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Article type: Research Article
Authors: Zhang, Qihang | Jiang, Jie*
Affiliations: School of Intellectual Property, Nanjing University of Science and Technology, Nanjing, Jiangsu, China
Correspondence: [*] Corresponding author: Jie Jiang, School of Intellectual Property, Nanjing University of Science and Technology, Nanjing, Jiangsu 210000, China. E-mail: [email protected].
Abstract: In the context of big data and the Internet of Things, this article explores the impact of corporate patent quality on corporate performance through analysis of corporate patent quality. Starting from multiple influencing factors, measure the patent value of enterprises to promote the economic benefits. Focus on the analysis of the practical conversion capacity of patents and the impact on corporate performance, establish a mathematical evaluation model, and explore the specificity of the patent quality through the method of regression analysis. This article has studied the important role of patent technology to improve the efficiency of enterprises, and explores the overall level of corporate performance management on how to maximizes the overall level of corporate performance management. The system of measures for patent technology to promote performance development under the background of big data constructed by the research has a very good reference for the sustainable development of enterprises at the current stage. From the existing research results, there are problems such as too few analysis samples and slow data updates, and these problems have been better solved in this study. The research analyzes the comprehensive impact of patent quality on enterprises through big data, highlights the positive relationship between patent quality and enterprise performance, and effectively fills the gap in current research.
Keywords: Big data, Internet of Things, patent quality, corporate performance, regression analysis, impact research, measures
DOI: 10.3233/JCM-226565
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 23, no. 1, pp. 457-467, 2023
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