research-article

Data Analysis of Railway Industry Patents

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Yao Zhang,

Gening ZhangAuthors Info & Claims

ICBDR '18: Proceedings of the 2nd International Conference on Big Data Research

Pages 27 - 32

https://doi.org/10.1145/3291801.3291836

Published: 27 October 2018 Publication History

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Abstract

In this paper, we analyze patents of railway industry in international patent database produced by the European Patent Office from year 2013 to 2017. Based on statistics of patent records in database, we build Auto-Regressive-Moving-Average (ARMA) models to predict the number of different types of patents in railway industry, the analysis results show that high prediction accuracy can be obtained. Furthermore, we propose a patent value evaluation scheme to evaluate patent values in railway industry. Through our work, the hot research spots, technology trajectories and development tendency of patents in railway industry can be adequately understood.

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https://worldwide.espacenet.com/classification. International patent database produced by the European Patent Office.

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Lee YHan YKim SLee C(2022)Patent Data Analytics for Technology Forecasting of the Railway Main TransformerSustainability10.3390/su1501027815:1(278)Online publication date: 24-Dec-2022
https://doi.org/10.3390/su15010278
Zhang YWang Q(2020)Patent Prediction Based on Long Short-Term Memory Recurrent Neural NetworkProceedings of the 9th International Conference on Computer Engineering and Networks10.1007/978-981-15-3753-0_28(291-299)Online publication date: 1-Jul-2020
https://doi.org/10.1007/978-981-15-3753-0_28

Index Terms

Data Analysis of Railway Industry Patents
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Data stream mining

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ICBDR '18: Proceedings of the 2nd International Conference on Big Data Research

October 2018

221 pages

ISBN:9781450364768

DOI:10.1145/3291801

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Shandong Univ.: Shandong University
University of Queensland: University of Queensland
Dalian Maritime University

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Association for Computing Machinery

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Published: 27 October 2018

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ICBDR 2018: 2018 The 2nd International Conference on Big Data Research

October 27 - 29, 2018

Weihai, China

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Lee YHan YKim SLee C(2022)Patent Data Analytics for Technology Forecasting of the Railway Main TransformerSustainability10.3390/su1501027815:1(278)Online publication date: 24-Dec-2022
https://doi.org/10.3390/su15010278
Zhang YWang Q(2020)Patent Prediction Based on Long Short-Term Memory Recurrent Neural NetworkProceedings of the 9th International Conference on Computer Engineering and Networks10.1007/978-981-15-3753-0_28(291-299)Online publication date: 1-Jul-2020
https://doi.org/10.1007/978-981-15-3753-0_28

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Software patents: a replication study

Patents: To file or not to file?

Using the comprehensive patent citation network (CPC) to evaluate patent value

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Software patents: a replication study

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Using the comprehensive patent citation network (CPC) to evaluate patent value

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