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| An Approval Time Prediction Method Based on Patent Characteristics |
| Xiang Shuxuan1, Li Rui2 |
1.Laboratory of Data Intelligence and Interdisciplinary Innovation, Nanjing University, Nanjing 210023
2.Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu 610207 |
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Abstract Predicting the approval speed of their competitors’ or their own patents is a crucial part of competitive intelligence analysis for innovation subjects. The examination time included the validity period of a patent. Some examiners took eight or even 12 years to approve a patent. This greatly reduces the validity period of a patent for up to 20 years from the date of application and has a considerable impact on its value. This study aimed to construct an information science method by mining a series of features of patent literature to form a prediction model, which can be used to predict whether a patent will be granted and the speed of approval. The research consisted of two logical parts: approval and speed prediction. First, correlation analysis and the Cox proportional risk regression model were applied to examine the selected characteristics. A patent approval time prediction model (MACP) based on the relation of examination decision and pendency was then constructed using the method of auxiliary learning, including the characteristics of technology internal capacity, technology structure, technology function, technology concept clarity, applicant, and inventor. Experimental results show that the MACP based on the combination of auxiliary tasks outperformed the existing baseline model. Because the MACP model can effectively learn and use more knowledge of the patent examination process, it can reduce the dependence on the amount of data and achieve a better prediction effect.
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Received: 12 March 2024
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