• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.392-397
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• 2015

Society has been developed through analogue, digital, and smart era. Every technology is going through consistent changes and rapid developments. In this competitive society, R&D strategy establishment is significantly useful and helpful for improving technology competitiveness. A patent document includes technical and legal rights information such as title, abstract, description, claim, and patent classification code. From the patent document, a lot of people can understand and collect legal and technical information. This unique feature of patent can be quantitatively applied for technology analysis. This research paper proposes a methodology for extracting core technology and patents based on quantitative methods. Statistical analysis and social network analysis are applied to IPC codes in order to extract core technologies with active R&D and high centralities. Then, core patents are also extracted by analyzing citation and family information.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.77-88
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• 2017

Recently, with the advent of knowledge based society where information and knowledge make values, patents which are the representative form of intellectual property have become important, and the number of the patents follows growing trends. Thus, it needs to classify the patents depending on the technological topic of the invention appropriately in order to use a vast amount of the patent information effectively. IPC (International Patent Classification) is widely used for this situation. Researches about IPC automatic classification have been studied using data mining and machine learning algorithms to improve current IPC classification task which categorizes patent documents by hand. However, most of the previous researches have focused on applying various existing machine learning methods to the patent documents rather than considering on the characteristics of the data or the structure of patent documents. In this paper, therefore, we propose to use two structural fields, technical field and background, considered as having impacts on the patent classification, where the two field are selected by applying of the characteristics of patent documents and the role of the structural fields. We also construct multi-label classification model to reflect what a patent document could have multiple IPCs. Furthermore, we propose a method to classify patent documents at the IPC subclass level comprised of 630 categories so that we investigate the possibility of applying the IPC multi-label classification model into the real field. The effect of structural fields of patent documents are examined using 564,793 registered patents in Korea, and 87.2% precision is obtained in the case of using title, abstract, claims, technical field and background. From this sequence, we verify that the technical field and background have an important role in improving the precision of IPC multi-label classification in IPC subclass level.

• Economic and Environmental Geology
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• v.49 no.3
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• pp.213-224
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• 2016

Non-conventional resource and alternative energy were researched for predicting oil peak. In this study, one of many non-conventional resources, specifically oil-sands, was investigated due to the increasing interest of oil-sands plant modularization in permaforst areas for reducing the construction periods through modular transportation while limiting local construction workers. Hence, tehcnological trends were analyzed for oil-sand plant modularization. Data used were between 1994 and 2015 for patent analysis while targets included Korea, US, Japan, Europe and Canada. Technology classification system consisted of mining, steam assisted gravity drainage(SAGD), separation/upgrading/tailors ponds, module design/packaging, module transportation and material/maintenance. Result of patent analysis, patent application accounts 89% in US and Canada. The main competitive companies were Shell, Suncor and Exxon-mobil. Unlike other oil developments, oil-sands have a long-term stable production characteristic, hence, it is important to ensure the competitiveness of oil-sands for obtaining a patent in the long run.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.5
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• pp.442-450
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• 2014

Technology opportunity discovery (TOD) based on technological capability is a process which identifies new product and technology items that can be developed by utilizing or improving a firm's existing products or technologies. By taking into consideration the investment risk of R&D and its practicality, developing technological capability-based TOD methodology is considered to be important for both business and research. To this end, we propose a technological capability-based TOD method and its system using TOD knowledge base. The method can support four types of TOD cases, which are based on a firm's existing technologies and products, and TOD knowledge base is developed by using function information extracted from patent documents. In this paper, we introduce the overall framework of the method and provide application examples on the four TOD cases using the prototype system.

• Industrial Engineering and Management Systems
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• v.14 no.1
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• pp.73-87
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• 2015

The development of a practical technology intelligence system requires a knowledge base that structures the core information and its relationship distilled from large volumes of technical data. Previous studies have mainly focused on the methodological approaches for technology opportunities, while little attention has been paid to constructing a practical knowledge base. Therefore, this study proposes a procedure to construct a function-based knowledge base for technology intelligence. We define the product-function-technology relationship and subsequently present the detailed steps for the knowledge base construction. The knowledge base, which is constructed analyzing 1110582 patents between 2009 and 2013 from the United States Patent and Trademark Office database, contains the functional knowledge of products and technologies and the relationship between products and technologies. This study is the first attempt to develop a large-scale knowledge base using the concept of function and has the ability to serve as a basis not only for furthering technology opportunity analysis methods but also for developing practical technology intelligence systems.

• Journal of KIISE:Software and Applications
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• v.36 no.5
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• pp.420-429
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• 2009

Patent text is a rich source for discovering technological trends. In order to automate such a discovery process, we attempt to identify phrases corresponding to the problem and its solution method which together form a technology. Problem and solution phrases are identified by a SVM classifier using features based on a combination of a language modeling approach and linguistic clues. Based on the occurrence statistics of the phrases, we identify the time span of each problem and solution and finally generate a trend. Based on our experiment, we show that the proposed semantic phrase identification method is promising with its accuracy being 77% in R-precision. We also show that the unsupervised method for discovering technological trends is meaningful.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.185-199
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• 2018

The purpose of this study is to derive the core technologies of VR using patent analysis and to explore the direction of social and public interest in VR using news analysis. In Study 1, we derived keywords using the frequency of words in patent texts, and we compared by company, year, and technical classification. Netminer, a network analysis program, was used to analyze the IPC codes of patents. In Study 2, we analyzed news articles using T-LAB program. TF-IDF was used as a keyword selection method and chi-square and association index algorithms were used to extract the words most relevant to VR. Through this study, we confirmed that VR is a fusion technology including optics, head mounted display (HMD), data analysis, electric and electronic technology, and found that optical technology is the central technology among the technologies currently being developed. In addition, through news articles, we found that the society and the public are interested in the formation and growth of VR suppliers and markets, and VR should be developed on the basis of user experience.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.65-79
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• 2022

This study identified smart manufacturing technologies using patent and topic modeling, and compared the technology development trends in countries such as the United States, Japan, Germany, China, and South Korea. To this purpose, this study collected patents in the United States and Europe between 1991 and 2020, processed patent abstracts, and identified topics by applying latent Dirichlet allocation model to the data. As a result, technologies related to smart manufacturing are divided into seven categories. At a global level, it was found that the proportion of patents in 'data processing system' and 'thermal/fluid management' technologies is increasing. Considering the fact that South Korea has relative competitiveness in thermal/fluid management technologies related to smart manufacturing, it would be a successful strategy for South Korea to promote smart manufacturing in heavy and chemical industry. This study is significant in that it overcomes the limitations of quantitative technology level evaluation proposed a new methodology that applies text mining.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2831-2845
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• 2022

Compared to the significant approach of project-based learning research, a data-driven design project-based learning has not reached a meaningful consensus regarding the most valid and reliable method for assessing design creativity. This article proposes an advanced information data-interactive learning system for creative design using a service design process that combines a design thinking. We propose a service framework to improve the convergence design process between students and advanced information data analysis, allowing students to participate actively in the data visualization and research using patent data. Solving a design problem by discovery and interpretation process, the Advanced information-interactive learning framework allows the students to verify the creative idea values or to ideate new factors and the associated various feasible solutions. The student can perform the patent data according to a business intelligence platform. Most of the new ideas for solving design projects are evaluated through complete patent data analysis and visualization in the beginning of the service design process. In this article, we propose to adapt advanced information data to educate the service design process, allowing the students to evaluate their own idea and define the problems iteratively until satisfaction. Quantitative evaluation results have shown that the advanced information data-driven learning system approach can improve the design project - based learning results in terms of design creativity. Our findings can contribute to data-driven project-based learning for advanced information data that play a crucial role in convergence design in related standards and other smart educational fields that are linked.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.