The measurement and assessment of technological innovation has attracted the interest of many actors. As reflected in national science and technology statistical publications, the demand for reliable innovation indicators has steadily increased over recent decades, both for macro and micro-economic analysis, and for policy use. The goal of the proposed research is to leverage advances in data/text analysis and visualization techniques to support knowledge discovery and to identify knowledge transfer based on (NSF) project funding and (USPTO) patent databases. As indicators of inventive output are mostly based on patents, efficient and innovative methodologies and frameworks for the analysis of the information related to technological innovation stored in patent databases are needed. Information about trends in NSF funding can be derived from the analysis of the NSF award abstracts and other relevant data. The research challenges are: uncertainty about the validity of using the award and patent data to approximate science and technology development, difficulty in intuitive presentation of analysis result, and precise identification of emerging topics and concepts in the NSF award and patent databases. This exploratory project aims to examine both technical issues and the fundamental hypotheses involved. Major information and computer science techniques adopted include: text-based linguistic analysis, 2D/3D knowledge map visualization, citation network analysis, and graphical display and analysis of temporal patterns. The current testbed is related to analysis of nano science and technology research in USA and other research described at http://ai.bpa.arizona.edu/.
