avenge

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?

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.

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.