An Income Model Using Historical Data, Power-Law Distributions and Monte Carlo Method for University Technology Transfer Offices

Abstract

Engineering education pushes the creation of new technology to solve community problems. The process of technology transfer promotes educational innovation in universities, a vital process that can improve citizens’ quality of life in cities and rural communities. As a result, university technology transfer offices (TTOs) have to create strategies that motivate students and researchers to generate technology. Thus, a primary challenge that TTOs face is to know and communicate the income potential compared to their much more predictable and limited expense budgets. Institutional budgeting for a TTO’s growth would be simplified if the office were on a solid financial footing, i.e., breaking even or making a financial return. Many offices assume that income is unpredictable, that it is a lottery, luck, and more stakes in the fire improve the odds of hitting a winner, etc. These common assumptions or beliefs provide only a vague insight into how to move an intellectual property (IP) portfolio strategy forward. How can a TTO be assessed for quantitative value and not just be a cost center adding qualitative value? This paper illustrates the first steps to understanding how to project potential income versus a much more predictable expense budget, which would allow universities to improve their technology transfer strategy and results. As a result, TTOs would operate under a more sustainable IP portfolio strategy, promote educational innovation in universities, and generate a more significant community impact.