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

An application of the Patent Rank Score will be used to validate the concept of economic development and firm value. Specifically, it will be shown that the Patent Rank Score will provide improved explanatory power over previous measures of radical innovation and firm performance. \citet{Mizik:2003} described the importance of strategic emphasis and value creation/appropriation on firm performance. In the analysis, firms were grouped into low-, stable-, and high- technology groups. In the first analysis, the firms are objectively grouped based on Patent Rank Scores. Further analysis will model firm performance based on stock returns and strategic emphasis \citep{Mizik:2003}. Mediation of innovation using Patent Rank Scores will also be considered.

Monte J. Shaffer is a fourth-year Ph.D. student and job market candidate (2011) in the Department of Marketing at Washington State University. Monte is currently working on his marketing dissertation in Entrepreneurial Innovations. Prior to joining Washington State University, Monte received a Bachelor in Mathematics / MBA in Marketing from Brigham Young University (BYU) in Provo, UT.

"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.

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.