Which type of dynamic indicators should be preferred to predict patent commercial potential?

Guancan Yang, Guoxuan Lu, Shuo Xu, Liang Chen and Yuxin Wen

Technological Forecasting and Social Change, 2023, vol. 193, issue C

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.

Keywords: Longitudinal Indicator; Cross-sectional indicator; Dynamic indicator; Commercial potential; Ex-ante perspective (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:193:y:2023:i:c:s0040162523003220

DOI: 10.1016/j.techfore.2023.122637

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