Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163644
Title: Analysis of patent application attention: a network analysis method
Authors: Mao, Shihao
Hu, Yuxia
Yuan, Xuesong
Zhang, Mengyue
Qiu, Qirong
Wu, Peng
Keywords: Science::Mathematics::Statistics
Issue Date: 2022
Source: Mao, S., Hu, Y., Yuan, X., Zhang, M., Qiu, Q. & Wu, P. (2022). Analysis of patent application attention: a network analysis method. Frontiers in Physics, 10, 893348-. https://dx.doi.org/10.3389/fphy.2022.893348
Journal: Frontiers in Physics 
Abstract: Patent is an important embodiment of innovation. Before patent application, many people will check a patent application process on the Internet to understand the steps of a patent application. In fact, these people’s search is also a means to understand whether innovative enterprises or individuals imply the importance of innovation. It has become a new crucial problem to obtain more information about time-series data. Research has found that the concept of VG can provide deeper information in time-series data so that it can understand the information of patent applications more comprehensively. After analyzing the data from 1 January 2011 to 31 December 2018, we find: i) there are very few peaks and valleys, and 80% of searches are in the normal range. ii) according to the central value of the ranking, it can be found that the peaks of the annual patent application search times time series occurred in December last year, after January, February of this year or after August-October, and iii) after clustering the time series data, we find that the attention of people shows noticeable segmentation effect.
URI: https://hdl.handle.net/10356/163644
ISSN: 2296-424X
DOI: 10.3389/fphy.2022.893348
Rights: © 2022 Mao, Hu, Yuan, Zhang, Qiu and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:NTC Journal Articles

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