1. Introduction
As the 4th Industrial Revolution unfolds, many advanced technologies have emerged, catalyzing significant transformations in the logistics industry [1]. Driven by big data, a core component of the revolution, the logistics sector has shifted its focus from suppliers to consumers [2,3]. This shift has accelerated the adaptation of various fields to swiftly accommodate consumer requirements, including providing tailored services, enhanced reliability, customized mass transportation, and automated logistics warehouses. Consequently, companies have maximized profits by improving customer satisfaction and resource efficiency. The COVID-19 pandemic has further underscored the importance of addressing uncertainties, such as global supply chain vulnerabilities, transportation disruptions, and rapid volume changes [4]. In response, logistics companies increasingly employ intelligent technologies to minimize supply chain uncertainties in the post-pandemic era [5].
Innovative logistics systems, encompassing advanced technologies such as artificial intelligence (AI), blockchain, robots, and intelligent mobility, have gained prominence [6]. The common thread among smart logistics technologies, such as the Internet of Things (IoT), AI, and cloud computing, is their ability to collect and analyze data, enabling real-time tracking and prediction [7]. These technologies have spurred novel developments in logistics, including supply chain management, demand management, and quality management. Big data is widely recognized as the cornerstone of smart logistics [8]. Companies are increasingly implementing projects such as Mega Hub, which leverages customer purchase history and pattern data to enable predictive delivery systems, manages supply chain risks using risk databases, and forecasts resource needs through big data analysis [9,10,11,12].
In the era of Logistics 4.0, technology development roadmaps are being announced, and R&D efforts are actively underway to secure advanced technologies [13,14]. Developing a system that offers services based on big data analysis or a new logistics technology utilizing 4th Industrial Revolution technologies necessitates the convergence and integration of multiple technologies [15]. Patent document analysis is crucial for identifying technology convergence relationships and informing future development strategies [16]. Patents encompass novel technologies and market attributes, enabling trend prediction and technology development planning through prior patent investigation and analysis [17]. Moreover, new technologies and relationships between technologies can be anticipated through various analyses, such as past and present technology trend analysis and interrelated technology analysis using patent documents. Technology forecasting studies have been conducted on patent data using various methodologies [18,19,20,21]. However, there have yet to be technology prediction studies using Temporal Network. We show that Temporal Network can be used to conduct technology forecasting research. This study aims to predict big data-driven logistics technologies using Temporal Network, informing R&D direction through trend and network analysis of patent documents and preventing redundant research efforts.
2. Previous Research
2.1. 4th Industrial Revolution Technology and Logistics Technology
The logistics industry refers to the overall process of the supply chain, including product production, inventory management, product distribution, and delivery [22]. It is possible to optimize the logistics process by establishing plans through accurate demand forecasting and automating logistics using machines, which leads to minimizing logistics costs and improving consumer satisfaction [23]. It is possible to derive optimized logistics processes with new technologies composed of 4th industrial revolution technologies such as artificial intelligence, extensive data analysis, and automated robots [24]. Based on the above, some countries, including the Netherlands, Germany, and Japan, and logistics-related companies are establishing data-based Information and Communications Technology (ICT) logistics technology development strategies using the 4th Industrial Revolution technology for future logistics competitiveness.
The European Union plans to improve efficiency by 30% in the logistics supply chain by 2030 via the Alliance for Logistics Innovation in Europe (Alice) project. The project aims to secure flexibility in logistics processes and design eco-friendly logistics chains by establishing a physical Internet, an information system that integrates logistics supply chains, and optimizing links between means of transportation [25]. Germany has tried digitalizing logistics supply chains through the Logistics 2030 Innovation Programme and High-Tech Strategy 2025. In this manner, they expect to build a fast and safe logistics process by digitally managing all transportation methods and establishing intelligent railroads, ports, and aviation systems [26]. Japan revised the “Comprehensive Logistics Policy” to apply the Internet of Things and artificial intelligence to the logistics system and announced establishment of a sustainable logistics system. In addition, automation technology is used as a solution to solve the shortage of workers in the logistics industry and the shortage of truck drivers due to the aging, low birth rate [27]. South Korea confirmed the “5th National Framework Plan for Logistics (2021–2030)”. It selected “Growth of Smart Digital Innovation in the Logistics Industry and Created a Win-Win Ecosystem” as its key theme. Based on this, the ten critical tasks announced include establishing a logistics system in preparation for the autonomous driving era, establishing an efficient, intelligent city logistics system, activating cold-chain, building only high-tech smart airports, innovating an eco-friendly shipping logistics system, and building a platform to distribute national logistics big data [28]. The logistics supply chain has a wide range, including logistics creation to delivery to consumers [29].
To apply the smart logistics system to the logistics supply chain, systematic development of various technologies and convergence between technologies are necessary [30]. For example, logistics centers that perform product storage, packaging, and classification, and Last Mile, where products are delivered to consumers, require classification automation robots and delivery optimal route technology to maximize efficiency individually. However, from the perspective of the overall logistics process, it is necessary to accurately predict the demand for products using advanced technologies such as artificial intelligence and big data. Finally, the optimization of the supply chain is designed by connecting the logistics center and Last Mile. In other words, data collection, extensive data analysis, and automation technologies should be applied to improve overall process efficiency and individual factors [31,32]. As mentioned above, since the scope of the logistics supply chain is broad, it is difficult to derive a systematic technology development strategy using a general technology development method. Therefore, to preoccupy excellent technologies in logistics technology, it is necessary to systematically predict technologies by applying analysis technologies based on accurate technology status surveys.
2.2. Patent Analysis
2.2.1. Technology Development Status Analysis
To predict technology, thorough investigation and analysis of technology development status are required [33]. There are two methods used to analyze the current status of technology development. One is a qualitative method, and the other is the quantitative method [34]. Qualitative analysis methods include Delphi, scenario composition, and brainstorming analysis methods. Significantly, the Delphi method is one of the methods of deriving problems based on expert empirical knowledge, presenting solutions through them, and finally predicting the future, and is widely used in various fields [35]. However, there is a possibility that qualitative analysis methods will be interpreted as false results because conclusions are drawn based on the researcher’s questionnaire form and combination and expert subjective opinions [36,37].
Quantitative analysis methods are used to analyze the current technology development status to overcome the limitations of qualitative analysis methods that can be interpreted as subjective opinions. Quantitative analysis methods encompass various methods, such as technology growth curve and technology level analysis. The technology growth curve was developed based on the growth curve. The growth curve is a model initially used in biology to measure the growth of living things over time, and a typical form is an S-curved sigmoid function [38]. It is known that this form is similar to the growth process of technology, and based on this, it was used as a technology growth curve [39]. After that, a method of evaluating the level of technology between countries using the technology growth curve, which is a method of analyzing the stage of technology development over time, was proposed [40]. In addition, it has been used to determine the current location of the technology using patent data. From a technical point of view, there is a case of identifying the stage of technology growth using patents filed in hydrogen energy, Radio-Frequency Identification (RFID), and transportation system [41,42,43]. Technology-level analysis is a method of analyzing the current status of technology development between technology fields. The number of patent applications and the number of applicants were analyzed in a two-dimensional graph on two axes [44]. However, technology-level analysis is unsuitable for the research because it compares relative technologies.
Therefore, this research identifies the development location of the technology using the technology growth curve and finally identifies the need for technology prediction.
2.2.2. Network Analysis
Network analysis derived from the technical analysis of graph theory is a methodology used to analyze interactions between nodes [45]. Relationships and relative locations between nodes in a network can identify characteristics in individual units and throughout the system [46]. Network analysis has been developed in social science and used to analyze various social networks, including politics and people [47]. Network analysis was used for technology analysis beyond the field of social science. Network analysis has also been performed to achieve visualization and analysis of citation networks and inventor–patent networks between light-emitting and laser diodes patents [48]. However, since patent citation analysis shows only the citation relationship between the two patents, it is difficult to grasp the overall context and internal relationship, and only citations and citation information are considered. Therefore, various network analyses have been developed to compensate for these limitations. In this analysis, a patent classification code, International Patent Classification (IPC), is used to analyze relationships between technical fields [49]. More specifically, to analyze the relationship between technical elements, the core words of the patent were extracted using text mining techniques, and network analysis was performed based on the keyword [50].
The network analysis used in the past is a type of static network, and it is challenging to express interactions between elements that change frequently. It was confirmed that the Temporal Network was devised and had excellent efficiency in simulation with the natural world compared to the static network to overcome the limitations of the above static network. [51,52]. In the Temporal Network, the characteristics to which the axis of time is applied were conducted in many studies. Studies were conducted to predict the spread of infectious diseases and explore infectious diseases concerning population movement over time through time series data [53], and to analyze temporary interaction patterns between people caused by information and communication technology [54]. In addition, research was conducted to analyze changes in investment groups over time by expressing venture capital (VC) and venture companies that received investment in the venture investment market as a kind of network (Temporal Network) [55]. These preceding studies reveal that changes in industrial technology over time can be analyzed through the Temporal network. Therefore, this research aims to identify the flow of technology development over time using the Temporal network for patent analysis and analyze the future direction of technology.
3. Methodological Framework
3.1. Overall Framework
The methodological framework performed in this paper consists of three major steps as shown in Figure 1. The first step is to extract patents through a search from the patent database and select valid patents suitable for analysis in this study. After that, patent data for technology analysis is preprocessed. The technology growth curve is expressed as an effective patent in the second step. Currently, a technology importance evaluation consisting of technology prospect analysis using technology innovation stage analysis and time series analysis is performed. In the last and third steps, the future technology is predicted by building a Temporal Network using the IPC code of the patent and analyzing the IPC network structure that changes over time. Detailed descriptions of each step are described below.
3.2. Detailed Explanation
3.2.1. Step 1: Collecting Patents and Preprocessing Data for Technology Analysis
The first phase is the initial step for technology analysis, as shown in Figure 2, and consists of patent collection and data preprocessing. This research starts with the collection of patent documents in a specific field, and patents can be collected from various patent institutions. Significantly, the United States Patent and Trademark Office (USPTO) stores, manages, and supervises world patents, so it is practical to use the USPTO database to produce reliable results [56]. In addition, a patent search formula is constructed for more reliable patent extraction by combining keywords consisting of analysis targets, purposes, and means [57]. Patents extracted through the above process confirm valid patents from experts in the relevant technology field.
Patents contain various information, including patent names, patent information, technical field, drawings, inventors, and applicants. However, this study uses only technical fields, application dates, and inventor information. One or more IPC codes, an internationally unified patent classification system, are assigned for systematic classification in the technology field. The IPC code has a hierarchical structure of section, class, subclass, main group, and subgroup, and is shown in Table 1. In order to analyze the technology field with IPC code, four digits must be used up to the subclass level [58]. Therefore, this study also extracts and analyzes IPC codes up to four digits.
Since there are one or more IPC codes in the patent, many IPC codes are in the extracted valid patent. Therefore, during the data preprocessing step, the patent-IPC matrix is derived using only the frequency of the IPC code and the IPC used above average. The patent-IPC matrix is configured by expressing the presence of IPC for each patent as 0 and 1, as shown in Table 2.
3.2.2. Step 2: Technology Development Status Analysis
Researching the characteristics and trends of a specific technology field for accurate technology prediction is essential [6,44]. Technology research should analyze the current status of technology and identify future trends. Therefore, in this step, the importance of technology consisting of technology prospect analysis is evaluated as a technology growth curve using effective patents as technology innovation stage analysis and time series analysis.
The technology growth curve, which analyzes the stages of technological innovation, is a graph of the number of applications and applicants divided over time and recognizes essential information about technology [59,60]. Since this curve can differentiate the characteristics of each stage, it checks the level of technological growth and maturity [61,62]. The technology growth curve is classified into five stages. It has two axes: patent applications and patent applicants. The number of patent applications is an indicator of the IP creation environment, which can be used to understand whether a technology group is able to mobilize funding to support IP creation. The number of patent applications is one of the most commonly used indicators in patent analysis, as it shows whether the research is continuing [63].
In this curve, innovation of new technologies begins in the first stage. Although the number is small, it is interpreted as the time of introduction when applicants and patents increase simultaneously. In the second stage, the number of applicants and patents increases rapidly simultaneously, and expectations for new technologies expand. The third stage is the maturity stage, and the rate of increase in patent applicants decreases. In the fourth stage, technology’s market size decreases as the number of patents and applicants decreases. In the fifth stage, as new technologies emerge that surpass existing ones, the market that shrinks in the fourth stage gradually grows as the number of patents and patent applicants increases. In this study, the stage and location of the current technology can be identified from a macro perspective with a patent-based technology growth curve [59].
Technology prospect analysis predicts the direction of future technologies through time series analysis. Time series data prediction is a representative method of trend analysis as an analysis method for predicting the future based on past data [64]. This analysis regularly groups patents, calculates the number, and processes them as time series data. There are several algorithms used to predict the future with time series data [65]. In this research, trend analysis is conducted using Authentic Integrated Moving Average (ARIMA), an algorithm for predicting time series. ARIMA is a model that considers both differences between observations for explaining autocorrelation, moving average, and non-stationary time series data in which previous values affect subsequent values [64]. Based on the direction of the time series prediction results, as shown in Figure 3, they are hot when the trend rises, cold when it falls, and active when the trend is maintained [49,66].
3.2.3. Step 3: Future Technology Prediction
The final step is network analysis with a patent–IPC matrix. Patent Temporal Network analysis visualizes the interrelationship between technologies and the combination of technologies over time. The IPC of the patent–IPC matrix is set as a node, and if one patent contains two or more IPCs, they are linked together. In this case, unlike the original static network analysis, the Temporal Network is used to examine changes in the network over time [67].
Unlike static networks, Temporal Network can analyze network changes over time by adding one dimension of time to existing networks. The left side of the Figure 4 is the Static Network, and the right side is the Temporal Network. For example, it is possible to express a dynamic system, such as the path through which the patient’s pathogen spreads over time in the network and the path through which the e-mail is delivered. Since the link is activated only at a specific time, changes in the connection state and weight of the link over time can be identified [68,69].
The time dimension plays a vital role in network analysis. In the existing static network, if node A and node B are connected, and node B and node C are connected, then node A and node C are indirectly connected to form a link between nodes. However, assuming that node A and node B, and node C are connected at different times in the Temporal Network, then it shows that node A and node C are not connected. It reveals that network analysis and analysis vary according to the time axis [70].
Technology development is well detected because development trends change over time, while public static networks have limitations that make detection more difficult because there is little change. Therefore, this study analyzes technology development trends by establishing an IPC network over time.
4. Future Logistics Technology Predicition Using Big Data
4.1. Logistics Patent Data Collection and Data Preprocessing
This research involves extracting patent data from the USPTO database to analyze logistics patents related to big data. The search period is from 1977 to 2021, and data from more than 75 countries, including the Republic of Korea, the United States, Japan, China, and PCT, were collected. The search keyword was composed of a patent search formula by dividing national policy and logistics-related terms, fields, targets, and purposes related to the 4th industrial revolution technology, and extracted a total of 2169 patents. In addition, 963 valid patents were selected through analysis of valid patents with logistics experts.
As a result of analyzing the four-digit IPCs (subclass) for 963 logistics-related valid patents, a total of 129 IPCs were identified. In the context of network analysis, interpreting the results can be challenging if the weight of links—representing connections between IPCs—consists predominantly of small values or has a value of one. Therefore, we limited our analysis to IPCs with above-average connectivity, i.e., the number of patents with two IPCs was above average when we plotted the connectivity graph for the IPCs of 963 patents. As a result of the analysis, out of 129 IPCs, 22 IPCs were used more than average, and the patent × IPC matrix of 963 × 22 was finally designed, as shown in Table 3.
4.2. Logistics Technology Development Status Analysis Based on Big Data
After analyzing the current status of technology development of 963 data based on valid logistics-related patents, big data is used to check the development stage and prospects of logistics technology. The purpose of the analysis is to secure the validity of technology prediction. Since patent data will not be released until one and a half years after filing, analysis of the technology innovation stage and technology outlook will be analyzed using only data until December 2020.
4.2.1. Technology Innovation Stage Analysis
The technology innovation stage analysis is performed to confirm the current location of technology development. If the technology development market is already deteriorating, technology prediction is unnecessary, but it is essential if it is a growing technology development market. Therefore, this analysis is applied before predicting future logistics technologies related to big data.
The analysis of the technology innovation stage visualizes the technology growth curve by dividing it into five sections for each period. Figure 5 is a graph representing the number of patent applications and the number of applicants in a two-dimensional chart from 1977 to 2020. When analyzing the trend of the technology growth curve, the current position of big data-related logistics technology is confirmed to be in the first two stages of entering the development stage after the introduction period.
4.2.2. Technology Prospect Analysis
The technology prospect analysis identifies future technology development trends, and a time series analysis based on the number of applications can confirm prospects. Technology prediction is necessary for promising technologies, so this analysis is applied before predicting big data-related logistics technologies.
It performs a time series analysis based on the number of patent applications by year of valid patents. Time series analysis can predict the prospect of the number of patents applied in the future using the ARIMA algorithm. Therefore, in this study, the ’auto.arima’ function of the ’forecast’ package of R software used the ARIMA algorithm. Figure 6 shows the results of technology prospect analysis using time series data. In big data-related logistics technology, Arima’s p, q, and r variables were derived as 0, 2, and 2. The graph is upward trending, indicating that the future of big data technology in logistics is a hot area that continues to rise.
4.2.3. Summary
The technology innovation stage analysis is an analysis of the current status of technology development. Technology prospect analysis is based on quantitative indicators and can be used as valid evidence for technology prediction establishment. As a step before predicting technology, the technology growth stage of big data-related logistics technology was confirmed, and the direction of future technology was grasped through trend analysis based on time series data.
Since logistics technology is growing from the introductory period, it is progressing from the first to the second stage of the technology growth curve. Therefore, it is expected to be in a transitional period that continues to develop soon. Because the current and prospects mean the growth of technology, big data-based logistics technology requires market interest and investment. Therefore, countries and companies should focus on and develop big data-based logistics technologies. Below, we will predict future technologies in this regard.
4.3. Future Logistics Technology Prediction Based on Big Data
It is necessary to identify future logistics technology changes by analyzing changes in big data-based logistics technologies from the past to predict future technologies. To confirm changes in logistics technology over time, a Temporal Network is established to confirm changes in logistics technology. In addition, the “teneto” package of R and the patent x IPC matrix designed in the data preprocessing process is used for this network. Table 4 shows the definitions of 22 IPCs for network analysis.
4.3.1. Logistics Technology Network Transition
Since not all 2021 patents have been released, we can design a Temporal Network based on meaningful data-driven logistics patents through 2020 to see technology convergence starting in 2016. This means that the logistics and big data industries will converge in 2017. The results of the Temporal Network are shown in Table 5 and Figure 7.
Table 5 represents the connection weights of IPC 1 and IPC 2 differently at the time. We find the links connected to G06Q and H04L in 2017, two links connected to G06Q-G06F, G06Q-H04L in 2018, three links connected to G06Q-H04L, G06Q-G06F, and G05B-H04L in 2019, and eight links connected to G06Q-G06K, G06Q-G06F, G06Q-G06N, G06Q-H04W, G06Q-H04L, G06F-G06K, G06Q-G06T, and G01S-H04W in 2020. This confirms the development of full-fledged big data-driven logistics technology since 2017. In particular, explosive logistics technology development took place in 2020.
As a result of network analysis and patent analysis, in 2017, wireless communication network technology was developed to apply IoT technology. In 2018, technology was developed to transmit and utilize digital data based on previously developed technology. In 2019, technologies for controlling and coordinating digital data over time are being developed. In 2020, computing technologies for effectively processing collected data, data collection and processing using video communication and speed, rather than simple data acquisition.
4.3.2. Network Analysis/Major IPC Selection
The IPC code of G06Q is identified as the core code by analyzing the Temporal Network. Figure 8 is an intuitive visualization of IPC code fusion from 2017 to 2020. The weights in the graph show how many years in total there were such connections between 2017 and 2020. Looking at the figure, IPC codes that have not yet been fused with the G06Q code are G05B and G01S codes. The previous four IPCs are relatively easily integrated with technology because they are networked with one or two IPCs. Therefore, corresponding IPC codes must be included to predict big data-based future logistics technology.
As shown in Table 6, we extract the IPC codes that appeared in 2021 patents and sort them according to their frequency of appearance. In Table 6, appearance is organized by examining whether the IPC codes extracted from 2021 patents appeared in patent IPC codes up to 2020. X is an IPC code that appeared for the first time in 2021 and O is an IPC code that appeared in a patent in the past. The new IPC codes in 2021 mean that they can be combined with past IPC codes to create new technologies. According to Table 6, the IPC analysis results of patents filed in 2021, the IPC of G06Q, G06K, and G06F were derived as high-frequency IPCs. Eight out of fourteen IPCs overlap with the derived IPCs up to 2020, so IPCs other than these should be selected. Since six IPCs are not duplicated, future logistics technologies can be predicted, including these technologies.
4.3.3. Future Logistics Technology Prediction
Detailed topic selection is possible with four essential IPCs and six IPCs that do not overlap to predict future logistics technologies. Since nonoverlapping IPCs are still in a difficult stage of convergence between technologies, the IPCs must be included individually. Therefore, the complicated topic is defined as one IPC that does not overlap with the required IPC when selecting the detailed topic. This study predicts future logistics technologies, as shown in Table 7 and Table 8. Table 7 shows that six innovations can be derived from our IPC code extraction. IPC 1 is a new code that emerged in 2021. IPC 2, IPC 3, and IPC 4 refer to the core patent derived in Figure 8 and the two IPC codes not directly linked to the core patent by 2020, respectively. This means that the core IPC codes, unconnected IPC codes, and newly emerged IPC codes that are centered on existing patents will converge to create new technologies. Table 8 provides a detailed description of the technologies that will emerge from the combination of IPC codes derived in Table 7.
5. Conclusions/Discussion
The transformation of the logistics supply chain process is expected to begin by establishing an integrated digital system of smart logistics technology that incorporates advanced technologies. Since the logistics supply chain process is a process that encompasses a wide range, it is not easy to develop a convergence technology that combines major technologies if the development level is different for each detailed technology field. Innovative logistics technology should be predicted based on systematic technology status analysis to solve the above problems. This research presents a methodology using patent analysis to present the direction of technology development by predicting future innovative logistics technologies. Technology innovation stage analysis and technology prospect analysis were constructed through valid patents. Technology status analysis was conducted to quantitatively analyze the hot technology field, showing an upward trend and that logistics technology is in the growth phase. To predict the logistics technologies that will evolve in the future, we designed a Temporal Network based on the IPC code to extract critical technologies. Based on this, the final future smart logistics technology through convergence with core technologies was predicted through IPC analysis of recent patents.
The six future smart logistics technologies derived in this study have many things in common with the logistics technology roadmap announced in countries worldwide. Table 9 is a comparative analysis table of national roadmaps and prediction technologies. The table shows ⊚ for direct application and ∘ for indirect application. Comparing and analyzing the results of this study with the eight areas, we can see that the predicted smart logistics technologies are included. In addition, compared to the national logistics technology roadmap with a comprehensive concept, the results of this study can be confirmed in a more detailed technology field. In other words, when we conducted technology forecasting using Temporal Network based on patent data, we found that it was consistent with the existing roadmap for developing big data-based logistics technology. This shows that our methodology can derive the same results as the existing technology development roadmap. Furthermore, since this study presents a methodology for conducting technology prediction from IPC codes of patents, it can be utilized to propose specific tasks for establishing technology development roadmaps and government policy roadmaps.
Predicting new technologies is crucial for providing ideas to researchers and policymakers and establishing policies or research plans. This study shows that applying Temporal Network to IPC codes can predict innovative logistics technologies. In addition, it is shown that the predicted technology could design more specific technology development roadmaps and policy roadmaps through analysis of technology factors. In general, technology development roadmap design requires a lot of time and effort because it consists of extensive research and analysis of corporate, market, and technology development trends. However, it has been shown that quantitative analysis using published patents alone can produce results similar to the existing technology development roadmap. As a methodology of this research, it will help derive a roadmap for technology development and predict future technologies by using it in various fields beyond smart logistics technology.
Conceptualization, J.S.; methodology, J.S.; formal analysis, J.S.; data curation, J.S.; writing—original draft preparation, K.K.; writing—review and editing, K.K.; supervision, J.S.; project administration, K.K.; funding acquisition, K.K. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Data on patents can be found at the United States Patent and Trademark Office USPTO.
The authors would like to thank Seongchan Jeon for giving detailed feedback on the paper.
The authors declare no conflict of interest.
Footnotes
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Enlarge this image.Figure 4. Static Network and Temporal Network. The left side is the Static Network, and the right side is the Temporal Network.
Enlarge this image.Figure 8. Results of the IPC network. The numbers in the graph represent connection weights. Each number represents how many years each connection appeared.
Example of IPC code.
| Classification | Symbol | Detailed Explanation |
|---|---|---|
| Section | G | Physics |
| Class | G01 | Measurement; Test |
| Subclass | G01B | Length, Thickness or Similar Straight Line, Angle Measurement |
| Main Group | G01B7 | An electrical or magnetic measuring device |
| Sub Group | G01B7/14 | For measuring the length or width of a moving object |
Example of Patent-IPC Matrix.
| IPC 1 | IPC 2 | ⋯ | IPC M | |
|---|---|---|---|---|
| Patent 1 | 1 | 0 | ⋯ | 1 |
| Patent 2 | 0 | 0 | ⋯ | 0 |
| Patent 3 | 0 | 0 | ⋯ | 0 |
| ⋮ | ⋮ | ⋮ | ⋱ | ⋮ |
| Patent N−1 | 0 | 0 | ⋯ | 0 |
| Patent N | 1 | 1 | ⋯ | 1 |
Patent-IPC Matrix (963 × 22).
| IPC 1 | IPC 2 | ⋯ | IPC 22 | |
|---|---|---|---|---|
| B07C | B25J | ⋯ | H04W | |
| Patent 1 | 0 | 0 | ⋯ | 0 |
| Patent 2 | 0 | 1 | ⋯ | 1 |
| Patent 3 | 0 | 0 | ⋯ | 0 |
| ⋮ | ⋮ | ⋮ | ⋱ | ⋮ |
| Patent 962 | 0 | 0 | ⋯ | 1 |
| Patent 963 | 1 | 1 | ⋯ | 1 |
Contents of the IPC code (Summary).
| IPC | Contents |
|---|---|
| B07C | Classification of individual items |
| B25J | Manipulator |
| B65B | Item/Material packaging machine and equipment |
| B65G | Transportation or storage |
| G01C | A rotating device with vibration mass |
| G01S | Wireless defense decision |
| G05B | Control or adjustment system |
| G05D | Non-electrical variance control |
| G06F | Electrical digital data processing |
| G06K | Data recognition, data display |
| G06N | Specific computational model computer system |
| G06T | Data processing system |
| G07C | Image data processing or generating |
| G07F | Machine work registration or display |
| G08B | Coin-input operating device |
| G08G | Signal or calling system |
| G09B | Traffic control system |
| G16Y | Educational or teaching equipment |
| H04L | IoT communication technology |
| H04N | Multiple communication |
| H04W | Video communication |
| G06Q | Wireless communication network |
Result of Temporal Network’s Arc.
| Num | IPC 1 | IPC 2 | Time | Definition |
|---|---|---|---|---|
| 1 | G06Q | H04L | 2017 | Internet of Things wireless communication |
| 2 | G06Q | G06F | 2018 | Digital data processing wireless communication |
| 3 | G06Q | H04L | 2018 | Internet of Things wireless communication |
| 4 | G06Q | H04L | 2019 | Internet of Things wireless communication |
| 5 | G06Q | G06F | 2019 | Digital data processing wireless communication |
| 6 | G05B | H04L | 2019 | IoT data control technology |
| 7 | G06Q | G06K | 2020 | Data recognition and communication through external sensors |
| 8 | G06Q | G06F | 2020 | Digital data processing wireless communication |
| 9 | G06Q | G06N | 2020 | Computing technology for data calculation |
| 10 | G06Q | H04W | 2020 | Video wireless communication |
| 11 | G06Q | H04L | 2020 | Internet of Things wireless communication |
| 12 | G06F | G06K | 2020 | Digital data display system |
| 13 | G06Q | G06T | 2020 | Wireless data processing system |
| 14 | G01S | H04W | 2020 | Video communication using speed and 3D position sensor |
Result of Patent’s IPC in 2021. Appearance indicates whether the subclass appeared in the subclass network of pre-2021 patents. O and X in Appearance indicate that the IPC code did or did not appear in a patent prior to 2021, respectively.
| IPC | Count | Appearance | |
|---|---|---|---|
| 1 | G06Q | 9 | X |
| 2 | G06K | 7 | X |
| 3 | G06F | 5 | X |
| 4 | G06T | 4 | X |
| 5 | G01S | 3 | X |
| 6 | G06N | 3 | X |
| 7 | G07C | 3 | O |
| 8 | B25J | 2 | O |
| 9 | B07C | 2 | O |
| 10 | B65G | 2 | X |
| 11 | G08G | 2 | O |
| 12 | G05D | 2 | O |
| 13 | G08B | 1 | O |
| 14 | H04W | 1 | X |
Result of Future Logistics Technology.
| IPC 1 | IPC 2 | IPC 3 | IPC 4 | |
|---|---|---|---|---|
| Technology 1 | G07C | G06Q | G05B | G01S |
| Technology 2 | B25J | G06Q | G05B | G01S |
| Technology 3 | B07C | G06Q | G05B | G01S |
| Technology 4 | G08G | G06Q | G05B | G01S |
| Technology 5 | G05D | G06Q | G05B | G01S |
| Technology 6 | G08B | G06Q | G05B | G01S |
Technology descriptions.
| Technology | Description |
|---|---|
| Technology 1 | Wireless communicating image data generation system (IDGS) and control system using IDGS |
| Technology 2 | A wireless control system using video communication |
| Technology 3 | Automatic classification system using 3D position sensors and other sensors attached machines |
| Technology 4 | An alarm system that collects and analyzes various data through sensors |
| Technology 5 | Automatic control system using data analysis technology |
| Technology 6 | A system that recognizes certain conditions through sensors and automation technology based on specific conditions |
Comparative Analysis with National Roadmaps.
| Technology 1 | Technology 2 | Technology 3 | Technology 4 | Technology 5 | Technology 6 | |
|---|---|---|---|---|---|---|
| Digital Logistics | ⊚ | ⊚ | ⊚ | ⊚ | ⊚ | ⊚ |
| Eco-friendly Logistics | ∘ | |||||
| Safe Logistics | ∘ | ⊚ | ⊚ | ∘ | ∘ | |
| IoT Logistics | ∘ | ⊚ | ⊚ | ⊚ | ||
| Smart-city Logistics | ⊚ | ∘ | ⊚ | ⊚ | ⊚ | ⊚ |
| Smart Airport/Harbor | ⊚ | ⊚ | ⊚ | ⊚ | ⊚ | ⊚ |
| Cold Chain | ∘ | ⊚ | ||||
| Worker Shortage Solution | ∘ | ⊚ | ⊚ | ⊚ | ⊚ |
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Details
; So, Jaeryong 2
1 Department of Shipping and Air Cargo & Drone Logistics, Youngsan University, 142, Bansong-sunhwan-ro, Haeundae-gu, Busan 48015, Republic of Korea
2 Department of Industrial Engineering, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea