Introduction

The artificial intelligence (AI) software market size in revenue is expected to reach $240.27 billion in 2024 with a compound annual growth rate (CAGR) of 16.7% (IDC, 2020). AI has attracted enormous attention not only in the information and communications technology (ICT) industry but also in a variety of other industries. In the healthcare industry, AI has already begun to transform a variety of aspects, such as offering monitoring, advice to patients and interpretation of scans (Houlton, 2018; Yu et al., 2018). In addition, AI is a key technology for autonomous driving, connectivity, electrification, and the shared mobility trend in the automobile industry (McKinsey, 2018a). Moreover, the expansion of robo-advisors using AI in the finance industry has been utilized (Deloitte, 2016). Likewise, AI is changing the landscape of various industries and applied area has increased gradually.

AI has led to not only technological progress and new innovations but also has the potential to be a general-purpose technology (GPT) (Liu et al., 2021). Research has confirmed how AI can affect technological innovation by improving knowledge creation, knowledge spillover, absorption capabilities, and by increasing investments in R&D, thus explaining the significant relationship between AI and technological innovation and also the positive impact of AI with regard to industry heterogeneity among high- and low-tech sectors (Liu et al., 2020). In addition, relevant study empirically has examined AI, showing that it plays a crucial role in increasing innovation performance at manufacturing enterprises (Yang et al., 2020). Moreover, AI has potential to become a GPT increasing direct productivity and spurring complementary innovations (Brynjolfsson et al., 2017). Because AI can drive innovations and lead to a new paradigm shift by combining various industries, there is a societal need to develop AI while considering its impact on various industries.

Technology convergence has been considered as a tool to drive technological innovation, and interdisciplinary research and the merging of different technologies have therefore increased (Kose & Sakata, 2019). Technology convergence refers to “the process by which two hitherto different industrial sectors come to share a common knowledge and technological base” (Athereye & Keeble, 2000; Rosenberg, 1976). By sharing technological characteristics, the erosion of distinct barriers has been accelerated among various industries (Wang et al., 2019b). Technology convergence leads to industry convergence (Choi et al., 2015; Nystrom, 2008), and industry convergence could only occur with the convergence of technologies (Nystrom, 2008). Therefore, this study attempts to examine the technology convergence of AI considering both technological and industrial perspectives.

Previous studies on the AI have insufficiently investigated the integrated approach considering both the overall industry and individual technology. Applied AI research has generally investigated a specific industrial sector, such as healthcare, vehicle, finance, etc. (Yu et al., 2018; Houlton, 2018; McKinsey, 2018a; Deloitte, 2016). In addition, research on AI patent analysis has analyzed AI technology itself by technological type, firm, and country level (Fujii & Managi, 2018; Tseng & Ting, 2013; WIPO, 2019a). Likewise, those studies generally have not focused on insights across various industries. Meanwhile, some reports on the industrial impact of AI have usually focused on comparing economic impact among industries (PWC, 2018; Deloitte, 2018; McKinsey, 2018b). However, those have not investigated comparing technological aspects according to industries from the perspectives of technology convergence. Therefore, this study attempts to investigate technology convergence of AI considering a set of industries and individual technology.

Research on technology convergence in relation to patent documents has been commonly divided in terms of three perspectives: 1) purpose, 2) methodology, and 3) object of the analysis (Kim & Lee, 2017). In particular, the purposes are two fold, identifying evolutionary trajectory and convergence pattern, and the methodologies are divided into two parts, patent co-citation to examine knowledge flow and patent co-classification to examine convergence phenomenon (Kim & Lee, 2017). Relevant studies of the object of the analysis can be divided according to whether the analysis targets one main technology category or more than two technology categories belonging to heterogeneous industry sectors. In most technology convergence research, these three perspectives have been combined depending on the research questions. The research related to one targeted main technology and the corresponding sub-technologies are as follows. Kim et al. (2014) analyzed the convergence of printed electronics technology based on its element technologies (i.e., device, ink, substrate, circuit, and control) to identify key technologies and their trajectories using co-citation. Han and Shon (2016) analyzed technological convergence in ICT using co-citation to identify crucial roles depending on the period. Wang et al. (2019b) identified emerging topics associated with 3D printing technology depending on time, comparing technology convergence with non-technology convergence environments based on co-classification. Meanwhile, the studies targeted two or more technologies belonging to heterogeneous industry sectors are as follows. Kim and Lee (2017) examine technology convergence in the IT and BT industries to identify key convergence technologies based on co-citation and to forecast future technology convergence. Curran and Leker (2011) analyzed convergence in the areas of NFF and ICT based on co-classification. Kose and Sakata (2019) identified technology convergence in robotics research considering related various sectors extracted from cluster categories such as robot control systems, surgical and medical systems, and automaton in biological and chemistry, among others, based on co-citation.

However, despite their invaluable and meaningful insights, the previous studies have several limitations. First, many of them identify convergence phenomena and trajectories, but there have been insufficient attempts to understand the characteristics of the technology from a multi-dimensional perspective. In other words, few studies have investigated how the technology is actually applied in a relation to the defined convergence phenomenon and/or trajectory. Second, attempts to examine technology convergence from a holistic industrial perspective have been insufficient. That is, many studies have explored convergence while focusing on technology itself and on sub-technologies (e.g., IT and corresponding sub-technologies such as devices and networks) or on combinations of technologies between heterogeneous industries (e.g., IT and BT). However, it is difficult to provide insight from a whole-industry perspective regarding technology.

To overcome these limitations, the paper proposes a two-way approach for technology convergence involving top-down and bottom-up approaches. The top-down approach here attempts to investigate technology convergence from a macro-perspective and to investigate notable industrial sectors and corresponding technology categories. This allows for a comparison of industry-specific technology categories from an all-encompassing perspective of industry. The bottom-up approach here attempts to investigate practical usage instances on a microscope, focusing on technology categories by industry. The integration of these two approaches provides an integrated and multidimensional understanding of technology convergence in terms of industry sectors, technology categories, and technology application levels.

We investigate this two-way approach based on patent documents. In this study, technology convergence is defined as when more than two technologies belonging to different sectors appear in one patent at the same time. If heterogeneous IPCs appear in one patent, the technology corresponding to each IPC is considered to be converged. In terms of technology convergence, the top-down approach serves to identify the patterns by which two IPCs converge, deriving significant IPCs in technology convergence. On the other hand, the bottom-up approach derives significant keywords regarding the convergence pattern though patent textual data, not covered from an IPC. Specifically, detailed procedures and explanations of the two-way approach are as follows. Using the top-down approach, this study conducts a network analysis in order to identify the central position in the convergence network using IPC codes that describe the technology category as a generally accepted classification scheme. Meanwhile, the bottom-up approach utilizes a clustering analysis, which is commonly used to derive characteristics from numerous of textual data. This approach groups patent documents based on similarities among the patent documents within industrial sectors. Overall, the contributions of the two-way approaches are to identify notable industrial sectors and influential technology categories based on the central position in the AI convergence network from the top-down approach and to identify significant keywords of actual use of the technology within the industrial sectors via the bottom-up approach.

The complementary aspects of the two-way approach are as follows. First, the top-down approach targets structured data, i.e., IPC data, which restricts the discovery of insights other than information in the technology category. In contrast, the bottom-up approach targets unstructured data, i.e., text data, including detailed explanations and information from the patent documents. Second, for unsupervised learning, in which the results of the clustering analysis are not strictly defined, the interpretation of the results is very important. To understand the results of the clustering analysis, the top-down approach, i.e., the network analysis, provides directions pertinent to the technology category.

The novelty of this paper is as follows. First, in order to identify the characteristics of technology convergence, this study attempts to compare the results of the technology categories from the network analyses and the results of the keywords from the cluster analysis. This study presents a new research framework by which to understand the technology convergence by discovering the structure of technology convergence patterns and additionally by investigating practical application aspects in the convergence patterns. Second, the study attempts to analyze AI technology convergence throughout various industries with a holistic and integrated approach that considers significant industries, technology categories, and related applications. In particular, research on general-purpose technology such as AI is crucial from an industry perspective, and the industry-specific AI convergence characteristics identified in this study can have significant implications for all AI-related industries.

The research questions of this study are as follows. (1) What are the notable industrial sectors in technology convergence of AI? (2) To compare the technological characteristics of AI convergence by the industrial sectors, which differential aspects do the two-way approaches have? The remainder of this paper is structured as follows. Section 2 describes the research framework and proposed methodology of this study. Sections 3 briefly shows the dataset in this study. Section 4 shows the results of network centrality analysis, ego-network analysis, clustering analysis, and the two-way approaches. Section 5 presents discussions and conclusions, and also proposes future research directions.

Proposed methodology

Research framework

Figure 1 shows the research framework for this study. A top-down approach focuses on hub nodes and their tie nodes in an IPC-based convergence network. A network centrality analysis is applied to determine the hub nodes (hubs, in short) which identify ‘notable industrial sectors’ and ‘influential technologies’. In addition, an ego-network analysis is done to understand the tie nodes (ties, in short) which are strongly ‘related technologies’ on hubs. The top-down approach, which means IPC-based network analyses, shows the results on the major technology convergence category with hubs and ties in each industrial sectors. Meanwhile, a bottom-up approach constructs clusters through the process of grouping the whole patents with similar topics. A text-based clustering analysis is performed, and the results show additional information not found in IPC-based network analyses. Generally, a patent is document that explains a new invention which are related to technology category, the problems to solve and how to solve them as well. Thus, text data in a patent document can be understand as information related to the application or method for implementing the technology, in addition to the structured technology categories identified from the IPC codes. Thus, keywords can be extracted from each cluster, and the aspects of technology applications can be examined by linking these aspects with the technology category derived from the network analysis. In this study, a technology application is defined as a practical way in which the technology can be used in the industry and technology categories.

Fig. 1
figure 1

Research framework

Full size image

Figure 2 shows the research procedure of this study, and following sections describe the background and provide a detailed explanation of each step. Because the purpose of the top-down and bottom-up approach differ, the range of the analysis dataset in each case is set differently. The top-down approach aims to extract significant industry and industry-specific technology categories using AI patents; thus, this step targets the entire AI patent. On the other hand, the bottom-up approach aims to identify specific and detailed features of technology applications in relation to the technology category regarding notable industrial sectors derived from the top-down approach. Accordingly, it targets AI patents classified by industrial sector. Meanwhile, the study uses abstract data from each patent document for the analysis of the text data.

Fig. 2
figure 2

Research procedure

Full size image

Data collection

There have been many trials to establish a new patent category in the area of AI. According to previous research, AI has been categorized into three broad areas: big data analytics, vision, and language (Tractica, 2016). In the same vein, to define the scope of our research, we categorized AI software technology into three groups: (1) learning and reasoning, (2) natural language processing, and (3) computer vision. In the previous research, IPC codes were used in selecting and classifying AI technology (Fujii & Managi, 2018; Tseng & Ting, 2013). Comprehensively, we collected the three groups of patents with related IPC codes. The IPC codes for each AI patent group in this study were according to the work of the Korean Intellectual Property Office (KIPO) in 2018 (KIPO, 2018). Table 1 shows the category of the AI technology and IPC codes used in this study. The descriptions of each IPC are referred to the WIPO (WIPO, 2019b).

Table 1 Category of AI technology
Full size table

This study collected AI-related patents which were registered at the United States Patent and Trademark Office (USPTO) from Google Patent Datasets. We constructed a set of standard SQL statements using the Google BigQuery platform for collecting AI-related patents with IPC codes during the period from 2000 to 2019 for the publication dates. In addition, from the Google Patents Search, we collected the forward citations of each patent. The total number of patents collected was 209,212, and one patent was incomplete; thus, 209,211 patents were selected for this study. Also, we extracted 2517 IPC codes at the group level from the patents.

Formation of IPC co-classification network

To measure technological convergence, previous research commonly used a co-classification network analysis and co-citation network analysis (Curran & Leker, 2011; Kwon et al., 2020). This study constructs a convergence network using co-classification for the following reasons. First, a co-citation is based on the relationships among the patent documents themselves, while co-classification is based on the relationships among the technology classification codes. Although a co-citation analysis is useful to understand knowledge flows (Lee et al., 2016), an IPC co-classification analysis can be a more direct indicator that explains specific technology areas (Choi et al., 2015). Therefore, co-classification is more adequate for the research purpose here, which is to identify technology categories in a convergence network. Second, co-classification is more consistent with the definition of technology convergence in this study. This study defined technology convergence as occurring when more than two technologies belonging to different sectors appear on one patent at the same time, and this definition corresponds to the co-classification concept.

Therefore, in this study, an IPC co-classification network was formed to analyze the convergence using patents. In the patent analysis, each IPC is considered as a node and the relationship between the IPCs as a link, and the weights mean the number of common patents for a pair of IPCs. We formed an undirected and weighted graph to analyze the network. The IPC co-classification network was created at the group-level (e.g. G06K-009) of each patent, a total of 2517 IPCs.

Extracting the hubs through network centrality analysis

Among the various network centrality indicators, degree centrality shows how connected the node is (Jackson, 2008). Degree centrality is an efficient indicator of measuring the power of each node (Borgatti et al., 2013) because a node with many links between other nodes has more advantages and influences on the network. Betweenness centrality shows the mediating role of the network among the nodes. If a node is located on the shortest path between a pair of nodes in the network, the node is considered to be on an advantageous position. Meanwhile, based on those two centralities, the network positions are categorized into four positions: the hub, bridge, core, and periphery (Baek et al., 2014). The hub position means highly connected with others and is important in connecting others, which has both a high degree and betweenness centrality. Therefore, in this study, the degree centrality and betweenness centrality were analyzed to investigate the hub nodes with advantages and influences on the network. From those hub nodes, this study identify the notable industrial sectors and influential technologies.

In terms of technology convergence, degree centrality can measure the direct influence in the technology convergence (Kim et al., 2014), and betweenness centrality is an indicator of the extent of the role of a node as a brokerage, and related to arbitration capabilities in technology convergence (Huang, 2017; Lee et al., 2012). Therefore, from the IPC co-classification network constructed in this study, the degree centrality finds IPCs which play a central role in terms of direct connectivity, whereas the betweenness centrality finds IPCs which play a central role in terms of intermediary connectivity.

In this study, the equation of node degree centrality can be defined as follows (Borgatti et al., 2002; Freeman, 1979).

$$C_{{D{ }}} (N_{i} ) = \mathop \sum \limits_{j = 1}^{g} x_{ij} \,i \ne { }j,$$

where \(g\) is the number of IPCs in the network, \(x_{ij}\) is the degree of strength of the relationship between IPC i and IPC j (\(0 \le x_{ij} \le \max\)).

In this study, the equation of node betweenness centrality can be defined as below (Borgatti et al., 2002; Freeman, 1979).

$$C_{B} { }(N_{i} ) = { }\mathop \sum \limits_{j < k} \frac{{g_{{jk{ }}} (N_{i} )}}{{g_{jk} }},{ }i{ } \ne j{ } \ne k{ }$$

where \(g_{jk}\) is the number of shortest paths between IPC \(j\) and IPC \({ }k\), and \(g_{{jk{ }}} (N_{i} )\) is the number of paths including IPC \({ }i\) in the shortest paths between IPC \(j\) and IPC \(k\).

The method for extracting the hubs through network centrality analysis is described as follows. In order to extract influential technology (i.e. hub) in the convergence network, the top 10 percent of the IPCs were considered. Appendix A. shows the distribution of the degree and betweenness centralities, respectively. The distribution shows the form of positive skewness with a long tail on the right. The top 10 percent was within rank 250 and explains 96.3% in the degree centrality (Sum of the top 10 percent centrality measure = 9,528,040 /Sum of the total centrality measure = 9,893,234), and describes 96.7% in the betweenness centrality of the total value (3,413,198 / 3,528,905). Thus, the top 10 percent of technology can represent the influential technology in this study.

Extracting ties on the hubs through ego-network analysis

For the selected hubs in Sect. “Extracting the hubs through network centrality analysis”, this section investigated the linked technology. An ego-network consists of a connection between one central node called an ego and other nodes called alters connected to that node. The ego-network was analyzed for each hub, and the strength of the connection with the alter was measured by the tie value which was based on the total number of ties in the ego-network. The nodes with the top 10 tie values were selected to derive the strong-tie in this study. The strong-ties were analyzed to identify the characteristics of the linked technologies among sectors. The linked technologies of the hub in each sector, which are ties, were investigated in terms of common or different technology within the sector compared to other sectors. Meanwhile, technology included in its own sector was not considered. For example, when analyzing the hubs in the medical sector, ties in the medical sector were excluded from the analysis.

Classification of dataset by industrial sector

To classify the patents by industrial sector, we referred to the WIPO IPC and technology concordance table (Schmoch, 2008) which was classified technology into thirty-five fields according to IPC codes. (See Appendix B). In this study, a term ‘sector’ was commonly used to indicate each ‘field’ in the IPC-concordance matrix. Among thirty-five sectors, to determine the industrial convergence sector in AI, we excluded sectors that are directly related with ICT and AI technology. Additionally, we excluded sectors that are specialized for a process or a machine itself and are difficult to identify in a specific industry. Also, in the furniture/game sector, only the game sector was examined in this study because those two are not considered as a same category in common, and a great amount of technology was included in game technology. Thus, the final sixteen sectors to be analyzed in this study were selected and the sectors were as follows: IT methods for management (referred to here as the finance/management sector representing included technology), semiconductors, analysis of biological materials, medical technology, biotechnology, transport, games, environmental technology, organic fine chemistry, pharmaceuticals, civil engineering, food chemistry, nanotechnology, basic materials chemistry, metallurgy, and polymers.

Keywords extraction through clustering analysis

For each industrial sector, there are number of patents and they are expected to have more than a single topic within a sector. To unveil topics consisting of a sector, clustering analysis using a Document-Term Matrix (DTM) could be applicable. From each cluster, keywords representing the core topics could be extracted.

Considering the characteristics of the dataset in this study, each cluster would be very close in an industrial sector. The patents within a sector do share similar topics. It is expected that there can be a degree of overlap among clusters. Also, a DTM of a sector could be a sparse matrix if there are number of clusters which share similar topics. Similar documents (i.e., patents) share a set of terms, and it is differentiated with others.

One of the widely used clustering methods is K-means clustering, which is quite simple and fast, but it has difficulty in handling inherent heterogeneity such that a certain data set is close to more than one cluster (Patel & Kushwaha, 2020). The resultant clusters of K-means clustering are disjoint because a data point is uniquely assigned to the cluster with the closest distance from the centroid which is the cluster center. Due to the disjoint nature, K-means clustering is not fit for clustering patents with similar topics.

Spectral clustering is one of the candidate solutions for patent clustering. It does not rely on distribution of the data. However, the DTM would be sparse and the affinity matrix for the spectral clustering also would be sparse. Thus, the spectral clustering is not suitable with our dataset because the spectral clustering requires a fully connected affinity network.

On the other hand, the Gaussian Mixture Models (GMM) cluster assigns a certain data set to the multivariate normal components maximizing the component posterior probability (Wang et al., 2019a). GMM finds complex patterns and then groups them into cohesive and homogeneous components which are a close representative of real patterns in the data set (Patel & Kushwaha, 2020). GMM is a density-based clustering algorithm which means that a resultant cluster has a high-density region surrounded by low-density regions. Patents with similar topics sometimes cannot be clustered with distinct boundaries, which can be clustered with a density-based model. Also, in GMM a data point can be expressed as a set of probabilities of cluster membership, which means the mixed membership.

GMM is an unsupervised clustering which finds out \(K\) Gaussian distributions from the given data, where \(K\) is the number of clusters. Thus, a probability density function of GMM \(p\left( {\mathbb{X}} \right)\) for a \(D\)-dimensional vector \({\mathbb{X}}\) is expressed as a superposition of \(K\) Gaussian probability densities (Bishop, 2006.)

$$p\left( {\mathbb{X}} \right) = \mathop \sum \limits_{k = 1}^{K} \pi_{k} {\mathcal{N}}({\mathbb{X}}|\mu_{k} , \Sigma_{k} )$$

where \(\pi_{k}\) is called the mixing coefficient, which indicates a selection probability of kth Gaussian distribution, \({\mathcal{N}}({\mathbb{X}}|{\varvec{\mu}}_{k} , {{\varvec{\Sigma}}}_{k} )\) is kth Gaussian density, \({\varvec{\mu}}_{k}\) is a \(D\)-dimensional mean vector of kth Gaussian distribution, and \({{\varvec{\Sigma}}}_{k}\) is a \(D \times D\) covariance matrix of kth Gaussian distribution. The parameters of the distributions are iteratively updated and converged using the Expectation–Maximization (EM) algorithm. These process means that the iterative estimation of parameters \(\pi_{k}\), \({\varvec{\mu}}_{k}\), and \({{\varvec{\Sigma}}}_{k}\) for given data \({\mathbb{X}}\).

The method for the patent clustering is described as follows. First, the abstracts of the patents were tokenized and lemmatized using the Python spaCy library to count terms in different forms in sum. Then a bigram DTM was constructed from the lemmatized text of the patent abstracts after removing stop words including patent-specific common terms such as ‘method' and ‘apparatus’. To weight the relatively important terms, a bigram Term Frequency-Inverse Document Frequency (TF-IDF) DTM was calculated from the DTM. Based on the TF-IDF DTM, the dimensionality of terms was reduced while preserving the hidden meaning of terms and reducing sparsity of the input DTM by applying Latent Semantic Analysis (LSA) with the target explained variance of 90% (See Appendix C). The patents were then clustered using GMM with the Python scikit-learn library by increasing the number of clusters from 1 to 10. In addition, to prevent EM from converging local maxima, each setting was executed 5 times with different initial random seeds.

The best-fitting models were chosen based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values. For a GMM clustering, AIC and BIC were measured to determine the appropriate number of clusters (Burnham & Anderson, 2002). Usually, the lower information criterion indicates the better clustering. Thus, the best-fitting model among different number of clusters could be chosen with the lowest AIC or BIC value. If the lowest AIC model and lowest BIC model were not the same, the resultant clusters of both models needed to be reviewed. In our dataset, however, the lowest AIC model was chosen because the lowest BIC was always the single cluster case.

Then, each resultant cluster had distinct top keywords according to the TF-IDF values. By taking the mean TF-IDF value of each bigram term in a cluster, the top keywords were populated from bigram terms of top mean TF-IDF values.

Dataset

To understand the dataset in this study thoroughly, we analyzed the patent data with the innovation performance indicators, which can compare the level of convergence by industrial sectors classified in the Sect. Classfication of Dataset by Industrial Sector. For measuring innovation performance, the number of patents or forward citations were used as indicators (Hagedoorn & Cloodt, 2003; Harhoff et al., 1999; Trajtenberg, 1990; Wartburg et al., 2005). The number of patents means a quantitative aspect in terms of the technological invention of new technology, process, and product. Whereas forward citations analysis implies a qualitative aspect (Hagedoorn & Cloodt, 2003).

The datasets classified into the industrial sectors were analyzed using the innovation performance indicators which were number of patents, CAGR of patent counts, and forward citations per patent (CPP) shown in Table 2. In addition, Fig. 3 shows the trend analysis according to the industrial sector based on the number of patents by the year. In terms of the total number of patents, the finance/management, medical, and transport sectors had a large patent count, over 6000, and the semiconductor, games, and biological materials had a patent count of 1000 to 2000. The environmental technology, organic chemistry, pharmaceuticals, civil engineering, food chemistry, nanotechnology, metallurgy, and polymers had a patent count below 1000. From a view of growth rate, the transport sector showed a noticeably high rate as 41% of CAGR. In addition, the game, finance/management, and civil engineering sector showed the high growth rates. In terms of forward CPP by sector, the micro-structure/nanotechnology, biological material, and finance/management sectors have shown significant numbers of citations over other sectors. Meanwhile, the following Sect. Results of network centrality analysis identifies the notable industrial sectors in this study among the sixteen sectors.

Table 2 Datasets by the industrial sector in AI patents
Full size table
Fig. 3
figure 3

Trends of datasets by the industrial sector in AI patents

Full size image

Analysis and results

Results of network centrality analysis

This study defined hub nodes as the degree and betweenness centralities within the top 10 percent. Obviously, a majority of hubs corresponded to AI-related or computer technology, such as pattern recognition, image analysis, and data processing. The IPC, G06K-009, which is related to pattern recognition, ranked first both degree and betweenness centralities (see Table 3) and positioned in the most central in the whole AI convergence network.

Table 3 Top 10 rank of the AI technology in AI convergence network
Full size table

Meanwhile, among the sixteen sectors in our dataset in the Sect. Dataset, the technologies in finance/management, medical, transport, semiconductor, game, biotechnology, and analysis of biological materials were included in the hub nodes in the AI convergence network. Table 4 shows the results of the hubs in each sector.

Table 4 Network centrality analysis on hubs by the industrial sector in AI convergence network
Full size table

In the finance/management sectors, the AI was used for commerce (G06Q-030), payment architectures (G06Q-020), business management systems (G06Q-010, 050), insurance and tax (G06Q-040). In the medical sector, the technologies, involved in diagnosis (A61B-005, 006, 008), examination or testing instruments (A61B-003), and surgical instruments (A61B-017), ranked high. For the transport sector, technologies related to protecting against accidents (B60R-021), optical devices (B60R-001, B60Q-001), vehicle control (B60W-030, 050, 040, 010), monitoring (B60R-011), seat management (B60N-002) and preventing theft (B60R-025) ranked high. In the semiconductor sector, technologies involved in semiconductor devices (H01L-027), manufacturing process (H01L-021), and details of semiconductor device (H01L-023) ranked high. In the game sector, the technologies involved in video games (A63F-013), sports appliances (A63B-071, 069), and controls for exercising (A63B-024) ranked high. For the analysis result of the biological materials sector, investigating and analyzing materials (G01N-033) ranked high, especially in the betweenness centrality, which means this technology has a tendency to mediate other technologies. In addition, measuring or testing processes (C12Q-001) ranked high in biotechnology sector. The biotechnology (C12Q-001) and biological materials (G01N-033), which were divided in the Sect. Dataset, were considered as one sector in this study since biological materials can be included in biotechnology sector.

Consequently, the six sectors were defined as notable industrial sectors in this study. In addition, hub nodes in each sector were determined as influential technology. Figure 4 shows the visualization of the six notable industrial sectors and influential technologies according to Table 3. Especially, for the finance/management sectors, most of the hubs (G06Q-010, 020, 030, 050) were positioned relatively high in both centralities. The medical sector has the highest technology (A61B-005) in both centralities among the hubs.

Fig. 4
figure 4

Hubs according to the industrial sector in the AI convergence network

Full size image

Results of ego-network analysis

This section analyzed the ego-network formed for the hubs to identify the linked technologies. From the ego-network analysis, the technology classification of strong-ties which were ranked in the top 10 were analyzed in this study. The strong-ties are shown in Table 5 according to each hub. (See Appendix D for each tie values). Obviously, a majority of strong-ties corresponded to common AI technology, such as pattern recognition, image analysis, data processing, and natural language processing. Thus, to determine differential strong-ties within the industrial sectors compared to other industrial sectors, we investigated strong-ties in terms of common or sector-specific technology. The footnotes in Table 5 shows a category of IPCs considering common or sector-specific technology among industrial sectors. Consequently, Table 6 presents a summary of the common ties among the industrial sectors and sector-specific ties by industrial sector.

Table 5 Ties according to each Hub in AI convergence network
Full size table
Table 6 Hub and ties by the industrial sector in AI convergence network
Full size table

The result showed that pattern recognition (G06K-009), image analysis (G06T-007), data processing (G06F-017), and speech recognition (G01L-015) were commonly used to almost all industrial sectors, and these technologies correspond to common AI-related technology. Especially, image analysis (G06T-007) was highly ranked in medical, transport, semiconductor, game, and biotechnology sectors. Also, data processing (G06F-017) was highly ranked in finance, medical, game, and biotechnology sectors. The speech recognition (G01L-015) was highly ranked in finance, transport, and game sectors.

On the other hand, in addition to these common AI-related technologies, different ties can be identified for each sector to understand the unique characteristics among the industrial sectors. Table 5 shows the summary ties in each sector. For the finance/management sector, against unauthorized activity (G06F-021) was widely used in payment architecture, commerce, business management systems and insurance (G06Q-020, 030, 040, 050). Moreover, selective content distribution (H04N-021) was highly connected with all. Additionally, speech recognition (G10L-015) technology was appeared in commerce (G06Q-030) and administrative area (G06Q-010). For the medical sector, image analysis (G06T-007) appeared to be commonly used in the sector. Additionally, a variety of image-related technologies such as image generation and enhancement (G06T-005, 001, 017) emerged in the others. In addition, diagnosis-related technology (A61B-005, 006, 007) showed high connectivity with the computational material science (G06F-019), which includes machine learning, data mining, and biostatistics. For the transport sector, image analysis (G06T-007) was shown to be mainly linked to various technologies. Especially, the first representative finding was control technology. The technology seen in transport-related nodes was identified as a traffic control system (G08G-001), which also included general control (G05D-001) and aircraft control (G08G-005). The second finding was directional guidance and detection technology. Navigation (G01C-021) and radio direction-finding (G01S) showed important connections. For the semiconductors sector, manufacturing processes (H01L-021) showed many connections with image analysis (G06T-007). Especially, semiconductor devices (H01L-027) had many connections with the sub-technology of the electric communication technique (H04). In addition, the optical (G01B-011) or photomechanical (G03F-001, 007), and also material analysis (G01N-021) were appeared in manufacturing processes. The game sector was divided into two categories: video games (A63F-013) and sports/exercises (A63B-071, 069, 024). In video games, not only image analysis (G06T-007) and but also speech recognition (G10L-015) showed high connections at the same time. On the other hand, sports/exercise had a high connection with diagnosis or identification (A61B-005). In addition, educational demonstration appliances (G09B-019) were appeared in the sports/exercise area. For the biotechnology sector, the computational material science (G06F-019) was also highly linked, and the analysis of materials (G01N-021, 015) appeared.

Results of clustering analysis

The top 30 keywords were extracted for each cluster, and the results of keywords and TF-IDF values are shown in Appendix E to J. This study selected representative keywords among the top 30, considering the discriminative meaning of each cluster, excluding redundant or common keywords among clusters. Table 7 shows the results of clustering analysis.

Table 7 Results of clustering analysis by industrial sectors in AI patents
Full size table

Two-way approaches on network analyses and clustering analysis

Based on the research framework in the Sect. ``Research framework", Fig. 5 summarizes the results of this study according to the hubs/ties from network analyses and keywords from clustering analysis according to the Sect. ``Results of network centrality analysis", ``Results of ego network analysis", and ``Results of clustering analysis". As shown Fig. 5, the hubs and ties show the influential technology and strongly related technology in each industrial sectors. In addition, the clusters show keywords regarding the technology application. An analysis of these two aspects can be complementary relative to both and can contribute to a better understanding of the characteristics of the convergence of AI and provide a deeper understanding of the industrial sectors.

The complementary aspects of two-way approach are follows. First, the top-down approach shows only information in the technology category, while the bottom-up approach shows information about practical usage examples via the extracted keywords, which are related to the applied target of the technology. This allows us to understand information about the data utilized in the implementation of the technology. For instance, the financial sector shown in Fig. 5 presents hub technology about the ‘payment architecture’ (referring to IPC G06Q-020) from the top-down, whereas the keywords of clusters related to ‘payment’ (cluster #3 in Table 7) provide information about significant applied areas, such as ‘user biometric information’. Furthermore, regarding the same technology category among the industrial sectors extracted from the top-down, the results from the bottom-up show different aspects to provide a clearer understanding of the applied areas. For instance, hub technology pertaining to ‘image analysis’ (G06T-007) is identical among the industrial sectors in the top-down, whereas the bottom-up allows us to identify differences in target data types for each sector used for the image analysis. The image analysis is mainly utilized for the recognition of electronic documents in the finance sector (cluster #2), projection images (i.e. CT, tomography, X-ray) in the medical sector (cluster #4), patterns of wafers in the semiconductor sector (cluster #1), for object detection in the transport sector (cluster #4), and for biological images (i.e. tissues, cells, specimen) in the biotechnology sector (cluster #1). Second, bottom-up approach gives information about an integrated form of various technology category extracted top-down approach. For instance, the transport sector shown in Fig. 5 shows the each influential technology pertaining to ‘control’ (G08G-001, G05D-001, G08B-005), ‘navigation & direction finding’ (G01C-021, G01S), and ‘image analysis’ (G06T-007) from the top-down. In contrast, the keywords of clusters show an integrated form and application of those technologies in transport, such as ‘unmanned aerial vehicle’ (cluster #0). For the game sector, the top-down shows individually each influential technology related to ‘video games’(A63F-013), ‘controls for exercising’ (A63B-024), ‘image analysis’ (G06T-007), and etc., whereas the keywords of clusters show an integrated form, such as ‘augment/virtual reality’(cluster #2). Third, the top-down complements the results of the bottom-up when defining the cluster structures and meanings. The bottom-up is a sort of unsupervised learning with unlabeled data, and meanings should be found by means of interpretations of randomly derived results. The results derived from the top-down approach can then serve as common criteria and important indicators for interpreting the outcomes from the bottom-up approach.

Fig. 5
figure 5

Summary of hubs, ties and clusters by the industrial sectors in AI patents

Full size image

Discussion and conclusion

This is an empirical study to understand technology convergence focusing on the case of AI. This study attempts to various analytical methods including network centrality analysis, ego-network analysis, and clustering analysis. As a result, this study identifies the notable six industrial sectors in technology convergence of AI implying the convergence level among industrial sectors in order to answer the first research question of this study. Additionally, to answer the second question in this study, we identify influential technology sectors through a network analysis, whereas keywords related to technology application aspects are found through a cluster analysis according to notable industrial sectors.

The theoretical contributions of this study on technology convergence research are as follows. This study suggests a new two-way approach, consist of the top-down and bottom-up approaches, to understand the characteristics of technology convergence. The framework of this study suggests integrated perspective on technology convergence based on industry, technology category, and related to applications. Consequently, it is possible not only to compare the characteristics of technology convergence by industrial sector, but also to define factors that can be revealed by each of the two-way approaches.

The methodological contributions in this study are as follows. The first is the combination of the network analysis using structured data in patent documents and the cluster analysis using unstructured data in patents. The network analysis provides a direct indicator by which to understand the patterns of technology convergence, while the clustering analysis provides implications related to practical applications considering each defined convergence pattern. Second, this study gives insight to technology convergence research by applying the GMM clustering algorithm. As the technology convergence dataset is a mixture of different categories of technologies, GMM explains well to classify clusters with various means and variances. This is meaningful study since there have been few previous studies related to the GMM clustering regarding technology convergence research.

The contributions and results of this study have practical implications for AI technology and related industries. From a strategic business perspective, AI must be considered as an opportunity to create a new paradigm from a disruptive innovation. For industrial AI companies, it is necessary to consider the current state of technology development in the main stream of industry-specific AI convergence, as posited by the analysis results here, and discover new business creation opportunities in new areas related to AI technology that have not been developed thus far. For AI companies, it is necessary to develop extensible general technologies that can be applied to various industries by considering the main stream of AI convergence among industries. Commonly required AI technologies for each industry as found in this study can present insights related to this scalable strategy.

From a policy perspective, it is necessary to establish R&D policies pertaining to AI by industry with respect to sustainable social development beyond pursing short-term technological innovations and growth. The results of this study revealed the status of each sector regarding AI convergence. While the technological trajectory of the cumulative AI patents has followed the S-curve over the past two decades and is in the growth stage of the technology life cycle (TLC), it appears that there is a difference in the level of growth stage in terms of the industrial sector. In particular, it was confirmed in the results of this study that industries or technologies directly related to the sustainable development do not stand out significantly in AI convergence. For instance, despite the fact that environment-related technology is essential for sustainable growth, the results of this study show that the degree of convergence of AI and environmental technology is still very low. In addition, previous study found that AI has had a significant impact on reducing energy consumption and energy intensity (Liu et al., 2021). However, it appears that the convergence of these sustainable technologies and AI has yet to make notable progress given the current status from the perspective of industry overall. Therefore, government R&D policies should be supported to promote the development of sustainable technologies on AI. Previous research found that enforced environmental policies by governments and subsidies developed to promote the invention of green technologies significantly increased green patent publication counts in China (Fujii and Managi, 2018). Comprehensively, governments in each country should understand the different development statuses with regard to the convergence of AI and various industries and should review R&D strategies considering policies or subsidies to encourage specific sectors in order to realize sustainable growth.

The limitations of this study and future research suggestions are as follows. First, with respect to the additional methodological approaches, it is recommended to compare the result of GMM with other soft clustering algorithm, such as fuzzy modeling and topic modeling. Second, this study considered AI patents based on three categories of technology: learning and reasoning, natural language processing, and computer vision. Thus, future studies should investigate patent data from more various categories and aspects to understand wide range of AI industry and technology. Third, future studies investigate each industrial sector respectively with a deep-depth understanding, or consider other industries not covered in this study. Since this study focuses on the technology and industrial sector with high centrality measure and strong-tie value, the research on the technology and industrial sector which have low centrality and tie values, but are still considered importance is necessary. Also, it is meaningful to examine the background and characteristics on the area where AI convergence occurred low.

Appendices

Appendix A. Distribution of the degree and betweenness centralities

figure a

Appendix B. WIPO IPC-technology concordance table (Schmock 2008)

Area

Field

IPC code

Electrical engineering

1

Electrical Machinery, apparatus, energy

F21#, H01B, H01C, H01F, H01G, H01H, H01J, H01K, H01M, H01R, H01T, H02#, H05B, H05C, H05F, H99Z

2

Audio-visual technology

G09F, G09G, G11B, H04N-003, H04N-005, H04N-009, H04N-013, H04N-015, H04N-017, H04R, H04S, H05K

3

Telecommunication

G08C, H01P, H01Q, H04B, H04H, H04J, H04K, H04M, H04N-001, H04N-007, H04N-011, H04Q

4

Digital Communication

H04L

5

Basic communication processes

H03#

6

Computer technology

(G06# not G06Q), G11C, G10L

7

IT methods for management

G06Q

8

Semiconductors

H01L

Measurement

9

Optics

G02#, G03B, G03C, G03D, G03F, G03G, G03H, H01S

10

Measurement

G01B, G01C, G01D, G01F, G01G, G01H, G01J, G01K, G01L, G01M, (G01N not G01N-033), G01P, G01R, G01S; G01V, G01W, G04#, G12B, G99Z

11

Analysis of biological materials

G01N-033

12

Control

G05B, G05D, G05F, G07#, G08B, G08G, G09B, G09C, G09D

13

Medical technology

A61B, A61C, A61D, A61F, A61G, A61H, A61J, A61L, A61M, A61N, H05G

Chemistry

14

Organic fine chemistry

(C07B, C07C, C07D, C07F, C07H, C07J, C40B) not A61K, A61K-008, A61Q

15

Biotechnology

(C07G, C07K, C12M, C12N, C12P, C12Q, C12R, C12S) not A61K

16

Pharmaceuticals

A61K not A61K-008

17

Macromolecular chemistry, polymers

C08B, C08C, C08F, C08G, C08H, C08K, C08L

18

Food chemistry

A01H, A21D, A23B, A23C, A23D, A23F, A23G, A23J, A23K, A23L, C12C, C12F, C12G, C12H, C12J, C13D, C13F, C13J, C13K

19

Basic materials chemistry

A01N, A01P, C05#, C06#, C09B, C09C, C09F, C09G, C09H, C09K, C09D, C09J, C10B, C10C, C10F, C10G, C10H, C10J, C10K, C10L, C10M, C10N, C11B, C11C, C11D, C99Z

20

Materials, metallurgy

C01#, C03C, C04#, C21#, C22#, B22#

21

Surface technology, coating

B05C, B05D, B32#, C23#, C25#, C30#

22

Micro-structure and nano-technolgy

B81#, B82#

23

Chemical engineering

B01B, B01D-000#, B01D-01##, B01D-02##, B01D-03##, B01D-041, B01D-043, B01D-057, B01D-059, B01D-06##, B01D-07##, B01F, B01J, B01L, B02C, B03#, B04#, B05B, B06B, B07#, B08#, D06B, D06C, D06L, F25J, F26#, C14C, H05H

24

Environmental technology

A62D, B01D-045, B01D-046, B01D-047, B01D-049, B01D-050, B01D051, B01D-052, B01D-053, B09#, B65F, C02#, F01N, F23G, F23J, G01T, E01F-008, A62C

Mechanical engineering

25

Handling

B25J, B65B, B65C, B65D, B65G, B65H, B66#, B67#

26

Machine tools

B21#, B23#, B24#, B26D, B26F, B27#, B30#, B25B, B25C, B25D, B25F, B25G, B25H, B26B

27

Engines, pumps, turbines

F01B, F01C, F01D, F01K, F01L, F01M, F01P, F02#, F03#, F04#, F23R, G21#, F99Z

28

Textile and paper machines

A41H, A43D, A46D, C14B, D01#, D02#, D03#, D04B, D04C, D04G, D04H, D05#, D06G, D06H, D06J, D06M, D06P, D06Q, D99Z, B31#, D21#, B41#

29

Other special machines

A01B, A01C, A01D, A01F, A01G, A01J, A01K, A01L, A01M, A21B, A21C, A22#, A23N, A23P, B02B, C12L, C13C, C13G, C13H, B28#, B29#, C03B, C08J, B99Z, F41#, F42#

30

Thermal processes and apparatus

F22#, F23B, F23C, F23D, F23H, F23K, F23L, F23M, F23N, F23Q, F24#, F25B, F25C, F27#, F28#

31

Mechanical elements

F15#, F16#, F17#, G05G

32

Transport

B60#, B61#, B62#, B63B, B63C, B63G, B63H, B63J, B64

Other fields

33

Furniture, games

A47#, A63#

34

Other consumer goods

A24#, A41B, A41C, A41D, A41F, A41G, A42#, A43B, A43C, A44#, A45#, A46B, A62B, B42#, B43#, D04D, D07#, G10B, G10C, G10D, G10F, G10G, G10H, G10K, B44#, B68#, D06F, D06N, F25D, A99Z

35

Civil engineering

E02#, E01B, E01C, E01D, E01F-001, E01F-003, E01F-005, E01F-007, E01F-009, E01F-01#, E01H, E03#, E04#, E05#, E06#, E21#, E99Z

Appendix C. Results of DTM (document-term matrix) and LSA (latent semantic analysis)

Sector

Number of patents

Number of bigram terms

Number of LSA-reduced features

Finance/Mgmt

12,603

30,862

3759

Medical

10,218

24,062

3404

Transport

6426

14,524

2177

Semiconductor

1896

5301

647

Game

1576

4495

522

Biotechnology

2956

8640

837

Appendix D. Top 10 Tie Value in Ego-network

Hub

Tie

Value

Hub

Tie

Value

Hub

Tie

Value

Hub

Tie

Value

G06Q-010

G06F-017

5294

G06Q-020

G06K-009

8061

G06Q-030

G06K-009

7737

G06Q-040

G06K-009

1273

G06K-009

5100

G06F-003

7208

G06F-017

6550

G06Q-020

922

G06F-003

2404

G06F-017

3223

G06F-003

5832

G06F-017

723

G06Q-030

2305

G06Q-030

2965

G06Q-020

2965

G06F-003

440

G06Q-050

1777

G06F-021

2861

H04N-021

2687

G06Q-030

384

H04W-004

1112

B41J-029

2765

G06Q-010

2305

H04N-021

359

H04N-021

1074

H04N-001

2761

H04N-001

1805

G06F-021

313

G10L-015

1041

H04N-021

2284

G06Q-050

1592

G06Q-010

289

G06K-007

1023

B41J-002

1901

G10L-015

1492

H04N-005

197

H04L-029

1017

G06K-019

1803

G06F-021

1385

G06Q-050

183

G06Q-050

G06F-017

2759

A61B-005

G06K-009

13,634

A61B-006

G06K-009

5704

A61B-008

A61B-005

2554

G06K-009

2417

G06T-007

8952

G06T-007

3817

G06K-009

2272

G06Q-010

1777

A61B-006

3389

A61B-005

3389

G06T-007

2166

G06Q-030

1592

G06F-019

3092

G06T-011

1138

A61B-006

1099

G06F-021

1437

A61B-008

2554

A61B-008

1099

G06T-011

449

G06F-003

1128

G06F-003

2059

G06T-005

889

G06F-019

447

A61B-005

665

G06F-017

1739

G06F-019

588

G01R-033

305

G06F-019

649

G01J-005

1631

G01R-033

434

G06F-017

294

H04L-029

645

G01R-033

1547

G06T-001

422

G06T-017

273

H04N-021

607

H04N-005

1401

G06T-017

342

A61B-034

270

A61B-003

G06K-009

2091

A61B-017

G06T-007

315

B60R-021

B60N-002

3157

B60R-001

G06K-009

2666

A61B-005

1344

A61B-005

308

G01S-015

2275

B60R-021

1122

G06T-007

928

G06K-009

306

G01S-007

1705

H04N-005

925

G06F-003

475

A61B-018

238

B60R-022

1698

G06T-007

764

G02B-027

386

A61B-006

187

G06K-009

1650

H04N-007

756

H04N-005

266

A61B-034

153

G01F-023

1368

G08G-001

588

A61F-009

260

A61B-008

140

G01S-017

1169

B60R-011

492

A61B-008

167

A61F-002

115

B60R-001

1122

B60N-002

483

A61M-021

143

A61B-090

88

B60R-016

1055

B60Q-001

420

G06T-005

140

A61B-010

82

G01S-013

703

G01S-015

346

B60N-002

B60R-021

3157

B60W-030

G06K-009

1723

B60Q-001

G06K-009

1296

B60W-050

G06K-009

818

G01S-015

1043

G08G-001

967

G08G-001

560

G08G-001

584

B60R-022

844

G05D-001

770

B60R-001

420

B60W-030

518

G01S-007

760

B60W-010

731

H04N-021

353

H04N-021

367

G01F-023

621

G06T-007

618

F21S-041

314

B60W-040

362

G01S-017

526

B60W-050

518

B60R-021

311

G06F-003

361

G06K-009

507

B60W-040

440

H04N-007

272

G05D-001

334

B60R-001

483

G01C-021

394

G01C-021

270

G01C-021

315

B60R-016

479

B60T-007

237

G06F-003

243

H04W-004

250

G01S-013

309

B60R-001

234

H04W-004

217

B60W-010

222

B60W-040

G06K-009

832

B60R-016

B60R-021

1055

B60R-025

H04N-021

557

B60R-011

G06K-009

1055

G08G-001

646

B60N-002

479

G06K-009

490

B60R-001

492

B60W-030

440

G10L-015

409

G08G-001

381

H04N-005

373

B60W-050

362

G01S-015

379

G06F-003

368

G06T-007

350

H04N-021

358

G06K-009

302

H04W-004

356

B60R-021

308

G01C-021

354

G01S-007

299

G01C-021

317

H04N-007

268

G06F-003

331

G06F-003

297

G06F-021

282

G08G-001

243

G05D-001

284

B60R-022

272

G06Q-030

238

B60Q-001

170

H04W-004

253

H04N-021

240

G07C-005

207

B60W-030

146

G06F-021

212

G01C-021

231

G05D-001

190

G01C-021

119

B60W-010

B60W-030

731

B64C-039

G06K-009

734

B64D-047

G06K-009

544

H01L-027

H04N-005

2883

G06K-009

510

G05D-001

438

B64C-039

295

H04L-012

1342

B60W-050

222

B64D-047

295

G05D-001

289

H04N-007

1065

G05D-001

203

G06T-007

261

G06T-007

214

G06K-009

1045

G08G-001

200

H04N-005

202

H04N-005

199

H04W-008

854

G06T-007

178

G08G-005

166

H04N-007

136

H04M-001

738

B60W-040

143

H04N-007

156

G08G-005

125

H04L-009

734

B60R-001

116

A01M-001

85

G06Q-010

102

H04W-028

706

B60T-007

87

G06Q-010

74

G06F-017

64

H04W-088

676

B62D-015

87

G06F-017

54

G06Q-050

63

H03M-013

628

H01L-021

G06K-009

1387

H01L-023

H01L-021

749

G01N-033

G06K-009

3042

C12Q-001

G06F-019

1679

H01L-023

749

G06K-009

689

G06F-019

2984

G01N-033

963

G01N-021

548

H01L-025

176

C12Q-001

963

G06K-009

510

G03F-001

501

H01L-027

135

G06F-017

934

G01N-021

232

G06T-007

359

H05K-001

81

G01N-021

869

G06T-007

190

G01B-011

280

G07F-007

72

G06F-007

824

C12M-001

187

G06T-001

223

G11B-020

63

G06K-007

761

C12N-015

171

G03F-007

159

G06T-001

56

G06T-007

715

G06F-017

164

H01L-027

159

A61B-005

50

G01N-015

557

G01N-015

110

G01R-031

136

H04N-001

50

G06Q-030

510

G01J-003

90

A63F-013

G06K-009

3258

A63B-071

A63B-069

444

A63B-069

A63B-071

444

A63B-024

G06K-009

320

G06F-003

2155

G06K-009

316

A61B-005

329

A61B-005

254

H04N-021

1734

A61B-005

302

G06K-009

282

A63B-071

163

G06T-007

1136

A63B-024

163

G09B-019

144

A63B-069

114

G06Q-020

865

G06F-003

154

G06F-003

131

G06T-007

94

H04N-005

861

G09B-019

127

A63B-024

114

G09B-019

71

G06F-017

739

A63B-021

126

A63B-021

93

G06F-003

66

G06Q-030

555

A63F-013

87

G06F-001

80

A63F-013

64

G10L-015

477

G06F-019

85

H04W-084

80

H04B-001

62

H04N-007

366

H04B-001

83

B33Y-010

79

H04N-005

61

Appendix E. Results of keywords and TF-IDF values (Finance and AI)

Rank

Cluster 0

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

1

Content

0.198377944

Datum

0.029212789

Document

0.17864845

User

0.087232717

Image

0.177121274

2

User

0.040252441

Base

0.017531206

Code

0.061838046

Information

0.059012376

Object

0.047988414

3

Content Item

0.035167167

Information

0.016409083

Datum

0.059727567

Biometric

0.033614463

Capture

0.041450252

4

Display

0.032519078

Use

0.015634365

Sensing

0.05333588

Transaction

0.031552755

Information

0.031399814

5

Item

0.031191526

Customer

0.01473762

Code datum

0.05100205

Authentication

0.028313665

Image datum

0.024372024

6

Page

0.027174535

Provide

0.014297695

Computer

0.035112772

Datum

0.022313967

Product

0.024329546

7

Medium

0.026360775

Product

0.014256802

Indicate datum

0.034483432

Voice

0.020990299

Datum

0.024130782

8

Information

0.025790752

Model

0.014034297

Surface

0.033739238

Provide

0.019496583

Image capture

0.022494829

9

Digital

0.022243898

Message

0.012991128

Print

0.032784042

Service

0.01893352

Capture image

0.021723515

10

Web

0.022192628

Determine

0.012944091

Identity

0.031411633

Base

0.017202477

Display

0.020512878

11

Datum

0.020624464

User

0.012837834

Indicate

0.031348711

Use

0.017170305

Unit

0.020382114

12

Base

0.019255619

Item

0.012461549

Indicative

0.029765584

Communication

0.017162653

Item

0.019177003

13

Provide

0.018451822

Computer

0.012348857

Interface surface

0.028668778

Server

0.016983829

Processing

0.018852817

14

Medium Content

0.017299259

Generate

0.012347353

Electronic document

0.028462684

Card

0.016597788

User

0.017716352

15

Identify

0.016048971

Plurality

0.012329854

Interface

0.027046014

Receive

0.01605443

Base

0.01717543

16

Digital content

0.015065079

Vehicle

0.011934425

Electronic

0.025883016

Electronic

0.015451682

Determine

0.01660745

17

Generate

0.01423348

Set

0.011662284

Use

0.023721453

Request

0.015377506

Plurality

0.015681891

18

Request

0.01389292

Identify

0.011510882

Form

0.023544227

Identification

0.015155556

Digital

0.015618133

19

Web page

0.013850067

Receive

0.011483505

Sense

0.023439435

Mobile

0.014984362

Identify

0.015582699

20

Multimedia

0.013385686

Associate

0.011457298

Datum indicative

0.022694584

Second

0.014465021

Use

0.015435008

21

Server

0.012770678

Text

0.011435366

Information

0.022689768

Input

0.013777312

Digital image

0.014805898

22

Receive

0.012665908

Time

0.011424035

Element

0.021656041

Application

0.013754555

Store

0.014418553

23

Audio

0.012664214

Data

0.011018341

Product item

0.021625903

Associate

0.012399184

Recognition

0.013856576

24

Unit

0.012663562

Object

0.01085272

User

0.019911184

Payment

0.012305476

Camera

0.013759393

25

Network

0.012359756

Second

0.010539817

Interactive element

0.019756694

Interface

0.012282748

Medical

0.013214398

26

Determine

0.012244654

Display

0.010466937

Image

0.019695849

Access

0.012200129

Second

0.013186597

27

Advertisement

0.012226459

Service

0.010221166

Transport

0.018801803

Terminal

0.012177946

Process

0.013069365

28

Use

0.012030406

Event

0.010021539

Interactive

0.0185945

Network

0.012159269

Feature

0.012719479

29

Client

0.012022752

Language

0.009718087

Interaction

0.017551788

Computer

0.011668643

Region

0.012668615

30

Signal

0.011840012

Process

0.00952546

Product

0.017465935

Determine

0.011592611

Vehicle

0.012644236

Appendix F. Results of keywords and TF-IDF values (Medical and AI)

Rank

Cluster 0

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

1

Image

0.12833039

User

0.044566694

Image

0.149681279

Light

0.101583329

Projection

0.119297612

Image

0.052189446

2

Unit

0.106690014

Signal

0.044505499

Image datum

0.027416184

Fingerprint

0.090448725

Ray

0.107269789

Datum

0.027815644

3

Processing

0.08023336

Datum

0.024469763

Datum

0.02627893

Sensor

0.054408846

Image

0.091765201

Region

0.02272248

4

Medical

0.0684575

Eye

0.021456577

Pixel

0.024506636

Finger

0.054060227

Datum

0.049854581

Use

0.02067914

5

Medical image

0.067533979

Sensor

0.021116344

Object

0.023767358

Surface

0.037915458

Projection image

0.049538731

Imaging

0.019572506

6

Image processing

0.06751749

Information

0.020196679

Second

0.023430128

Object

0.035298815

Projection datum

0.049110468

Patient

0.019263426

7

Information

0.051642611

Patient

0.01926218

Capture

0.021532835

Image

0.035106531

Ray Image

0.045568751

Object

0.017973157

8

Display

0.04751378

Base

0.018273905

Display

0.020177258

Light source

0.032372541

Reconstruct

0.04154275

Model

0.017096626

9

Unit configure

0.03899422

Control

0.017792294

Processing

0.019100076

Electrode

0.031413606

Ct

0.040786627

Medical

0.017020779

10

Region

0.033572651

Use

0.017542315

Value

0.018310521

Source

0.030607139

Reconstruction

0.039133382

Tissue

0.016149597

11

Configure

0.032858229

Determine

0.01708166

Information

0.016447049

Layer

0.030330429

Object

0.030652927

Dimensional

0.015811229

12

Datum

0.029595938

Configure

0.015528522

Acquire

0.016266851

Unit

0.029090611

Source

0.024814282

Base

0.015616778

13

Plurality

0.025654308

Biometric

0.015280724

Set

0.01591795

Identification

0.024699352

Detector

0.024249885

Determine

0.014949172

14

Image datum

0.024733249

Receive

0.015170428

Iris

0.01584503

Capture

0.022607802

Generate

0.022544378

Feature

0.014583906

15

Processing image

0.023606485

Provide

0.014980987

Plurality

0.015392949

Fingerprint sensor

0.020796236

Reconstruct image

0.021832223

Set

0.014091084

16

Acquire

0.021248901

State

0.014854827

Region

0.014996175

Element

0.020529027

Imaging

0.020953452

Point

0.012976287

17

Obtain

0.020051507

Display

0.013994934

Frame

0.014963367

Information

0.020291217

Temperature

0.020715122

Volume

0.012956456

18

Generate

0.019192259

Subject

0.013966003

Base

0.014554158

Contact

0.020132733

Tomography

0.019721685

Provide

0.012928826

19

Second

0.018233156

Detect

0.013610199

Image processing

0.014245878

Pattern

0.019472063

Tomosynthesis

0.019598999

Generate

0.012915165

20

Storage

0.017663728

Individual

0.013408652

Imaging

0.014233494

Array

0.019455031

Use

0.019343465

Second

0.012445498

21

Position

0.016984598

Person

0.013269696

Obtain

0.014162927

Sensing

0.018890653

Compute

0.019306941

Value

0.012395759

22

Circuitry

0.016695628

Speech

0.013112183

Generate

0.013896711

Capacitance

0.018545644

Core temperature

0.018794324

Position

0.011962686

23

Area

0.016131952

Camera

0.013015364

Use

0.013721902

Emit

0.017608535

Body core

0.018794324

Identify

0.011714824

24

Diagnosis

0.016119545

Input

0.012849453

Subject

0.01345284

Eye

0.017537753

Image datum

0.018453915

Structure

0.011460095

25

Extract

0.015930934

Time

0.012812523

Second image

0.013415665

Second

0.017303589

Artifact

0.018279601

Image datum

0.011383725

26

Base

0.015386953

Processor

0.012164025

Process

0.013351851

Substrate

0.017104841

Measurement

0.018275404

Subject

0.011382012

27

Section

0.015161255

Voice

0.012074533

Section

0.013123026

Form

0.016517986

Compute tomography

0.018237567

Information

0.011279962

28

Processing circuitry

0.014942322

Physiological

0.011244492

Radiation

0.012503388

Portion

0.016430622

Source point

0.018226013

Display

0.011222254

29

Imaging

0.014822018

Unit

0.011235695

Determine

0.011809622

Circuit

0.015973076

Measurement external

0.018226013

Scan

0.010942954

30

Tomographic image

0.014507676

Response

0.01103956

Image frame

0.011575773

Position

0.015310686

External source

0.017990564

Plurality

0.010866449

Appendix G. Results of keywords and TF-IDF values (Transport and AI)

Rank

Cluster 0

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Cluster 6

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

1

Aerial

0.159427233

Vehicle

0.031056294

Vehicle

0.096151063

Driver

0.160409504

Object

0.156138695

Image

0.13412893

Lane

0.279929199

2

Uav

0.145661685

Image

0.030292462

Control

0.047991409

Vehicle

0.058581736

Light

0.064978981

Display

0.077100225

Vehicle

0.072954673

3

Aerial vehicle

0.142521953

Sensor

0.028861171

User

0.036221433

Information

0.03931589

Image

0.058780659

Vehicle

0.069974302

Line

0.053775216

4

Unmanned

0.134573367

Occupant

0.025066266

Parking

0.034826919

Drive

0.037710207

Vehicle

0.04289003

Camera

0.061163007

Travel

0.046231281

5

Unmanned aerial

0.116606092

Datum

0.023229803

Information

0.03387836

State

0.030959532

Detection

0.040101062

Capture

0.04924199

Lane change

0.045317069

6

Flight

0.05441209

Signal

0.019014479

Voice

0.027270317

Assistance

0.028984218

Unit

0.038623791

View

0.042136822

Change

0.041479546

7

Vehicle

0.048996729

Position

0.018430637

Autonomous

0.024645105

Gaze

0.02569218

Detect

0.033569292

Image datum

0.040922548

Image

0.035969317

8

Datum

0.035075087

Determine

0.018028235

Unit

0.024396807

Driver assistance

0.024306248

Object detection

0.027210971

Datum

0.03847697

Road

0.033542202

9

Vehicle uav

0.033635654

Use

0.01795721

Speech

0.022978872

Unit

0.024076081

Region

0.026264719

Unit

0.033593735

Boundary

0.030161741

10

Image

0.033429338

Base

0.01770307

Command

0.022113133

Image

0.023990315

Source

0.023025796

Capture image

0.029347439

Travel lane

0.029410796

11

Landing

0.033115104

Information

0.017147233

Base

0.021048951

FINGERPRINT

0.023723216

Information

0.022428436

Control

0.027182451

Lane Mark

0.029223709

12

Structure

0.031329129

Camera

0.016754367

Recognition

0.019435878

Determine

0.022911094

Light source

0.02222869

Processing

0.024307513

Unit

0.029202966

13

Location

0.027392452

Detect

0.014940307

Input

0.017964097

Vehicle driver

0.022681827

Area

0.021011946

Second

0.023564644

Detect

0.029111632

14

Camera

0.024072838

Video

0.014822091

Display

0.017905456

Driver vehicle

0.021946511

Second

0.020922634

Area

0.019879779

Mark

0.029030678

15

Flight path

0.023507981

Unit

0.014491309

Determine

0.017509464

Driving

0.021296849

Camera

0.020169578

Processor

0.019678465

Control

0.027330377

16

Target

0.023366543

Area

0.014135431

Datum

0.017251797

Base

0.020289442

Determine

0.020018552

Image capture

0.019659964

Lane boundary

0.026474082

17

Use

0.02324508

Second

0.014035532

Drive

0.017200359

Detect

0.02028685

Configure

0.018680393

Configure

0.018959778

Lane line

0.026366581

18

Inspection

0.023187405

Control

0.0139589

Configure

0.016609577

Behavior

0.020003265

Detect object

0.017903007

Image processing

0.018357075

Marking

0.026134196

19

Object

0.022544702

Provide

0.013449327

Vehicle control

0.016406075

Configure

0.019591766

Position

0.017532529

Field

0.017836405

Vehicle lane

0.023587277

20

Unmanned aircraft

0.021658919

Point

0.013389051

Signal

0.016384093

Datum

0.018354341

Base

0.017026624

Field view

0.017425634

Recognize

0.023400175

21

Sensor

0.020468828

Aircraft

0.013294046

Autonomous vehicle

0.016192819

Provide

0.017186052

Target

0.015741914

Video

0.017173459

Information

0.023005102

22

Control

0.019872358

Detection

0.013118654

Controller

0.01603727

Fact check

0.017167639

Capture

0.015699335

Imaging

0.016904874

Determine

0.022772694

23

Information

0.019386384

Configure

0.013108036

Provide

0.015433222

Fact

0.01701888

Dimensional

0.014939229

Region

0.016245116

Lane marking

0.022094068

24

Fly

0.019241628

Seat

0.013065

Sensor

0.015428558

FACE

0.016975401

Object detect

0.014715535

Object

0.016130362

Point

0.021735698

25

Path

0.019021107

Value

0.012675534

Operation

0.015032859

Driver state

0.016829022

Control

0.014583048

Vision

0.015902234

Position

0.020982387

26

Capture

0.018277651

Feature

0.012621097

Receive

0.014855577

Eye

0.016721036

Use

0.01428023

Detect

0.015749778

Detect lane

0.020286683

27

Receive

0.017994064

Receive

0.012565294

Motor

0.014831073

Monitor

0.01668831

Traffic light

0.014278756

Rear

0.015735298

Base

0.019967848

28

Rooftop

0.017598594

Plurality

0.012556094

Detect

0.014382639

Direction

0.016681767

Traffic

0.014135008

Driver

0.015485735

Departure

0.019700223

29

Aircraft

0.017561018

Road

0.012529721

Motor vehicle

0.014134266

Duration

0.016436393

Datum

0.013785172

Process

0.015212184

Vehicle travel

0.019125669

30

Configure

0.017507896

Surface

0.011833398

Communication

0.013981051

Alert

0.016247663

Distance

0.013653862

Road

0.015167621

Datum

0.018656464

Appendix H. Results of keywords and TF-IDF values (Semiconductor and AI)

Rank

Cluster 0

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

1

Signal

0.044702592

Pattern

0.108125686

Light

0.147184061

defect

0.250480901

sensor

0.096859752

2

Pixel

0.035166852

Image

0.083281964

Display

0.122139957

inspection

0.120098083

layer

0.091713197

3

Circuit

0.0303897

Wafer

0.048132518

Emit

0.069394297

image

0.113537734

fingerprint

0.078581499

4

Image

0.030184402

Datum

0.047816355

Fingerprint

0.068853102

pattern

0.08121038

surface

0.066872685

5

Sensor

0.029509571

Mask

0.041385668

Light emit

0.063671234

inspect

0.052762786

chip

0.065993618

6

Array

0.028699363

Inspection

0.036879544

Layer

0.060014388

detect

0.047682959

fingerprint sensor

0.059097502

7

Element

0.026141355

Position

0.030430035

Panel

0.053804267

candidate

0.040216658

sensing

0.055443751

8

Second

0.024502222

Second

0.026302931

Substrate

0.045932114

pattern inspection

0.038987837

electrode

0.054315777

9

Datum

0.023922522

Reference

0.026121717

Display panel

0.045335622

defect candidate

0.038512054

package

0.052778546

10

Substrate

0.02104948

Edge

0.025437432

Sensor

0.044821112

defect inspection

0.03674768

substrate

0.050240005

11

Light

0.020837783

Semiconductor

0.025205057

Optical

0.042653287

wafer

0.034416902

structure

0.040057451

12

Semiconductor

0.019296188

Object

0.024340352

Pixel

0.03975595

reference

0.033249917

form

0.037756285

13

Line

0.019261878

Use

0.024225027

Electrode

0.036813508

classification

0.032691374

conductive

0.035978669

14

Unit

0.018957725

Inspect

0.024183914

Sensing

0.034345266

value

0.030955755

second

0.035715671

15

Information

0.018206322

Region

0.023990048

Unit

0.033452424

unit

0.030821797

circuit

0.03434731

16

Output

0.018028201

Value

0.023737108

Recognition

0.03314576

reference image

0.029270298

die

0.034211313

17

Layer

0.017821151

Exposure

0.023651016

Dispose

0.031829755

detection

0.028878046

pad

0.02665942

18

Object

0.017703665

Measure

0.023420336

Fingerprint recognition

0.030024992

detect defect

0.027800169

connection

0.02640326

19

Use

0.017229621

Obtain

0.02276489

Organic

0.029888434

Sample

0.026861223

Connect

0.024482952

20

Control

0.017111873

Process

0.022333478

Plurality

0.02855115

Defect image

0.026787822

Dielectric

0.024316756

21

Channel

0.016341966

Optical

0.021761987

Identification

0.028415769

Datum

0.026483719

Portion

0.023274139

22

Detection

0.015834037

Unit

0.020266575

Organic light

0.027707207

Inspection condition

0.026323708

Electrically

0.022969956

23

Processing

0.015516258

Design

0.019899876

Array

0.027135693

Image defect

0.024251549

Cover

0.022713028

24

Process

0.014981377

Processing

0.01928304

Element

0.026772149

Condition

0.023292133

Contact

0.02180223

25

Sensing

0.014679848

Correction

0.019276089

Fingerprint identification

0.026619077

Inspection image

0.022985454

Dispose

0.021795028

26

Configure

0.014505143

Plurality

0.019130024

Region

0.026614971

Information

0.022942306

Finger

0.021667264

27

Provide

0.014295471

Measurement

0.018463426

Photosensitive

0.025447119

Obtain

0.022364682

Material

0.021526337

28

Image sensor

0.013897129

Detect

0.018446415

Oled

0.025377127

Compare

0.022319001

Dielectric layer

0.021258494

29

Form

0.013848361

Determine

0.018338073

Source

0.025243756

Use

0.021711746

Plate

0.021160379

30

Cell

0.013842385

Calculate

0.017847281

Light source

0.025209527

Pattern defect

0.02169489

Semiconductor

0.02069056

Appendix I. Results of keywords and TF-IDF values (Game and AI)

Rank

Cluster 0

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

1

Voice

0.080636368

User

0.056927493

Reality

0.232453193

Object

0.43868811

Image

0.171958667

Game

0.067825941

2

Signal

0.050224917

Datum

0.055877063

Augment reality

0.225395519

Capture

0.177600925

Card

0.119606654

Video

0.059295019

3

Audio

0.046373366

Motion

0.04830412

Augment

0.213153391

Information

0.152080695

Face

0.050237501

Object

0.056198027

4

Sound

0.043668412

Sensor

0.045016688

Reality display

0.121915096

Address correspond

0.151615894

Processing

0.040308931

Image

0.0403155

5

Speech

0.042752214

Exercise

0.036717848

World

0.113477717

Information address

0.151615894

Information

0.038907048

User

0.039687433

6

Robot

0.041001165

Event

0.033750717

Passable world

0.091772342

Capture identification

0.150979901

Object

0.035160213

Gesture

0.038438559

7

Control

0.040628489

Activity

0.02634334

Passable

0.091772342

Image

0.150808477

Capture

0.034964776

Player

0.03795768

8

Input

0.03950159

Information

0.025593977

World model

0.091772342

Identification process

0.149741889

Display

0.032678618

Golf

0.034766763

9

Command

0.03571509

Movement

0.023126028

Display

0.091545322

Use access

0.149119244

Section

0.029071137

Camera

0.031573598

10

Character

0.035622645

Use

0.022092086

Individual augment

0.088029668

Process digital

0.148484742

Unit

0.028571637

Depth

0.028391094

11

Information

0.03558271

Unit

0.020711602

Model datum

0.085965192

Object database

0.147882605

Capture image

0.028386674

Ball

0.026640296

12

Game

0.033377825

Provide

0.019940811

Waveguide

0.075197316

Communication pertinent

0.147165537

Detect

0.027820283

Virtual

0.026504529

13

Unit

0.029224434

Base

0.019641776

Model

0.06336711

Pertinent object

0.147165537

Information processing

0.026776362

Use

0.026256791

14

Output

0.02911663

Athletic

0.019339101

Individual

0.062621649

Initiate communication

0.147165537

Area

0.025953933

Determine

0.025900724

15

Datum

0.024423045

Toy

0.018716625

Pass

0.061063022

Information initiate

0.147165537

Image processing

0.024842534

Position

0.023614954

16

Message

0.022798916

Analysis

0.018553061

Comprise

0.050209962

Object recognize

0.146653117

User

0.023421889

Base

0.023264422

17

Communication

0.022145106

Tag

0.018017177

Datum

0.044756867

Address

0.146272932

Datum

0.02328261

Information

0.023241233

18

Generate

0.021571009

Body

0.017155502

Planar waveguide

0.044367352

Capture object

0.146000332

Region

0.022670175

Capture

0.022559429

19

Processing

0.020771693

Performance

0.016964766

Planar

0.043032665

Pertinent

0.145906691

Position

0.022422868

Provide

0.021166177

20

Text

0.019757139

Signal

0.016798959

Virtual

0.042669155

Digital image

0.145766315

Extract

0.022265651

Video game

0.020576439

21

Display

0.019359831

Receive

0.016777097

Doe

0.041106619

Access information

0.145294647

Image datum

0.022040247

Control

0.020411992

22

Data

0.019269211

Processor

0.016497447

Object

0.041042742

Recognize plurality

0.144888864

Camera

0.021327564

Location

0.019835285

23

User

0.019119122

Time

0.016310078

Set

0.040731824

Object capture

0.144751453

Set

0.019801488

Club

0.01952646

24

Receive

0.019008362

Analyze

0.016168934

Augmented

0.0402833

Object use

0.142621069

Base

0.019186749

Second

0.019158768

25

Recognition

0.018232912

Configure

0.016112217

Map point

0.040218541

Plurality object

0.141614644

Second

0.018326374

Display

0.019015473

26

Conversation

0.018006436

Generate

0.015586703

Real world

0.039140088

Database Information

0.14099138

Acquire

0.018265498

Datum

0.018607618

27

Mean

0.017700223

Sport

0.015533802

Set map

0.038909688

Initiate

0.140764922

Generate

0.018245853

Track

0.018386891

28

Operation

0.017157954

Determine

0.015438573

Point

0.037892553

Image object

0.13912926

Hmd

0.018053191

Play

0.017989826

29

Microphone

0.017014063

Example

0.015084341

Reality augment

0.037739146

Correspond object

0.138428774

Point

0.017493771

Computer

0.017731684

30

Provide

0.016361572

Application

0.015024328

Image

0.037487396

Digital

0.134547617

Register

0.016696607

Plurality

0.017222084

Appendix J. Results of keywords and TF-IDF values (Biotechnology and AI)

 

Cluster 0

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Cluster 6

Rank

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

Keyword

Mean TF-IDF

1

Datum

0.036473279

Image

0.156658117

Code

0.173355978

Sequence

0.168139919

Sample

0.103804642

Gene

0.074982537

Cell

0.249888437

2

Use

0.024491862

Object

0.038706815

Product Item

0.165962072

Chip

0.073742299

Biological

0.050898444

Expression

0.054858939

Image

0.057863485

3

Determine

0.018885495

Light

0.034858575

Item

0.154643534

Database

0.069136125

Biological sample

0.032702629

Cancer

0.052521331

Unit

0.034151346

4

Value

0.018420464

Pixel

0.031148781

Product

0.147050332

Oligonucleotide

0.063335794

Analysis

0.027891585

Disease

0.046226299

Culture

0.027509779

5

Base

0.017852577

Tissue

0.030449241

Datum

0.123147121

Model

0.044599847

Signal

0.025045956

Invention

0.043425127

Blood cell

0.027130116

6

Set

0.017378239

Cell

0.0268223

Code datum

0.108370305

Information

0.043993096

Nucleic acid

0.024570137

Treatment

0.032086037

Blood

0.026437477

7

Measure

0.016570474

Color

0.02678293

Interface surface

0.105965646

Probe

0.04297725

Nucleic

0.024570137

Patient

0.031713597

Analysis

0.024753323

8

Signal

0.016528994

Capture

0.026582046

Identity

0.100365016

Nucleotide

0.039583624

Acid

0.024219381

Gene Expression

0.03086849

Cell cell

0.023976884

9

Array

0.015775794

Specimen

0.022120576

Interface

0.098022844

Acid

0.03843464

Microbiome

0.024005384

Use

0.02939628

Sample

0.023760615

10

Sensor

0.015517946

Processing

0.021232978

Code data

0.090562877

Fiber

0.03829139

Cell

0.023295319

Present invention

0.029353071

Provide

0.020640079

11

Provide

0.014924164

Imaging

0.021168146

Surface

0.087748252

Nucleotide sequence

0.038161032

Provide

0.022441868

Subject

0.028935993

Identify

0.019906672

12

Detect

0.014720327

Sample

0.020879066

Data

0.08171961

Peptide

0.03791799

Composition

0.022221726

Present

0.028095427

Determine

0.019544511

13

Test

0.014624313

Area

0.019834573

Data portion

0.080793378

Cluster

0.035695158

Image

0.021939285

Provide

0.028013307

Colony

0.019171383

14

Information

0.014587642

Analysis

0.01958918

Indicate datum

0.079544553

Array chip

0.035557393

Analyze

0.021192761

Biomarker

0.02696519

Target

0.018813344

15

Feature

0.014573884

Obtain

0.01954078

Portion

0.079357652

Nucleic

0.033188994

Use

0.020734013

Relate

0.026797458

Detect

0.018252357

16

Plurality

0.014482166

Value

0.018873391

Sensing

0.079181473

Nucleic acid

0.033188994

Condition

0.019911701

Identify

0.024544193

Feature

0.0180671

17

Analysis

0.013916717

Use

0.018401931

Indicative

0.072968414

Protein

0.031659175

Generate

0.019817458

Drug

0.023791878

Cell image

0.017901893

18

Plant

0.013620567

Process

0.017318653

Sense

0.072515478

Array

0.030876366

Base

0.019252419

Marker

0.023438864

State

0.017561688

19

Time

0.013342118

Plurality

0.017180416

Indicate

0.065400597

Database model

0.029801976

Determine

0.019170932

Invention relate

0.022895685

Cell population

0.017512512

20

Genetic

0.013196075

Biological

0.016232473

Sense code

0.057712256

Probe array

0.028351435

Comprise

0.019022345

Risk

0.021198079

Use

0.017261805

21

Model

0.012928349

Identify

0.016165183

Datum indicative

0.057610649

Sample

0.027982863

Dna

0.018385415

Predict

0.020590024

Target cell

0.016239512

22

Computer

0.011881703

Digital

0.016118842

Indicative identity

0.054971961

Organize information

0.026252613

Blood

0.018227272

Diagnosis

0.019536176

Plurality

0.015968902

23

Product

0.011797208

Image analysis

0.016074127

Identity product

0.0526563

Provide

0.025278891

Subject

0.018011882

Response

0.019291142

Imaging

0.015958532

24

Unit

0.01179494

Colony

0.015875143

Scanning

0.046985715

identify

0.024696997

Detect

0.017252995

Tumor

0.019054098

Information

0.015800876

25

Comprise

0.011793525

Information

0.015852125

Threshold

0.044426225

Use

0.024473405

Particle

0.016378128

Sample

0.018256374

Analysis cell

0.015479632

26

Process

0.011703455

Feature

0.015821996

Time pcr

0.040259911

Information relate

0.023505797

Second

0.016371287

Level

0.017849214

Population

0.015455206

27

Second

0.011584073

Second

0.015565325

Baseline

0.039091157

Organize

0.023057981

Specimen

0.016134434

Determine

0.017335211

Optical

0.015037281

28

Result

0.011465704

Determine

0.015558044

User

0.034381374

Acid sequence

0.022637953

Measurement

0.01609807

Invention provide

0.017306212

Cell culture

0.014921321

29

Protein

0.011311549

Analyze

0.015456845

Adapt

0.033804075

Target

0.022221809

Dataset

0.016004942

Genetic

0.017102623

Step

0.014885546

30

Select

0.01107696

Optical

0.015390779

Real time

0.031954265

Code

0.018777318

Analyte

0.015919984

Base

0.016971216

Tissue

0.013886119