The measurements and determinants of patent technological value: Lifetime, strength, breadth, and dispersion from the technology diffusion perspective

Song, Haoyang; Hou, Jianhua; Zhang, Yang

doi:10.1016/j.joi.2022.101370

Cited by 4 publications

(1 citation statement)

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“…Consequently, supported by strategic initiatives and policy encouragement from various countries, the global patent application volume has been increasing year by year. According to a report released by the World Intellectual Property Organization, the number of patent applications submitted by over 150 countries and regions worldwide reached 3.4 million in 2022 (Kong et al 2023). However, as the volume of patent applications continues to rise, the quality of patents varies greatly.…”

Section: Introductionmentioning

confidence: 99%

Research on High-Value Patent Identification Model from Perspective of Patent Transfer

Wu,

Li,

Han

et al. 2024

Preprint

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Accurately identifying high-value patents can be difficult with the dramatic increase in the number of patent applications. This leads to a low rate of commercialization of patent achievements. Whether a patent is transferred or not is an important reflection of the value of the patent. In order to solve above problems, we proposed a high-value patent identification model that combines hybrid sampling technology and ensemble learning algorithm. First, we add technical capacity of patentees based on traditional high-value patent identification indicators to reconstruct the indicator system. Then we reduce the identification indicator system for high-value patents to eliminate redundant indicators. Second, we use Adaptive Synthetic Sampling - Local Outlier Factor (ADASYN-LOF) to expand minority samples to balance the data. Finally, we use Genetic Algorithm (GA) to optimise the parameters of AdaBoost. For clarity, this model is called the ADASYN-LOF-GA-AdaBoost model. To test the effectiveness of above model, we use patent data in field of scientific instruments. The results demonstrate that the proposed model achieves ACC of 94.47%, AUC of 94.87%, recall of 97.54%, and F1-score of 95.23%. The results show that ADASYN-LOF-GA-AdaBoost model performs better than other models. Therefore, this model can effectively identify high-value patents with transfer potential.

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Section: Introductionmentioning

confidence: 99%