Mapping technological innovation dynamics in artificial intelligence domains: Evidence from a global patent analysis

• Published in: PloS one Vol. 16; no. 12; p. e0262050
• Main Authors: Liu, Na, Shapira, Philip, Yue, Xiaoxu, Guan, Jiancheng
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 31.12.2021; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Artificial Intelligence; Automation - methods; Bibliometrics; Computer and Information Sciences; Data processing; Diffusion of Innovation; Humans; Innovations; Intellectual Property; Inventions; Keywords; Liu, Na; Machine learning; Models, Organizational; Models, Statistical; Patent searches; Patents as Topic; People and Places; Physical Sciences; Public policy; Research and Analysis Methods; Social Sciences; Software; Technological change; Technology; Technology - methods; Technology - trends; Trends; China; Beijing China; Georgia; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0262050

Artificial intelligence (AI) is emerging as a technology at the center of many political, economic, and societal debates. This paper formulates a new AI patent search strategy and applies this to provide a landscape analysis of AI innovation dynamics and technology evolution. The paper uses patent analyses, network analyses, and source path link count algorithms to examine AI spatial and temporal trends, cooperation features, cross-organization knowledge flow and technological routes. Results indicate a growing yet concentrated, non-collaborative and multi-path development and protection profile for AI patenting, with cross-organization knowledge flows based mainly on interorganizational knowledge citation links.