A Study on the Deduction and Diffusion of Promising Artificial

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Article A Study on the Deduction and Diﬀusion of Promising Artiﬁcial Intelligence Technology for Sustainable Industrial Development

Hong Joo Lee * and Hoyeon Oh

Department of Industrial and Management, Kyonggi University, Suwon 443760, Gyeonggi, Korea; [email protected] * Correspondence: [email protected]

Received: 14 May 2020; Accepted: 8 July 2020; Published: 13 July 2020

Abstract: Based on the rapid development of Information and Communication Technology (ICT), all industries are preparing for a paradigm shift as a result of the Fourth Industrial Revolution. Therefore, it is necessary to study the importance and diffusion of technology and, through this, the development and direction of core technologies. Leading countries such as the United States and China are focusing on artificial intelligence (AI)’s great potential and are working to establish a strategy to preempt the continued superiority of national competitiveness through AI technology. This is because artificial intelligence technology can be applied to all industries, and it is expected to change the industrial structure and create various business models. This study analyzed the leading artificial intelligence technology to strengthen the market’s environment and industry competitiveness. We then analyzed the lifecycle of the technology and evaluated the direction of sustainable development in industry. This study collected and studied patents in the field of artificial intelligence from the US Patent Office, where technology-related patents are concentrated. All patents registered as artificial intelligence technology were analyzed by text mining, using the abstracts of each patent. The topic was extracted through topic modeling and defined as a detailed technique. Promising/mature skills were analyzed through a regression analysis of the extracted topics. In addition, the Bass model was applied to the promising technologies, and each technology was studied in terms of the technology lifecycle. Eleven topics were extracted via topic modeling. A regression analysis was conducted to identify the most promising/mature technology, and the results were analyzed with three promising technologies and five mature technologies. Promising technologies include Augmented Reality (AR)/Virtual Reality (VR), Image Recognition and Identification Technology. Mature technologies include pattern recognition, machine learning platforms, natural language processing, knowledge representation, optimization, and solving. This study conducts a quantitative analysis using patent data to derive promising technologies and then presents the objective results. In addition, this work then applies the Bass model to the promising artificial intelligence technology to evaluate the development potential and technology diffusion of each technology in terms of its growth cycle. Through this, the growth cycle of AI technology is analyzed in a complex manner, and this study then predicts the replacement timing between competing technologies.

Keywords: artificial intelligence technology; promising technology; Bass diffusion model; USPTO analysis; technology diffusion; innovation

Sustainability 2020, 12, 5609; doi:10.3390/su12145609 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 5609 2 of 15

1. Introduction

The Business Phenomenon Industries worldwide, including manufacturing, are preparing for a paradigm shift as a result of the Fourth Industrial Revolution. Artificial intelligence (AI) technology has emerged as a key growth engine in the Fourth Industrial Revolution. In the past, artificial intelligence has repeatedly experienced stagnation due to the limitations of technology, but advancements have recently been made due to the rapid development of information and communication technology (ICT). According to an International Data Corporation (IDC) report on the “Artificial Intelligence System Market”, the global cognitive computing and artificial intelligence system market is expected to grow at an annual average of 55.1% over the next five years from 2016 to 2020. Tractica forecasts that the AI market will expand from about USD 60,000 in 2016 to about USD 368 billion in 2025 in the Global Market Forecast report [1]. In addition, McKinsey predicted that the ripple effect of knowledge and labor automation through artificial intelligence would range from USD 5.2 trillion to USD 6.7 trillion per year by 2025. Hence, it is important to study the prospects of leading technologies in the field that can utilize artificial intelligence because companies are curious about which AI technologies to use and the lifecycle length of the technology. In other words, to ensure industrial growth, it is necessary to present the leading research and direction of AI technology in the industry based on the importance, maturity, and market acceptance of this technology. This will lead to continuous development and enhanced competitiveness. Thus, this paper analyzed AI patent technology and AI technology trends. Patents are known to contain more than 90% of the technical information existing in the world, and 80% of the technical information existing in patent documents is not published in any other form of document [2]. Therefore, patents are a major competitive strategy to categorize commercial value based on the source information of technology; researchers use patent analysis as a practical tool to infer various types of information [3,4]. The patent analysis is obtained from various pieces of information included in the patent documents. The patent documents consist of technology implementation contents, the technology classification code, citation information, and owner information [5]. Analyzing these patents is important because it means that technology changes, trends, levels, and commercial values can be identified [6,7]. Information about the technology contained in the patent document can be used to explore changes in the field and obtain information for technological innovation. In addition, it is possible to monitor the technology to be analyzed and develop a technology development strategy [8]. Accordingly, many researchers have conducted studies using patent information to analyze and predict technology trends, establish strategic technology plans, and explore promising technologies [9]. The following research questions were prepared to solve this realistic problem:

First, which AI technology will be most useful in future industrial sites? • Second, in which direction is the most promising AI technology developing? • Third, how can one predict the lifecycle of AI technology? • This study used topic modeling to derive detailed technologies (topics) in the field of artificial intelligence. For the detailed technology (topics) derived, promising technology is derived through periodic trend analysis and prospect/decrease analysis. The growth cycle of technology was analyzed via the Bass diffusion model, which can predict the growth curve of promising technology. Through the abstracts of patents, this paper explored artificial intelligence technology and studied promising technologies via topic modeling to analyze the connectivity and potential of keywords using text mining. Sustainability 2020, 12, x FOR PEER REVIEW 3 of 17 Sustainability 2020, 12, 5609 3 of 15

2. Theoretical Background 2. Theoretical Background 2.1. Diffusion of Technology 2.1. Diffusion of Technology The diffusion of technology can be seen as innovation occurring by exchange and distribution withinThe a disocialffusion system of technology for a certain can amount be seen of as time innovation, through occurring a diffusion by exchangechannel [10]. and The distribution form of withindiffusion a socialis expressed system in for a acumulative certain amount graph ofover time, time, through which ais di generallyffusion channel called the [10 Growth]. The form Curve of diModelffusion [11]. is expressedThe growth in pattern a cumulative assumed graph by overthe growth time, which curve ismodel generally shows called the overall the Growth cumulative Curve Modelrate in [the11]. form The growthof an S shape pattern, as assumed the growth by the rate growth increases curve at model the beginning shows the and overall then cumulativedecreases after rate ina certain the form period of an of S shape, time [12]. as the The growth growth rate model increases is used at the to beginningpredict the and demand then decreases and diffusion after a pattern certain periodof new oftechnologies time [12]. Theand new growth products model and is used to establish to predict the thetime demand of new andproduct diffusion launch patternes, the time of new of technologiesdisposal of existing and new products, products and to the establish time of the research time of and new development product launches, [12]. the The time growth of disposal curve ofmodel existing is used products, as a and representative the time of research analysis and method development to analyze [12 ]. the The development growth curve stage model of is specific used as atechnologies. representative The analysis model method can be to analyze used to the monitor development the level stage of of development specific technologies. or to predict The model the candevelopment be used to of monitor technology the level [13], of and development research is or being to predict conducted the development to analyze of the technology technology [13’s], andlifecycle research. is being conducted to analyze the technology’s lifecycle. The lifecycle of technology consists of four steps: introduction, introduction, growth, growth, maturity, maturity, and and decline. decline. ThisThis can be schematized as in Figure1 1[14 [14].].

Figure 1. Technology lifecycle.lifecycle.

2.2.2.2. Bass Diffusion Diffusion Model As a method for analyzing the technology lifecycle lifecycle,, the Bass diffusion diffusion model is known to show the size ofof thethe marketmarket basedbased on on past past data data [ 15[15].]. It It was was first first proposed proposed by by Frank Frank M. M. Bass Bass in in 1969 1969 [16 [16]] and and is ais mathematicala mathematical conversion conversion model model based based on Rogers’ on Rogers (1962)’ (1962) [10] innovation[10] innovation diffusion diffusion theory. theory. The model The ismodel used is to used predict to thepredict demand the demand for new productsfor new products or services. or Itservices. is a typical It is methoda typical to method express theto express spread ofthe innovation spread of i asnnovation an initial as model an initial of the model S-shaped of the function S-shaped model. function model. The Bass Bass model model currently currently considers considers only only two two segments, segments, the themarket market and andpotential potential market, market, and andassumes assumes that thattwo factors, two factors, innovation innovation and imitation, and imitation, have have the greatest the greatest impact impact on innovation on innovation [15]. [The15]. Theinnovation innovation effect eff isect defined is defined as anas an innovator innovator for for a agroup group that that adopts adopts new new technologies technologies and and new products by making independent decisions without external influence,influence, and uses the innovation innovation coecoefficientfficient (p) as a parameterparameter to reflectreflect this.this. The imitation effect effect d definesefines a group that imitates other people to purchase new products due to internal influences,influences, and uses the imitation coecoefficientfficient (q) as a parameter to reflectreflect this. The assumption is that someone who has not yet adopted an innovation follows the linear function.function. This can be expressed as EquationEquation (1). F(t) is the differentiation differentiation of F((tt).). 푓f (t푡)) == p푝++qF푞퐹((t푡)) (1(1)) 11 − F퐹((t푡)) − where F(t): probability of total potential demand being adopted by time t (cumulative probability density function); f(t): probability that some of the potential demand for innovative technologies will Sustainability 2020, 12, 5609 4 of 15 where F(t): probability of total potential demand being adopted by time t (cumulative probability density function); f(t): probability that some of the potential demand for innovative technologies will be adopted at time t (probability density function); p: innovation factors affecting the adoption of innovative technologies (independent influence independent of existing demand); q: imitation coefficient (how the existing demand affects the demand at time t) indicating how much people are trying to imitate innovative technology. When the total new demand (potential demand) of the product for the entire period at time t is N(t) m, and the number of existing adopters is N(t), it can be expressed as F(t) = m . Using this, we can derive F(t) from Equation (1) as in Equation (2).

1 e (p+q)t F(t) = − − (2) q (p+q)t 1 + p e−

2 (p+q)t dF(t) p(p + q) e− f(t) = = (3) 2 dt (p+q)t p + qe− Multiplying Equation (2) by the potential demand m and F(t) yields N(t), the cumulative number of adopters for innovative technologies at time t, as in Equation (4). In the Bass diffusion model, N(t) is determined by the expression of the innovation coefficient p, the imitation coefficient q, and the potential demand m, and the demand model is determined by estimating it.

(p+q)t 1 e− N(t) = m − (4) q (p+q)t 1 + p e−

2 (p+q)t dN(t) p(p + q) e− n(t) = = (5) (p+q)t dt (p + qe− In the Bass model, the left and right sides are asymmetrical around the inﬂection point. In addition, even if the scale of m is unknown, the parameters p and q can be directly estimated if only the accumulated demand N(t) is known. However, because the estimates of m and p, q are closely related, if the estimation is inaccurate due to insuﬃcient data, the probability of the incorrect estimation of both p and q increases [17].

3. Literature Review

3.1. Method of Patent Analysis It is statistically simple to compile and compare patents filed in the general way. However, recent studies have been changing to apply a precise patent analysis methodology using large amounts of bibliographic information [9]. At the macroscopic level of patent analysis, studies measuring the economic impact of technology development or evaluating the technological competitiveness of the country are common. Through this, it is possible to grasp the internal and external linkage structure of technology development trends. In the microscopic aspect of patent analysis, studies are being conducted to determine the technical strengths and weaknesses of competitors, plan corporate technology development activities, derive the priorities of research and development, find technical gaps, and present opportunities for new technologies [18,19]. Suzuki et al. (2008) [20] used International Patent Classification (IPC) codes to classify the Research and development status of companies in terms of the integration of technologies in the same field and the integration of heterogeneous technologies and judged them from a technology convergence perspective through simultaneous classification analysis. Sustainability 2020, 12, 5609 5 of 15

Jeong (2011) [21] developed a technology roadmap through patent analysis and applied text Sustainability 2020, 12, x FOR PEER REVIEW 5 of 17 mining to perform technology planning based on a technology roadmap. Through this, undeveloped technologiesCurran andand characteristics Leker (2011) of [ technologies22] observed that a need convergence to be developed between in the technologies future were using proposed. the simultaneousCurran and classification Leker (2011) analysis [22] observed of IPC a and convergence grasped the between degree technologies of convergence using technology the simultaneous based classificationon the possibility analysis that of the IPC IPC and of grasped a specific the patent degree appears of convergence simultaneously technology in patents based onin ot theher possibility fields. that theKim IPC et ofal. a(2012) specific [23 patent] studied appears a methodology simultaneously and system in patents for expressing in other fields. technology causality in technologyKim et al.information (2012) [23 as] studied a network a methodology using patent andinformation system forand expressing providing technologyknowledge causalityto support in technologytechnology information convergence. as a network using patent information and providing knowledge to support technologyKarvonen convergence. et al. (2012) [24] analyzed the technology convergence using the patent’s bibliographic informationKarvonen and et conducted al. (2012) [ 24a patent] analyzed citation the relationship technology convergenceanalysis at the using intersection the patent’s of the bibliographic traditional informationpaper industry and and conducted the electronic a patent industry citation via relationship Radio-Frequency analysis Identification at the intersection (RFID of) cases. the traditional paper industry and the electronic industry via Radio-Frequency Identification (RFID) cases. 3.2. Promising Technology Analysis Method 3.2. Promising Technology Analysis Method Today, the rapid development of technology has shortened the lifecycle of technology, and the needToday, for continuous the rapid technologydevelopment development of technology is increasing. has shortened Accordingly, the lifecycle many of technology,companies and and thecountries need for are continuous focusing on technology discovering development core technologies is increasing. that can Accordingly, lead industries, many companies products, and and countriesservices. areAs focusingthe importance on discovering of technology core technologies grows, it is, that in turn can, lead becoming industries, increasingly products, important and services. to Asanalyze he importance the technology of technology information grows, that it emerges is, in turn, globally becoming and increasinglypredict new importanttechnologies. to analyzeHowever, the technologypredicting technologies information is that time emerges-consuming globally and andcostly; predict it is difficult new technologies. to obtain corresponding However, predicting results. technologiesTherefore, is time-consuming researchers are andstudying costly; various it is di ffitechnologycult to obtain forecasting corresponding methodologies results. to analyze promisingTherefore, technologies researchers that are can studying lead to new various technologies technology and innovative forecasting ideas methodologies [25]. The concept to analyze of a promisingpromising technologiestechnology can that be can interpreted lead to new in various technologies ways depending and innovative on the ideas viewpoint. [25]. The It is concept often ofreferred a promising to as a technology future technology, can be interpreted promising in technology, various ways emerging depending technology, on the new viewpoint. technology, It is oftenbreakthrough referred to technology, as a future or technology, key technology. promising technology, emerging technology, new technology, breakthroughPromising technology, technologies or keycan technology.be found by predicting the trend and status of technologies that can be noticedPromising in the technologies future for technologies can be found in bya specific predicting field. the There trend are and two status main of approaches technologies to finding that can bepromising noticed in technologies the future for [5 technologies]: first, there in a are specific trend field. analyses There and are two bibliometrics main approaches as method to findings of promisingquantitative technologies technology [5 ]:prediction first, there, using are trend statisti analysescal analysis and bibliometrics and machine as learning methods algorithms of quantitative [5]. technologyThe second prediction, is a qualitative using technology statistical prediction analysis and methodology machine learning for observing algorithms technology [5]. The trends second via is aexpert qualitative meetings technology and disagreements; prediction methodology this includes for scenario observing analysis, technology tree relevance trends via methods, expert meetingsand the andDelphi disagreements; method. this includes scenario analysis, tree relevance methods, and the Delphi method.

4.4. ResearchResearch MethodMethod

4.1.4.1. ResearchResearch ProcessProcess TheThe research research process process for for this this study study is is as as FigureFigure2 2.. First, First, patent patent data data in the fieldfield of artificialartificial intelligenceintelligence werewere collectedcollected fromfrom the United States Patent Patent and and Trademark Trademark Office Office (USPTO) (USPTO) website. website. Second,Second, the the abstractsabstracts ofof patentspatents werewere analyzed,analyzed, andand the main keywords were extracted as as nouns. nouns. Third,Third, aa datadata preprocessingpreprocessing processprocess waswas conductedconducted for analysis. Fourth, Fourth, artificial artificial intelligence intelligence technologytechnology was was extracted extracted and and defined defined using using the the topic topic modeling modeling technique. technique. Finall Finally,y, promising promising technologiestechnologies were were studied studied throughthrough aa regressionregression analysisanalysis ofof the extracted technologies.

FigureFigure 2. Research process process..

4.2. Data Collection Sustainability 2020, 12, 5609 6 of 15

4.2. Data Collection In this study, patent data registered in the United States Patent and Trademark Office (USPTO) were used to determine the scope of patents in the field of artificial intelligence. This study collected 9556 registered patents from the US Patent and Trademark Office website between 2010 and 2019. Table1 shows that Class 706 (the classification code assigned by the USPTO to the field of artificial intelligence technology) includes patents that describe the use of artificial intelligence technology in data processing. The USPTO further defines Class 706 as “the classification of patents for artificial intelligence computers, digital data processing systems, and corresponding data processing methods and knowledge processing systems”. Table1 shows the classifications of sub-technologies in the AI field. However, the USPTO changed the patent classification system to Cooperative Patent Classification (CPC) instead of US Class classification system in 2015 and the USPTO has been using the CPC instead of the US Class for international universality. In this paper, patents registered after 2016 in the artificial intelligence field were collected from the CPC codes G06N, G06K, and G06Q.

Table 1. USPTO artiﬁcial intelligence technology classiﬁcation code.

Artiﬁcial Intelligence Field Technology Code Sub-Technology Code Major Technology -Fuzzy logic hardware Problem inference and solution 706 1~11 -Multiple treatment system -Speciﬁc user interface Machine learning 706 12~25 -Machine learning -Adaptation system Network structure 706 26~44 -Neural network -Knowledge processing system -Knowledge expression and Knowledge processing system 706 45~62 reasoning technology -Rule-based reasoning system -Fuzzy logic AI application 706 900~934 -AI application

The registered patents were more valuable than patent pending because they recognize patent registration for technologies with a certain level of newness. In general, after a patent is ﬁled, it goes through a review process, and the patent is released after about 18 months [2]. In this paper, the registered patents were collected and analyzed from 2010, and the number of patents collected is the same as Table2.

Table 2. Number of patent applications for each year subject to analysis.

Year Number of Patent Applications Year Number of Patent Applications 2010 1011 2015 852 2011 1195 2016 399 2012 1250 2017 497 2013 1482 2018 566 2014 1577 2019 727

4.3. Data Processing This study utilized the abstracts of the patents that summarize key elements of the patent. Since the patent document is text-based, unstructured data and a preprocessing process that can standardize the data that are required for analysis. The entire process of data preprocessing was performed with the statistical package R via a text mining package. Here, 9556 patent documents were collected and reﬁned based on ﬁve criteria (Table3). Thus, words were extracted in noun units from the abstract of the patent. In addition, uppercase letters were changed to lowercase letters for the extracted words Sustainability 2020, 12, 5609 7 of 15

(lowercase). In addition, stop words such as surveys, articles, and special characters included in the extracted words were removed. Next, stemming was performed for the plural and past forms of each standard English word. Based on the keywords created through the above process, this paper created a matrix between documents and keywords and constructed a document-term matrix (DTM)—a matrix with a frequency value. Finally, this study constructed and analyzed DTMs with 9556 documents and 10,098 keywords.

Sustainability 2020, 12, x FORTable PEER REVIEW 3. Preprocessing standard of patent abstract. 8 of 17

CriteriaTable 3. Preprocessing Method standard of patent abstract. Example Distributed // management // with // embedded // 1 Tokenization Separate by keyword Criteria Method agents // in // enterpriseExample // apps // is // disclosed Distributeddistributed // management// management // with // embeddedwith // embedded // // 2 Lowercase1 Tokenization conversion Separate Lowercase by keyword conversion agentsagents // in // //enterprisein // enterprise // apps // isapps // disclosed// is // disclosed Lowercase distributed // management // with // embedded // Eliminate stop words such as articles, distributed // management // // embedded // agents // 3 Stop2 words removed Lowercase conversion conversion investigations, and conjunctions agents// //enterprise in // enterprise// apps // apps// // disclosed // is // disclosed Stop words Eliminate stop words such as articles, distributed // management // // embedded // 3 Extract words that are the root of distribute // management // // embed // agent // // 4 Stem extractionremoved investigations, and conjunctions agents // // enterprise // apps // // disclosed keywords (remove -es, -s, -ed, etc.) enterprise // app // // disclose Extract words that are the root of distribute // management // // embed // agent // 4 Stem extraction distribute // management // // embed // agent // // // 5 Low-frequency removalkeywords Remove words (remove used -es, less-s, -ed, than etc.) 10 times// enterprise // app // // disclose app // // disclose Low-frequency distribute // management // // embed // agent // 5 Remove words used less than 10 times removal // // app // // disclose 4.4. Patent Abstract Topic Modeling 4.4. Patent Abstract Topic Modeling The topic model is a method for ﬁnding a topic within a document cluster and classifying the The topic model is a method for finding a topic within a document cluster and classifying the documents according to the topic. It is a generative probabilistic model that allows the subject to be documents according to the topic. It is a generative probabilistic model that allows the subject to be extractedextracted through through a grouping a grouping technique technique that that hashas a similar meaning meaning to words to words that are that latent are in latent the in the documentdocument group. group. The algorithmsThe algorithms used used in in the the topictopic model model include include Latent Latent Semantic Semantic Indexing Indexing (LSI), (LSI), ProbabilisticProbabilistic Latent Latent Semantic Semantic Indexing Indexing (pLSI),(pLSI), and and Latent Latent Dirichlet Dirichlet Allocation Allocation (LDA). In (LDA). general, Inthe general, the LDALDA algorithm algorithm is is widely widely used [26 [26]. ]. The The LDA LDA was suggestedwas suggested by supplementing by supplementing the absence the of a absence of a document-leveldocument-level probability probability model model in in the the existing existing pLSI. pLSI. The The LDA LDA algorithm algorithm is a probabilistic is a probabilistic algorithm based on a Dirichlet distribution, where each document is considered to have multiple algorithmsubjects. based In on addition, a Dirichlet a word distribution, is generated where for each each subject, document and the probability is considered of generating to have amultiple subjects.document In addition, is derived a word by is modeling generated the fordistribution each subject, of subjects and thein the probability document ofand generating the probability a document is derivedthat by a word modeling is generated the distribution for each subject of subjects[26]. in the document and the probability that a word is generated forIn each addition, subject LDA [26 makes]. the basic assumption of words interchangeable. The bag-of-words In addition,method focuses LDA on makes which words the basic appear assumption rather than the of wordsorder in interchangeable.which they appear. For The example, bag-of-words “The apple is red” and “Red is the apple” are recognized as the same sentence. LDA estimates the methodparameter focuses ons (α, which β) through words the word appear distribution rather thanand frequency the order of inthe whichreceived they documents. appear. Through For example, “The applethese is parameters, red” and the “Red word is (W) the is apple” determined arerecognized for each topic as via the the sametopic ratio sentence. (θ) of each LDA document estimates the parametersand (theα, allocationβ) through topic the (Z) word of each distribution word. Figure and3 is a frequency model that visually of the receivedshows the documents.process of LDA Through these parameters,[27]. Topic modeling the word can (W) classify is determined documents more for eachflexibly topic because via it the uses topic a method ratio of (θ estimating) of each the document and theposterior allocation probability topic (Z) under of eachconditions word. that Figure are not3 independent is a model of that words visually in each showsdocument the in processthe of parameter estimation process. LDA [27]. Topic modeling can classify documents more ﬂexibly because it uses a method of estimating Therefore, in this study, topic modeling characteristics were applied to analyze patents in the the posteriorfield of probability artificial intelligence. under conditions Through this, that technologies are not independent in the field of of wordsartificial in intelligence each document were in the parameterextracted, estimation and prospective process. technologies were studied for extracted technologies.

Figure 3. Latent Dirichlet Allocation (LDA) processing. Note: whole documentation (D), each individual Figure 3. Latent Dirichlet Allocation (LDA) processing. Note: whole documentation (D), each documentindividual (d), number document of words (d), number (N), number of words of(N topics), number (K ),of determiningtopics (K), determining the parameters the parameters (θ) that (θ) indicate which subjectthat indicate proportions which subject the proportions document the will document make upwill (makeα), Dirichlet up (α), Dirichlet distribution distribution (β), (β topic), topic ratio for documentratiod (forθd ),document topic assignment d (θd), topic forassignment the nth for word the innth document word in documentd (Zd,n ),d and(Zd,n nth), and word nth word observed in documentobservedd (Wd,n in ).document d (Wd,n).

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SustainabilityTherefore, 2020, 1 in2, thisx FOR study, PEER REVIEW topic modeling characteristics were applied to analyze patents in the9 fieldof 17 of artificial intelligence. Through this, technologies in the field of artificial intelligence were extracted, 5.and Research prospective Results technologies were studied for extracted technologies.

5.1.5. Research Topic Modeling Results

5.1. TopicTopic Modeling modeling was conducted to extract technologies in the field of artificial intelligence. Topic modeling requires researchers to select the number of topics in advance. One of the methods for determiningTopic modeling the optimal was number conducted of topics to extract is to select technologies the lowest in number the field of of topics artificial measured intelligence. in the LDATopic result, modeling i.e., requiresthe topic researchers-keyword matrix to select [28 the]. number of topics in advance. One of the methods for determiningIn this study, the optimal the cosine number similarity of topics for each is to document select the was lowest used number to determine of topics the measuredoptimal number in the ofLDA topics result, (=K). i.e., The the cosine topic-keyword similarity matrix is closer [28 ].to one as the similarity between documents becomes higher.In thisTherefore, study, thethere cosine is less similarity topic similarity for each documentwith a lower was cosine used tosimilarity. determine The the selection optimal numbercriteria forof topicsthe number (=K). Theof topics cosine were similarity based on is closerthe low to cosine one as similarity. the similarity Figure between 4 show documentss how the similarity becomes valuehigher. change Therefore,s with there the isK lessvalue, topic such similarity as from with K = afive lower to cosine11. These similarity. are clear The data selection that show criteria the for K selection.the number of topics were based on the low cosine similarity. Figure4 shows how the similarity value changes with the K value, such as from K = five to 11. These are clear data that show the K selection.

Figure 4. AnalysisAnalysis of of cosine cosine similarity similarity value value change with K value value..

In this study, the number of topics was set from ﬁve to 11 by referring to Table1, and the results of In this study, the number of topics was set from five to 11 by referring to Table 1, and the results cosine similarity by topic number were equal to Table4. of cosine similarity by topic number were equal to Table 4. Finally, in this study, the number of topics was 11, and the number of sampling repetitions was Finally, in this study, the number of topics was 11, and the number of sampling repetitions was 5000. The basic values of α and β for LDA were used, and the Gibbs sampling method was used. 5000. The basic values of α and β for LDA were used, and the Gibbs sampling method was used. For For the entire process of topic modeling, R packages “topic models” [29] and “LDA” [30] were used. the entire process of topic modeling, R packages “topic models” [29] and “LDA” [30] were used. The LDA model should estimate the post-probability of the latent variable when the document Table 4. Cosine similarity by number of topics. is given. One of the inference methods for this is Gibbs sampling [31]. Gibbs sampling is a probabilistic algorithm thatK generates Cosine Similarity a series of samples K from Cosine a combined Similarity probability distribution of two or more random5 variables [0.031031]. Gibbs sampling 9 is one of 0.0296 the Markov chain Monte Carlo (MCMC) algorithms that6 estimates the 0.0307 distribution mix 10 [31]. The Gibbs 0.0285 Sampler has a disadvantage in that the convergence is7 slow due 0.0296to the large dependence 11 between 0.0276 variables. To improve this, the Collapsed Gibbs Sampler8 omits some 0.0295 unnecessary variables from the sampling [32].

The LDA model should estimateTable 4. Cosine the post-probability similarity by number of the of latent topics. variable when the document is given. One of the inference methods for this is Gibbs sampling [31]. Gibbs sampling is a probabilistic K Cosine Similarity K Cosine Similarity algorithm that generates a series of samples from a combined probability distribution of two or more 5 0.0310 9 0.0296 6 0.0307 10 0.0285 7 0.0296 11 0.0276 8 0.0295 Sustainability 2020, 12, 5609 9 of 15 random variables [31]. Gibbs sampling is one of the Markov chain Monte Carlo (MCMC) algorithms that estimates the distribution mix [31]. The Gibbs Sampler has a disadvantage in that the convergence is slow due to the large dependence between variables. To improve this, the Collapsed Gibbs Sampler omits some unnecessary variables from the sampling [32].

5.2. Extraction and Prospect of Artificial Intelligence Technology through Topic Modeling The results for 11 topics are summarized in Table5 due to the analysis of patents in the field of artificial intelligence since 2010 via topic modeling. Topics were selected from the top 50 words by topic, and 15 words representing each topic are shown in Table5. To verify the topic labeling suggested in Table5, this paper administered a questionnaire to some 30 experts. Table5 shows the analysis results of reliability verification through the Cronbach’s Alpha coefficient. Experts participating in the survey are those who have been involved in research and work for more than 20 years in the field of AI technology.

Table 5. Technology and core linked words by artiﬁcial intelligence ﬁeld.

Artificial Intelligence Cronbach’s Alpha Topic Core Linked Words Technology Coefficient user, predict, content, event, recommend, behavior, item, Topic 1 Action Recognition 0.6977 interaction, service, social, model, action, web, profile, media node, graph, state, pattern, problem, network, constraint, Topic 2 Pattern Recognition 0.7188 tree, transit, variable, quantum, path, sequence, layer, match image, video, reality, augment, optic, recognition, audio, Augmented Reality Topic 3 scene, virtual, display, motion, garment, signal, 0.6258 (AR)/Virtual Reality (VR) individuality, content image, document, item, color, user, product, identification, Topic 4 Image Recognition 0.7244 object, device, person, capture, content, account, card, digit print, image, inform, code, locate, product, item, medic, Topic 5 Vision AI 0.6081 device, tag, vehicle, transport, object, patient train, feature, classification, vector, predict, model, set, Topic 6 Machine Learning Platforms 0.7931 cluster, value, data, sample, learn, class, probable, weight Natural Language document, train, score, classification, feature, query, set, Topic 7 0.6571 Processing model, rank, predict, text, word, entity, page, search neuron, signal, card, magnet, neural, spike, circuit, Topic 8 VLSI emulation, payment, synapse, layer, pulse, stripe, input, 0.7240 reader rule, knowledge, service, semantic, policy, decision, Topic 9 Knowledge Representation ontology, set, engine, system, execution, manage, event, 0.6235 request, application model, predict, parameter, solution, control, optimal, state, Topic 10 Optimization and Solving estimate, value, conditional, calculation, measure, behavior, 0.6939 learn, time device, image, wireless, authenticator, communication, tag, Topic 11 Identification Technology mobile, secure, access, reader, capture, RFID, card, object, 0.7231 biometric

5.3. Analysis of Promising and Declining Technologies in Artificial Intelligence Prospective and mature technologies were analyzed through linear regression analysis by using the trend of the specific gravity of topics by year for each technology (topic), extracted through the topic model. A regression analysis was also performed using year as an independent variable and the weight of each technology (topic) as a dependent variable. The coefficient of regression (slope) of linear regression was used as a criterion for determining the rise and fall of each technique (topic) [33]. In addition, in this study, the Durbin–Watson value was used to test the suitability of the regression model [31]. The regression analysis assumes the independence of the residuals. In particular, time series data can suffer from the independence of the residuals. This is because the residuals of the previous Sustainability 2020, 12, 5609 10 of 15 Sustainability 2020, 12, x FOR PEER REVIEW 11 of 17 periodThe are frequency more likely of tothe aff appearanceect the residuals of artificial of the later intelligence period. Among technologies the topics over with time significant was also analyzedprobability (Figures within 4 the and significance 5) via the technology level (p-value analyzed< 0.05) in of Table the regression 6. analysis, the subjects were those with Durbin–Watson values greater than one and less than three. The frequencyTable of the6. Technology appearance and of key artificial link words intelligence by artificial technologies intelligence over (AI time) field. was also analyzed (FiguresTopic4 and 5) via the technologyName analyzed in TableCoefficient6. p-Value Durbin–Watson Hot/Cold Topic 1 Action Recognition −0.00686 0.048 0.945 X Table 6. Technology and key link words by artificial intelligence (AI) field. Topic 2 Pattern Recognition −0.00867 0.000 1.114 Cold TopicTopic 3 Augmented Reality Name (AR)/Virtual Reality (VR) Coe ffi0.01279cient p-Value0.002 Durbin–Watson1.343 HotHot/Cold TopicTopic 1 4 ActionImage Recognition Recognition 0.006860.02168 0.0480.001 1.071 0.945 Hot X − Topic 2 Pattern Recognition 0.00867 0.000 1.114 Cold Topic 5 Vision AI − 0.02615 0.000 0.928 X Topic 3 Augmented Reality (AR)/Virtual Reality (VR) 0.01279 0.002 1.343 Hot TopicTopic 4 6 Machine Image Recognition Learning Platforms 0.02168−0.01495 0.0010.001 1.006 1.071 Cold Hot TopicTopic 5 7 Natural Vision Language AI Processing 0.02615−0.00953 0.0000.026 1.095 0.928 Cold X Topic 6 Machine Learning Platforms 0.01495 0.001 1.006 Cold Topic 8 VLSI − 0.00124 0.311 0.917 X Topic 7 Natural Language Processing 0.00953 0.026 1.095 Cold − TopicTopic 8 9 Knowledge VLSI Representation 0.00124−0.01924 0.3110.000 1.543 0.917 Cold X Topic 9 Knowledge Representation 0.01924 0.000 1.543 Cold Topic 10 Optimization and Solving − −0.02109 0.001 1.087 Cold Topic 10 Optimization and Solving 0.02109 0.001 1.087 Cold − TopicTopic 11 11 IdentificationIdentification Technology Technology 0.018480.01848 0.0130.013 1.135 Hot Hot

Figure 5. Promising (Hot) artificial intelligence technology. Figure 5. Promising (Hot) artificial intelligence technology. The results are summarized in Table6. The technique (topic) showing a positive regression The results are summarized in Table 6. The technique (topic) showing a positive regression coefficient was analyzed in three categories: Topic 3, 4, and 11. An analysis of the promising technologies coefficient was analyzed in three categories: Topic 3, 4, and 11. An analysis of the promising is shown in Augmented Reality (AR)/Virtual Reality (VR), Image Recognition, and Identification technologies is shown in Augmented Reality (AR)/Virtual Reality (VR), Image Recognition, Technology (Table6); these are marked as Hot. Five technologies (topics) classified as mature and Identification Technology (Table 6); these are marked as Hot. Five technologies (topics) technologies (pattern recognition, machine learning platform, natural language processing, knowledge classified as mature technologies (pattern recognition, machine learning platform, natural representation and optimization and solving) were analyzed, and described as Cold in Table6. language processing, knowledge representation and optimization and solving) were In addition, the range of the utilization of AR/VR technology expanded with the recent development analyzed, and described as Cold in Table 6. of 5G technology, and the market (equipment, content, etc.) using realistic media has been attracting attention. The market was originally formed in entertainment industries such as games and videos, In addition, the range of the utilization of AR/VR technology expanded with the recent development of 5G technology, and the market (equipment, content, etc.) using realistic media has Sustainability 2020, 12, x FOR PEER REVIEW 12 of 17 Sustainability 2020, 12, 5609 11 of 15 been attracting attention. The market was originally formed in entertainment industries such as games and videos, but it has recently been spreading to various industries such as medical care, but it has recently been spreading to various industries such as medical care, education, shopping, education, shopping, and manufacturing. and manufacturing. In addition, the development of image Identification Technology such as facial recognition can In addition, the development of image Identification Technology such as facial recognition can be used for crime investigation, smart cities, etc. It can improve the safety and convenience of life. be used for crime investigation, smart cities, etc. It can improve the safety and convenience of life. Meanwhile, the development of technologies related to identification seems to be expected, as the Meanwhile, the development of technologies related to identification seems to be expected, as the importance of information protection and security becomes more important due to the development importance of information protection and security becomes more important due to the development of of new technologies. new technologies. The technologies classified as mature technologies are early technologies in the field of artificial The technologies classified as mature technologies are early technologies in the field of artificial intelligence or technologies that have appeared actively in the past and are not dealt with much at intelligence or technologies that have appeared actively in the past and are not dealt with much at present. Mature technologies are actively developed and have reached a plateau of development. present. Mature technologies are actively developed and have reached a plateau of development. Figure 6 is a result of analyzing the mature (cold) artificial intelligence technology by year and ration Figure6 is a result of analyzing the mature (cold) artificial intelligence technology by year and ration of topic. of topic.

Figure 6. MatureMature ( (Cold)Cold) artificial artiﬁcial intelligence technology.

5.4. Diffusion Diffusion M Modelodel of Promising Artificial Artificial Intelligence Using the Bass DiDiffusionffusion ModelModel Three promising technologies were derived through regression analysis among 11 technologies (topics) extracted by analyzing patents in the fieldfield of artificialartificial intelligenceintelligence viavia topictopic modeling.modeling. In this study, three promisingpromising technologies (topics) were analyzedanalyzed using the Bass diffusion diffusion model using the cumulative number of each year; these were then analyzed in terms of the technology growth cycle to examine the development stage of each technologytechnology until 2040. In In addition, when the number of patents expectedexpected forfor eacheach year year reached reached the the maximum maximum value, value the, the maturity maturity period period was was analyzed, analyzed, and and the thegrowth growth period period and and the maturitythe maturity period period were were classified classified based based on the on maximum. the maximum. In addition, In addition, the data the weredata were analyzed analyzed when when the rate the ofrate patent of patent production production flattened flattened or began or began to decline. to decline.

Bass Diffusion Diffusion Model Parameter Estimation MethodMethod The Bass diffusion diffusion model should estimate three parameters: potential market size m, innovation coefficientcoefficient p, and and imitation imitation coe coefficientfficient qq.. Methods Methods for for estimating parameters include initial data utilizatiutilization,on, expert expert experience, experience, and and similar similar case applicationcase application [31]. There [31]. are There three are methods three for methods estimating for parametersestimating parameters using the initial using data. the initial data. First, there is an ordinary least squares approach using the parameter estimation method of the multiple regression regression Equatio equationn ( [5)15. ].Second, Second, there there is maximum is maximum likelihood likelihood estimation estimation that that establishes establishes the likelihoodthe likelihood function function using using the probability the probability function function of given of data; given this data; is used this to is estimate used to the estimate parameter the Sustainability 2020, 12, x FOR PEER REVIEW 13 of 17

[31]. Third, there is a nonlinear least square (NLS) method—a multidimensional nonlinear parameter estimationSustainability method2020, 12 ,that 5609 minimizes the sum of squared errors between the difference12 of 15between the estimated value and the measured value [34]. Of these, the NLS method is known to have the highest predictionparameter accuracy [31]. Third, [33]. there Therefore, is a nonlinear the least NLS square method (NLS) was method—a used multidimensional in this study, and nonlinear the innovation parameter estimation method that minimizes the sum of squared errors between the difference between coefficient (p), imitation coefficient (q), and potential market (m) were estimated. The NLS analysis the estimated value and the measured value [34]. Of these, the NLS method is known to have the was carriedhighest predictionout using accuracy R’s “nls [33”]. function. Therefore, the NLS method was used in this study, and the innovation coefficient (p), imitation coefficient (q), and potential market (m) were estimated. The NLS analysis was 5.5. Diffusioncarried out Analysis using R’s of “nls” AI T function.echnology

5.5. Diffusion Analysis of AI Technology 5.5.1. Augmented Reality and Virtual Reality (AR/VR) 5.5.1. Augmented Reality and Virtual Reality (AR/VR) AR/VR technology is forecast to have 2899 cumulative patents by 2040; it likely matured in 2018 and will declineAR/VR technology in 2027. In is the forecast “Top to 10 have Strategic 2899 cumulative Technology patents Trends by 2040; For it likely 2019 matured” announced in 2018 by Gartner, technologyand will fo decliner immersive in 2027. Inexperience the “Top 10s Strategichas been Technology selected Trendsas a core For future 2019” announced technology by Gartner,for building next- technology for immersive experiences has been selected as a core future technology for building generationnext-generation digital digitalbusiness business ecosystems. ecosystems. It It is is expected to createto create innovative innovative services services via integration via integration with withvarious various industries. industries. AR/VR AR/VR technology technology is used is used for rehabilitation for rehabilitatio treatmentn intreatment the medical in field the and medical field and monitoringmonitoring and and quality quality control control in the in manufacturing the manufacturing field. It is field. expected It is that expected AR/VR technology that AR/VR will technology will bebe usedused in in other other fields fields such assuch education as education to create new to addedcreate value. new Figureadded7 is value. the result Figure of analyzing 7 is the result of analyzingthe spread the spread trend of ARtrend/VR of technology. AR/VR technology.

Figure 7. Augmented Reality (AR)/Virtual Reality (VR). Figure 7. Augmented Reality (AR)/Virtual Reality (VR). 5.5.2. Image Recognition 5.5.2. ImageImage Recognition Recognition technology is expected to have 2814 cumulative patents by 2040; its maturity likely occurred in 2018, and a decline is expected in 2028. Image Recognition technology allows one to Imageaccurately Recognition distinguish technology and recognize is the expected shape of thingsto have rather 2814 than cumulative people. This patents oﬀers appropriate by 2040; its maturity likelyinformation occurred andin 2018, services and to users.a decline is expected in 2028. Image Recognition technology allows one to accuratelyThese distinguish technologies and will recognize enable companies the shape to innovate of things products rather and than services. people. Given This that offers all of appropriate informationthe information and services we use to is imageable,users. Image Recognition technology can likely be applied to most industries. Figure8 is the result of analyzing the spread trend of image recognition technology. These technologies will enable companies to innovate products and services. Given that all of the information we use is imageable, Image Recognition technology can likely be applied to most industries. Figure 8 is the result of analyzing the spread trend of image recognition technology. Sustainability 2020, 12, x FOR PEER REVIEW 14 of 17 Sustainability 2020, 12, 5609 13 of 15 Sustainability 2020, 12, x FOR PEER REVIEW 14 of 17

Figure 8. Image Recognition. Figure 8. Image Recognition. Figure 8. Image Recognition. 5.5.3. Identification Technology 5.5.3. Identification Technology 5.5.3.Identification Identification Technology is estimated to have matured in 2016 with peak technological Identification Technology is estimated to have matured in 2016 with peak technological development in 2025. The number of cumulative patents in 2040 is estimated to be 2373. This certifies developmentIdentification in 2025. Technology The number is of estimated cumulative to patents have matured in 2040is in estimated 2016 with to be peak 2373. technological This certifies that individuals can use biometrics as an Identification Technology. Biometrics will be a key developmentthat individuals in 2025. can The use number biometrics of cumulative as an Identification patents in 2040 Technology. is estimated Biometrics to be 2373. will This be certifies a key authentication means for information and communication technology (ICT) services. thatauthentication individuals means can for use information biometrics and as an communication Identification technology Technology (ICT). Biometrics services. will be a key Automatically identified results have reduced the hassle of processes, and users are provided authenticationAutomatically means identified for information results haveand communication reduced the hassle technology of processes, (ICT) service and userss. are provided with convenience and efficiency. These technologies will be used to detect personal security and risk withAutomatically convenience and identified efficiency. results These have technologies reduced the will hassle be usedof process to detectes, and personal users are security provided and situations such as airport search stations or to customize the personalized automation used in withrisk situationsconvenience such and as effici airportency. search These stations technologies or to customizewill be used the to personalized detect personal automation security and used risk in autonomous vehicles. Figure 9 is the result of analyzing the spread trend of identification technology. situationsautonomous such vehicles. as airport Figure search9 is the stations result of or analyzing to customize the spread the personalized trend of identification automation technology. used in autonomous vehicles. Figure 9 is the result of analyzing the spread trend of identification technology.

Figure 9. Identiﬁcation Technology. Figure 9. Identification Technology. 6. Conclusions Figure 9. Identification Technology. 6. Conclusions This study analyzed three promising technologies from the perspective of technology growth 6. Conclusions cyclesThis by applyingstudy analyzed the Bass three model; promising these were technologies summarized from in Table the7 perspective. The technologies of technology with the highestgrowth cyclescumulativeThis by studyapplying number analyzed ofthe patents Bass three model; (market promising these size) weretechnologies appeared summarized as ARfrom/VR, thein Image Table perspective Recognition,7. The oftechnologies technology and Identiﬁcation with growth the cycleshighestTechnology, by cumulative applying in that the order.number Bass The ofmodel; resultspatents these suggest(market were that summarizedsize) Image appeared Recognition in asTable AR/VR, 7. will T heImage likely technologies Recognition, have the with greatest andthe highestIdentificationdevelopment cumulative potentialTechnology number in, the in of future.that patents order. (market The results size) suggest appeared that as Image AR/VR, Recognition Image Recognition, will likely have and Identificationthe greatest development Technology ,potential in that order. in the The future. results suggest that Image Recognition will likely have the greatest development potential in the future. Sustainability 2020, 12, 5609 14 of 15

Table 7. Technology lifecycle of promising technologies in artiﬁcial intelligence.

Promising Technology in Cumulative Number of Technology Limits Topic Maturity Artiﬁcial Intelligence Patents (Market Size) (Decline) Topic 3 AR/VR 2899 2018 2027 Topic 4 Image Recognition 2814 2018 2028 Topic 11 Identiﬁcation Technology 2373 2016 2025

This study can find meaning as an early piece of academic research exploring artificial intelligence technology using patents in the field of artificial intelligence. In addition, a quantitative analysis using patent data as a method of deriving promising technologies was conducted to present objective results. In addition, while exploring promising technologies, we applied the Bass model to predict the maturity and decline stages through a detailed analysis of the technology growth cycle. We then suggested the development potential of each technology. The results can be used as basic data for R&D and product and service planning for each technology.

Research Limitations and Future Research In this paper, USPTO patent data were collected and analyzed, but the data provided were limited and diﬃcult to analyze. Therefore, it is necessary to collect and analyze patent data from other leading countries.

Author Contributions: Conceptualization, H.J.L. and H.O.; data curation, H.J.L. and H.O.; formal analysis, H.J.L. and H.O.; funding acquisition, H.J.L.; methodology, H.J.L. and H.O.; resources, H.J.L. and H.O.; Validation, H.J.L. and H.O.; Visualization, H.J.L. and H.O.; writing and editing, H.J.L. and H.O. The views and opinions expressed in this paper are those of the authors alone. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the Gyeonggi-do Regional Research Center (GRRC) program of Gyeonggi province. Conﬂicts of Interest: The author declares no conﬂict of interest.

References

1. Tractica. Artificial Intelligence for Enterprise Applications. Tractica. 2015. Available online: https: //tractica.omdia.com/research/artificial-intelligence-for-enterprise-applications/ (accessed on 7 May 2020). 2. Zha, X.; Chen, M. Study on early warning of competitive technical intelligence based on the patent map. J. Comput. 2010, 5, 274–281. [CrossRef] 3. Campbell, R.S. Patent trends as a technological forecasting tool. World Pat. Inf. 1983, 5, 137–143. [CrossRef] 4. Lee, S.; Yoon, B.; Park, Y. An approach to discovering technology opportunities: Keyword-based patent map approach. Technovation 2009, 29, 481–497. [CrossRef] 5. Choi, J.; Kim, H.; Im, N. Keyword network analysis for technology forecasting. J. Intell. Inf. Syst. 2011, 17, 227–240. [CrossRef] 6. Yoon, B.; Park, Y. Development of new technology forecasting algorithm: Hybrid approach for morphology analysis and conjoint analysis of patent information. IEEE Trans. Eng. Manag. 2007, 54, 588–599. [CrossRef] 7. Von Wartburg, I.; Teichert, T.; Rost, K. Inventive progress measured by multi-stage patent citation analysis. Res. Policy 2005, 34, 1591–1607. [CrossRef] 8. Tseng, Y.H.; Lin, C.J.; Lin, Y.I. Text mining techniques for patent analysis. Inf. Process. Manag. 2007, 43, 1216–1247. [CrossRef] 9. Abbas, A.; Zhang, L.; Khan, S.U. A literature review on the state-of-the-art in patent analysis. World Pat. Inf. 2014, 37, 3–13. [CrossRef] 10. Rogers, E.M. Diffusion of Innovations; Simon and Schuster: New York, NY, USA, 1962. 11. Kim, D.H.; Part, S.S.; Shin, Y.G.; Jang, D.S. Forecasting the diffusion of technology using patent information: Focused on information security technology for network-centric warfare. J. Korea Contents Assoc. 2009, 9, 125–132. [CrossRef] Sustainability 2020, 12, 5609 15 of 15

12. Lee, D.U. Exploratory Research on the Analysis of National R&D Programs Using Growth Model; Korea Institute of S&T Evaluation and Planning: Chungcheongbuk-do, Korea, 2013. 13. Liu, C.Y.; Wang, J.C. Forecasting the development of the biped robot walking technique in Japan through S-curve model analysis. Scientometrics 2010, 82, 21–36. [CrossRef] 14. Gao, L.; Porter, A.L.; Wang, J.; Fang, S.; Zhang, X.; Ma, T.; Wang, W.; Huang, L. Technology life cycle analysis method based on patent documents. Technol. Forecast. Soc. Chang. 2013, 80, 398–407. [CrossRef] 15. Norton, J.A.; Bass, F.M. A diffusion theory model of adoption and substitution for successive generations of high-technology products. Manag. Sci. 1987, 33, 1069–1086. [CrossRef] 16. Bass, F.M. A new product growth for model consumer durables. Manag. Sci. 1969, 15, 215–227. [CrossRef] 17. Park, D.; Chung, J.; Chung, Y.; Lee, D. Development of market growth pattern map based on growth model and self-organizing map algorithm: Focusing on ICT products. J. Intell. Inf. Syst. 2014, 20, 1–23. [CrossRef] 18. Cho, Y.; Kim, E.A. Corporate strategy on technological convergence through analyzing patent networks and strategic indicators. J. Intellect. Prop. 2014, 9, 191–221. [CrossRef] 19. Lee, W.; Park, Y.T.; Yoon, B.U.; Shin, J.; Choi, C.W.; Han, Y.J.; Kim, E.H. Analysis of Technology-Industry Linkage and Korean Firm’s Patent Strategy utilizing Information from Patent Database. Available online: http://www.stepi.re.kr/module/pubDownFile.jsp?categCd=A0201&ntNo=344&r= (accessed on 13 May 2020). 20. Suzuki, K.; Sakata, J.; Hosoya, J. Innovation position: A quantitative analysis to evaluate the efficiency of research and development on the basis of patent data. In Proceedings of the 41st Annual Hawaii International Conference on System Sciences, Waikoloa, HI, USA, 7–10 January 2008. 21. Jeong, B.G.; Kim, J.W.; Yoon, J.H. Patent-based competitive intelligence analysis of augmented reality technology: Application of topic modeling. In Proceedings of the Conference on Smart Industry and Future, Seoul, Korea, 7 November 2015; pp. 2265–2270. 22. Curran, C.S.; Leker, J. Patent indicators for monitoring convergence–examples from NFF and ICT. Technol. Forecast. Soc. Chang. 2011, 78, 256–273. [CrossRef] 23. Kim, H.; Park, H.; Lim, J.; Jung, C.; Kim, K. Function-property based causality network of patents for technology convergence. In Proceedings of the Conference on Overcoming the Global Economic Crisis, Ansan, Korea, 2 November 2012; pp. 1205–1219. 24. Karvonen, M.; Lehtovaara, M.; Kässi, T. Build-up of understanding of technological convergence: Evidence from printed intelligence industry. Int. J. Innov. Technol. Manag. 2012, 9.[CrossRef] 25. Jun, S. Technology forecasting using bayesian discrete model. J. Korean Inst. Intell. Syst. 2017, 27, 179–186. [CrossRef] 26. Blei, D.M.L.; Ng, A.Y.; Jordan, M.I. Latent dirichlet allocation. J. Mach. Learn. Res. 2003, 3, 993–1022. Available online: http://www.jmlr.org/papers/v3/blei03a (accessed on 4 May 2020). 27. Wang, B.; Liu, S.; Ding, K.; Liu, Z.; Xu, J. Identifying technological topics and institution-topic distribution probability for patent competitive intelligence analysis: A case study in LTE technology. Scientometrics 2014, 101, 685–704. [CrossRef] 28. Blei, D.M. Probabilistic topic models. Commun. ACM 2012, 55, 77–84. [CrossRef] 29. Hornik, K.; Grün, B. Topic models: An R package for fitting topic models. J. Stat. Softw. 2011, 40, 1–30. [CrossRef] 30. Griffiths, T.L.; Steyvers, M. Finding scientific topics. Proc. Natl. Acad. Sci. USA 2004, 101, 5228–5235. [CrossRef] 31. Kim, Y.; Jeong, K. Micro-segmentation strategy for big data analytics using a topic model. J Korean Off. Stat. 2016, 17–44. 32. Chang, J.; Chang, M.J. Package ‘lda’. Available online: http://citeseerx.ist.psu.edu/viewdoc/download?doi= 10.1.1.216.2273&rep=rep1&type=pdf (accessed on 4 May 2020). 33. Kim, T.; Choi, H.; Lee, H. A study on the research trends in fintech using topic modeling. J. Korea Acad.-Ind. Coop. Soc. 2016, 17, 670–681. [CrossRef] 34. Schmittlein, D.C.; Mahajan, V. Maximum likelihood estimation for an innovation diffusion model of new product acceptance. Mark. Sci. 1982, 1, 57–78. [CrossRef]

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