"Monte from Montana" was born and raised near Glacier National Park. He is a strong, sober mind that likes to solve problems in order to help people. Following in his father's footsteps, he began teaching high school mathematics (BYU: mathematics with minors in Physics and Spanish). The excitement of the dot-com era led Monte to Monterey California where he became a Senior Software Engineer doing web-application development for an Internet Company. Following the bubble-burst, he returned to BYU (MBA: Marketing Research). Monte is concurrently working toward his Ph.D. in Marketing and a M.S. in Statistics at WSU in Pullman, Washington. Generally, he likes to identify innovative statistical techniques that can help solve marketing problems. Specifically, his interests are in Entrepreneurial Innovation, U.S. Patent Data, and Internet Consumer Behavior. Outside of Marketing, Monte enjoys his family, a good game of basketball, golf, and chess.

Patent data is publicly available, serves as a instrument for doing patent-level and firm-level analysis for both private and public firms, and amidst the modern information age, may be the only way to secure intellectual property. Patent counts or forward-citation counts have been traditionally used to measure the innovation portfolio of a firm. Using network analysis, a variation of Google's PageRank algorithm is introduced to the patent citation network to define an objective measure for radical innovation -- ``Patent Rank". Two model types are considered: simple structure and technology ``class-match" using two temporal forms: cumulative network and five-year moving window. All utility patents from 1976--2009 will be analyzed; over 5.6 million patents and 40 million citations are evaluated to produce 332 million Patent Rank scores. Useful distributional properties are considered and these objective scores are compared to a recent subjective survey performed by PBS to assess the question: What are the most radical innovations of the modern era?

Assessing a firm's innovation portfolio is a challenge? Even more difficult is estimating its future value? This paper applies the principles of the Bass model of diffusion of innovation \citep{Bass:1969} to the estimation of forward citations, ``class-match" dampened forward citations, and the newly introduced Patent Rank Scores. The cumulative diffusion will be modeled using a generalized logistic function known as the Richards' curve \citep{Richards:1959}. To estimate the parameters of the the model, the Newton-Raphson method is used. Over 22,000 randomly selected patents from 1976--2008 will be individually modeled, and diffusion patterns will be classified based on the parameters of the model. Valuation of innovation can be objectively assessed, and future valuation can be predicted based on each innovation's specific diffusion pattern.

There has been a call for 'new' patent data (Kortum - see Tellis et al. 2009). I believe that I can contribute to the field of marketing strategy by improving the data available, and describing its potential uses. The new data source allows for large and rich information regarding patents that can be used in many types of strategic analyses. The most recent run of these data consisted of 73 IT firms in the S&P 500. Collecting data from January 1996 to June 2009 provides over 192,000 patents with information about forward/backward citations, classification matches, and more. The programming process to run this list took nearly 36 hours as it had to analyze over 3 million patents to create the informative dataset. This is my definition of new data, and the process is continuous and ongoing: (1) All Patent Data has been harvest (8 million patents); (2) Parsed Data is currently being stored in database format; (3) Firm boundary issues [IBM, Internation Business Machines, mergers, misspellings, etc.]; (4) with an intent to do new modeling research on the patent data: (a) Diffusion of Radical Innovations (patents); (b) Patent Rank (e.g., Page Rank applied to patent network of citations) - structural and weighted ranks (e.g., classification matching); (c) EIQ; (d) Race to the Patent Office; (e) Patent Pending

My interests in marketing strategy are related to entrepreneurs, entrepreneurial startups, and their perceptions. Specifically, how do they make sense of the information they perceive in the market place and how do these perceptions influence their marketing strategies for their entrepreneurial ideas.