Abstract: The rapid development of Artificial Intelligence (AI), especially Deep Learning (DL) technologies, has brought unprecedented challenges and opportunities for Intellectual Property (IP) protection and management. In this paper, we employ Bibliometrix and Biblioshiny to conduct a bibliometric analysis of global research at the intersection of AI-driven innovation and IP frameworks over the past decade. The findings reveal a significant annual growth rate of 15.34 per cent in publications, with an average of 5.82 citations per study, reflecting increasing academic interest. China, the United States, and India dominate the research output, but the cross-country collaboration rate is only 10.74 per cent, indicating that there is still room for improvement in global collaborative research. The current major research groups in the field, as well as different research themes, are identified through collaborative network and thematic analyses, respectively. Although the field has achieved remarkable results in technological innovation, the deep integration of legal, economic and ethical dimensions is still at an early stage. The study highlights the urgent need for interdisciplinary collaboration and enhanced international cooperation to address pressing issues such as AI-generated content (AIGC) attribution, legal applicability, and the societal impact of DL technologies in IP protection. These findings aim to support academia and industry in clarifying ownership and promoting synergistic innovation in the AI era.

Jing Li and Quanwei Huang, “Intellectual Property Protection in the Age of AI: From Perspective of Deep Learning Models” International Journal of Advanced Computer Science and Applications(IJACSA), 16(4), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160424

@article{Li2025,
title = {Intellectual Property Protection in the Age of AI: From Perspective of Deep Learning Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160424},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160424},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {4},
author = {Jing Li and Quanwei Huang}
}