International nanotechnology development in 2003: Country,institution, and technology field analysis based on USPTO patent database

Nanoscale science and engineering (NSE) have seen rapid growth and expansion in new areas in recent years. This paper provides an international patent analysis using the U.S. Patent and Trademark Office (USPTO) data searched by keywords of the entire text: title, abstract, claims, and specifications. A fraction of these patents fully satisfy the National Nanotechnology Initiative definition of nanotechnology (which requires exploiting specific phenomena and direct manipulation at the nanoscale), while others only make use of NSE tools and methods of investigation. In previous work we proposed an integrated patent analysis and visualization framework of patent content mapping for the NSE field and of knowledge flow pattern identification until 2002. In this paper, the results are updated for 2003, and the new trends are presented.     

Patent citation network in nanotechnology (1976–2004)

The patent citation networks are described using critical node, core network, and network topological analysis. The main objective is understanding of the knowledge transfer processes between technical fields, institutions and countries. This includes identifying key influential players and subfields, the knowledge transfer patterns among them, and the overall knowledge transfer efficiency. The proposed framework is applied to the field of nanoscale science and engineering (NSE), including the citation networks of patent documents, submitting institutions, technology fields, and countries. The NSE patents were identified by keywords “full-text” searching of patents at the United States Patent and Trademark Office (USPTO). The analysis shows that the United States is the most important citation center in NSE research. The institution citation network illustrates a more efficient knowledge transfer between institutions than a random network. The country citation network displays a knowledge transfer capability as efficient as a random network. The technology field citation network and the patent document citation network exhibit a␣less efficient knowledge diffusion capability than a random network. All four citation networks show a tendency to form local citation clusters.     

Longitudinal Nanotechnology Development (1991--2002): National Science Foundation Funding and its Impact on Patents

Nanotechnology holds the promise to revolutionize a wide range of products, processes and applications. It is recognized by over sixty countries as critical for their development at the beginning of the 21st century. A significant public investment of over $1 billion annually is devoted to nanotechnology research in the United States. This paper provides an analysis of the National Science Foundation (NSF) funding of nanoscale science and engineering (NSE) and its relationship to the innovation as reflected in the United States Patent and Trade Office (USPTO) patent data. Using a combination of bibliometric analysis and visualization tools, we have identified several general trends, the key players, and the evolution of technology topics in the NSF funding and commercial patenting activities. This study documents the rapid growth of innovation in the field of nanotechnology and its correlation to funding. Statistical analysis shows that the NSF-funded researchers and their patents have higher impact factors than other private and publicly funded reference groups. This suggests the importance of fundamental research on nanotechnology development. The number of cites per NSF-funded inventor is about 10 as compared to 2 for all inventors of NSE-related patents recorded at USPTO, and the corresponding Authority Score is 20 as compared to 1.8.     

A Bounded Measure for Estimating the Benefit of Visualization (Part II): Case Studies and Empirical Evaluation

Many visual representations, such as volume-rendered images and metro maps, feature a noticeable amount of information loss due to a variety of many-to-one mappings. At a glance, there seem to be numerous opportunities for viewers to misinterpret the data being visualized, hence, undermining the benefits of these visual representations. In practice, there is little doubt that these visual representations are useful. The recently-proposed information-theoretic measure for analyzing the cost–benefit ratio of visualization processes can explain such usefulness experienced in practice and postulate that the viewers’ knowledge can reduce the potential distortion (e.g., misinterpretation) due to information loss. This suggests that viewers’ knowledge can be estimated by comparing the potential distortion without any knowledge and the actual distortion with some knowledge. However, the existing cost–benefit measure consists of an unbounded divergence term, making the numerical measurements difficult to interpret. This is the second part of a two-part paper, which aims to improve the existing cost–benefit measure. Part I of the paper provided a theoretical discourse about the problem of unboundedness, reported a conceptual analysis of nine candidate divergence measures for resolving the problem, and eliminated three from further consideration. In this Part II, we describe two groups of case studies for evaluating the remaining six candidate measures empirically. In particular, we obtained instance data for (i) supporting the evaluation of the remaining candidate measures and (ii) demonstrating their applicability in practical scenarios for estimating the cost–benefit of visualization processes as well as the impact of human knowledge in the processes. The real world data about visualization provides practical evidence for evaluating the usability and intuitiveness of the candidate measures. The combination of the conceptual analysis in Part I and the empirical evaluation in this part allows us to select the most appropriate bounded divergence measure for improving the existing cost–benefit measure.     

A Bounded Measure for Estimating the Benefit of Visualization (Part I): Theoretical Discourse and Conceptual Evaluation

Information theory can be used to analyze the cost–benefit of visualization processes. However, the current measure of benefit contains an unbounded term that is neither easy to estimate nor intuitive to interpret. In this work, we propose to revise the existing cost–benefit measure by replacing the unbounded term with a bounded one. We examine a number of bounded measures that include the Jenson–Shannon divergence, its square root, and a new divergence measure formulated as part of this work. We describe the rationale for proposing a new divergence measure. In the first part of this paper, we focus on the conceptual analysis of the mathematical properties of these candidate measures. We use visualization to support the multi-criteria comparison, narrowing the search down to several options with better mathematical properties. The theoretical discourse and conceptual evaluation in this part provides the basis for further data-driven evaluation based on synthetic and experimental case studies that are reported in the second part of this paper.