Modeling of Published Patent Applications to Patent Grants by Regression Analysis and Neural Network
Date Issued
2007
Date
2007
Author(s)
Ji, Jing-Yi
Abstract
A suitable technology forecasting method can help managers grasp the latest trends of market in specific technologies and make the best decisions on product and process developing policies. The number of published patent applications on specific technologies can reflect the significance of those technologies before they are granted. There are considerable amount of researches that use the methods of neural network and statistical regression on modeling and forecasting systems in the domains of business and production management. The objective of this research is to first study the relationships between published patent applications and patent grants, and determine the publish-to-grant time lag. A second step was then conducted to choose the most appropriate modeling time span based on the best fitting of data acquired from USPTO. Finally the models established can be used to estimate the future numbers of patent grants and carry out technology forecasting. Two methods, statistical regression and neural network, were used in the implementation of the proposed methodologies, and three case studies were conducted for presentation. On BPNN, it used MAPE as the effective or ineffective determination of modeling. On Statistic regression analysis, it used T, P and F test as the modeling determination.
Subjects
technological forecasting
backpropagation
BPNN
multinomial regression analysis
polynomial regression analysis
Type
thesis
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