Mejia Cristian, Kajikawa Yuya
Institute for Future Initiatives, The University of Tokyo, Tokyo, Japan.
Front Res Metr Anal. 2025 Jan 8;9:1484685. doi: 10.3389/frma.2024.1484685. eCollection 2024.
Patent analytics is crucial for understanding innovation dynamics and technological trends. However, a comprehensive overview of this rapidly evolving field is lacking. This study presents a data-driven analysis of patent research, employing citation network analysis to categorize and examine research clusters. Here, we show that patent research is characterized by interconnected themes spanning fundamental patent systems, indicator development, methodological advancements, intellectual property management practices, and diverse applications. We reveal central research areas in patent strategies, technological impact, and patent citation research while identifying emerging focuses on environmental sustainability and corporate innovation. The integration of advanced analytical techniques, including AI and machine learning, is observed across various domains. This study provides insights for researchers and practitioners, highlighting opportunities for cross-disciplinary collaboration and future research directions.
专利分析对于理解创新动态和技术趋势至关重要。然而,目前缺乏对这一快速发展领域的全面概述。本研究对专利研究进行了数据驱动的分析,采用引文网络分析对研究集群进行分类和考察。在此,我们表明专利研究的特点是相互关联的主题,涵盖基本专利制度、指标开发、方法进步、知识产权管理实践以及各种应用。我们揭示了专利战略、技术影响和专利引文研究中的核心研究领域,同时确定了新兴的关注重点,即环境可持续性和企业创新。在各个领域都观察到了先进分析技术(包括人工智能和机器学习)的整合。本研究为研究人员和从业者提供了见解,突出了跨学科合作的机会和未来的研究方向。