While “open innovation” has exploded on the R&D/technology scene, there has been little recognition of how to implement it, observes Alan Porter, director of R&D for Search Technology, Inc., and co-director of the Technology Policy and Assessment Center at Georgia Tech. Porter observes that open innovation requires good intelligence about the external R&D environment, and that “tech mining” of science and technology information resources can help to obtain that intelligence quickly and efficiently. “For instance,” he adds, “tech mining can help you identify and profile research centers active in your field of interest, including their most active researchers and research topics.”
Porter goes on to explain the integration of open innovation and tech mining. Here’s what he says: The tech mining framework for competitive technical intelligence (CTI) starts with technology managers’ information needs. It postulates 13 recurring issues, that spawn about 40 explicit questions, which can be addressed via some 200 empirical innovation indicators. It offers a systematic approach to mining information resources (see Table, below). Blending technical with contextual content is important. Ideally an organization taps all three tiers of source types: databases (wherein others compile and filter content), the Internet, and human expertise. My colleagues and I are continually astounded to find that some organizations miss significant portions, especially the “low-hanging fruit” of the R&D databases.
The following, necessarily over-simplified, illustration of tech mining applications in the “nano” arena shows how one can derive intelligence from S&T information resources. Our nano data derive largely from a substantial project dataset of nano-related articles and patents, collected at Georgia Tech with funding from the National Science Foundation through NC State University and Arizona State University.
We have over 1 million paper abstract records and some 60,000 international patent families, and intend to update soon. The first results illustrate broad “research landscaping,” which can discern patterns across the global R&D enterprise. For tech mining, such analyses typically pursue a mid-level of detail. Overall “nano trends” make almost no sense as the field is a general-purpose technology with diffuse elements. Rather, we need to parse these data to uncover technological thrusts that could lead to emergent capabilities of interest. Put another way, we want to combine “who, what, where, when” elements to show:
• Trends in nano patenting by selected international patent classes (which ones are gaining steam?)
• Geographic concentrations (which areas bringacademic and government research labs together with industrial counterparts?)
• Key players’ emphases (which organizations are pursuing what topics?)
Tech mining can probe deeper to help analysts get at the “how and why?” that underlie observed R&D patterns. For instance, Alencar et al. (in press) analyzed nano patenting. They blended two types of information — patent classes and text mining of Derwent patent abstracts and claims — to position patents along a nano value chain. In this way they could distinguish possible corporate strategies as targeting basic materials, intermediates, or final products. That intelligence, in turn, could help spot potential open innovation partners who complement one’s own strategic thrusts.
The notion of researcher networking complements these interests in strategically understanding research communities — vital for open innovation. Intellectually, we can look to the interchange of ideas, approaches, tools, and empirical results across researchers and institutions. Socially, this translates into “who shares ideas with whom?” The social network diagram at left illustrates a form of “knowledge network analysis.” The circled entity is a small business (Transgenex Nanobiotech, Inc.) that has collaborated with the University of South Florida. Those researchers, in turn, have collaborated with many others. Jue Wang, at Georgia Tech, used this to illustrate how one could construct a social network that could be depicted as a hierarchy reaching out from Transgenex. One could further explore the first- and secondorder articles, for instance, to get at the intellectual network — which topics are interrelated? In terms of open innovation, building such networks of contacts and resources can open up new possibilities. Innovative product development beyond the reach of one’s own enterprise can be enabled by suitable partnering. Tech mining can help develop the contacts and intelligence to help identify bold opportunities and to locate the right partners to bring them to fruition.
To better generate the CTI that enables such open innovation, Search Technology aspires to bolster the tech mining process. Some of our aims are to:
• Combine technological with contextual content mined from database searches to yield “greater than the sum” insights.
• Improve agent retrieval and formatting of Internet content to enable seamless integration with the database findings.
• Reach beyond direct relationship text mining to filter indirect relationship measures, enabling “Literature Based Discovery” that goes beyond information retrieval.
We’d love to work with companies on real innovation challenges upon which to refine these elements.
Alan L. Porter
Search Technology, Inc.
Georgia Tech University
For Further Reading
Alencar, M.S.M., Porter, A.L. and A.M.S. Antunes (in press). Nanopatenting Patterns in Relation to Product Life Cycle, Technological Forecasting & Social Change.
Porter, A.L. and Cunningham, S.W. (2005). Tech Mining: Exploiting New Technologies for Competitive Advantage, Wiley, New York.
Porter, A.L., Kongthon, A. and J-C Lu (2002). Research profiling: Improving the literature review, Scientometrics, Vol. 53, 351-370