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.
Chen, Dar-Zen; Lin, Chang-Pin; Huang, Mu-Hsuan; and Chan, Yi-Tung
"TECHNOLOGY FORECASTING VIA PUBLISHED PATENT APPLICATIONS AND PATENT GRANTS,"
Journal of Marine Science and Technology: Vol. 20:
4, Article 1.
Available at: https://jmstt.ntou.edu.tw/journal/vol20/iss4/1