**3. Data and methodology**

Advanced bibliometric analysis is regarded as a powerful method to answer questions, such as "How can we keep track of the increasing number of scientific articles? Are there specific patterns hidden in this mass of published knowledge at a meta-level, and if so, how can these patterns be interpreted?" which enable us to analyze structures and dynamics of fields [31, 32]. Forty-eight articles in English identified by merging the query of terms<sup>1</sup> in the scope of e-mobility (e.g., electric vehicles, hybrid electric vehicle, etc.) with the topic search of patent (TS = patent\*) from the Web of Science™ Core Collection (WoS) database up to 2017 are discussed in this chapter aiming to investigate the current progress of patent research on e-mobility. Visualizations are addressed throughout the discussion by explaining how they are produced and how they can be interpreted. Extrinsic data to the text such as the publication year, keywords, and citations are synthetically measured in a co-occurrence analysis, a technique that captures the frequency of pairs of words, phrases, or references in and between articles [33]. The first step is to represent the association of research topics and to observe the progress along with the time, source, and flow of knowledge, eventually to understand the development of scientific fields. The common base and expansion of knowledge are structured through backward and forward references by performing a co-citation and bibliographic coupling analysis, respectively, and the former depends on the frequency when two documents are cited together whereas the latter occurs when two works reference a common third work [33, 34]. Then, intrinsic information regarding the reason for performing patent analysis of e-mobility issues, research limitations, and trends dug out from abstracts, methods, conclusive parts, and recent highly cited papers are collected and categorized on a sentence-by-sentence basis in order to advocate for greater attention to article content in addition to the bibliometric analysis.

> explored ranging from the topics of patent-based indicators and approaches to technologies and the automotive industry as well as green products and market since 2013. Recently, the focus of patent research lies with the emergence of hybrid devices. E-mobility issues are inevitably tied to carbon emissions, efficient strategy, and sustainable development, which is

Patent Research in a Period of Industry Transformation: A Focus on Electromobility

http://dx.doi.org/10.5772/intechopen.75579

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Filtered by the minimum number of citations, 51 of 1788 references are collected and form four main clusters (**Figure 2**). The paper published by van den Hoed in 2005 [36] is represented as a key node among a group of emerging technology-based studies in red at the interface between discussions on emerging eco-innovation evaluation (green nodes on the top) and the cluster of papers adopting patent-based indicators in measuring technological change (yellow nodes in the middle). Note that citation is more frequent and probably more disciplined on the overall innovation performance research side, which also provides us with different kinds of evidence for the deficiency in e-mobility patent study. Among the technological forecasting-focused papers on the right side of **Figure 2** (blue nodes), the co-citation analysis highlights authors [37–39] who have engaged in discussions with the joint use of bibliometric and patent analysis. The first cluster indicated in red is led by research from van den Hoed and Bakker [36, 40], who share an interest in the development of fuel cell technology. Citations categorized into the second cluster have an earlier average publication year than that of the first cluster, including studies on e-mobility innovation coupled with policy, economic, and

proved in cluster 5 and 6.

*4.1.2. Citation-based knowledge flows*

**Figure 1.** Co-occurrence analysis of keywords with average publication year.
