Title page for 964401023


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Student Number 964401023
Author Chih-hsi Kao(°ª§Ó©ý)
Author's Email Address No Public.
Statistics This thesis had been viewed 774 times. Download 5 times.
Department Business Administration
Year 2010
Semester 2
Degree Ph.D.
Type of Document Doctoral Dissertation
Language English
Title Visualization Methods for Patent Analysis to Explore Enterprise Technological Deployment and R&D Strategies
Date of Defense 2011-06-17
Page Count 88
Keyword
  • Cluster analysis
  • Correspondence analysis
  • Data envelopment analysis
  • Patent analysis
  • Patent indicator
  • Patent portfolio
  • Abstract The rate of technological progress in the hi-tech industry is increasing rapidly. If enterprise managers in industrial environments can identify the trends of emerging potential technology or the technological progress of its opponents, they could seize the opportunity to build patent fences and increase the entry obstacles of related technology. This, in turn, would allow them to maintain technological competitiveness and achieve dominance in technological capabilities or markets. Preempting the deployment of their patented technology could enhance the competitiveness of enterprises. Therefore, the development of analysis methods as reference for corporate research and development (R&D) and patent deployment has become an important issue. Patent indicators are simple and effective patent analysis methods that, through the use of multiple indicators or variables, form patent portfolio maps for enterprise managers or R&D personnel. This study develops two innovative models of patent portfolio analysis to explore the R&D planning and patent deployment of enterprise technologies and the practices in empirical patent analyses for related industries as a reference approach. Model 1 uses the characteristics of data envelopment analysis (DEA) to calculate the optimal weight set of patent indicators and variables for forming patent portfolio maps. This model allows enterprises to freely choose the patent indicator or variable weight set that is most advantageous by DEA evaluation, limiting the numerical values from 0 to 1. In addition, it increases the weight objectivity of indicators or variables, enhances the degree of distinguishability and accuracy, and increases the function of enterprise benchmark learning. Model 1 is evidenced in the lithium iron phosphate battery technology. Model 2 integrates the visualization methods of correspondence analysis and cluster analysis to visually present the effects of complex multiple patent indicators (or variables) corresponding with the relationships, which could be used in data mining to extract important patent information. This model allows enterprises to explore the associations among patent indicators or variables according to geometry distance or similarity, and to simultaneously visualize the results on a two-dimensional graphical display. Model 2 is evidenced in the thin film photovoltaic technology. These analysis results provide reference for patented deployment and R&D planning of enterprise technologies.
    Table of Content ABSTRACT IN CHINESE¡@i
    ABSTRACT IN ENGLISH¡@ii
    TABLE OF CONTENTS¡@iv
    LIST OF FIGURES¡@vi
    LIST OF TABLES¡@vii
    CHAPTER 1 INTRODUCTION¡@1
    1.1 Research background¡@1
    1.2 Research motivation¡@4
    1.3 Research purpose¡@5
    1.4 Framework and outlines of this study¡@6
    CHAPTER 2 THEORETICAL BACKGROUND¡@9
    2.1 Patent analysis and patent indicators¡@9
    2.2 Patent portfolio and map¡@11
    2.3 DEA basic model¡@15
    2.4 Correspondence analysis and cluster analysis¡@17
    CHAPTER 3 ANALYSIS PROCESS OF PROPOSED MODEL 1 AND LITHIUM IRON PHOSPHATE BATTERY EMPIRICAL STUDY¡@22
    3.1 Research process and steps¡@22
    3.1.1 Collecting patent data¡@23
    3.1.2 Patent data analysis¡@26
    3.1.3 Obtaining information and formulating R&D strategies¡@27
    3.2 Research result¡@30
    3.2.1 Patent portfolio on the company level¡@30
    3.2.2 Patent portfolio on the technological level¡@34
    3.3 Discussion and recommendations¡@42
    3.3.1 Conclusion of the methodology¡@42
    3.3.2 Conclusion at the practical application¡@44
    3.3.3 Recommendations for Model 1¡@47
    3.4 Summary¡@47
    CHAPTER 4 ANALYSIS PROCESS OF PROPOSED MODEL 2 AND THIN FILM PHOTOVOLTAIC EMPIRICAL STUDY¡@49
    4.1 Research process and steps¡@49
    4.1.1 Collecting patent data¡@50
    4.1.2 Patent data analysis¡@52
    4.1.3 Obtaining information and formulating R&D strategies¡@53
    4.2 Research Result¡@54
    4.2.1 Patent portfolio for enterprise technological deployment and industrial technological gap¡@54
    4.2.2 Patent portfolio for enterprise technological movement and industrial technological development trends¡@58
    4.3 Discussion and recommendations¡@61
    4.3.1 Enterprise situation¡@61
    4.3.2 Technological development situation¡@62
    4.3.3 Recommendations for Model 2¡@63
    4.4 Summary¡@63
    CHAPTER 5 CONCLUSIONS¡@65
    5.1 Discussion and conclusions¡@65
    5.1.1 Discussion and comparison of the two models¡@65
    5.1.2 Conclusions¡@66
    5.2 Future research¡@67
    REFERENCE¡@68
    APPENDIX 1¡@75
    APPENDIX 2¡@76
    APPENDIX 3¡@78
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    Advisor
  • Dong-shang Chang(±iªF¥Í)
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    Date of Submission 2011-06-23

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