Please wait a minute...
Data Analysis and Knowledge Discovery
Current Issue | Archive | Adv Search |
Research on Influencing Factors Patent Examination Cycle: Case Study of Artificial Intelligence in China
Ou Guiyan,Pang Na,Wu Jiang
(School of Information Management, Wuhan University, Wuhan 430072, China) (Department of Information Management, Peking University, Beijing 100871, China)
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

[Purpose] We aim to examine the factors that may affect the patent examination cycle and explore the mechanism behind the patent examination cycle in the field of artificial intelligence in China. [Method] This article takes 78,254 invention patent applications in the field of artificial intelligence in China as the research object, uses the Kaplan-Meier method in survival analysis and the COX proportional hazard regression model to explore the overview of patent examination in the field, and analyzes the characteristics of patent objects and patent subjects based on characteristics, explore the factors that significantly affect the patent examination cycle in this field. [Results] The results show that the average survival period of the overall Chinese invention patent examination process in the field of AI is 32.81 months. Among them, the number of claims, the number of IPC classification numbers, and the number of inventors is the protective factors of the patent examination cycle, which promotes its extension; the number of patent citations is a risk factor, and the more patent citations, the shorter the time required to obtain authorization. Among the types of applicants, universities and scientific research institutions, as well as institutions and organizations, all spend a shorter time on patent examination than individuals. Surprisingly, companies will reduce the risk rate of patent application-authorization, which requires a longer patent examination cycle. [Limitations] The patent examination cycle is closely related to the examination process of the patent office and the personal characteristics of patent examiners. The article failed to obtain more fine-grained data related to it for analysis. [Conclusion] In order to optimize the patent examination procedure and shorten the patent granting cycle, this paper proposes that we can further combine different technical fields and the characteristics of the applicant to establish a diversified examination mode, strengthen the use of automated technology in the patent examination process, and establish classification examination standards to improve the overall patent examination efficiency.

Key words patent examination cycle      survival analysis      artificial intelligence      
Published: 20 June 2022
ZTFLH:  G251  

Cite this article:

Ou Guiyan, Pang Na, Wu Jiang. Research on Influencing Factors Patent Examination Cycle: Case Study of Artificial Intelligence in China . Data Analysis and Knowledge Discovery, 0, (): 1-.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021-1233     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y0/V/I/1

[1] Zhang Huaping, Li Linhan, Li Chunjin. ChatGPT Performance Evaluation on Chinese Language and Risk Measures[J]. 数据分析与知识发现, 2023, 7(3): 16-25.
[2] Zhang Zhixiong, Yu Gaihong, Liu Yi, Lin Xin, Zhang Menting, Qian Li. The Influence of ChatGPT on Library & Information Services[J]. 数据分析与知识发现, 2023, 7(3): 36-42.
[3] Qian Li, Liu Yi, Zhang Zhixiong, Li Xuesi, Xie Jing, Xu Qinya, Li Yang, Guan Zhengyi, Li Xiyu, Wen Sen. An Analysis on the Basic Technologies of ChatGPT[J]. 数据分析与知识发现, 2023, 7(3): 6-15.
[4] Ou Guiyan, Pang Na, Wu Jiang. Influencing Factors of Patent Examination Cycle: Case Study of Artificial Intelligence in China[J]. 数据分析与知识发现, 2022, 6(8): 20-30.
[5] Song Ruoxuan,Qian Li,Du Yu. Identifying Academic Creative Concept Topics Based on Future Work of Scientific Papers[J]. 数据分析与知识发现, 2021, 5(5): 10-20.
[6] Lv Xueqiang,Luo Yixiong,Li Jiaquan,You Xindong. Review of Studies on Detecting Chinese Patent Infringements[J]. 数据分析与知识发现, 2021, 5(3): 60-68.
[7] Lu Wei,Luo Mengqi,Ding Heng,Li Xin. Image Annotation Tags by Deep Learning and Real Users: A Comparative Study[J]. 数据分析与知识发现, 2018, 2(5): 1-10.
[8] Huang Kun,Fu Shaohong. Some Related Problems Faced by the Application of It in Information Retrieval[J]. 现代图书情报技术, 2001, 17(3): 26-29.
[9] Wang Yong,Ni Bo,Ding Wei,Cheng Bin. Advances of Computer Software Technology in 20 Century[J]. 现代图书情报技术, 2000, 16(6): 6-9.
[10] Niu Yun,Zhu Xianyou. The Prospect of the Application of Neural Network in Text Retrieval[J]. 现代图书情报技术, 1997, 13(3): 19-21.
[11] Zhang Daofu,Li Yiping. THE BASIC MODEL AND INFO RMATION TECHNOLOGIES OF MODERN LIBRARIES AND INFORMATION CENTRES[J]. 现代图书情报技术, 1995, 11(2): 40-45.
[12] . [J]. 现代图书情报技术, 1995, 11(1): 43-45.
[13] He Qin. THE POTENTIAL APPLICATION OF KNOWLEDGE REPRESENT ATION IN INFORMATION RETRIEVAL SYSTEM[J]. 现代图书情报技术, 1994, 10(5): 47-49.
[14] . [J]. 现代图书情报技术, 1994, 10(1): 42-46.
[15] . [J]. 现代图书情报技术, 1991, 7(3): 46-49.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn