Development of Patent Technology Prediction Model Based on Machine Learning

被引:3
作者
Lee, Chih-Wei [1 ]
Tao, Feng [1 ]
Ma, Yu-Yu [2 ]
Lin, Hung-Lung [3 ]
机构
[1] Jinan Univ, Inst Ind Econ, Guangzhou 510632, Peoples R China
[2] Minnan Normal Univ, Sch Educ Sci, 36 Shi Qian Zhi St, Zhangzhou 363000, Peoples R China
[3] Sanming Univ, Sch Econ & Management, 25 Ching Tung Rd, Sanming 365004, Peoples R China
关键词
patent technology; intellectual property; automobile industry; artificial neural network; machine learning; ensemble learning; RISK; NETWORK; STORAGE;
D O I
10.3390/axioms11060253
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Intellectual property rights have a great impact on the development of the automobile industry. Issues related to the timeliness of patent applications often arise, such as the inability of firms to predict new technologies and patents developed by peers. To find the proper direction of product development, the R&D departments of enterprises need to accurately predict the technology trends. Machine learning adopts calculation through a large amount of data through mathematical models and methods and finds the best solution at the fastest speed through repeated simulation and experiments, to provide decision makers with a reference basis. Therefore, this paper provides accurate forecasts through established models. In terms of the significance of management, the planning of future enterprise strategy can be divided into three stages as a short-term plan of 1-3 years, a medium-term plan of 3-5 years, and a long-term plan of 5-10 years. This study will give appropriate suggestions for the development of automobile industry technology.
引用
收藏
页数:26
相关论文
共 54 条
[41]   Dynamic relationships of knowledge creation activities in supply chains: Evidence from patent data in the US auto industry [J].
Lin, Yan ;
Chen, Jian ;
Chen, Yan .
AFRICAN JOURNAL OF BUSINESS MANAGEMENT, 2011, 5 (32) :12563-12576
[42]   Application of risk-based fuzzy decision support systems in new product development: An R-VIKOR approach [J].
Mousavi, Seyedeh Anahita ;
Seiti, Hamidreza ;
Hafezalkotob, Ashkan ;
Asian, Sobhan ;
Mobarra, Rouhollah .
APPLIED SOFT COMPUTING, 2021, 109
[43]  
Phan K., 2013, RES TECHNOL MANAGE, P189, DOI [10.1007/978-1-4471-5097-88, DOI 10.1007/978-1-4471-5097-88]
[44]   Life Cycle Assessment as a decision-making tool: Practitioner and managerial considerations [J].
Pryshlakivsky, Jonathan ;
Searcy, Cory .
JOURNAL OF CLEANER PRODUCTION, 2021, 309
[45]   Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble Learning [J].
Suhail, Yasir ;
Upadhyay, Madhur ;
Chhibber, Aditya ;
Kshitiz .
BIOENGINEERING-BASEL, 2020, 7 (02) :1-13
[46]   Technology opportunity discovery of proton exchange membrane fuel cells based on generative topographic mapping [J].
Teng, Fei ;
Sun, Yuling ;
Chen, Fang ;
Qin, Aning ;
Zhang, Qi .
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2021, 169 (169)
[47]   A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry [J].
Tsang, Yung Po ;
Wong, Wai Chi ;
Huang, G. Q. ;
Wu, Chun Ho ;
Kuo, Y. H. ;
Choy, King Lun .
ENERGIES, 2020, 13 (15)
[49]   Making the right business decision: Forecasting the binary NPD strategy in Chinese automotive industry with machine learning methods [J].
Wang, Xinyi ;
Zeng, Deming ;
Dai, Haiwen ;
Zhu, You .
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2020, 155
[50]  
Xiao F., 2013, P FISITA 2012 WORLD, P447