Multiple patent network analysis for identifying safety technology convergence
| Published date | 01 July 2019 |
| Pages | 269-285 |
| DOI | https://doi.org/10.1108/DTA-09-2018-0077 |
| Date | 01 July 2019 |
| Author | Jeonghwan Jeon,Yongyoon Suh |
| Subject Matter | Library & information science |
Multiple patent network analysis
for identifying safety
technology convergence
Jeonghwan Jeon
Department of Industrial & Systems Engineering/Engineering Research Institute,
Gyeongsang National University, Jinju, The Republic of Korea, and
Yongyoon Suh
Department of Safety Engineering,
Pukyong National University, Busan, The Republic of Korea
Abstract
Purpose –Using the large database of patent, the purpose of this paper is to structure a technology
convergence network using various patent network analysis for integrating different results according to
network characteristics.
Design/methodology/approach –The patent co-class analysis and the patent citation analysis are applied
to discover core safety fields and technology, respectively. In specific, three types of network analysis, which
are centrality analysis, association rule mining analysis and brokerage network analysis, are applied to
measure the individual, synergy and group intensity.
Findings –The coresafety fields derived fromthree types of network analysisused by different natureof data
algorithms are compared with each other to understand distinctive meaning of cores of patent class such as
medical safety, working safety and vehicle safety, differentiating network structure. Also, to be specific, the
authors find thedetailed technology contained in the corepatent class using patent citationnetwork analysis.
Practical implications –The results provide meaningful implications to various stakeholders in organization:
safety management, safety engineering and safety policy. The multiple patent network enables safety manager to
identify core safety convergence fields and safety engineers to develop new safety technology. Also, in the view of
technology convergence, the strategy of safety policy can be expanded to collaboration and open innovation.
Originality/value –This is the initial study on applying various network analysis algorithms based on patent
data (class and citation) for safety management. Through comparison among network analysis techniques, the
different results are identified and the collective decision making on finding core of safety technology convergence
is supported. The decision maker can obtain the various perspectives of tracing technology convergence.
Keywords Technology convergence, Safety technology, Patent network analysis, Centrality analysis,
Association rule, Brokerage analysis
Paper type Research paper
1. Introduction
As both industries and society are continuously developing, the importance of managing
industrial accidents is increasing in various perilous fields such as manufacturing factories,
chemical plants, construction sites and everyday life (Strauch, 2015). As risks of these
industrial accidents have been derived from technology and industry convergence (Kohler
and Som, 2014), many types of accidents are emerging in modern convergence industries
(Swuste et al., 2010). Thus, for occupational safety, the systematic management of these
industrial accidents is inevitable. Aboveall, it should be noted that these industrial accidents
have pointed out a distinctfeature of safety convergencebetween industrial fields. Becauseof
the complex convergence of various technologies such as electricity, mechanics, chemicals,
ergonomics, construction, and information technologies, it has become difficult to discover
critical problems and faults for occupational safety (Abdat et al., 2014; Hollnagel, 2014). Data Technologies and
Applications
Vol. 53 No. 3, 2019
pp. 269-285
© Emerald PublishingLimited
2514-9288
DOI 10.1108/DTA-09-2018-0077
Received 10 September 2018
Revised 21 February 2019
30 March 2019
Accepted 20 April 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2514-9288.htm
This work was supported by the Ministry of Education of the Republic of Korea and the National
Research Foundation of Korea (NRF-2018S1A5A8027985).
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Multiple patent
network
analysis
Despite this difficulty, however, manyindustries have still attempted toinvestigate solutions
in their own field alone. Thus, to solve such problems derived from industrial convergence,
safety technology is being developed through the convergence among various technologies
(Kokangul etal., 2017; Patriarca et al., 2017). It is also requiredto consider the interdisciplinary
approach of ergonomics, machinery, chemistry, textile and building systems (Hale and
Hovden, 1998; Song and Suh, 2019).
The research on technology convergence has been carried out in the last decade in a
variety of fields based on a lot of data sets (No and Park, 2010; Caviggioli, 2016). A number
of methodologies for data analytics have already been proposed to investigate a core of
technology convergence in terms of function, field and industry (Paulhiem, 2014). In
particular, the social network effect has been focused to explore market opportunities
through interaction among industrial knowledge flows (Kang and Kim, 2017). However, in
safety management, these approaches to identifying state-of-the-art safety technology
convergence have not been applied so far because safety issues in an individual industry
field are usually considered. As technology convergence is critical to designing recently
advanced systems, risks in recent industrial systems have become more complicated than
the previously simple systems (Wahlstrom and Rollenhagen, 2014).
For identifying these knowledge flows in technology convergence network, the patent
analysis is a usefulapproach to identifying thedrivers of technology convergence(Caviggioli,
2016). For the levelof analysis, patent analysis is dividedinto two parts: class level and patent
level. On the one hand, it is especially true that the patent is a fruitful resource for
investigating the safety convergence using the information classified into various industry
fields according to patent classification (Leydesdorff et al., 2014; Park and Yoon, 2014). The
co-classification (hereafter referred to as “co-class”) information is manly used through the
International Patent Classification (IPC) code for identifying the converging fields of safety
technology. In general, patents have multiple classes because most patents can beapplied in
various technology fields. These multiple classes are defined as the co-class. For example,
when such a patent related to information technology includes mobile technology for
unmanned vehicles,this patent has multiple codes related to mobile technology and vehicles.
In this case, it should be noted that this patent represents convergence for safety between
mobile and vehicle technologies. Thus, the co-class is a significant measure for monitoring
convergence of industries. On the other hand, the patent itself has the information of
knowledge flowin terms of citing and cited patents. The patentcitation network is structured
according to the knowledge flows from cited to citing patents. Technologyknowledge shared
across the technology classes is also identified by using text analytics on patent documents.
In this respect, data technologies using patent data set on class, citation, and text
information can be applied to the field of safety management, focusing on network analysis.
To be specific,this study aims to explore technologyopportunities and solutions in thefield of
safety for structuring various formsof convergence network using multiple network analysis
approaches. The multiple network analysis is used to sequentially identify the important
convergence network with respect to different perspectives in terms of field-level and
technology-level analyses (Yan and Guan,2018). The field-level analysis asthe macro-level is
to explore core fields of relating safety technologies and the technology-level analysis as the
micro-level is to identify the specific technology convergence in each of the core fields.
First, for the field-level analysis,three types of network analysis techniques are conducted
to represent multiple safety convergence networks including core nodes and their patterns:
centrality analysis, brokerage network analysis and association rule mining (ARM) analysis.
Previous studieson identifying convergencepatterns have been usually limited in conducting
single network analysis that has only unique purpose of node-based or group-based analysis
(Park and Yoon, 2014;Suh and Kim, 2015; Kim et al., 2016). As a result, core nodes which are
discovered from single network analysis are not convincing and are biased according to
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