Patent Data Analytics for Technology Forecasting of the Railway Main Transformer

Abstract

The railway main transformer is considered one of the most important electrical equipment for trains. Companies and research institutes around the world are striving to develop high-performance railway main transformers. In order to be the first mover for railway main transformer technology, companies and research institutes should predict vacant technology based on the analysis of promising detailed technology areas. Therefore, in this study, a patent analysis to predict vacant technologies based on identified promising IPC technology areas is provided. In order to identify promising detailed IPC technology areas, the technology mapping analysis, the time series analysis, and the social network analysis are conducted based on the patent-IPC matrix, extracted from the data information of 707 patents from the patent database of Korea, China, Japan, United States, Canada, and Europe. Then, through the GTM analysis based on promising detailed IPC technology areas, one vacant technology node and three analysis target nodes surrounding the vacant technology node are obtained to predict vacant technologies. From the analysis, we predict the following three groups of vacant technologies: (1) blowerless technology, (2) oil-free technology, and (3) solid-state technology. This study provides insights on the technology trend in railway main transformers, as well as the analysis framework for the development of R&D strategies based on the patent data.

1. Introduction

The railway main transformer is one of the heaviest and bulkiest of a train’s electrical equipment. To meet the requirements for high-speed rail, passenger comfort, open space for passengers, and a heightened level of supplemental power, the railway main transformer should have technological innovations such as high power, high efficiency, and reduced weight and volume [1,2]. In recent years, many companies and research institutes all over the world are investing in the development of railway main transformer technology. To be competitive in the market, we need more advanced technology than ordinary technology [3,4]. In these circumstances, due to factors such as increased efficiency and reduced costs, more creative and progressive transformer technologies will help to improve railway competitiveness in the future market. Considering the current market situation in which the technological importance of railway main transformers is increasing, it is required to secure technological competitiveness through investment by companies for the development of technology. Therefore, it is necessary to strategically establish an investment direction by predicting a vacant technology group that has not yet been activated based on the promising detailed technology areas of the railway main transformer.

Technology forecasting through patent analysis is a method that is widely used in establishing future strategies for technology development [5]. Patents can provide a variety of valuable information about researched and developed technologies, including indicators of technology trends [6,7]. In addition, the patent includes information on the applicant who applied for the patent and the period and date of application, which can be used to identify competitors in the market through technology trend analysis [8]. Patent analysis to predict vacant technology based on promising IPC technology areas can be useful in establishing technology development strategies for targeting markets and promising technologies in railway main transformers. However, previous research literature mainly focused on qualitative methods through expert evaluations and opinions, and they did not consider the quantitative patent analysis and technology trends at the same time. The qualitative evaluation method is a disadvantage in that a lot of time and costs are incurred.

To overcome the above limitations, this study aims to provide an analysis framework for simultaneously performing technology trend analysis and predicting vacant technology based on promising detailed IPC technology areas identified through quantitative patent analysis. Through this, in the current situation where many companies and research institutes are actively developing railway main transformers, we intend to contribute to providing an analysis framework for R&D strategy development based on patent data by predicting vacant technology based on promising detailed technology areas.

The analysis process of the paper is as follows. First, patent analysis in railway main transformer technology is obtained to identify the result of technology trend analysis. Then, to identify promising detailed IPC technology domains, we obtain technology mapping analysis, time series analysis, and social network analysis results based on the patent-IPC matrix extracted from patent data information. Moreover, through GTM analysis of promising detailed IPC technology areas, vacant technology node and analysis target nodes surrounding the vacant technology node to predict vacant technology are obtained. Lastly, we predict the groups of vacant technology using patent information contained in each node of the obtained analysis target nodes. Furthermore, we discuss the vacant technology of railway main transformers based on our analysis results. The composition of the sectional papers below is as follows. First, a prior literature review related to this study is provided in Section 2. Then, the analysis methodology used in this study is presented in Section 3, and the analysis results are presented in Section 4. Finally, Section 5 and Section 6 conclude the paper by presenting the contributions and limitations through discussion and conclusions on the analysis results.

2. Literature Review

Patents are a valuable source of technological information and a measure of the research and development activities of companies and research institutes around the world [9]. The World Intellectual Property Organization (WIPO), an international organization that discusses policies and institutions related to intellectual property, describes patent applications as a process to protect inventions internationally [10]. Patent analysis is a valuable method for gathering information about new technologies and for measuring the impact of research and development activities [9]. Intellectual property (IP) rights are a form of monopoly rights and are considered a key strategic resource for businesses to improve their overall competitiveness. As carriers of intellectual property, patents allow organizations to use and exploit their invention in a unique way [11]. Patents are guaranteed authority for 20 years from the date of filing. This means that a company can hold onto the specific technology for a long time [12]. Patent data provide valuable information about technology, geography, technology, and applicants [13]. Moreover, patent data provide a valuable source of information about the latest technological developments and potential business opportunities [9]. Therefore, it is important to further explore the technology in real or potential applications by analyzing the related patents [14].

Patent information consists of unstructured and structured data [15]. Patent data are organized in structure, which makes them easy to find and access specific information [9]. Patent analysis is a common method used to evaluate technological innovations and provide insights into their development status [16]. In addition, patent analysis is an important method for technology monitoring because it can show how innovation is developing in a specific sector [17]. Patent analysis can trace the emergence of technology from the past to identify recent technological changes [18]. By analyzing patent information recorded in patent documents, it is possible to identify competitors related to technology by identifying trends in technology and identifying promising detailed technology areas [19]. Patents can be analyzed by extracting the number of applications and inventors corresponding to the IPC class as time series data using the IPC class, explaining the detailed technology area of the patent, and measuring the technology of the patent using the number of family size and the citations [20].

The prior academic literature discussed analysis methods using patent documents to visualize valuable information in the market, and through this, it intended to understand the status of R&D of competitors. In addition, technology trends analysis can be used to establish R&D plans by predicting promising technologies through understanding them [21]. Moreover, previous patent research has used text information, such as titles and abstracts, to analyze patents. For example, previous patent research extracted some keywords to analyze development patterns [22,23,24,25,26] and create technology maps [27,28,29,30,31,32]. In addition, Noh et al. extracted the term frequency–inverse document frequency (TF-IDF) using keywords and IPCs included in the patent information for patent analysis [33]. Traditional keyword-based patent analysis cannot always understand the contextual meaning of words in a broad technology sector [34]. To overcome the limitations of existing analysis methods, our analysis is performed based on the IPC of the patent. The IPC standard taxonomy is used to sort, organize, classify, determine, and search patent documents [35]. IPC (International Patent Classification) is the name of a field in patent documents that was originally established by the Strasbourg Agreement of 1971. IPC information is currently available from the WIPO (World Intellectual Property Organization). The ninth edition of the IPC report was released on 1 January 2009. The IPC structure is well designed and has a high level of public confidence. The classification has a five-level hierarchical structure, with subsections at the top, classes at the middle, subclasses below classes, groups below subclasses, and subgroups below groups. The hierarchy contains 8 sections, 120 classes, 600 subclasses, and around 70,000 groups. Each group can be further broken down into main groups and subgroups. There are main groups in the population of 7000, while the remaining 63,000 individuals are subgroups. The following sentences are examples of an IPC code hierarchical structure (“H01L 27/18”). The section “H” represents a section of the code, and the class “01” represents a class of the code. The subclass “L” represents a subclass of the class, and the main group “27” represents the main part of the code. The subgroup “18” represents a subpart of the main group. IPC codes can be thought of as a subject label regarding the content of a patent document. IPC codes are used to enrich the retrieval method for patent documents [21,30,31,32,34,36].

Patents provide information about relationships between different technologies, which can help companies to choose the most effective technologies for their needs. This information can also help firms make better decisions about which technologies to develop or use. Technology forecasting will help companies make informed decisions, prioritize R&D departments, and leverage knowledge [37]. Previous literature used qualitative and quantitative analysis methods to predict promising technology using patent data. Methods of quantitative analysis were included” “(A) Time Series Analysis (TSA)” and “(B) Social Network Analysis (SNA)”. First, through “(A) Time Series Analysis (TSA)”, the frequency of patent applications by technology was extracted as time series data to predict promising future technology [21,24,28,29,30,31,34,38,39,40,41,42,43,44]. Then, through “(B) Social Network Analysis (SNA)”, the relationship between nodes as a quantitative indicator through centrality indices was extracted, and promising detailed descriptive areas based on the extracted indicators were predicted [22,24,25,26,27,28,29,30,31,32,34,39,40,43,45,46,47,48,49,50]. In addition to the quantitative analysis methods, the qualitative analysis method of “(C) Technology Mapping Analysis (TM)” was also used in previous studies. Through “(C) Technology Mapping Analysis (TM)”, the importance of the level of technology for each area was obtained, and priorities for technology development were identified [21,24,29,30,31,38,40,45,46,47,48,49,50,51]. In addition to predicting promising technology, previous studies have identified vacant technology by analyzing patent information of analysis target nodes through “(D) Generative Topographic Mapping Analysis (GTM)”, identifying empty technology [24,27,28,40,52]. Table 1 summarizes the prior literature using patent analysis to forecast the technology. The methodology is the use of the alphabet mentioned above.

In addition, recent research has been conducted in technology analysis and prediction for railways using patent data. Xiao analyzed patent applications related to railway control system technology and summarized the status of various train control system technologies, but the analysis needs to obtain the direction of innovation through various analysis methods [59]. Du et al. attempted to analyze the market by analyzing the invention patents filed by Thales SEC Transportation in the field of train signal control technology, but there is a limitation in that it only identified trends [53]. Liu and Yang have attempted to explore the technological innovation capabilities (TICs) of high-speed railways (HSRs) using patent information, but there is a limit to the limited use of patent information [51]. Peng and Yu accessed patent data from the patent information platform of China’s Intellectual Property Network and calculated the quantity and composition of patents held by China’s two railway systems, but the country to be analyzed has a limitation of bias [55]. Sun et al. look at how China’s high-speed railway (HSR) construction has led to knowledge ripple effects. As a result, by using patent information, Sun wanted to discover that HSR stimulated the growth of new businesses and patent applications, but there was a limit in considering only the single impact of variables [41]. Hanley et al. attempted to understand the effect of high-speed railways on patent cooperation between Chinese urban class companies, but they were used for analysis using only the simple frequency of patent applications [42]. Zhang and Zhang conducted research on the patent activities of vibration reduction control technology in the field of high-speed railway vehicle systems applied in China, but only a bibliographic analysis was conducted [58]. By mining patent data on magnetic levitation (linear motor car) transportation technology, Gou analyzed patents to understand global competitive trends in magnetic levitation transport technology, but only analyzed trends [56]. Zhang and Zhang analyzed patents from the railway industry in the international patent database produced by the EPO (European Patent Office), but there was a limitation in that the variables used in the analysis were limited [57]. Cho et al. researched to obtain R&D strategies through technology prediction through patent analysis based on patent data to predict vacant technology in electric motor technology applied to railway vehicles, but only through qualitative analysis [52]. Karasev et al. researched the technology trend of foreign railway using bibliometric patent analysis, but there is a limitation in that it is limited to bibliographic analysis [54]. Moreover, Salmi and Torkkeli researched the patent trend of utilizing satellite navigation systems in the railway industry, but there is a limitation in that it is limited to bibliographic analysis [60].

To meet the requirements for high-speed rail, passenger comfort, open space for passengers, and a heightened level of supplemental power, the railway main transformer should have technological innovations such as high power, high efficiency, and reduced weight and volume [1,2]. Based on the above, the analysis of patents related to railway main transformer technologies is essential for technological innovation. Nevertheless, the topic of railway main transformer technology has not yet been researched. Therefore, forecasting vacant technology based on promising detailed technology areas through patent analysis under the theme of railway main transformers is a suitable topic for research. Furthermore, the existing study is lacking in-depth and various approaches to the detailed railway technologies sector. To overcome the above problem, including the existing patent analysis methods and indicators, other in-depth and various approaches, such as patent-IPC matrix extraction, technology mapping analysis, time series analysis, social network analysis, generative topographic mapping, and so on, would improve this research more comprehensively. In this study, we will discuss vacant technology in the railway main transformer through technology trend analysis using patent data and predict vacant technology based on promising detailed IPC technology areas identified through quantitative patent analysis.

3. Methodology

3.1. Research Framework

Figure 1 show that patent analysis is conducted in three stages to identify vacant technology of railway main transformer that is predicted based on promising detailed IPC technology areas identified through quantitative patent analysis.

First, we collected patent data to be analyzed for main transformer technology based on each specific search keyword extracted from the opinions of railway experts. If collected patent data was not related to the railway main transformer, it could not be included in the analysis. Then, the unstructured data of the collected patent data was preprocessed to enable effective data analysis [61,62]. Since raw unstructured data includes unnecessary noise in the analysis, unstructured data that was not related to the data analysis was removed through preprocessing, and then valid unstructured data was extracted [63]. In addition, we obtained a result of technology trend analysis from the collected patent data to understand technology trends for railway main transformer.

Secondly, the patent-IPC matrix was extracted from patent data by text mining. Then, the results of technology mapping, time series, and social network analysis were derived to identify promising detailed IPC technology areas [64].

Lastly, based on the obtained promising detailed IPC technology areas, the study forecasted vacant technology. Generative topographic mapping (GTM) was used to predict vacant technology [65,66]. Through GTM analysis of promising detailed IPC technology areas, vacant technology nodes, and analysis target nodes, surrounding vacant technology nodes to predict vacant technology were obtained. To predict vacant technology, we analyzed patent data containing each analysis target node. Then, based on the results of the patent analysis, the vacant technology of the railway transformer was discussed. Table 2 shows the position of our analysis method in the relevant prior literature about technology forecasting in the railway field. Our study provides a diversified analysis framework compared to previous literature.

3.2. Preprocessing and Patent-IPC Matrix Extraction

Patent-IPC matrix is a matrix representation of the frequency of each IPC code that appears in each patent data. The frequency of the IPC code appearing in each data is expressed as a matrix value, as demonstrated in the example shown in Figure 2. Patent-IPC matrix can be quantified to compare the corresponding IPC codes between patent data, and promising detailed IPC technology areas can be derived by analyzing the various indicators of patent data corresponding to each IPC code. Text data must be preprocessed before analysis to improve the accuracy of the analysis [67]. Even in patent legal documents with many professional legal terms and abbreviations, preprocessing work is required [68]. Preprocessing of text data is a process of converting text data from patent documents into a format suitable for analysis by cleaning text and removing unnecessary words [69]. IPC information of patent data specifying detailed technology may contain punctuation, special symbols, and other details that must be cleaned up before analysis. To extract IPC information for analysis, it is possible to build a patent-IPC matrix that can confirm the IPC included in the patent document by preprocessing the patent IPC data. For example, if the IPC code contained in the patent data is extracted as “H01L 27/18”, remove the unnecessary special symbol (", /) to preprocess it and obtain it into the top four digits (H01L) corresponding to the section (H), class (01), and subclass (L) of the code. This process was carried out for the IPC data specifying detailed technology. We extracted keywords using the DTM function of R programming and converted them into a patent-IPC matrix, as shown in Figure 2.

3.3. Technology Level Evaluation of IPC

Qualitative and quantitative analysis were used in combination to evaluate the technology level of the IPC. Qualitative evaluation is a method of evaluating the relative level of technology for information on patent data [70]. We can identify the technology level by applying the technology mapping analysis [71]. In addition, through quantitative evaluation, the indicator of centrality and time series were extracted from each detailed IPC technology area, and the results of the analysis were confirmed through social network analysis and time series analysis [72,73].

3.3.1. Technology Mapping Analysis

Qualitative analysis of the quality of technology and its market securing ability was conducted by measuring the PFS (patent family size) and CPP (cites per patent) values. The patent family refers to all patents related to an application patent when applied in a foreign country based on a patent filed in its own country [74,75,76]. If an applicant wants to plan to enter the global market through technology, they apply for patents in various countries at the domestic and international patent bodies, so the high level of family patents shows high marketability [77,78,79,80]. PFS is calculated as in Equation (1):

(1)$\mathrm{PFS}=\frac{\mathrm{Count}\mathrm{of}\mathrm{family}\mathrm{patents}\mathrm{on}\mathrm{each}\mathrm{detailed}\mathrm{IPC}\mathrm{technology}\mathrm{area}}{\mathrm{Number}\mathrm{of}\mathrm{patents}\mathrm{on}\mathrm{each}\mathrm{detailed}\mathrm{IPC}\mathrm{technology}\mathrm{area}}$

CPP is a metric that quantifies how frequently per patent is cited [81]. A higher number of citations indicates that the technology possesses a high level of qualitative value, and a low number of citations indicates that the technology has a low level of qualitative value [82]. Higher levels of citations indicate that the patent is likely to be significant and may have value as an indicator of the qualitative quality of patents [83]. CPP (cites per patent) is calculated as in Equation (2):

(2)$\mathrm{CPP}=\frac{\mathrm{Count}\mathrm{of}\mathrm{citing}\mathrm{patents}\mathrm{on}\mathrm{each}\mathrm{detailed}\mathrm{IPC}\mathrm{technology}\mathrm{area}}{\mathrm{Number}\mathrm{of}\mathrm{patents}\mathrm{on}\mathrm{each}\mathrm{detailed}\mathrm{IPC}\mathrm{technology}\mathrm{area}}$

In this study, to assess the technological level assessment of the per IPCs, we performed a technology level evaluation analysis through technology mapping analysis. There are four quadrants in which technology can be located: the 1st, 2nd, 3rd, and 4th. In each quadrant, different technology areas can be found [84]. In the first quadrant, both PFS and CPP indicators are high, which means the level of the program is high. In the second and third quadrants, one of the PFS and CPP indicators is high, which means the level of the program is medium. Finally, the fourth quadrant is defined as low when all indicators are low. Figure 3 below is an example of a technology mapping analysis. The detailed IPC technology area corresponding to “IPC_4” corresponds to the first quadrant, the detailed IPC technology area corresponds to “IPC_1,2,3” corresponds to the second and third quadrants, and the detailed IPC technology area corresponds to “IPC_5”.

3.3.2. Time Series Analysis

Time series analysis is a statistical analysis method based on time series data for forecasting technology trends [85]. Time series data consist of an independent variable representing a time point and a dependent variable representing the frequency that can predict the future trend [86,87]. Among the models for analyzing time series data, the ARMA model is used as a representative [88,89]. ARIMA is a generalized model of the automatic regression moving average (ARMA) model. ARIMA model can describe the current and future time series values using past observations and errors. In this study, we predicted the future trend of each detailed IPC technology area through time series analysis using ARIMA models by converting the IPC frequency of appearance by year into time series data. As shown in Figure 4, the fields were predicted as hot, active, and cold according to the future trend for each detailed IPC technology area.

3.3.3. Social Network Analysis

Social network analysis was performed using a correlation matrix based on the IPC classification [90]. Matrix was used to identify common IPCs used in patent data and to analyze the correlations between them [91]. The correlation between IPCs was analyzed, and relevant technology was identified by those with a high correlation [92]. In Table 3, patent 1 contains references to IPC 1, 2, 3 and, as such, can be interpreted as containing associations between these technologies. Each patent IPCs is correlated in the patent-IPC matrix shown in Table 4. Therefore, SNA could be combined to obtain significant technological implications.

In social network analysis, IPCs are represented as nodes, and their relationships are illustrated by the edges between them. This reveals the patterns and relationships between IPCs. SNA can be used to illustrate network structures and quantify the relationships between nodes through centrality indices [93]. IPCs can be visualized as nodes in a network, with each node representing IPC and the connections between nodes representing the relationships between the IPCs [94]. In this study, we could obtain IPC nodes with high centrality, and promising detailed technology areas could be predicted based on the connections between the nodes [95]. Centrality indices can calculate “Degree”, “Closeness”, and “Betweenness” through the following equation [96].

Equation (3) is a formula for measuring how many nodes in a network are directly connected to an analysis target node:

(3)$$\begin{gathered} \mathrm{Degree}\mathrm{Centrality}:D_i={{\displaystyle \sum }}_{j=1}^N{Z}_{ij} \\ {Z}_{ij}Number of direct connections from node i to j \end{gathered}$$

Equation (4) is a formula that measures not only direct connections but also indirect connections by calculating the distances of all nodes connected to one node in a network:

(4)$$\begin{gathered} \mathrm{Closeness}\mathrm{Centrality}: C_c={\left[{{\displaystyle \sum }}_{j=1}^{N}d\left(i,j\right)\right]}^{-1} \\ d\left(i,j\right) The length of the shortest path from node i to j \end{gathered}$$

Equation (5) is a formula that measures how well a node acts as a hub; if the betweenness centrality is large, it is likely to affect the flow of connections between nodes in the network:

(5)$$\begin{gathered} \mathrm{Betweenness}\mathrm{Centrality}:C_B={{\displaystyle \sum }}_{j<k}^{ }\frac{g_{jk}\left(N_i\right)}{g_{jk}} \\ {g}_{jk}\left(N_i\right): Number of paths that include node i, the shortest path from node j to k \\ {g}_{jk}:Shortest path between nodes j and k \end{gathered}$$

3.4. Identification of Vacant Technology through Generative Topographic Mapping (GTM)

Based on the derived promising detailed IPC technology area, we applied a method of patent-IPC matrix-based generative topographic mapping (GTM) to identify vacant technologies that may be of interest to researchers and investors. GTM-based patent mapping patents split into two groups based on how many distributions of promising detailed IPCs are in each patent document. Technology mapping analysis through generative topographic mapping (GTM) was constructed through a grid of square map size N $\times$ N [97]. It does not have a specific rule to specify the map size of the map [98]. If the map size is too small, you cannot obtain empty nodes; if it is too large, you can obtain too many empty nodes, making it difficult to analyze empty nodes [99]. Therefore, in this study, we utilized a method of incrementally increasing the map size of a small size through a blanking method of selecting an appropriate map size [40]. The patent mapping, obtained from the GTM analysis presented in Figure 5, consists of nodes with and without patents. Vacant technology identifies technology that is not patented, connecting non-patent nodes [100]. To determine the corresponding vacant technology, patents in the vicinity of the nodes must be analyzed [101]. Since there was a patent of the analysis target nodes surrounding vacant technology, the vacant technology was defined using the patent information of the target nodes.

4. Analysis Results

4.1. Patent Data Collect and Technology Trend Analysis

4.1.1. Patent Data Collect

Patent data on railway main transformer technology were collected based on technology keywords extracted from the opinions of railway experts. Patent data of open application were collected over a period of years, covering a variety of the major patent applicant region. The data used to analyze patents were taken from applications filed from 1990 to 2020 issued by the six regions of Korea, China, Japan, the USA, Canada, and Europe. Because patent information is available after 18 months from open application, patent data after 18 months was therefore used in the analysis [102,103,104]. Although it will not be used for patent analysis, data for 2021 and 2022 will also be extracted separately to identify technology trend analysis. To extract patent data, a patent search using technology keywords was performed using the platform’s patent database search engine. Effective data was extracted by filtering, as the raw data contains unnecessary noisy data in the analysis [105]. If the technology was not related to the railway main transformer, it could not be included in the study. The result of technology keywords extraction regarding railway main transformer technology was that the “Frame”, “Core”, “Coil”, “Insulating Oil”, and “Cooling Fan” keywords were extracted. Furthermore, for the results of the data extraction and filtering based on the technology keywords, a total of 707 effective data were extracted, and 67 data for technology trend analysis in 2021 and 2022 were extracted.

4.1.2. Technology Trend Analysis

Figure 6 shows the number of collected patents data from 1990 to 2020. Because patents are granted confidential status for up to 18 months at the request of the applicant [102,106], some of the patents filed in 2021 and 2022 are not reflected yet. Therefore, technology trend analysis was conducted on patent data for the accuracy of the study from 1990 to 2020. Railway main transformer technology-related patents have continuously increased since 1990; it can be classified around 2006 when patent applications in China rapidly increased. From 1990 to 2006, there was an average annual increase rate of 10.5%, and a continuous increase in patent applications according to the passing year. Then, since 2006, there was an average annual increase rate of 18.1% and a rapid increase in patent applications. In addition, more than 20 patents have been filed every year since 2006, except in 2007, when the application was temporarily slowed down.

Moreover, Figure 6 shows the trend of patent application rates by region from 1990 to 2020. China, where about 48.38% of effective data were filled, is a large region for patent applications related to railway main transformer technology, and applicants from China are interested in recent technology. In 1990–2006, application activities in China were relatively minimal. However, since 2007, the number of patent applications has increased every year. The number of patent applications in Korea has increased slightly since 2007, with 6.38% of the effective data. The USA, where about 7.51% of effective data were filled, shows a continuously decreasing number of patent applications since the year 2010. In the case of Canada, which had a market share of about 0.57%, the overall application activity was insignificant. In addition, Japan had the highest patent application ratio between 1990 and 2006, but its effective data share continued to decline to 18.86%. Nevertheless, the continuous application was shown. Lastly, Europe, which is filled with about 18.3% of the effective data, has been found to have a consistently high number of patent applications since 1990 when the investigation began.

Table 5 shows the 10 most active applicants who applied for technical patents from 1990 to 2020. The companies listed in this table account for about 33.5% of the entire patents filed in technology; “CRRC Co., Ltd.” and “Mitsubishi Electric Corp.” are the two most prominent filing companies. As expected, companies in China are dominant in filing patents.

Figure 7 displays the patent filing patterns of the ten most active applicants and the number of patents filed by each company in the ten most active patent applicants. As expected, the frequency of patent applications by Chinese companies has been increasing recently. In the case of Japan, even though the market share of the Japanese patent is decreasing, Japanese companies are still showing high patent applications.

Finally, Table 6 shows the 10 applicants with the most active patent applications for the 233 technical patents filed over the past five years from 2018 to 2022. Patents are given confidential status for up to 18 months at the request of the applicant. Therefore, patent data in 2021 and 2022 are not reflected in the data and cannot produce accurate statistics. Nevertheless, the companies in the table are responsible for about 43% of the entire patents filed in technology, and “CRRC Co., Ltd.” is the most prominent company filing. “CRRC Co., Ltd.” accounts for 15% of all patent applications. Chinese application patents account for 74.3% of all patents over the past five years. Moreover, it is noteworthy that new companies such as “Bombardier Inc.” and “Zhuzhou Lince Group Co., Ltd” have emerged over the past five years.

4.2. Technology Level Evaluation of IPC

In this study, the top four digits classes of the IPC code were used for technology classification, and railway main transformer technology was divided into 127 detailed IPC technology areas through the patent-IPC matrix. To identify promising detailed IPC technology areas, we conducted a qualitative and quantitative evaluation of social network analysis, technology mapping analysis, and time series analysis. Social network analysis confirms the linkage between IPCs. We analyzed the centrality indicators of “Betweenness”, “Closeness”, and “Degree”, which are indicators that can be confirmed through social network analysis. The network analysis result is shown in Figure 8 and Table 7.

Each of the 127 detailed IPC technology areas of railway main transformer technology has a different technology qualitative level and market-securing ability. Therefore, we analyze the quality level of technology and the ability to secure markets by deriving PFS and CPP indices for each detailed IPC technology group through technology mapping analysis. The technology level according to the PFS and CPP indices is divided into three stages, and the relative position of the technology level by IPC is shown in Figure 9 and Table 8 through the technology mapping analysis.

In this study, we analyzed future technology trends by detailed IPC technology through time series analysis. We identified the future potential by each 127 detailed IPC technology through the ARIMA model. In this analysis, we defined the parameters p, d, and q, which are variables related to the order, difference, and moving average of the model, using the R programming package via “auto.arima”. Since patents are disclosed 18 months after filing, the number of patent applications by detailed IPC technology from 1990 to 2020 was extracted as time series data and used for time series analysis. The analysis results were classified into hot, active, and cold fields according to future trends, as shown in Table 9.

In this study, qualitative and quantitative analysis were used together to evaluate the technology level for each IPC to obtain promising detailed IPC technology areas. The level of technology for each detailed technology was analyzed based on the PFS index and CPP index derived from the technology mapping analysis, which is a qualitative analysis. In addition, through quantitative evaluation, the indicator of centrality and time series were extracted from each detailed IPC technology area, and the analysis results of the technology level were confirmed through social network analysis and time series analysis. The results of the three integrated analysis frameworks are summarized below, as shown in Table 10. As shown in the table below, the analysis results are different for each analysis procedure, so if all three analysis results are integrated, the results can be predicted as promising detailed IPC technology in the future. We would like to obtain the vacant technology through GTM analysis based on the obtained promising detailed IPC technology areas.

Promising detailed IPC technology areas regarding railway main transformer technology are derived from integrated analysis. A total of 11 IPCs were collected, and promising detailed IPCs are provided in Table 11. There are a total of 450 patents in the promising detailed IPC technology area, accounting for 63.6% of the patents to be analyzed.

4.3. Forecasting of Vacant Technology

4.3.1. Identification of Vacant Technology Based on Promising Detailed IPC Technology Areas: Generative Topographic Mapping (GTM) Analysis

To forecast vacant technology, based on the derived detailed IPC technology, a patent-IPC matrix was obtained, and a GTM-based patent map was obtained through GTM analysis to predict vacant technology. For the results of the GTM-based patent map presented demonstrated in Figure 10 below, we applied a 5 × 5 sized map for GTM analysis. Looking at the GTM-based patent map, in Group 1, 17 technology patents were identified; in Group 2, 26 technology patents were identified; and in Group 3, 47 technology patents were identified. There are a total of 90 patents in analysis target nodes, accounting for 12.7% of the patents to be analyzed.

4.3.2. Forecasting of Vacant Technology in Each Group

The patent information of patents included in the analysis target nodes to be analyzed and identified through GTM analysis was investigated, and the vacant technology of each group in analysis target nodes was defined, as shown in Table 12. In predicting vacant technology topics, among patents filed in each group, it was selected based on technology keywords that can represent the group. The technology keyword is obtained from the keyword related to the technology of each group that shows the most frequency in the text data included in the patent for each group.

Existing main transformers were one of the reasons for reducing the efficiency of electric energy consumption because they are one of the largest parts of the electrical equipment installed in railways. Therefore, railway main transformers should have technological innovations such as high power, high efficiency, and reduced weight and volume. Looking at the results of the obtained vacant technology in each group, overall, the focus is on lightening, reduction of maintenance costs, and reduction of noise.

Group 1 was identified as a blowerless technology in the main transformer. As a result of searching for technologies keywords through investigating the patent documents of Group 1, the five technology keywords were identified as “(1) fanless”, “(2) blowerless”, “(3) inverter”, “(4) cooling”, and “(5) nature”. The analysis shows that the technology keyword will correspond to the fan field of the main transformer corresponding to “F04D” and “H01F” among the obtained promising detailed IPC technology areas. Blowerless technology that does not require a fan is a technology that provides necessary cooling through natural cooling without using a fan at the main transformer. Since the main transformer fan is so noisy, some high-speed railways do not operate the fan at the station, and the fan is turned after the train leaves the station. If a fan is not used, the main transformer is cooled using a natural cooling method, so the weight of the main transformer can be greatly reduced, and noise can be greatly reduced because no fan occupies most of the noise. The blowerless cooling method is a cost-effective cooling solution, and the technology is applied to electric multiple units, but this technology has not been applied to high-speed railways yet, so it needs to be developed through research.

Group 2 was identified as an oil-free technology in the main transformer. As a result of searching for technologies keywords through investigating the patent documents of Group 2, the five technologies keywords were identified as “(1) refrigerant”, “(2) insulating”, “(3) dry “, “(4) leak”, and “(5) oilfree”. The analysis shows that the technology keyword will correspond to the insulating oil field of the main transformer corresponding to “F04D”, “H01B”, and “H01F” among the obtained promising detailed IPC technology areas. Insulating oil occupies a lot of volume in the main transformer and causes weight gain. In addition, this technology is applied to electric multiple units, but this technology has not been applied to high-speed railways yet, so it needs to be developed through research. Therefore, it is necessary to develop an oil-free main transformer technology. This technology is expected to reduce the weight and volume of a railway because insulation oil is not required.

Group 3 was identified as a solid-state technology in the main transformer. As a result of searching for technologies keywords through investigating the patent documents of Group 3, the seven technologies keyword were identified as “(1) solidstate”, “(2) highfrequency”, “(3) semiconductor”, “(4) converter”, and “(5) modular”, “(6) electronic”, “(7) multilevel”. The analysis shows that the technology keyword will correspond to the solid-state field of the main transformer corresponding to “H01B”, “H01F”, and “H02M” among the obtained promising detailed IPC technology areas. In accordance with the identified technology keyword, related technology development is currently underway around multilevel converter, AC/DC, and DC/AC power conversion technology. The railway main transformer should have technological innovations such as high power, high efficiency, and reduced weight and volume. Therefore, it is expected that the efficient application of solid-state technology would be possible to reduce the weight and volume of railway main transformers. It is also expected to increase railway efficiency while leading to the reduction of maintenance costs.

5. Discussion

The main implication of this study is to provide an analysis framework for simultaneously performing technology trend analysis and predicting vacant technology based on promising detailed IPC technology areas identified through quantitative patent analysis. Previous research literature mainly focused on qualitative methods through expert evaluations and opinions, and they did not consider the quantitative patent analysis and technology trends at the same time.

Therefore, to overcome the above limitations, in this study, firstly, a patent analysis in the railway main transformer technology was obtained to identify the result of technology trend analysis. Then, to identify promising detailed IPC technology domains, we obtained technology mapping analysis, time series analysis, and social network analysis results based on the patent-IPC matrix extracted from patent data information. Moreover, through GTM analysis of promising detailed IPC technology areas, a vacant technology node and analysis target nodes surrounding the vacant technology node to predict vacant technology were obtained. Lastly, we predicted the groups of vacant technology using patent information contained in each node of the obtained analysis target nodes.

Despite the advancement of the analysis methodology aspects mentioned above, the study has some limitations, and it is intended to supplement them with follow-up research. Patent information is available after 18 months from open application; therefore, there are limitations in using the recently applicated patent data for analysis. To overcome these limitations, we will establish an analysis framework that can further justify the validity of the results derived in this study through subsequent studies. The results of this study are expected to be widely used in follow-up studies to support decision-making by researchers and investors within the field of the railway main transformer and to apply analysis framework for the railway main transformer not only to the railway industry but also to various other equipment industries. In addition, we are expected to contribute to the establishment of a patent information-based analysis framework to improve technology competitiveness.

6. Conclusions

In this study, we researched vacant technology groups for forecasting future railway main transformer technology based on promising detailed IPC areas. In addition, the purpose of this study was to provide insight into the technology trend in railway main transformers, as well as an analysis framework that can be used to make patent-based informed decision-making. First, we attempted to identify the result of technology trend analysis through patent analysis in railway main transformer technology. Then, to identify promising detailed IPC technology domains, we obtained technology mapping analysis, time series analysis, and social network analysis results based on the patent-IPC matrix extracted from patent data information. Moreover, the results of GTM analysis of promising detailed IPC technology areas were obtained to maximize the possibility of identifying vacant technology groups. Lastly, we discussed the future technology of railway main transformers based on our analysis results. As a result of our study, technology trend analysis using patent data was performed, and we confirmed that patent applications in China have been increasing rapidly recently. Furthermore, through GTM analysis of promising detailed IPC technology areas, one vacant technology node and three analysis target nodes surrounding the vacant technology node to predict vacant technology were obtained. Then, we predicted the groups of vacant technology using patent information contained in each node of the obtained analysis target nodes. Through this process, the vacant technologies discovered by our patent analysis framework are (1) blowerless technology in main transformer; (2) oil-free technology in main transformer; and (3) solid-state technology in main transformer.

To verify whether our analysis results were reasonable, we compared the future technology roadmap of the related organization about the railway main transformer. Currently, related research is being conducted in related organizations for technology development, and as shown in Table 13, many future technology roadmaps have been established. First, the International Union of Railways (UIC) has established a future railway strategy for Europe. Second, the Railway Technical Research Institute (RTRI) aims to commercialize the technology within the next 10–20 years with the main goal of improving safety, improving convenience, and reducing costs through the roadmap derived from “Master plan Research 2020”. Third, the Korea Railroad Research Institute (KRRI) analyzed the value-added effect, production inducement effect, and job creation effect in the railway transportation-related industry, and a technology roadmap with promising commercialization of technology development in the future was established. The above organizations established detailed goals for establishing future railway strategies as follows: (1) reduction of maintenance costs, (2) reduction of noise, (3) high power, (4) high efficiency, and (5) lightening. The above-detailed goals were found to be like the expectation effectiveness of the vacant technology forecasted results obtained from each group.

In this study, the research results can predict a vacant technology group of railway main transformer technology for strategic research and investment. According to the result of our study, first, the group that applies blowerless technology in main transformers is expected to be in the spotlight. Since this technology does not need a fan, it is expected to reduce noise and volume. This technology is applied to electric multiple units, but it has not been applied to high-speed railways yet, so it needs to be developed through research. Therefore, we expect that research on applying this technology to main transformers of high-speed railways will be actively conducted in the future. Second, the group that applies oil-free technology in main transformers is expected to be in the spotlight. Since this technology does not need insulation oil, it is expected to reduce the volume and weight of main transformers. Moreover, this technology is applied to electric multiple units, but it has not been applied to high-speed railways yet, so it needs to be developed through research. Therefore, we expect that research on applying this technology to the main transformer will be actively conducted in the future. Third, the group that applies solid-state technology in main transformers is expected to be in the spotlight. The railway main transformer should have technological innovations such as high power, high efficiency, and reduced weight and volume. Therefore, it is expected that the efficient application of solid-state technology would make it possible to reduce the weight and volume of the railway main transformer. It is also expected to increase railway efficiency while leading to reduced maintenance costs. Therefore, we expect that research on applying this technology to the main transformer will be actively conducted in the future.

Author Contributions

Y.-J.L. performed data curation, conceptualization, data analysis, investigation, methodology, validation, and the writing of the original manuscript; Y.J.H. contributed to conceptualization, methodology, funding acquisition, supervision, editing of the manuscript, and project administration; S.-S.K. contributed to funding acquisition, editing of the manuscript, and project administration; C.L. contributed to funding acquisition, supervision, writing review, editing of the manuscript, and project administration. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All the data for this study are available in public domain (the sources are provided in the relevant sections).

Acknowledgments

This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant RS-2022-0014-3496). Moreover, the authors thank Irena Yosephine for proofreading this article and providing valuable comments.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Research methods and processes.

Figure 2. Examples of patent-IPC matrix extraction through patent data.

Figure 3. Example of technology mapping analysis.

Figure 4. Example of time series analysis.

Figure 5. The above GTM-based patent map shows the distribution of patents; this information can be used to indicate where technology development has taken place. (a) Light blue nodes represent where patent application has occurred, dark blue nodes represent where patent apply has occurred frequently; (b) red nodes represent vacant technology, and yellow area represents nodes that need to be analyzed [40].

Figure 6. Number of patents according to each region.

Figure 7. Number of railway main transformer technology patents filed each year by each company in the ten most active applicants.

Figure 8. Result of social network analysis.

Figure 9. Result of technology mapping analysis.

Figure 10. Results of GTM-based patent map based on promising detailed IPC technology areas.

Table 1

Literature on using patent analysis to predict technology.

Year of Publication	Author	Technology	Methodology
2022	Liu and Chen [21] 2	Semiconductor industry	(A), (C)
2022	Mao et al. [22] 1	Industrial wastewater treatment	(B)
2022	Park and Jun [23] 1	Cognitive diagnosis model
2022	Yang et al. [24] 1	Hydrogen fuel cell	(A), (B), (C), (D)
2022	Choi and Woo [34] 2	Hydrogen	(A), (B)
2022	Kwon et al. [40] 1	Logistics	(A), (B), (C), (D)
2022	Du et al. [53] 1	Train signal control
2022	Anatolyevna [46] 2	Labor market	(B), (C)
2022	Hingley and Dikta [48] 1	Patent filings counts forecasts	(B), (C)
2022	Durmusoglu and Durmusoglu [43] 2	Respiratory	(A), (B)
2021	Durmusoglu and Durmusoglu [44] 2	Traffic control system for road vehicles	(A)
2021	Feng et al. [28] 1	Mining machinery	(A), (B), (D)
2021	Yuan and Li [29] 1	Battery electric vehicles	(A), (B), (C)
2021	Ferreira et al. [30] 2	Cerrado plant species	(A), (B), (C)
2021	Sun et al. [41] 1	High-speed railways	(A)
2021	Hanley et al. [42] 1	High-speed railways	(A)
2021	Cho et al. [52] 2	Electric motors of railway vehicles	(D)
2020	Karasev et al. [54] 1	Foreign railway companies
2019	Kim et al. [39] 1	Wireless power transfer	(A), (B)
2019	Liu and Yang [51] 1	High-speed rail industry	(C)
2018	Yoon and Magee [27] 1	3D printing	(B), (D)
2018	Cho et al. [38] 1	High-rise building construction	(A), (C)
2018	Feng and Yu [55] 1	China high-speed railway
2018	Gou [56] 1	Maglev transportation
2018	Zhang and Zhang [57] 1	Railway industry
2017	Durmuşoğlu [50] 2	Chemical industry	(B), (C)
2017	Suominen et al. [32] 2	Telecommunication industry	(B)
2016	Madani and Weber [26] 1	Pattern recognition	(B)
2016	Nikolopoulos et al. [47] 2	Generic pharmaceuticals	(B), (C)
2015	Lee, et al. [31] 2	Light-emitting diode (LED)	(A), (B), (C)
2014	Choi and Hwang [25] 1	LED and wireless broadband	(B)
2014	Zhang and Zhang [58] 2	Vibration-reduction control
2012	Hidalgo and Gabaly [49] 1	Spanish patents applications	(B), (C)
2011	Xiao [59] 1	Train control system
2009	Baser and Buchbauer [45] 1	Art of eugenol	(B), (C)
2009	Salmi and Torkkeli [60] 1	Satellite navigation systems

¹ Keyword-based patent search. ² IPC-based patent search.

Table 2

Position of our analysis method in prior literature in the field of railways.

Papers	TSA (A)	SNA (B)	TM (C)	GTM (D)
Our paper	✓	✓	✓	✓
Sun et al. [41]	✓
Hanley et al. [42]	✓
Liu and Yang [51]			✓
Cho et al. [52]				✓
Du et al. [53]
Karasev et al. [54]
Feng and Yu [55]
Gou [56]
Zhang and Zhang [57]
Zhang and Zhang [58]
Xiao [59]
Salmi and Torkkeli [60]

✓ Use the corresponding methodology.

Table 3

Examples of patent-IPC matrix based on patent data.

Patent	IPC_1	IPC_2	IPC_3	IPC_4	IPC_5
1	1	1	1	0	0
2	0	1	0	1	1
3	0	0	0	1	1
4	1	0	0	1	1
5	0	1	1	0	1

Table 4

Example of IPC classification-based correlation matrix.

IPC	IPC_1	IPC_2	IPC_3	IPC_4	IPC_5
IPC_1	1	−0.53452248	−0.1336306	−0.25	−0.51449576
IPC_2	−0.5345225	1	0.7857143	−0.3118048	0.04583492
IPC_3	−0.1336306	0.78571429	1	−0.8017837	−0.50418417
IPC_4	−0.25	−0.31180478	−0.8017837	1	0.91465912
IPC_5	−0.5144958	0.04583492	−0.5041842	0.9146591	1

Table 5

Top ten applicants filing patents in railway main transformer technology.

Rank	Applicant	Region/City	Number of Patents Filed (Patent Share)
1	CRRC Co., Ltd.	China/Beijing	59 (8.4%)
2	Mitsubishi Electric Corp.	Japan/Tokyo	46 (6.5%)
3	ABB Group.	Europe/Zurich	25 (3.5%)
4	Toshiba Corp.	Japan/Tokyo	20 (2.8%)
5	Wolong Electric Group Co., Ltd.	China/Shaoxing	18 (2.6%)
6	Hitachi, Ltd.	Japan/Tokyo	16 (2.3%)
7	Southwest Jiaotong University	China/Chengdu	15 (2.1%)
8	China Railway Engineering Equipment Group Co., Ltd.	China/Zhengzhou	13 (1.8%)
9	Siemens AG.	Europe/Munich	13 (1.8%)
10	Alstom S.A.	Europe/Saint-Ouen	11 (1.6%)

Table 6

Top ten applicants filing patents in railway main transformer technology in the last five years.

Rank	Applicant	Region/City	Number of Patents Filed (Patent Share)
1	CRRC Co., Ltd.	China/Beijing	35 (15%)
2	Southwest Jiaotong University	China/Chengdu	15 (6.4%)
3	Hitachi, Ltd.	Japan/Tokyo	14 (6%)
4	China Railway Engineering Equipment Group Co., Ltd.	China/Zhengzhou	9 (3.8%)
5	Bombardier Inc.	Canada/Montreal	7(3%)
6	Mitsubishi Electric Corp.	Japan/Tokyo	6 (2.5%)
7	Zhuzhou Lince Group Co., Ltd	China/Zhengzhou	5 (2.14%)
8	Wolong Electric Group Co., Ltd.	China/Shaoxing	4 (1.7%)
9	Alstom S.A.	Europe/Saint-Ouen	4 (1.7%)
10	Toshiba Corp.	Japan/Tokyo	3 (1.28%)

Table 7

Social network analysis results.

IPC	Betweenness (X)	Closeness (X)	Degree (X)	Priority
“B01D”, “B09B”, “B22D”, “B22F”, “B23K”, “B32B”, “B60L”, “B60M”, “B60R”, “B61C”, “B61D”, “B61L”, “B62D”, “C04B”, “C08G”, “C08J”, “C08K”, “C08L”, “C21D”, “C22C”, “C22F”, “F01D”, “F03D”, “F04D”, “F16F”, “F16M”, “F28D”, “G01D”, “G01N”, “G01R”, “G05D”, “G05F”, “G08B”, “H01B”, “H01F”, “H01H”, “H01L”, “H02B”, “H02H”, “H02J”, “H02K”, “H02M”, “H02P”, “H03H”, “H04L”, “H05B”, “H05K”	100 < X	40 < X	5 < X	High
“A61F”, “A61L”, “A61N”, “A62D”, “B01J”, “B21D”, “B29C”, “B60K”, “B61B”, “B61F”, “B61H”, “B66C”, “C01B”, “C07B”, “C07C”, “C08F”, “C09D”, “C09J”, “E04G”, “E04H”, “E21C”, “E21F”, “F02C”, “F27B”, “F27D”, “F28C”, “F28F”, “G01B”, “G01K”, “G01M”, “G05B”, “G06G”, “G09B”, “G10K”, “H01J”, “H03K”, “H04B”, “H04J”	10 < X < 100	30 < X < 40	2 < X < 5	Medium
“B08B”, “B21B”, “B23D”, “B24B”, “B60C”, “B60H”, “B60P”, “B61K”, “B65D”, “B65G”, “B65H”, “B66D”, “C01G”, “C09K”, “C23C”, “C25C”, “C30B”, “E01H”, “F01K”, “F02B”, “F02P”, “F16D”, “F16H”, “F16K”, “F16L”, “F24S”, “G01F”, “G01P”, “G01V”, “G06F”, “G06K”, “G06Q”, “G08C”, “H01Q”, “H01R”, “H01S”, “H01T”, “H02G”, “H02S”, “H03M”, “H04N”, “H05G”	X < 10	X < 30	X < 2	Low

Table 8

Normalization results of technology mapping analysis.

IPC	Technology Level
“A62D”, “B01D”, “B01J”, “B09B”, “B22D”, “B22F”, “B23K”, “B60L”, “B60M”, “B60R”, “B61H”, “B65D”, “C04B”, “C08F”, “C08G”, “C08J”, “C08K”, “C08L”, “C09K”, “C21D”, “C22C”, “C22F”, “E21F”, “F01K”, “F02C”, “F04D”, “F16K”, “F27B”, “F27D”, “G01B”, “G01N”, “G05F”, “H01B”, “H01H”, “H01L”, “H02G”, “H02H”, “H02J”, “H02K”, “H02M”, “H05B”, “H05K”, “H01F”, “F01D”, “F03D”, “H03H”, “B65H”, “F16H”, “F02P”, “H02B”, “B61C”, “H03K”	High
“F16M”, “H02P”, “H03M”, “H04B”, “H04N”, “H05G”	Medium
“A61F”, “A61L”, “A61N”, “B08B”, “B21B”, “B21D”, “B23D”, “B24B”, “B29C”, “B32B”, “B60C”, “B60H”, “B60K”, “B60P”, “B61B”, “B61C”, “B61D”, “B61F”, “B61K”, “B61L”, “B62D”, “B65G”, “B65H”, “B66C”, “B66D”, “C01B”, “C01G”, “C07B”, “C07C”, “C09D”, “C09J”, “C23C”, “C25C”, “C30B”, “E01H”, “E04G”, “E04H”, “E21C”, “F01D”, “F02B”, “F02P”, “F03D”, “F16D”, “F16F”, “F16H”, “F16L”, “F24S”, “F28C”, “F28D”, “F28F”, “G01D”, “G01F”, “G01K”, “G01M”, “G01P”, “G01R”, “G01V”, “G05B”, “G05D”, “G06F”, “G06G”, “G06K”, “G06Q”, “G08B”, “G08C”, “G09B”, “G10K”, “H01J”, “H01Q”, “H01R”, “H01S”, “H01T”, “H02B”, “H02S”, “H03H”, “H03K”, “H04J”, “H04L”	Low

Table 9

Result of time series analysis.

IPC	Fields
“B24B”, “B60L”, “B60M”, “B61C”, “C09D”, “E04G”, “E04D”, “F04D”, “F04H”, “F16H”, “F28C”, “F28F”, “G01D”, “G01R”, “G05F”, “G06F”, “G08B”, “G08C”, “H01B”, “H01F”, “H01R”, “H02H”, “H02J”, “H02K”, “H02M”, “H04L”, “H05K”	Hot
“B01D”, “B61L”, “B66D”, “C08L”, “E01H”, “F03D”, “F16F”, “F28D”, “G01K”, “G01N”, “G05D”, “G06G”, “G09B”, “H02B”, “H02P”, “H05B”	Active
“A61F”, “A61L”, “A61N”, “A62D”, “B01J”, “B08B”, “B09B”, “B21B”, “B21D”, “B22D”, “B22F”, “B23D”, “B23K”, “B29C”, “B32B”, “B60C”, “B60H”, “B60K”, “B60P”, “B60R”, “B61B”, “B61D”, “B61F”, “B61H”, “B61K”, “B62D”, “B65D”, “B65G”, “B65H”, “B66C”, “C01B”, “C01G”, “C04B”, “C07B”, “C07C”, “C08F”, “C08G”, “C08J”, “C08K”, “C09J”, “C09K”, “C21D”, “C22C”, “C22F”, “C23C”, “C25C”, “C30B”, “E21C”, “E21F”, “F01D”, “F01K”, “F02B”, “F02C”, “F02P”, “F16D”, “F16K”, “F16L”, “F16M”, “F24S”, “F27B”, “F27D”, “G01B”, “G01F”, “G01M”, “G01P”, “G01V”, “G05B”, “G06K”, “G06Q”, “G10K”, “H01H”, “H01J”, “H01L”, “H01Q”, “H01S”, “H01T”, “H02G”, “H02S”, “H03H”, “H03K”, “H03M”, “H04B”, “H04J”, “H04N”, “H05G”	Cold

Table 10

Results of the integrated analysis frameworks.

IPC	Qualitative Evaluation	Quantitative Evaluation		Priority
	Technology Mapping Analysis	Social Network Analysis	Time Series Analysis
“B60L”, “B60M”, “F04D”, “G05F”, “H01B”, “H01F”, “H02H”, “H02J”, “H02K”, “H02M”, “H05K”	High	High	Hot	1
“B01D”, “C08L”, “G01N”, “H05B”	High	High	Active	2
“B61C”, “G01D”, “G01R”, “G08B”, “H04L”	Low	High	Hot	3
“B09B”, “B22D”, “B22F”, “B23K”, “B60R”, “C04B”, “C08G”, “C08J”, “C08K”, “C21D”, “C22C”, “C22F”, “H01H”, “H01L”	High	High	Cold	3
“H02P”	Medium	High	Active	3
“C09D”, “E04G”, “E04H”, “F28C”, “F28F”	Low	Medium	Hot	4
“A62D”, “B01J”, “B61H”, “C08F”, “E21F”, “F02C”, “F27B”, “F27D”, “G01B”	High	Medium	Cold	4
“F16M”	Medium	High	Cold	4
“B61L”, “F03D”, “F16F”, “F28D”, “G05D”, “H02B”	Low	High	Active	4
“B24B”, “F16H”, “G06F”, “G08C”, “H01R”	Low	Low	Hot	5
“B65D”, “C09K”, “F01K”, “F16K”, “H02G”	High	Low	Cold	5
“B32B”, “B61D”, “B62D”, “F01D”, “H03H”	Low	High	Cold	5
“H04B”	Medium	Medium	Cold	5
“G01K”, “G06G”, “G09B”	Low	Medium	Active	5
“A61F”, “A61L”, “A61N”, “B21D”, “B29C”, “B60K”, “B61B”, “B61F”, “B66C”, “C01B”, “C07B”, “C07C”, “C09J”, “E21C”, “G01M”, “G05B”, “G10K”, “H01J”, “H03K”, “H04J”	Low	Medium	Cold	6
“H03M”, “H04N”, “H05G”	Medium	Low	Cold	6
“B66D”, “E01H”	Low	Low	Active	6
“B08B”, “B21B”, “B23D”, “B60C”, “B60H”, “B60P”, “B61K”, “B65G”, “B65H”, “C01G”, “C23C”, “C25C”, “C30B”, “F02B”, “F02P”, “F16D”, “F16L”, “F24S”, “G01F”, “G01P”, “G01V”, “G06K”, “G06Q”, “H01Q”, “H01S”, “H01T”, “H02S”	Low	Low	Cold	7

Table 11

Promising detailed IPC technology areas by IPC code.

IPC	Title
B60L	(1) Propulsion of electric vehicles.(2) Power the auxiliary equipment for electric propulsion.(3) Generally, electrodynamic braking systems for vehicles.(4) Vehicle magnetic suspension or injury.(5) Monitoring of operating variables of electric propulsion vehicles.(6) Electrical safety equipment for electric propulsion vehicles.
B60M	(1) Electric propulsion vehicle power supply lines.(2) A device along rails.
F04D	(1) Non-positive-displacement pumps (engine fuel-injection pumps; electrodynamic pumps; cooling; diminishing heat transfer; heating; ion pumps).
G05F	(1) A system for adjusting electrical or magnetic variables.
H01B	(1) Material selection for cable, conductor, insulator, conductive insulation, or dielectric properties.
H01F	(1) Magnet inductance; transformer; selection of materials for magnetic properties.
H02H	(1) Emergency protective circuit device.
H02J	(1) A circuit device or system for power supply or distribution.(2) Electrical energy storage system.
H02K	(1) Dynamo electromechanical.
H02M	(1) Equipment for conversion between AC and AC, between AC and DC, or between DC and DC, and for use in mains or similar power supply systems.(2) Convert DC or AC input power into surge output power.(3) Control or regulation.
H05K	(1) Printed circuit.(2) Casings or structural details of electrical equipment.(3) Manufacture of electrical assembly.

Table 12

Forecasted vacant technology in each group.

Group	Forecasted Vacant Technology	Technology Keywords for Each Identified Group
1	Blowerless Technology in Main Transformer	fanless, blowerless, inverter, cooling, nature
2	Oil-Free Technology in Main Transformer	refrigerant, insulating, dry, leak, oilfree
3	Solid-State Technology in Main Transformer	solid-state, highfrequency, semiconductor, converter, modular, electronic, multilevel

Table 13

Comparison between our analysis results and future technology roadmap of related organization about railway main transformer.

Forecasted Vacant Technology	International Union of Railways (UIC)Rail Technical Strategy Europe	Railway Technical Research Institute (RTRI)Master Plan Research 2020	Korea Railroad Research Institute (KRRI)National Transportation Research and Planning Project
Blowerless Technology in Main Transformer	√	√	√
Oil-Free Technology in Main Transformer	√	√	√
Solid-State Technology in Main Transformer	√	√	√

√ It means that the future technology roadmap of the relevant institutions has the same direction as our analysis results.

References

1. Paul, S.G.; Ravichandran, C. High frequency transformer in electric traction with bidirectional DC-DC converter using customized embedded system. Microprocess. Microsyst.; 2020; 77, 103162. [DOI: https://dx.doi.org/10.1016/j.micpro.2020.103162]

2. Song, W.; Jiang, Z.; Staines, M.; Badcock, R.A.; Wimbush, S.C.; Fang, J.; Zhang, J. Design of a single-phase 6.5 MVA/25 kV superconducting traction transformer for the Chinese Fuxing high-speed train. Int. J. Electr. Power Energy Syst.; 2020; 119, 105956. [DOI: https://dx.doi.org/10.1016/j.ijepes.2020.105956]

3. Serrano-Jiménez, D.; Abrahamsson, L.; Castaño-Solís, S.; Sanz-Feito, J. Electrical railway power supply systems: Current situation and future trends. Int. J. Electr. Power Energy Syst.; 2017; 92, pp. 181-192. [DOI: https://dx.doi.org/10.1016/j.ijepes.2017.05.008]

4. Türker Ahi, M.; Yildiz, K. Determining performance criteria of railway market: A case study. J. Open Innov. Technol. Mark. Complex.; 2018; 4, 6.

5. Yun, S.; Cho, W.; Kim, C.; Lee, S. Technological trend mining: Identifying new technology opportunities using patent semantic analysis. Inf. Process. Manag.; 2022; 59, 102993. [DOI: https://dx.doi.org/10.1016/j.ipm.2022.102993]

6. Wang, C.; Geng, H.; Sun, R.; Song, H. Technological potential analysis and vacant technology forecasting in the graphene field based on the patent data mining. Resour. Policy; 2022; 77, 102636. [DOI: https://dx.doi.org/10.1016/j.resourpol.2022.102636]

7. Tseng, F.-M.; Hsieh, C.-H.; Peng, Y.-N.; Chu, Y.-W. Using patent data to analyze trends and the technological strategies of the amorphous silicon thin-film solar cell industry. Technol. Forecast. Soc. Change; 2011; 78, pp. 332-345. [DOI: https://dx.doi.org/10.1016/j.techfore.2010.10.010]

8. Linares, I.M.P.; De Paulo, A.F.; Porto, G.S. Patent-based network analysis to understand technological innovation pathways and trends. Technol. Soc.; 2019; 59, 101134. [DOI: https://dx.doi.org/10.1016/j.techsoc.2019.04.010]

9. Sun, X.; Jin, L.; Zhou, F.; Jin, K.; Wang, L.; Zhang, X.; Ren, H.; Huang, H. Patent analysis of chemical treatment technology for wastewater: Status and future trends. Chemosphere; 2022; 307, 135802. [DOI: https://dx.doi.org/10.1016/j.chemosphere.2022.135802]

10. Joung, J.; Kim, K. Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data. Technol. Forecast. Soc. Change; 2017; 114, pp. 281-292. [DOI: https://dx.doi.org/10.1016/j.techfore.2016.08.020]

11. Wang, X.; Daim, T.; Huang, L.; Li, Z.; Shaikh, R.; Kassi, D.F. Monitoring the development trend and competition status of high technologies using patent analysis and bibliographic coupling: The case of electronic design automation technology. Technol. Soc.; 2022; 71, 102076. [DOI: https://dx.doi.org/10.1016/j.techsoc.2022.102076]

12. Maehara, Y.; Kuku, A.; Osabe, Y. Macro analysis of decarbonization-related patent technologies by patent domain-specific BERT. World Pat. Inf.; 2022; 69, 102112. [DOI: https://dx.doi.org/10.1016/j.wpi.2022.102112]

13. Janse, M.E.; Zinkweg, D.B.; Larsen, O.F.; van de Burgwal, L. Innovations in the veterinary intestinal health field: A patent landscape analysis. One Health; 2022; 15, 100419. [DOI: https://dx.doi.org/10.1016/j.onehlt.2022.100419] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36277087]

14. Jin, L.; Sun, X.; Ren, H.; Huang, H. Hotspots and trends of biological water treatment based on bibliometric review and patents analysis. J. Environ. Sci.; 2023; 125, pp. 774-785. [DOI: https://dx.doi.org/10.1016/j.jes.2022.03.037]

15. Jafery, W.A.Z.W.C.; Omar, M.S.S.; Ahmad, N.A.; Ithnin, H. Classification of patents according to industry 4.0 pillars using machine learning algorithms. Proceedings of the 2019 6th International Conference on Research and Innovation in Information Systems (ICRIIS); Johor Bahru, Malaysia, 2–3 December 2019; pp. 1-6.

16. Zhu, H.; Du, Z.; Wu, J.; Sun, Z. Innovation environment and opportunities of offshore wind turbine foundations: Insights from a new patent analysis approach. World Pat. Inf.; 2022; 68, 102092. [DOI: https://dx.doi.org/10.1016/j.wpi.2021.102092]

17. Ampah, J.D.; Jin, C.; Fattah, I.M.R.; Appiah-Otoo, I.; Afrane, S.; Geng, Z.; Yusuf, A.A.; Li, T.; Mahlia, T.I.; Liu, H. Investigating the evolutionary trends and key enablers of hydrogen production technologies: A patent-life cycle and econometric analysis. Int. J. Hydrogen Energy; 2022; [DOI: https://dx.doi.org/10.1016/j.ijhydene.2022.07.258]

18. Kim, D.; Kim, S. Role and challenge of technology toward a smart sustainable city: Topic modeling, classification, and time series analysis using information and communication technology patent data. Sustainable Cities Soc.; 2022; 82, 103888. [DOI: https://dx.doi.org/10.1016/j.scs.2022.103888]

19. Wei, F.; Zhou, H.; Gao, G.; Zheng, Q. Analysis of trends in patent development for coronavirus detection, prevention, and treatment technologies in key countries. J. Biosaf. Biosecurity; 2022; 4, 23. [DOI: https://dx.doi.org/10.1016/j.jobb.2021.10.005]

20. Baumann, M.; Domnik, T.; Haase, M.; Wulf, C.; Emmerich, P.; Rösch, C.; Zapp, P.; Naegler, T.; Weil, M. Comparative patent analysis for the identification of global research trends for the case of battery storage, hydrogen and bioenergy. Technol. Forecast. Soc. Change; 2021; 165, 120505. [DOI: https://dx.doi.org/10.1016/j.techfore.2020.120505]

21. Liu, K.-T.; Chen, C.-H. Formulation of research and development strategy by analysing patent portfolios of key players the semiconductor industry according to patent strength and technical function. World Pat. Inf.; 2022; 70, 102125. [DOI: https://dx.doi.org/10.1016/j.wpi.2022.102125]

22. Mao, G.; Han, Y.; Liu, X.; Crittenden, J.; Huang, N.; Ahmad, U.M. Technology status and trends of industrial wastewater treatment: A patent analysis. Chemosphere; 2022; 288, 132483. [DOI: https://dx.doi.org/10.1016/j.chemosphere.2021.132483] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34624344]

23. Park, S.; Jun, S. Technological cognitive diagnosis model for patent keyword analysis. ICT Express; 2020; 6, pp. 57-61. [DOI: https://dx.doi.org/10.1016/j.icte.2019.09.004]

24. Yang, H.; Han, Y.J.; Yu, J.; Kim, S.; Lee, S.; Kim, G.; Lee, C. Exploring Future Promising Technologies in Hydrogen Fuel Cell Transportation. Sustainability; 2022; 14, 917. [DOI: https://dx.doi.org/10.3390/su14020917]

25. Choi, J.; Hwang, Y.-S. Patent keyword network analysis for improving technology development efficiency. Technol. Forecast. Soc. Change; 2014; 83, pp. 170-182. [DOI: https://dx.doi.org/10.1016/j.techfore.2013.07.004]

26. Madani, F.; Weber, C. The evolution of patent mining: Applying bibliometrics analysis and keyword network analysis. World Pat. Inf.; 2016; 46, pp. 32-48. [DOI: https://dx.doi.org/10.1016/j.wpi.2016.05.008]

27. Yoon, B.; Magee, C.L. Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction. Technol. Forecast. Soc. Change; 2018; 132, pp. 105-117. [DOI: https://dx.doi.org/10.1016/j.techfore.2018.01.019]

28. Feng, J.; Liu, Z.; Feng, L. Identifying opportunities for sustainable business models in manufacturing: Application of patent analysis and generative topographic mapping. Sustain. Prod. Consum.; 2021; 27, pp. 509-522. [DOI: https://dx.doi.org/10.1016/j.spc.2021.01.021]

29. Yuan, X.; Li, X. Mapping the technology diffusion of battery electric vehicle based on patent analysis: A perspective of global innovation systems. Energy; 2021; 222, 119897. [DOI: https://dx.doi.org/10.1016/j.energy.2021.119897]

30. Ferreira, R.B.; Parreira, M.R.; Nabout, J.C. Is there concordance between Science and Technology in Natural Science? Mapping the relationship among number of papers and patents from research on Cerrado plants. World Pat. Inf.; 2022; 69, 102108. [DOI: https://dx.doi.org/10.1016/j.wpi.2022.102108]

31. Lee, C.; Kang, B.; Shin, J. Novelty-focused patent mapping for technology opportunity analysis. Technol. Forecast. Soc. Change; 2015; 90, pp. 355-365. [DOI: https://dx.doi.org/10.1016/j.techfore.2014.05.010]

32. Suominen, A.; Toivanen, H.; Seppänen, M. Firms’ knowledge profiles: Mapping patent data with unsupervised learning. Technol. Forecast. Soc. Change; 2017; 115, pp. 131-142. [DOI: https://dx.doi.org/10.1016/j.techfore.2016.09.028]

33. Noh, H.; Jo, Y.; Lee, S. Keyword selection and processing strategy for applying text mining to patent analysis. Expert Syst. Appl.; 2015; 42, pp. 4348-4360. [DOI: https://dx.doi.org/10.1016/j.eswa.2015.01.050]

34. Choi, H.; Woo, J. Investigating emerging hydrogen technology topics and comparing national level technological focus: Patent analysis using a structural topic model. Appl. Energy; 2022; 313, 118898. [DOI: https://dx.doi.org/10.1016/j.apenergy.2022.118898]

35. Kang, I.-S.; Na, S.-H.; Kim, J.; Lee, J.-H. Cluster-based patent retrieval. Inf. Process. Manag.; 2007; 43, pp. 1173-1182. [DOI: https://dx.doi.org/10.1016/j.ipm.2006.11.006]

36. Chen, Y.-L.; Chiu, Y.-T. An IPC-based vector space model for patent retrieval. Inf. Process. Manag.; 2011; 47, pp. 309-322. [DOI: https://dx.doi.org/10.1016/j.ipm.2010.06.001]

37. Abbas, A.; Zhang, L.; Khan, S.U. A literature review on the state-of-the-art in patent analysis. World Pat. Inf.; 2014; 37, pp. 3-13. [DOI: https://dx.doi.org/10.1016/j.wpi.2013.12.006]

38. Cho, H.P.; Lim, H.; Lee, D.; Cho, H.; Kang, K.-I. Patent analysis for forecasting promising technology in high-rise building construction. Technol. Forecast. Soc. Change; 2018; 128, pp. 144-153. [DOI: https://dx.doi.org/10.1016/j.techfore.2017.11.012]

39. Kim, K.H.; Han, Y.J.; Lee, S.; Cho, S.W.; Lee, C. Text mining for patent analysis to forecast emerging technologies in wireless power transfer. Sustainability; 2019; 11, 6240. [DOI: https://dx.doi.org/10.3390/su11226240]

40. Kwon, K.; Jun, S.; Lee, Y.-J.; Choi, S.; Lee, C. Logistics Technology Forecasting Framework Using Patent Analysis for Technology Roadmap. Sustainability; 2022; 14, 5430. [DOI: https://dx.doi.org/10.3390/su14095430]

41. Sun, D.; Zeng, S.; Ma, H.; Shi, J.J. How do high-speed railways spur innovation?. IEEE Trans. Eng. Manag.; 2021; 22, 72. [DOI: https://dx.doi.org/10.1109/TEM.2021.3091727]

42. Hanley, D.; Li, J.; Wu, M. High-speed railways and collaborative innovation. Reg. Sci. Urban Econ.; 2022; 93, 103717. [DOI: https://dx.doi.org/10.1016/j.regsciurbeco.2021.103717]

43. Durmuşoğlu, A.; Durmuşoğlu, Z.D.U. Remembering Medical Ventilators and Masks in the Days of COVID-19: Patenting in the Last Decade in Respiratory Technologies. IEEE Trans. Eng. Manag.; 2022; [DOI: https://dx.doi.org/10.1109/TEM.2022.3151636]

44. Durmusoglu, A.; Durmusoglu, Z.D.U. Traffic control system technologies for road vehicles: A patent analysis. IEEE Intell. Transp. Syst. Mag.; 2020; 13, pp. 31-41. [DOI: https://dx.doi.org/10.1109/MITS.2020.3037319]

45. Baser, K.H.C.; Buchbauer, G. Handbook of Essential Oils: SCIENCE, Technology, and Applications; CRC Press: Boca Raton, FL, USA, 2009.

46. Anatolyevna, K.O. Analysis of technological trends to identify skills that will be in demand in the labor market with open-source data using machine learning methods. Известия Саратoвскoгo университета. Нoвая серия. Серия Математика. Механика. Инфoрматика; 2022; 22, pp. 123-129.

47. Nikolopoulos, K.; Buxton, S.; Khammash, M.; Stern, P. Forecasting branded and generic pharmaceuticals. Int. J. Forecast.; 2016; 32, pp. 344-357. [DOI: https://dx.doi.org/10.1016/j.ijforecast.2015.08.001]

48. Hingley, P.; Dikta, G. Use of Information Criteria for finding Box-Jenkins Time Series Models for Patent Filings Counts Forecasts. IAENG Int. J. Appl. Math.; 2022; 52, pp. 1-11.

49. Hidalgo, A.; Gabaly, S. Use of prediction methods for patent and trademark applications in Spain. World Pat. Inf.; 2012; 34, pp. 19-29. [DOI: https://dx.doi.org/10.1016/j.wpi.2011.09.001]

50. Durmuşoğlu, A. Effects of clean air act on patenting activities in chemical industry: Learning from past experiences. Sustainability; 2017; 9, 862. [DOI: https://dx.doi.org/10.3390/su9050862]

51. Liu, X.; Yang, X. Identifying technological innovation capability of high-speed rail industry based on patent analysis. Proceedings of the 2019 8th International Conference on Industrial Technology and Management (ICITM); Cambridge, UK, 2–4 March 2019; pp. 127-131.

52. Cho, Y.; Han, Y.J.; Hwang, J.; Yu, J.; Kim, S.; Lee, C.; Lee, S.; Yi, K.P. Identifying technology opportunities for electric motors of railway vehicles with patent analysis. Sustainability; 2021; 13, 2424. [DOI: https://dx.doi.org/10.3390/su13052424]

53. Du, W.; Niu, K.; Yang, Z.; Li, D.; Nai, W. Analysis of Representative Invention Patents on Train Signal Control Technique of Thales SEC Transport. Proceedings of the 2022 IEEE 12th International Conference on Electronics Information and Emergency Communication (ICEIEC); Beijing, China, 15–17 July 2022; pp. 259-263.

54. Karasev, O.; Zheleznov, M.; Trostyansky, S.; Shitova, Y.A. Comprehensive Analysis of Forms of Innovative Activity of Foreign Railway Companies. World Transp. Transp.; 2020; 18, pp. 158-170. [DOI: https://dx.doi.org/10.30932/1992-3252-2020-18-158-170]

55. Feng, L.; Yu, X. A study on the integration innovation mode of China railway high-speed (CRH) technology. Proceedings of the 2018 Portland International Conference on Management of Engineering and Technology (PICMET); Honolulu, HI, USA, 19–23 August 2018; pp. 1-5.

56. Gou, J. Development status and global competition trends analysis of maglev transportation technology based on patent data. Urban Rail Transit; 2018; 4, pp. 117-129. [DOI: https://dx.doi.org/10.1007/s40864-018-0087-3]

57. Zhang, Y.; Zhang, G. Data analysis of railway industry patents. Proceedings of the 2nd International Conference on Big Data Research; Barcelona, Spain, 3–5 August 2018; pp. 27-32.

58. Zhang, F.; Zhang, X. Patent activity analysis of vibration-reduction control technology in high-speed railway vehicle systems in China. Scientometrics; 2014; 100, pp. 723-740. [DOI: https://dx.doi.org/10.1007/s11192-014-1318-3]

59. Xiao, J.Y. The development and innovative direction of train control system. Proceedings of the 2011 2nd International Conference on Intelligent Control and Information Processing; Harbin, China, 25–28 July 2011; pp. 442-445.

60. Salmi, P.; Torkkeli, M. Inventions Utilizing Satellite Navigation Systems in the Railway Industry: An Analysis of Patenting Activity. J. Technol. Manag. Innov.; 2009; 4, pp. 46-58. [DOI: https://dx.doi.org/10.4067/S0718-27242009000300005]

61. Lee, J.Y.; Yoon, Y.G.; Oh, T.K.; Park, S.; Ryu, S.I. A study on data pre-processing and accident prediction modelling for occupational accident analysis in the construction industry. Appl. Sci.; 2020; 10, 7949. [DOI: https://dx.doi.org/10.3390/app10217949]

62. Lee, H.-Y.; Kim, D.-H.; Park, K.-R. Pest diagnosis system based on deep learning using collective intelligence. Int. J. Electr. Eng. Educ.; 2019; [DOI: https://dx.doi.org/10.1177/0020720919833052]

63. Tarle, B.; Jena, S. Ant lion optimization based medical data classification using modified neuro fuzzy classifier. Wirel. Pers. Commun.; 2021; 117, pp. 1223-1242. [DOI: https://dx.doi.org/10.1007/s11277-020-07919-6]

64. Kim, C.; Kwon, S.; Kim, J. The evaluation of korea’s competitiveness in lubricants industries using patent index analysis. Korean Chem. Eng. Res.; 2016; 54, pp. 332-339. [DOI: https://dx.doi.org/10.9713/kcer.2016.54.3.332]

65. Horvath, D.; Baskin, I.; Marcou, G.; Varnek, A. Generative topographic mapping of conformational space. Mol. Inform.; 2017; 36, 1700036. [DOI: https://dx.doi.org/10.1002/minf.201700036]

66. Jeong, Y.; Lee, K.; Yoon, B.; Phaal, R. Development of a patent roadmap through the Generative Topographic Mapping and Bass diffusion model. J. Eng. Technol. Manag.; 2015; 38, pp. 53-70. [DOI: https://dx.doi.org/10.1016/j.jengtecman.2015.08.006]

67. Marques, N.C.; Lopes, G.P. Tagging with small training corpora. Proceedings of the International Symposium on Intelligent Data Analysis; Cascais, Portugal, 13–15 September 2001; pp. 63-72.

68. Dewolf, L. Study of new concepts, acronyms and borrowing in the terminology of the road traffic and their anchoring in beige road regulation. META; 2015; 60, pp. 173-192. [DOI: https://dx.doi.org/10.7202/1032405ar]

69. IŞIK, M.; Dağ, H. The impact of text preprocessing on the prediction of review ratings. Turk. J. Electr. Eng. Comput. Sci.; 2020; 28, pp. 1405-1421. [DOI: https://dx.doi.org/10.3906/elk-1907-46]

70. Chulanova, Z.K.; Satybaldin, A.A.; Koshanov, A.K. Methodology for assessing the state of human capital in the context of innovative development of the economy: A three-level approach. J. Asian Financ. Econ. Bus.; 2019; 6, pp. 321-328. [DOI: https://dx.doi.org/10.13106/jafeb.2019.vol6.no1.321]

71. Chun, E.; Jun, S.; Lee, C. Identification of Promising Smart Farm Technologies and Development of Technology Roadmap Using Patent Map Analysis. Sustainability; 2021; 13, 10709. [DOI: https://dx.doi.org/10.3390/su131910709]

72. Nordberg, G.F. Historical perspectives on cadmium toxicology. Toxicol. Appl. Pharmacol.; 2009; 238, pp. 192-200. [DOI: https://dx.doi.org/10.1016/j.taap.2009.03.015]

73. Cohen, S.M.; Klaunig, J.; Meek, M.E.; Hill, R.N.; Pastoor, T.; Lehman-McKeeman, L.; Bucher, J.; Longfellow, D.G.; Seed, J.; Dellarco, V. Evaluating the human relevance of chemically induced animal tumors. Toxicol. Sci.; 2004; 78, pp. 181-186. [DOI: https://dx.doi.org/10.1093/toxsci/kfh073]

74. Akinsolu, F.T.; de Paiva, V.N.; Souza, S.S.; Varga, O. Patent landscape of neglected tropical diseases: An analysis of worldwide patent families. Glob. Health; 2017; 13, pp. 1-13. [DOI: https://dx.doi.org/10.1186/s12992-017-0306-9]

75. Phirouzabadi, A.M.; Savage, D.; Blackmore, K.; Juniper, J. The global patents dataset on the vehicle powertrains of ICEV, HEV, and BEV. Data Brief; 2020; 32, 106042. [DOI: https://dx.doi.org/10.1016/j.dib.2020.106042]

76. Kim, G.; Bae, J. A novel approach to forecast promising technology through patent analysis. Technol. Forecast. Soc. Change; 2017; 117, pp. 228-237. [DOI: https://dx.doi.org/10.1016/j.techfore.2016.11.023]

77. Svensson, R. Patent value indicators and technological innovation. Empir. Econ.; 2022; 62, pp. 1715-1742. [DOI: https://dx.doi.org/10.1007/s00181-021-02082-8]

78. Caviggioli, F.; Colombelli, A.; De Marco, A.; Paolucci, E. How venture capitalists evaluate young innovative company patent portfolios: Empirical evidence from Europe. Int. J. Entrep. Behav. Res.; 2020; 26, pp. 695-721. [DOI: https://dx.doi.org/10.1108/IJEBR-10-2018-0692]

79. Fu, B.-R.; Hsu, S.-W.; Liu, C.-H.; Liu, Y.-C. Statistical analysis of patent data relating to the organic Rankine cycle. Renew. Sustain. Energy Rev.; 2014; 39, pp. 986-994. [DOI: https://dx.doi.org/10.1016/j.rser.2014.07.070]

80. Xu, Z.; Parry, M.E.; Song, M. The Impact of Technology Transfer Office Characteristics on University Invention Disclosure. IEEE Trans. Eng. Manag.; 2011; 58, pp. 212-227. [DOI: https://dx.doi.org/10.1109/TEM.2010.2048915]

81. Kim, S.; Kim, H.; Kim, E. How knowledge flow affects Korean ICT manufacturing firm performance: A focus on open innovation strategy. Technol. Anal. Strateg. Manag.; 2016; 28, pp. 1167-1181. [DOI: https://dx.doi.org/10.1080/09537325.2016.1182150]

82. Shema, H.; Bar-Ilan, J.; Thelwall, M. Do blog citations correlate with a higher number of future citations? Research blogs as a potential source for alternative metrics. J. Assoc. Inf. Sci. Technol.; 2014; 65, pp. 1018-1027. [DOI: https://dx.doi.org/10.1002/asi.23037]

83. Franceschet, M.; Colavizza, G. Quantifying the higher-order influence of scientific publications. Scientometrics; 2020; 125, pp. 951-963. [DOI: https://dx.doi.org/10.1007/s11192-020-03580-9]

84. Dharmayanti, N.L.S.; Suryani, E. Strategic Planning for System and Information Technology Using Anita Cassidy in PT. XYZ. IPTEK J. Proc. Ser.; 2019; 5, pp. 559-568. [DOI: https://dx.doi.org/10.12962/j23546026.y2019i5.6431]

85. Li, H.W.W.; Huang, P.; Du, L.; Zhang, X. State Prediction Method Research in NC Machine Tool Based on Multidimensional Time Series. Gongcheng Kexue Yu Jishu/Adv. Eng. Sci.; 2018; 50, pp. 187-195.

86. Chen, M.; Chen, C. Utilizing Various Statistical Models on Time Series Temperature Data; Stanford Online High School: San Jose, CA, USA, 2022.

87. Shakeel, M.; Srivastava, B. Stylized facts of high-frequency financial time series data. Glob. Bus. Rev.; 2021; 22, pp. 550-564. [DOI: https://dx.doi.org/10.1177/0972150918811701]

88. Bürkner, P.-C.; Gabry, J.; Vehtari, A. Approximate leave-future-out cross-validation for Bayesian time series models. J. Stat. Comput. Simul.; 2020; 90, pp. 2499-2523. [DOI: https://dx.doi.org/10.1080/00949655.2020.1783262]

89. Lugo-Martinez, J.; Ruiz-Perez, D.; Narasimhan, G.; Bar-Joseph, Z. Dynamic interaction network inference from longitudinal microbiome data. Microbiome; 2019; 7, pp. 1-14. [DOI: https://dx.doi.org/10.1186/s40168-019-0660-3]

90. Ampornphan, P.; Tongngam, S. Exploring technology influencers from patent data using association rule mining and social network analysis. Information; 2020; 11, 333. [DOI: https://dx.doi.org/10.3390/info11060333]

91. Park, H.; Yoon, J. Assessing coreness and intermediarity of technology sectors using patent co-classification analysis: The case of Korean national R&D. Scientometrics; 2014; 98, pp. 853-890.

92. Meena Kowshalya, A.; Valarmathi, M. Evaluating twitter data to discover user’s perception about social Internet of Things. Wirel. Pers. Commun.; 2018; 101, pp. 649-659. [DOI: https://dx.doi.org/10.1007/s11277-018-5709-2]

93. Kim, D.; Kim, S. The role of mobile technology in tourism: Patents, articles, news, and mobile tour app reviews. Sustainability; 2017; 9, 2082. [DOI: https://dx.doi.org/10.3390/su9112082]

94. Son, S.; Cho, N.-W. Technology fusion characteristics in the solar photovoltaic industry of South Korea: A patent network analysis using IPC co-occurrence. Sustainability; 2020; 12, 9084. [DOI: https://dx.doi.org/10.3390/su12219084]

95. Oh, H.; Lee, H.J.; Chang, T.-W. A study on the technology convergence of artificial intelligence through patent analysis. ICIC Express Lett.; 2018; 12, pp. 699-706.

96. Park, Y.-N.; Lee, Y.-S.; Kim, J.-J.; Lee, T.S. The structure and knowledge flow of building information modeling based on patent citation network analysis. Autom. Constr.; 2018; 87, pp. 215-224. [DOI: https://dx.doi.org/10.1016/j.autcon.2017.12.023]

97. Son, C.; Suh, Y.; Lee, Y.; Park, Y. A probabilistic approach to identifying technology vacuum: GTM-based patent map. Proceedings of the Picmet 2010 Technology Management For Global Economic Growth; Phuket, Thailand, 18–22 July 2010; pp. 1-8.

98. Wu, F.; Mi, L.; Li, X.; Huang, L.; Tong, Y. Identifying potential standard essential patents based on text mining and generative topographic mapping. Proceedings of the 2018 IEEE International Symposium on Innovation and Entrepreneurship (TEMS-ISIE); Beijing, China, 30 March–1 April 2018; pp. 1-9.

99. Hickman, T.L.; Choi, E.; Whiteman, K.R.; Muralidharan, S.; Pai, T.; Johnson, T.; Parikh, A.; Friedman, T.; Gilbert, M.; Shen, B. BOXR1030, an anti-GPC3 CAR with exogenous GOT2 expression, shows enhanced T cell metabolism and improved antitumor activity. bioRxiv; 2021.

100. Sandri, P.F.; Portocarrero, A.R.; Ciupa, L.; karina Da Veiga, F.; Falkowski, G.F.S.; Benvenutti, M.J.; de Souza Rodrigues, W.N.; Aleixo, D.L.; Araújo, S.M. Clinical and parasitological assessment in mice treated with highly diluted Atropa belladonna. Int. J. High Dilution Res. -ISSN 1982-6206; 2014; 13, pp. 122-124. [DOI: https://dx.doi.org/10.51910/ijhdr.v13i47.734]

101. Jeong, Y.; Park, I.; Yoon, B. Identifying emerging Research and Business Development (R&BD) areas based on topic modeling and visualization with intellectual property right data. Technol. Forecast. Soc. Change; 2019; 146, pp. 655-672.

102. Hegde, D.; Luo, H. Patent publication and the market for ideas. Manag. Sci.; 2018; 64, pp. 652-672. [DOI: https://dx.doi.org/10.1287/mnsc.2016.2622]

103. Lück, S.; Balsmeier, B.; Seliger, F.; Fleming, L. Early disclosure of invention and reduced duplication: An empirical test. Manag. Sci.; 2020; 66, pp. 2677-2685. [DOI: https://dx.doi.org/10.1287/mnsc.2019.3521]

104. Saidi, F.; Žaldokas, A. How does firms’ innovation disclosure affect their banking relationships?. Manag. Sci.; 2021; 67, pp. 742-768. [DOI: https://dx.doi.org/10.1287/mnsc.2019.3498]

105. Peddoju, S.K.; Upadhyay, H.; Soni, J.; Prabakar, N. Natural language processing based anomalous system call sequences detection with virtual memory introspection. Int. J. Adv. Comput. Sci. Appl.; 2020; 11, [DOI: https://dx.doi.org/10.14569/IJACSA.2020.0110559]

106. Alshrari, A.S.; Hudu, S.A.; Imran, M.; Asdaq, S.M.B.; Ali, A.M.; Rabbani, S.I. Innovations and development of COVID-19 vaccines: A patent review. J. Infect. Public Health; 2021; 15, pp. 123-131. [DOI: https://dx.doi.org/10.1016/j.jiph.2021.10.021] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34742639]