Effective Identification of Technological Opportunities for Radical Inventions Using International Patent Classification: Application of Patent Data Mining

Yang, Wendan; Cao, Guozhong; Peng, Qingjin; Zhang, Junlei; He, Chuan

doi:10.3390/app12136755

Cited by 2 publications

(1 citation statement)

References 48 publications

(59 reference statements)

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“…AdaBoost (Adaptive Boosting) algorithm proposed by Freund and Schapire [35]. The basic idea of the algorithm is as follows: Initially, each of the 𝐺 samples in the dataset is assigned the same weight of 1/𝐺.…”

Section: Adaboost Algorithm Optimized Based On Gamentioning

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: Adaboost Algorithm Optimized Based On Gamentioning

confidence: 99%