Because of the flexibility and complexity of Newly Emerging Science and Technologies (NESTs), traditional statistical analysis fails to capture technology evolution in detail. Tracking technology evolution pathways supports industrial, governmental, and academic decisions to guide future development trends. Patents are one of the most important NESTs data sources and are pertinent to developmental paths. This paper draws upon text analyses, augmented by expert knowledge, to identify key NESTs sub-domains and component technologies. We then complement those analyses with patent citation analysis to help track developmental progressions. We identify key sub-domain patents, associated with particular component technology trajectories, then extract pivotal patents via citation analysis. We compose evolutionary pathways by combining citation and topical intelligence obtained through term clumping. We demonstrate our approach with empirical analysis of dye-sensitized solar cells (DSSCs), as an example of a promising NESTs, contributing to the remarkable growth in the renewable energy industry. The systematic approach we proposed not only offers a macro-perspective covering technology development levels and future trends, but also makes R&D information accessible for micro-level probes as needed. We work to uncover developmental trends and to compile mentions of possible applications, and this study informs NESTs management by spotting prime opportunities for innovation.
Author(s): Ying Huang, Yi Zhang, Jing Ma, Alan L. Porter, Xuefeng Wang, Ying Guo
Organization(s): Beijing Institute of Technology, University of Technology Sydney
Source: Anticipating Future Innovation Pathways Through Large Data Analysis pp 153-172