Empirical Analysis of Technological Trajectories Using Patent Information: Methods, Findings, and Implications

Abstract

This paper presents an empirical study that employs patent information to analyze technological trajectories across various industries and domains. Technological trajectories represent the evolutionary paths of innovation within specific technological fields, capturing the patterns of technological change, knowledge accumulation, and innovation dynamics over time. Drawing upon patent data and quantitative analysis techniques, this study examines the trajectories of technological development within selected industries, identifying key technological breakthroughs, emerging trends, and areas of future innovation potential. Through a combination of descriptive statistics, network analysis, and machine learning algorithms, the study reveals insights into the patterns of patent activity, the distribution of technological knowledge, and the dynamics of knowledge diffusion and collaboration among inventors and organizations. Additionally, the paper discusses the methodological challenges and limitations associated with using patent data for studying technological trajectories, such as data quality issues, patent classification schemes, and patent citation biases. By synthesizing empirical findings and methodological insights, this research contributes to advancing our understanding of technological change and innovation dynamics, and offers practical implications for policymakers, industry practitioners, and researchers seeking to harness patent information for strategic decision-making, technology forecasting, and innovation management.