A patent time series processing component for technology intelligence by trend identification functionality

Technology intelligence indicates the concept and applications that transform data hidden in patents or scientific literatures into technical insight for technology strategy-making support. The existing frameworks and applications of technology intelligence mainly focus on obtaining text-based knowledge with text mining components. However, what is the corresponding technological trend of the knowledge over time is seldom taken into consideration. In order to capture the hidden trend turning points and improve the framework of existing technology intelligence, this paper proposes a patent time series processing component with trend identification functionality. We use piecewise linear representation method to generate and quantify the trend of patent publication activities, then utilize the outcome to identify trend turning points and provide trend tags to the existing text mining component, thus making it possible to combine the text-based and time-based knowledge together to support technology strategy making more satisfactorily. A case study using Australia patents (year 1983–2012) in Information and Communications Technology industry is presented to demonstrate the feasibility of the component when dealing with real-world tasks. The result shows that the new component identifies the trend reasonably well, at the same time learns valuable trend turning points in historical patent time series.