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Identification and Analysis of Converging Technology Based on Patent Co-Classification Relationship

Authors:

Lucheng Lv,

Tao Han,

Yajuan Zhao,

Xuezhao Wang,

Ping ZhaoAuthors Info & Claims

JCDL '18: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries

Pages 363 - 364

https://doi.org/10.1145/3197026.3203903

Published: 23 May 2018 Publication History

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Abstract

Patent documents are an ample source for technical knowledge, and increased dramatically in recent years. This paper aims at identifying and analyzing converging technology based on patent analysis. The identification method of Converging technology is through cluster analysis based on USPC co-occurrence matrix calculated by the cross USPC class patents of five parties during the 2005-2015 years. Finally, 161 converging technologies are identified. Converging technology is mainly distributed in the new generation of information technology, new material industry. High end equipment manufacturing industry is the most active industry in technological convergence.

References

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[2]

Geum Y, Kim C, Lee S, et al. Technological Convergence of IT and BT: Evidence from Patent Analysis{J}. ETRI Journal, 2012, 34(3):439--449

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Tijssen R J W. A quantitative assessment of interdisciplinary structures in science and technology: Co-classification analysis of energy research {J}. Research Policy, 1992, 21(1):27--44

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Shaikh AAlhoori HDownie SZhu QHahn J(2019)Predicting patent citations to measure economic impact of scholarly researchProceedings of the 18th Joint Conference on Digital Libraries10.1109/JCDL.2019.00089(400-401)Online publication date: 2-Jun-2019
https://dl.acm.org/doi/10.1109/JCDL.2019.00089

Index Terms

Identification and Analysis of Converging Technology Based on Patent Co-Classification Relationship
1. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Cluster analysis

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Information

Published In

JCDL '18: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries

May 2018

453 pages

ISBN:9781450351782

DOI:10.1145/3197026

General Chairs:
Jiangping Chen
College of Information, UNT, USA
,
Marcos André Gonçalves
, Brazil
,
Jeff M. Allen
College of Information, UNT, USA
,
Program Chairs:
Edward A. Fox
Virginia Tech, USA
,
Min-Yen Kan
National University of Singapore, Singapore
,
Vivien Petras
Humboldt-Universität zu Berlin, Germany

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 May 2018

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JCDL '18

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JCDL '18: The 18th ACM/IEEE Joint Conference on Digital Libraries

June 3 - 7, 2018

Texas, Fort Worth, USA

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JCDL '18 Paper Acceptance Rate 26 of 71 submissions, 37%;

Overall Acceptance Rate 415 of 1,482 submissions, 28%

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Shaikh AAlhoori HDownie SZhu QHahn J(2019)Predicting patent citations to measure economic impact of scholarly researchProceedings of the 18th Joint Conference on Digital Libraries10.1109/JCDL.2019.00089(400-401)Online publication date: 2-Jun-2019
https://dl.acm.org/doi/10.1109/JCDL.2019.00089

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