Abstract
The current patent value evaluations increasingly focus on serving realistic predictive scenarios, emphasizing the commercial potential of patents at the early stage from the ex-ante perspective. This requirement poses a serious challenge: those classical dynamic indicators that have been proved to be effective in the literature may not be valid for commercial patent potential prediction from the ex-ante perspective. Thereupon, this study groups the dynamic indicators into cross-sectional indicators and longitudinal indicators. Then, a patent commercial potential prediction framework is proposed from the ex-ante perspective, in which the impact of the chronological order on predictive models is investigated comprehensively. More specifically, this study collects the USPTO cancer-related dataset from 2003 to 2013 as the training set, and combines three dynamic indicators (cross-sectional, longitudinal, and mixed) with classical static indicators to test the prediction performance for the following five years (2014–2018). The biased results caused by the ex-post perspective are indeed observed, and the longitudinal indicators are more sensitive to commercial patent potential, especially in the early stage. The effect of the ex-post perspective will gradually weaken over time, and the cross-sectional indicators provide stable prediction performance three years later. These findings will be helpful for subsequent improvements of commercial patent potential prediction models.
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