasting

Scenarios Combined procedure of subjective, cognitiveand quantitative procedure

It is not a formal technique. It serves as a guide which helps to foresee the future. It tires toidentify treats and opportunities for the firms

Technologicalvigil

Combined procedure of subjective, cognitiveand quantitative procedure

It is the administration of the flow of the scientific, technical and technological information, inorder to aid the innovation process

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detailed processes of technology development using traditional forecasting methodologies. Hence, providing sufficientinformation about technological change is still necessary for successful technological forecasting. Second, a systematic approachis necessary to explain the detailed development processes of technology. Even though various methodologies such as statisticalanalysis, diffusion modeling, and trend extrapolation can be applied to technological forecasting to enhance the objectivity ofanalysis results, they cannot explain the complex structure of the detailed development of technologies. They can describe only theoverall directions and processes of technological development at the macro level. To overcome these limitations, we take asystematic approach to identifying organic and complex structures of current technology.

In this context, we propose a technology development map to analyze changes in technology in detailed micro-level. This mapis developed using patent analysis, because this is the most widely used methodology in formalized and systematic approaches toidentifying and managing technological change. Patents are the primary output of the R&D activity of firms. The utilization ofpatent data, however, is not limited to protecting the legal right to developed technology. Patents and patent data can supportmany aspects of technology management [9].

In the remainder of the paper, first we describe technological forecasting, patent analysis, and patent citation analysis as thebackground for this research, in Section 2. Second, the proposed algorithm to identify the patent development path is explained inSection 3. As an example and application of the suggested approach, development paths for flash memory system patents arepresented and discussed in Section 4. The implications of this research are discussed in Section 5. Finally, in Section 6, we concludewith a summary and the implications of our results.

2. Background

2.1. Technological forecasting

Technological forecasting is of great interest for both theory and practice in the establishment of technological strategy andplanning. Technological change requires a new set of engineering and scientific principles. It may reinforce the dominance of firms inthe market or open up new market [7,10–12]. On the other hand, many studies show that an inadequate reaction to technologicalchange may lead to the demise of established company [10,13,14]. Insufficient information on technological trends and managerialincompetence are the main reasons for failure in the market. Hence, societies, scientists, planners and decision-makers shouldendeavor to discover the current status of technology and anticipate future events [15]. This has been called technological forecasting.

Many researchers have suggested various observations of technological trends to forecast technological change at an early stageand increase the effectiveness of technological decision-making. In the literature, many terms are used for this process ofacquisition, assessment and communication of information on technological trends to detect opportunities and threats in a timelymanner [7]. Furthermore, a detailed analysis of studies that were carried out in different industries shows that the ability of firmsto forecast technological change is a major factor in managing the risk of organizational failure in the face of rapid technologicaldevelopment [16,17].

Porter et al. (1991) argued that the cornerstone for technological forecasting is identification of current technology [18]. It is vital inits own right to comprehend “who is doing what now”with respect to a given technology. This underpins forecasting in two criticalways — forthcoming technological change is foreshadowed by current developments and will be influenced by changes in relatedtechnologies, and relevant contextual influences are the most essential ingredients in effective technological forecasting [19].

Typical techniques for identifying and forecasting technological change are the consensus method, the Delphi method,structural models, scenarios and technological vigilance. The characteristics of these techniques may be described with subjectiveprocedure, quantitative procedure and combined procedure as shown in Table 1 [15]. First, the consensus method and the Delphimethod are subjective procedures. The subjective procedure depends on the experts' qualitative and intuitive knowledge. This,however, may be biased because of the subjectivity of experts' knowledge and opinion. Moreover, even after a basic innovation,some expertsmay be too pessimistic about progress because they are familiar with the problems and difficulties of new technology[20,21]. The structural model, as a representative quantitative procedure, may eliminate these subjective factors. This modelisolates certain elements that are important to the technological generation process, explaining and expressing mathematicallysome of the functional relationships among the elements involved. These models, however, tend to be abstractions. Omissions of

756 C. Choi, Y. Park / Technological Forecasting & Social Change 76 (2009) 754–768

certain elements that are not judged to be relevant for the formulation of the model may occur. The combined approaches help toidentify threats and opportunities in the decision-making process through the information collected. These approaches, however,also require a given amount of information and it is still difficult to obtain informative data and to provide objective information fortechnological forecasting for technological strategizing [15]. In this context, a systematic approach is needed to gather and analyzedata based on scientific methodologies for technological forecasting.

2.2. Patent analysis

Patents are major outputs of research and development. Hence, they represent the origin and features of a new technology.They have long been recognized as a very fruitful source of data for the study of innovation and technical change and thus fortechnology management research. There are, of course, important limitations on using patent data as an indicator of technologydevelopment. First, not all inventions are patented, because not all inventionsmeet the patentability [22]. Second, the inventor hasto make a strategic decision to file for a patent registration, as opposed to relying on secrecy or other appropriate means. Thisstrategic decision-making differs across companies and industries [23]. The results and interpretations of patent analysis are,therefore, not consistent across technology fields. Third, changes in patent law over the years make it difficult to analyze trendsover time. The protection afforded to patentees worldwide has been improved since the early 1980s, and companies are nowmoreinclined to file for a patent registration than used to be the case [22].

Patent data, however, should not be discarded entirely as a statistical indicator simply because of these limitations. In fact,quantitative and empirical analyses of technological innovation using patents have been performed with various objectives [24].Consequently, patent data represent a unique opportunity to satisfy the need for conceptual or qualitative analysis of technologicalchange [25]. Furthermore, patent data can empirically explainmost aspects of technological innovation in developed countries [23]. Insummary, patent information can help researchers and technology developers make technological decisions, facilitate policy-makingand long-term, national R&D strategies in government offices and assist in R&D strategy-making andmanagement in individual firms.

Research related to patent analysis can be divided into macro-level research of national or industrial analysis and micro-levelresearch of particular technological analysis and forecasting. In the macro-level research, the major topics are the economic effectof technological innovation [26], and the evaluation of technological competitiveness of nations [27]. In the micro level, researchactivities, such as identifying technological advantages and disadvantages of competitors and planning of technologicaldevelopment activities, are conducted with a focus on data related to individual firms [28,29]. This patent information can helppractitioners derive R&D priorities [30], draw a patent map to discover technological vacuums [31], analyze technological trendsand opportunities [32,33], and examine the effects of technological change on firm performance [34].

2.3. Patent citation network

Patent citation analysis is widely used method for advanced analysis on technological change. It is conducted through theexamination of citation links among various patents. Patent citation information can be used to analyze technological valuation,impact, or diffusion [35]. The number of patent citations can be used as an objective indicator of the market value of innovationoutput, or to estimate the firm's value for M&A [36]. Additionally, an economic model for measuring the international knowledgeflow has been developed by using patent citation information as a proxy for knowledge flow between actors and investigating co-authorship, citation, and scientific activities [37,38].

The objective of this research is to identify change in the development and innovation of technology at themicro-level. To this end,analysis on citation information of individual patent is required. Recentworks have attempted to construct a patent citation network atthe individual patent level [39–42]. In particular, Mina et al. (2007) constructed a large citation network and applied a path searchalgorithm to it to analyze the growth and transformation of medical knowledge [40]. This research was conducted based on analgorithmsuggestedbyHummonandDoreian (1989) [41] and developed andutilized byVerspagen (2007) [42]. HummonandDorian(1989) suggested an algorithm for evaluating the weight of an arc (a patent citation in a patent citation network) in a large networkgraph. Based on this research, a few studies have been carried out on enhancing the efficiency of search path algorithms and onconstructing patent citation networks in the field of fuel cells applying Hummon and Dorian's (1989) methodology [40,42].

However, these studies have some limitations. Even though early research by Hummon and Doreian (1989) is worthwhile, theirbasic algorithm is simply based on the exhaustive path search algorithm of general network theory. This can be an advantage from theperspective of efficiency, since such a method uses existing research in the network theory field; however, the method doesn't applythe innate characteristics of patent citation analysis. Specifically, themethod results in a simple analysis of network structurewithoutthe applying the characteristics of patent citation. Other studies are also extensions of HummonandDorian's (1989)DNA algorithmoruse applications of the algorithm provided by commercial software for social network analysis, such as Pejek and Ucinet. The presentresearch suggests a developed algorithm for identifying the path of a patent's development, applying the characteristics of patentcitation to overcome the limitations of previous research.

3. Suggested algorithm for patent development paths

The number of patents is escalating as the competitiveness of technology development is intensifying and the speed oftechnology development is accelerating. Hence, it is difficult to identify and understand the detailed structure of technologydevelopment at the level of individual patents with the large patent citation network, because it has too complex structure. To

Fig. 1. Overall process of suggested algorithm.


overcome this problem, a systematic algorithm is suggested to extract the main paths of patent development and track history oftechnology development from patents and patent citation information.

The algorithm suggested for tracing the paths of patent development is composed of the six steps as shown in Fig. 1. First, apatent's data is collected from the public patents database. Second, a patent citation matrix is constructed, and patents areclassified into four groups based on citation relationships. Third, the weights of all arcs in the patent citation matrix are calculatedbased on the number of forward citations. Fourth, all origin patents among the collected patents are selected as the starting pointsof the patent development path. Fifth, main development paths among arcs are selected recursively based on the weight of thearcs. Finally, this algorithm terminates when all of the patent development paths from every origin patent meet the terminuspatents. The detailed process of this algorithm is explained in following sections.

3.1. Step 1: data collection

Patent information should be collected as data to monitor and track the technology development. Patent data is widely used toexplain technology development, and this data is provided to public users as an open database. As the first step, patents in giventechnology field are collected based on the various search conditions from patent database.

3.2. Step 2: construction of the patent citation matrix

A general network consists of nodes and arcs which link two nodes. Columns and rows of matrix represent the nodes and eachvalue in the matrix is defined as the existence or strength of arc between two nodes. The nodes and arcs in a patent citation matrixare defined in this step. The patent citationmatrix represents citations among patents. Nodes represent the individual patents, andarcs between two nodes are citations. The patent citation matrix P is defined with these nodes and arcs as follows:

P=

p11 p12 : : : p1np21 p22 : : : p2nv v O v

pn1 pn2 : : : pnn

0BB@

1CCA

pij =1 : if patent jcites patent i0 : otherwise

�

e patent citation matrix P is a n×n square matrix, where n is the number of patents. If the nodes are arranged by increasing
Thor decreasing order of patent number, the matrix P is a triangular matrix. The patent citation network is a directed network,

Table 2Types of patents in a citation network.

Types of patents Description Condition

Isolated patents Patents that have no citation and are not cited dI(ni)=dO(ni)=0Origin patents Patents that have no citation, but are cited dI(ni)=0 and dO(ni)N0Terminus patents Patents that have citations, but are not cited dI(ni)N0 and dO(ni)=0Intermediate patents Patents that have citations and are cited dI(ni)N0 and dO(ni)N0


therefore all arcs have a direction. An individual matrix element pij is the directed linkage from node i to node j, representing thefact that patent j cites patent i. In general network theory, node i is named as the sender and node j the receiver [43]. Using thisdirected network, various approaches have been proposed for explaining the relationships and characteristics of an individualnode and arc. Wasserman and Faust (1994) classify the nodes in a directed network into four types [43]. In the same manner, wedefine the four types of patents in this research based on their classifications, as shown in Table 2. The first column shows the typesof patents, and the second column describes the citation relationships of each type. The last column shows the mathematicalconditions on the types of patents. The variable dI(ni) is the number of nodes adjacent to the node i and represents the number ofbackward citations for patent i. Also, dO(ni) is the number of nodes adjacent from the node i, representing the number of patents inwhich patent i is cited, i.e., the number of forward citations.

3.3. Step 3: calculating the weights of arcs

As explained above, it is difficult to understand a complex patent citation network in its entirety. Hence, this research proposesa methodology for identifying the main development path, which aids in understanding the history of a technology. The main arcsare used to identify the patent development path. Because this main path is selected based on theweight of all arcs, themethod fordefining the weight of arcs is the most important part of identifying the patent development path.

We call the indicator of theweight of the arcs the forward citation node pair (FCNP). The FCNP considers the node pair linked bya selected arc. The FCNP is calculated by multiplying the number of forward citations of the two linked nodes. In this calculation,the number of forward citations includes the linked patent itself, thereby preventing the weight of terminus patents from beingcalculated as zero. This process is illustrated in Fig. 2.

In Fig. 2 FCNP (Aij) is the weight of arc ij, where patent j cites patent i. Also, ni and nj are the number of forward citations ofpatent i and patent j. This FCNP (Aij) indicator can be calculated for each arc in the patent citation network, and the patentdevelopment paths are defined based on the FCNP (Aij) indicators.

The proposed FCNP considers the outward linkages, that is, forward citations. Forward citation and backward citation havedifferent utilities in patent analysis. Backward citations are used to investigate knowledge flows, or spillovers between patents ortechnologies; and forward citations are applied as a measure of inventive quality in terms of technological or economic value[23,39]. Hence, only the number of forward citations is applied to the indicator to reflect the technological or economic value ofpatents in selecting the patent development paths.

3.4. Step 4: selecting origin patents

All origin patents in the original patent citation network are selected in this stage. The origin patents are developed andregistered in the early time of technology history. These patents are the starting point of development paths that derived from theoriginal patent citation network. Hence, the identification process starts from the origin patents.

3.5. Step 5: selecting the linked patent with the highest FCNP

The next step is extending the patent development path from the origin patents. Every arc starting from an origin patent isselected. These arcs are linked to the patents that cite the origin patent. Arcs are evaluated based on the comparison of the FCNP of

Fig. 2. Calculation of weight of arc ij.

Table 3Large components in the network of patent development paths for flash memory system.

Components Size (number of patents) Components Size (number of patents)

C1 113 C8 17C2 33 C9 16C3 30 C10 11C4 29 C11 11C5 28 C12 10C6 21 C13 10C7 20


each arc. One or more arcs that have the highest FCNP among linked arcs from the origin patent are selected. In this process,generally only one arc is selected. Two or more arcs, however, can be selected if the FCNPs of these arcs are same. The selected arcsare the first elements that compose the development path from an origin patent.

A patent located at the end of the selected arc becomes a new starting point for another arc of development path. That is, the arcand patent located at the end of an arc are reclusively added to the development path by evaluating the arcs linked to the recentlyadded patent based on the FCNP.

3.6. Step 6: evaluation of the exit condition

The algorithm is terminated when a latterly selected patent is a terminus patent. Viewing the algorithm as a whole, the patentdevelopment path starts from origin patents and ends at terminus patents. The origin patents are the oldest patents and theterminus patents aremost recently registered patents. The process of identifying a patent development path for selected patents ina given field of technology is performed from every origin patent to every terminus patent. Therefore, various patent developmentpaths in a specific technology field can be identified with this process.

Our algorithm keeps the highly weighted arcs in the original patent citation network and reduces the arcs linked from theorigin patents. Patents that are linked to the selected arcs, hence, can be evaluated as highly valued patents. Also, patents located atthe position where various development paths gather are interpreted as points of converging technology, where a technology isdeveloped by converging two or more technologies that have different objectives or characteristics. These patents can be the maintargets of technology monitoring.

4. Illustrative example: Patent development path in flash memory

4.1. Data collection

In this section, the development paths of flash memory patents are analyzed using the suggested algorithm. Patent data forflash memory were collected from the United States Patent and Trademark Office (USPTO) homepage. The search keyword was‘flash memory system,’ and a total of 2796 patents before June, 2007 were collected.

4.2. Components in the patent development paths

The components in a general network graphmean the connected subgraphs. In a directed graph, the two nodes are in the sameweak component if all pairs of nodes areweakly connected [43]. Various components are identified by applying the algorithm aftereliminating the isolate patents among the 2796 patents collected. Each component in the network of patent development pathscan be interpreted as an independent technology group. Table 3 shows the thirteen large components in the network of patentdevelopment paths for flash memory systems.

4.3. Patent development paths for the largest component

As an illustrative example, we constructed the patent development paths for the largest component in order to apply theproposed algorithm. From the thirteen components, we selected component 1, which has 113 patents, to draw the patentdevelopment paths shown in Fig. 3. The color of each node denotes the types of patents. White nodes are origin patents, gray nodesare intermediate patents and black nodes are terminus patents. The direction of the arrow represents the development direction offorward citations. The main development paths for flash memory systems are composed with connected arcs from many originpatents to many terminus patents. Of these patents, some can be evaluated as valuable patents located where two or moredevelopment paths gather. For example, 6901499 (System andmethod for tracking data stored in a flashmemory device) is locatedin the position at which five development paths from various origin patents gather and develop toward the new technology,7076599 (Transactional file system for flash memory) and 7178061 (Power failure detection and correction in a flash memorydevice). And two terminus patents, 7111121 (USB storage device and program) and 7102671 (Enhanced compact flash memorycard), can be evaluated as valuable patents since they are located in the gathering positions.

Fig. 3. Patent development path for flash memory systems.


4.4. Clustering of the largest component

We carried out clustering analysis to understand the detailed inner structure of the networks. Hierarchical clustering analysiswas applied to the largest component of the 113 patents in the previous section. Five clusters were delivered based on the citationrelationship. The title and patents of each cluster are described in Table 4, and Fig. 4 shows the result of clustering analysis. Weasked two experts on flash memory system to make the titles of clusters. The titles were decided by discussion of the experts onflash memory system. Cluster 1, programmed value sensing, includes technologies used for sensing programmed value in memory

Fig. 4. Clustering result of the largest component.


cells. There are two kinds of technologies in cluster 2: software download to flash memory and diagnostic systems. Patents fordata-handling technology are gathered in cluster 3. Cluster 4, file systems, includes data recovery, tracking data, transactional filesystems, and power failure detection technologies for flash memory systems. Patents in cluster 5 are practical applications ofremovable storage or portable memory.

4.5. Converging patents in patent development paths

Of the various patents in this map, those located at the position where various development paths converge are interpreted asconverging patents. Because this map is drawn by starting from origin patents and selecting arcs of the highest weight, it is very

Table 4Five clusters of patents for flash memory systems.

Cluster Title

1 Programmed value sensing2 Software download to flash memory and diagnostic system3 Data handling4 File system5 Usage as portable memory


likely that patents located at the converging positions would be economically or technologically valuable ones. Furthermore, thesepatents are expected to serve as seeds for another advanced technology. Table 5 shows the converging patents in each cluster andadditional attributes of registered year, main classifications, type and number of inflows.

4.6. Development stages of flash memory technology

Visualization of the technology development over time based on the changes of development path map can provide moreuseful information for identifying the complex structure of technology. The development process of flash memory technology,especially, selected 113 patents in five clusters (as mentioned before, these patents are not all patents for flash memorytechnology because the only one component of 113 patents is selected from the constructed patent development paths for theconvenience of illustration) can be divided into four stages. Table 6 shows the development process from introduction stage toconverging stage.

First, early origin patents, especially, included in the cluster 2 (Software download to flash memory & diagnostic system) andcluster 4 (file system) started to be registered at the introduction stage in Fig. 5. Thismeans that the technology for file systems andsoftware download was developed earlier than other technology fields. Representative patents cited more than other patents inthe introduction stage are 5440632 (reprogrammable subscriber terminal) and 5463766 (system and method for loadingdiagnostics routines from disk) in cluster 2 and 5410707 (bootstrap loading from external memory including disabling a reset froma keyboard controller while an operating system load signal is active) and 5463757 (command interface between user commandsand a memory device) in cluster 4.

Second, at the growth stage, origin patents in all five technology fields in Table 4 were developed and early part of technologydevelopment path can be seen in cluster 3 (data handling) and cluster 4 (file system) in Fig. 6. Technology development is mostactive in cluster 4. 6279069 (interface for flash EEPROM memory arrays) is outstanding as a converging patent in cluster 4.

Third, at the maturity stage, there are various technology development paths in all technology fields including cluster 1(programmed value sensing), cluster 2 (software download to flash memory and diagnostic system), and cluster 5 (usage asportable memory) in Fig. 7. This means that technology development activity was carried out actively at this stage and manyoutputs were registered as patents. Various converging patents were emerged and the structure of the development path becamemore complex. Converging patents such as 6615404 (method and apparatus for downloading software into an embedded-system),6901499 (system and method for tracking data stored in a flash memory device), and 6842794 (method for starting a dataprocessing system via a flash memory device) are observed.

Table 5Converging patents in patent development paths.

Cluster Patent number Year Main class Type Number of inflows

1 6914823 2005 365/185.22 Terminus patents 32 6449735 2002 714/25 Intermediate patents 4

6615404 2003 717/173 Intermediate patents 47055148 2006 717/172 Terminus patents 3

3 7000064 2006 711/103 Intermediate patents 37102671 2006 348/231.9 Terminus patents 37215580 2007 365/189.02 Terminus patents 3

4 6279069 2001 711/103 Intermediate patents 76901499 2005 711/205 Intermediate patents 5

5 6842794 2005 710/10 Terminus patents 47111121 2006 711/115 Terminus patents 4

Table 6Development stages of flash memory technology.

Stage Description Development path map

Introduction stage (∼1995) Development of early origin patents Fig. 5Growth stage (∼2001) Development of origin patents in all five technology fields Fig. 6Maturity stage (∼2005) Expansion of technology development paths Fig. 7Converging stage (∼2007) Emergence of links among various technology fields Fig. 4

Fig. 5. Technology development map for flash memory at introduction stage (till 1995).


Finally, all technology fields from cluster 1 to cluster 5 were connected at the converging stage in Fig. 4. Many convergingpatents were registered and linked among technology fields. Representative converging patents are 7102671 (enhanced compactflash memory card), 7215580 (non-volatile memory control), 7076599 (transactional file system for flash memory) and 7111121(USB storage device and program).

With this technology development map on flash memory system, we asked two experts to identify key patents on flashmemory. Patent 5627784 was selected as one of the core flash memory technologies. This patent includes technology fornonvolatile data storage structure for memory, and control parameters and methods. A NAND flash memory using the technologyin that patent has been produced. Flash memory is evaluated in the semiconductor market as a fusion semiconductor that willmigrate previous high-quality technologies, single-level cell (SLC) and multilevel cell (MLC), into one chip. This technology

Fig. 6. Technology development map for flash memory at growth stage (till 2001).


integrates SLC's high functionality and MLC's high capacity. This patent is one example that shows that the suggested map reflectsthe development of technology and product in the market.

5. Discussion

The role of technology as a resource for building competitive advantage has been critical not only in manufacturingindustries, but also in service industries. With the increasing importance of technologies, practitioners are interested in learninghow to manage and plan technologies more effectively, and how to develop organizational strategies. However, technologymanagement is a challenging topic, because technological complexity, the rate of technological change, and global technology

Fig. 7. Technology development map for flash memory at maturity stage (till 2005).


sourcing are more dynamic than ever. In this context, this research provides a systematic tool for analyzing complex anddynamic structures of technological change to support strategic technology management. In particular, the patent-basedtechnology development map identifies the development paths of a technology. It can be a useful tool to represent the dynamichistory of technology development and to understand its past and present. It shows the dynamic path of technologydevelopment and also reflects the composition of increasingly complex systems. It is a helpful tool in ascertaining likely futuredevelopment paths based on the objective database, that is, each path to the terminal patents is a possible path for likely futuretechnology development.

This can help researchers, R&D managers, and decision makers to evaluate technology, analyze competitors more objectively,and to develop technology strategy. First, it can be a data source for decisions on technology transactions. It can be used to identify

Table 7Comparison of proposed approaches with general technological forecasting methods.

Technique Characteristics Comparison with proposed approaches

Consensusmethod

- Provide various options for experts to formulate more correcttechnology forecasting

- Use practical technology database to identify current trends of technologydevelopment

Delphimethod

- Share and purify knowledge by group dynamics

Structuralmodels

- Eliminates subjective factors - Focus on the analysis of the complex structure and dynamic change of thestructure

- Explicate and express mathematically some functionalrelationships among the elements involved

Scenarios - Identify threats and opportunities for business - Provide objective information about the current structure and change oftechnology

Technologicalvigil

- Help manager by providing information in the decision-makingprocess

- Identify and understand the complex structure of technologicaldevelopment based on systematic methodologies


the core technology in a specific technology field. For example, if a firm seeks a technological resource and legal rights for a filesystem using flash memory technology, 6279069 (interface for flash EEPROM memory arrays) and 6901499 (system and methodfor tracking data stored in a flash memory device) are the first candidates for a technology transaction. Second, opportunities forlicensing patent rights can be discovered using the suggested map. We can understand the trend of technology development andidentify the companies that have the legal right to a specific technology by tracking the development paths. This process can beused to determine the licensing opportunities required for the business operation of a company. Third, this can provide objectiveinformation for the development of new technology. The development paths represent the development history of technologyitself. Therefore, we can identify the development of a specific technology. For example, if a firm is preparing for the developmentof the next-generation portable USB memory, the R&D manager can track the development paths of cluster 5 in the suggestedtechnology development paths.

The patent-based technology development map proposed in this paper has different characteristics than traditionaltechnological forecasting techniques (Table 7). First, the consensus method and the Delphi method are subjective techniquesthough they are used differently for specific forecasting processes. This paper applies a systematic approach to draw objectiveanalysis results from a practical technology database. Second, a structural model eliminates subjective factors and expressesdevelopment trends using mathematical functions. However, the forecasting area that may be analyzed based on mathematicalfunctions is limited. Furthermore, mathematical functions can deal only with overall trends at the macro level. Even though thisapproach is very useful for forecasting sales, since demand and stock prices support strategic decisions surrounding generalmanagement issues, it is not applicable for wide use in technological forecasting. Therefore, quantitative serial data areinadequate for technological forecasting because technology is naturally uncertain and unclear. Therefore, patent-basedtechnology development paths focus on the analysis of the complex structure and dynamic change of the structure, and not onthe forecasting of numerical changes in technological data. Third, the scenario model serves as an outline to use in predicting thefuture as it is a combined procedure of intuitive cognitive and mechanistic approaches. The accuracy and usefulness depends onhow rich the information about current trends is in these kinds of approaches. In this context, the main purpose of theseproposed methodologies is to provide objective information about the current structure and change in specific technologyfields. Finally, technological vigilance includes various analytical methods for identifying and understanding technologydevelopment. The main purpose of this technique is to provide information to managers to help in the decision-making process.However, it is difficult to provide objective information about technological development because technology is generallyembedded in products, processes or services. In this context, patent-based technology development analysis are practicalapproaches to identifying and understanding the complex structure of technological development at a detailed level based onsystematic methodologies.

The method for analysis of complex structure and change in technology proposed in this research provides beneficialinformation for establishing technological strategy in the private sector and technology policy in the public sector. Our approachovercomes the problems with traditional patent analysis in the following ways. First, systematic and quantitative analysis oftechnology development may be conducted. It is possible to analyze issued patent data from an empirical perspective tounderstand the process of technology development. Second, this quantitative analysis supports expert intuition and judgment.This research may also provide valuable support for decisions on investment in and development of specific technologies in boththe private and public sectors. Third, we may draw a technology development map for multiple technology fields. Analysis byexperts may provide accurate and effective output in single specific technology fields; however, it is difficult to assemble all theexpert opinion required to assess all technology fields. By comparison, the analysis in this paper covers the wide scope oftechnology fields while using a specific patent database. Fourth, comparisons between technology fields may be conducted usingthesemaps. Our approachmay also be used to analyze the complex and dynamic structure of technology development. In previousresearch, various patent analyses were conducted based on the simple counting of patents. Such a traditional approach is useful forunderstanding the overall trend at the macro level, but it fails to illustrate the detailed structure and trends of technologydevelopment at the micro level. These results may be used to understand these complex relationships and developments.

These results should allow decision makers to (1) assess the attractiveness of technologies, especially new technologies posinga threat, or a new opportunity for existing business, (2) recognize strategic changes in the firm's competitive environment, (3)


identify and assess external sources of knowledge generation, and (4) assess the patent situation in new business areas thatmay beexplored. For these reasons, the results may be taken as a strategic information source, contributing to the effective and efficientmanagement of technology.

6. Conclusion

We have suggested an algorithm for identifying patent development paths based on patent citation analysis and we haveconstructed an example of patent development paths for flash memory systems. By identifying the development history ofindividual patents, the proposedmap provides insights into the technology development process and into forecasting the directionof technology development. It can be a powerful tool to identify the historical development of technology. It represents thedynamic paths of technology development and simplifies the complex and organic structure of technology development.Moreover, this map can be used to obtain ideas for new technology development, and identify core technology and search forpatent technology for licensing.

We believe that with the suggested algorithm it is possible to understand the microprocess of technology innovation and thatthis algorithm is applicable to various research domains. However, this research also has some limitations. First, it does notovercome the innate limitations of patent analysis, that is, fundamental problems such as the differences in patent application andregistration strategy across various industries and the influence of legal change [22]. Second, information loss occurs duringanalysis because only the major development path is constructed, and patents on other paths are discarded. Analysis of theunselected paths and patents cannot be conducted with the suggested algorithm. This is why the main objective of our algorithm,is to effectively visualize technological development and to enhance the readability of large patent maps. Trade-offs betweenreadability and information are inevitable. Third, the algorithm suggested in this paper is focused on a limited sample. That is, thedevelopment paths are constructed based on only the retrieved patents. To identify development paths outside the selectedpatents, additional retrieval of patents is necessary. This, however, can be an expanded application of the suggested algorithm. Forexample, the development paths over two or more technology fields can be identified with patents retrieved from thosetechnology fields.

Somepossible future research topics related to thiswork are as follows. First, additional indicators could bedevelopedbased on thesuggested algorithms. Second, comparisons between industries or countries based on the suggested map and indicators could beconducted. Third, the suggested algorithmstarts fromoriginpatents, screens thehighlyweightedarcs and selects intermediate patentsand terminus patents. This process focuses on high-value, recent patents and those located at converging points. If we began thisalgorithmwith terminus patents, we could analyze the important early patents and identify those located at points of divergence.

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Changwoo Choi is a doctoral candidate in the Department of Industrial Engineering of Seoul National University (SNU). He holdsMS in industrial engineering fromSNU. His research interests lie in R&D policy, technology management, patent analysis, and new service creation. He has authored published papers inTechnological Forecasting and Social Change, and Expert Systems with Applications.

Yongtae Park is a professor in the Department of Industrial Engineering at SNU. He holds a BS in industrial engineering from SNU, and anMS and PhD in operationsmanagement, both from the University of Wisconsin-Madison. His research topics cover a wide variety of areas including technology management, technologicalinnovation, knowledge management, and service engineering.

Technological Forecasting and Social Change

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