Zero Inflated Poisson and Negative Binomial Regressions for Technology Analysis
Technology analysis is to understand target technology by analyzing diverse information of developed technologies. Using the results of technology analysis, we can perform the technology management such as technology forecasting, technological innovation, and technology valuation for research and development (R&D) planning. In addition, the R&D planning is built upon in order to improve technological competitiveness of a company. Patent analysis is a popular approach to technology analysis. Many researches on patent analysis have been done because patent documents contain diverse and complete information on developed technology. However, the documents are not suitable for patent analysis based on statistics. So, in much of the work on patent data analysis, the researchers transformed the patent documents into structured data using text mining techniques. Generally, the structured data set has a sparsity problem, that is, most elements of the data are zero valued. The existing researches in patent analysis have not considered this zero inflated problem, but it places serious limits on performance when we analyze the patent data. In this paper, to overcome this problem, we propose a methodology for patent analysis using zero inflated Poisson and negative binomial regressions. We apply the proposed methodology based on zero inflated Poisson and negative binomial regression models to Apple's technology analysis.