Technology Forecasting Via Published Patent Applications and Patent Grants

Abstract

The objective of this research is to study and establish the relationships between published patent applications and patent grants for exploring the technology development trend on a specific technology since more and more patents have their applications published before they are granted. Two modeling algorithms based on the patent grant/publish ratio as well as one long-term modeling algorithm based on the average publish-to-grant lag, were developed accordingly. The relationships between patent grants and published patent applications were constructed through two case studies on Magnetic Random Access Memory and Organic Light-Emitting Diode technologies and corresponding forecasts were then conducted. Comparing to the traditional time-series Autoregressive Integrated Moving Average method, the predicting power of the modeling algorithms based on the patent grant/publish ratio was satisfactory. On the other hand, the modeling algorithm based on the characteristic of average publish-to-grant lag has shown superior predicting power. Results from these two applications help us to validate the proposed methods and appropriate tools for forecasting the patent grants.