A Time-Series-Based Technology Intelligence Framework by Trend Prediction Functionality

Technology Intelligence (TI) indicates the concept and applications that transform data hidden in patents or scientific literature into technical insight for technology development planning and strategies formulation. Although much effort has been put into technology trend analysis in existing research, the majority of the results are still obtained from expert opinions on the basis of historical trends presented by content-based Technology Intelligence tools. To improve this situation, this paper proposes a time-series-based framework for TI that enables the system to be more effective when dealing with trend prediction requirements. Time-series analysis module is first applied in TI framework to process patent time series for technology trend predictions in a real sense, at the same time overcome the problem that prediction of future data points' values is insufficient to support TI construction. Based on explicit patent attributes and unknown patterns learned from the historical data, the framework combines the "trend" and "content" knowledge by analyzing both time-related property and semantic attributes of patent data, to support technology development planning more efficiently and satisfactorily. A case study is presented to demonstrate the validity of trend prediction functionality, which is the emphasis of the whole framework.