Technology Opportunity Discovery in the biomedical sector : An Integrated Analysis of Scientific Publications and Patents
없음
- 주제(키워드) Technology Opportunity Discovery , biomedical
- 주제(DDC) 006.31
- 발행기관 아주대학교 일반대학원
- 지도교수 손경아
- 발행년도 2026
- 학위수여년월 2026. 2
- 학위명 박사
- 학과 및 전공 일반대학원 인공지능학과
- 실제URI http://www.dcollection.net/handler/ajou/000000035696
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
Scientific and technological knowledge is mainly accumulated through two major information sources: publications and patents. Patents focus on the implementation of technologies and the protection of intellectual property rights, showing a strong orientation toward commercialization. Publications, in contrast, emphasize experimentation, analysis, and interpretation for the purpose of sharing scientific knowledge. Despite their complementary nature, prior research has rarely compared and integrated the characteristics of these two sources in a systematic and strategic way. To address this gap, this dissertation proposes an integrated Technology Opportunity Discovery (TOD) framework combining Technology-Push and Market-Pull perspectives. The framework aims to analyze and guide structural technological change in both routine and demand-driven innovation environments. The study is guided by two research questions: RQ1. What are the informational characteristics of publications and patents, and how can they be strategically utilized for technology opportunity discovery? RQ2. How do societal shocks such as pandemics reshape patterns of technological change and stability, and how can these changes be incorporated into opportunity discovery? Three empirical analyses were conducted. First, a comparison of publications and patents was performed using three indicators—Relevance, Completeness, and Accuracy. The analysis revealed that patents are strong in practicality and accuracy, whereas publications excel in comprehensiveness and diversity. This finding confirms their complementary roles. This comparison serves as the foundation for constructing the TOD framework, clarifying how each information source can be strategically used in opportunity discovery. Second, from a Technology-Push perspective, a drug repurposing study was conducted. Two disease networks—one derived from publications and the other from patents—were constructed. Together, these networks demonstrated the potential of reusing existing drugs for new therapeutic indications. Third, from a Market-Pull perspective, a patent-based study examined technological shifts driven by the COVID-19 pandemic. A technology-tree structure and the Relatedness Value (RV) coefficient were applied to identify patterns of structural stability and contributions among digital health care technologies. In addition, to validate the applicability of the proposed TOD framework, this study applies the analytical approaches developed in Modules 2 and 3 to a case study on Chronic Fatigue. Following the pandemic, the demand for treatments for this disease has grown rapidly, yet no established therapies are available. From the Technology-Push perspective, the drug repurposing approach developed in Module 2 was applied, while technological change patterns from Module 3 were used to assess post-pandemic digital therapeutic potential. By integrating these two analyses, the case study demonstrates how the TOD framework links knowledge-based technological expansion with demand-driven structural adaptation. This connection ultimately forms a resilient innovation pathway. This dissertation makes three contributions. First, it provides a quantitative comparison of the informational characteristics of publications and patents, offering evidence for their strategic utilization. Second, it conducts empirical analyses of Push-based drug repurposing and Pull-based technological change monitoring. These analyses demonstrate how the two approaches work together to uncover new innovation opportunities. Third, it presents a data-driven framework for resilient and adaptive innovation management. The framework is designed to be applicable across diverse technological and industrial contexts. These results provide actionable implications for technology strategy formulation and policy design. They are relevant not only in the face of societal shocks but also in broader contexts of technological transition and market transformation.
more초록/요약
Scientific and technological knowledge is primarily accumulated through publications and patents, which play complementary roles in innovation. Patents emphasize technological implementation and commercialization, while publications focus on scientific exploration and knowledge dissemination. However, their characteristics have rarely been systematically compared and strategically integrated for technology opportunity discovery. To address this gap, this dissertation proposes an integrated Technology Opportunity Discovery (TOD) framework that combines Technology-Push and Market-Pull perspectives to analyze structural technological change under both routine and demand-driven innovation environments. The study investigates (1) the distinct informational characteristics of publications and patents and their strategic use in opportunity discovery, and (2) how societal shocks, such as pandemics, reshape technological change and stability. Three empirical analyses are conducted. First, publications and patents are quantitatively compared using relevance, completeness, and accuracy, revealing their complementary strengths. Second, a Technology-Push analysis applies drug repurposing methods using publication- and patent-based disease networks. Third, a Market-Pull analysis examines COVID-19–induced technological shifts using a technology-tree structure and the Relatedness Value (RV) coefficient. The framework is further validated through a case study on chronic fatigue, integrating drug repurposing analysis with post-pandemic digital therapeutic opportunity assessment. Overall, this study demonstrates how integrating knowledge-based technological expansion with demand-driven structural adaptation enables resilient and adaptive innovation management, offering practical implications for technology strategy and policy design across diverse industrial contexts.
more목차
Chapter 1. Introduction 1
1.1 Background and Motivation 1
1.2 Research Objectives and Questions 2
(1) Module 1. Information Source Characteristics 2
(2) Module 2. Technology-Push: Drug Repurposing Analysis 2
(3) Module 3. Market-Pull: Technological Change Analysis 2
(4) Module 4. Case Study: Application of Modules 2 and 3 to Chronic Fatigue Syndrome (CFS/ME) 2
(5) Research Questions 3
1.3 Scope and Methodology Overview 3
1.4 Structure of the Dissertation 4
Chapter 2. Theoretical Background 5
2.1 Publications and Patents as Information Sources 5
2.1.1 Functional Characteristics of Publications and Patents 6
2.1.2 Major Research Trends on Publication–Patent Linkage Analysis 6
2.2 Technology Opportunity Discovery (TOD) Framework 7
2.3 Drug Repurposing as a Data-Driven TOD Approach 8
2.4 Interacting Drivers of Technological Change: Technology-Push and Market-Pull 10
2.4.1 Technology-Push: Internal Dynamics of Innovation 10
2.4.2 Market-Pull Perspective 11
2.4.3 Integrating Push and Pull: Complementarity, Path Dependence, and Feedback 12
(1) Complementarity: Aligning Technological Possibility and Societal Demand 12
(2) Path Dependence: Overcoming Technological and Institutional Lock-In 12
(3) Feedback: Learning and Reinforcing Innovation Loops 12
2.5 Limitations of Previous Studies and Research Gap 13
Chapter 3. Research Framework and Data 14
3.1 Overall Research Framework 14
3.2 Network-Based Analytical Framework 15
3.3 Data Collection and Preprocessing 16
3.3.1 Overview of Data Sources and Preprocessing Workflow 16
3.3.2 Data Collection Procedures 17
(1) Publications 17
(2) Patents 18
(3) Clinical Trials 18
(4) Drug Information 19
(5) Temporal Boundary Setting for COVID 19 Analysis 19
3.3.3 Data Standardization and Integration 19
(1) Terminology Mapping 19
(2) Text Preprocessing 20
(3) Cross-Linking 20
(a) Publication–Patent Mapping 20
(b) Comparative Validation of Publication and Patent Data Using Phase IV Clinical Trials 21
(4) Data Normalization and Weight Adjustment 21
(5) Analytical Design and Module-Specific Frameworks 21
(a) Modules 1 and 2 (Publication–Patent Comparative Analysis) 21
(b) Module 3 (Structural Analysis of Digital Health Care Technologies: Pre/Post-COVID-19) 22
(c) Module 4 (CFS/ME Case Study) 22
(6) Analytical Readiness 23
Chapter 4. Module 1: Comparative Characteristics of Publications and Patents 24
4.1 Literature Review on Comparative Studies 24
4.2 Research Objectives and Procedures 26
4.3 Data and Analytical Methods 26
4.3.1 Analytical Framework of Module 1 26
4.3.2 Data Collection and Preprocessing 27
4.3.3 Development of Disease Network 30
4.3.4 Disease Relationship Comparison Using Network Analysis 31
4.3.5 Comparison of Data Characteristics 31
4.4 Results 34
4.5 Discussion 35
4.6 Summary and Implications 36
Chapter 5. Module 2: Drug Repurposing through Network Analysis 36
5.1 Literature Review on Drug Repurposing 36
5.2 Research Objectives and Procedures 37
5.3 Data and Analytical Methods 38
5.3.1 Analytical Framework of Module 2 38
5.3.2 Data Collection and Preprocessing 39
5.3.3 Development of Disease Network 39
5.3.4 Identification of Technology Opportunities 40
(1) Network comparison 40
(2) Network integration 42
5.4 Results 42
5.4.1 Publication-based Disease Causal Network 42
5.4.2 Patent-based Disease Co-treatment Network 44
5.4.3 Identification of Technology Opportunities 45
(1) Network comparison 45
(2) Network integration 48
5.5 Discussion 48
5.6 Summary and Implications 49
Chapter 6. Module 3: Technological Change and Stability under the COVID-19 Pandemic 50
6.1 Literature Review on COVID-19 and Technological Change 50
6.1.1 Concept of Societal Shocks and Technological Response 50
6.1.2 Crisis-Driven and Problem-Driven Innovation 51
6.1.3 Previous Studies on Digital Health Care Innovation Triggered by the Pandemic 52
6.1.4 Evidence of Market Demand Shift during the COVID-19 Pandemic 53
6.2 Research Objectives and Questions 54
6.2.1 Analytical Focus and Research Questions (M3-RQ1 & M3-RQ2) 54
6.2.2 Scope and Contribution of This Module 55
6.3 Data and Analytical Methods 56
6.3.1 Analytical Framework of Module 3 56
6.3.2 Data Source and Utilization 57
(1) Data Source and Collection 57
(2) Data Overview and Utilization 59
6.3.3 Technology Tree Construction 60
6.3.4 Indicator Design 63
(1) Relatedness Value (RV) Coefficient for Structural Stability 63
(2) Contribution Indicator for Sub-Technology Influence 64
6.4 Results 66
6.4.1 Analysis of the First-Level Technology Groups in the Technology Tree 66
6.4.2 Analysis of the Second-Level Technology Groups in the Technology Tree 67
6.4.3 Analysis of the Third-Level Technology Groups in the Technology Tree 71
6.5 Discussion 73
(1) Structural Stability Patterns of Technology Groups (M3-RQ1) 73
(2) Shifts in Contributions of Sub-Technology Groups (M3-RQ2) 75
(3) Interpretation Based on Crisis-Driven Innovation and Resilience Perspectives 76
(4) Differentiated Policy Strategies by Technology Stability Type 77
6.6 Summary and Implications 78
Chapter 7. Case Study: Chronic Fatigue Syndrome (CFS/ME) 79
7.1 Research Motivation: Rising Demand and Lack of Treatments in CFS/ME 79
7.2 Application of Technology-Push Perspective: Drug Repurposing 80
7.2.1 Objective 80
7.2.2 Data Collection 81
7.2.3 Methodology for Network Analysis 81
7.2.4 Key Results 82
7.2.5 Implications 85
7.3 Application of Market-Pull Perspective: Digital Health Care Technologies 85
(1) Stability Group – Standardization and Diffusion Phase 86
(2) Increasing Stability Group – Infrastructure and Connectivity Enhancement Phase 86
(3) Turbulence Group – Reliability and Safety Validation Phase 86
7.4 Integrated Implications of Push and Pull Approaches 88
Chapter 8. General Discussion 88
8.1 Complementarity of Information Sources 89
8.2 Implementation of TOD from the Technology-Push Perspective 90
8.3 Expansion of TOD from the Market-Pull Perspective 90
8.4 Integration of Push and Pull Perspectives through the CFS/ME Case 91
8.5 Summary 92
Chapter 9. Contributions and Limitations 92
9.1 Theoretical Contributions 93
9.2 Managerial and Policy Implications 95
9.3 Limitations and Future Research 96
References 100
Appendix 125
