• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.221-241
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• 2018

As we enter the knowledge society, the importance of information as a new form of capital is being emphasized. The importance of information classification is also increasing for efficient management of digital information produced exponentially. In this study, we tried to automatically classify and provide tailored information that can help companies decide to make technology commercialization. Therefore, we propose a method to classify information based on Korea Standard Industry Classification (KSIC), which indicates the business characteristics of enterprises. The classification of information or documents has been largely based on machine learning, but there is not enough training data categorized on the basis of KSIC. Therefore, this study applied the method of calculating similarity between documents. Specifically, a method and a model for presenting the most appropriate KSIC code are proposed by collecting explanatory texts of each code of KSIC and calculating the similarity with the classification object document using the vector space model. The IPC data were collected and classified by KSIC. And then verified the methodology by comparing it with the KSIC-IPC concordance table provided by the Korean Intellectual Property Office. As a result of the verification, the highest agreement was obtained when the LT method, which is a kind of TF-IDF calculation formula, was applied. At this time, the degree of match of the first rank matching KSIC was 53% and the cumulative match of the fifth ranking was 76%. Through this, it can be confirmed that KSIC classification of technology, industry, and market information that SMEs need more quantitatively and objectively is possible. In addition, it is considered that the methods and results provided in this study can be used as a basic data to help the qualitative judgment of experts in creating a linkage table between heterogeneous classification systems.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.103-106
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• 2007

The importance of intellectual property right becomes larger and the number of the person who uses a patent search is increasing. When considering the search pattern of the general user who uses only one or two search terms, it is not easy task to find desirable search result in the massive patent documents. So we present patent search system based on IPC Clustering which helps users confine the search result by using international patent classification (IPC) which provided from all patent documents. By using this system, the general users can find patent search result more effectively.

• International Journal of Contents
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• v.13 no.2
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• pp.66-74
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• 2017

Patent classification is becoming more critical as patent filings have been increasing over the years. Despite comprehensive studies in the area, there remain several issues in classifying patents on IPC hierarchical levels. Not only structural complexity but also shortage of patents in the lower level of the hierarchy causes the decline in classification performance. Therefore, we propose a new method of classification based on different criteria that are categories defined by the domain's experts mentioned in trend analysis reports, i.e. Patent Landscape Report (PLR). Several experiments were conducted with the purpose of identifying type of features and weighting methods that lead to the best classification performance using Support Vector Machine (SVM). Two types of features (noun and noun phrases) and five different weighting schemes (TF-idf, TF-rf, TF-icf, TF-icf-based, and TF-idcef-based) were experimented on.

• Journal of Information Management
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• v.41 no.3
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• pp.27-44
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• 2010

Most research about patent data analyzes the trend of technologies using a Patent Map(PM), and suggests the frequencies and trend of patents in a certain topic using tables or graphs in Excel. However, more advanced analysis tools are recently needed to compare the trends among national and international industries. This research discussed why statistical analysis is needed to improve the reliability in PM analysis, and the research compares the trends of patents in Korea between 1990 and 2004 by years, International Patent Classification(IPC) sections, and countries using the frequencies and Poisson regression model. The statistical analysis is also suggested and applied to R&D studies.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.25-32
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• 2019

With the rapid development of artificial intelligence technology, the patenting activities related to the fields of AI is increasing worldwide. In particular, a share of patent filed in China has exploded in recent years and overtakes the numbers in the US. In the present study, we focus our attention on the patenting activity of China and the US. We analyzed 6,281 and 13,664 patent applications in the US and China respectively between 2008 and 2018, and belonging to the "G06F(Electric Digital Data Processing)", "G06N(Computer Systems Based on Specific Computational Models)", "H04L(Transmission of Digital Information)" and nine more relevant technological classes, as indicated by the International Patent Classification(IPC). Our analysis contributes to: first, the understanding of patent application trends from foreign countries filed in the US and China, 2) patent application status by applicants category such as companies, universities and individuals, 3) the development direction and forecasting vacant technology of AI according to main IPC code. Through the analysis of this paper, we can suggest some implications for patent research related to artificial intelligence in Korea. Plus, by analyzing the most recent patent data, we can provide important information for future artificial intelligence technology research.

• Journal of Society of Preventive Korean Medicine
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• v.18 no.2
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• pp.147-154
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• 2014

Objective : The purpose of this study is to analyze for the patent application tendency in Korean Oriental Pediatric Medicine. Recently, many studies are published in Korean Oriental Pediatric Medicine. However, in the patent area, the research results are few. Method : For patent analysis, we searched the database kipris (http://www.kipris.or.kr) which managed in the Korean Intellectual Property Office by keywords about Korean Oriental Pediatrics. They are technically analysed by year and IPC, patent's contents, main applicant and organization. Results : 1. Patents by year have been on the rise 2 in 1997 to 32 in 2011. 2. Our findings are Section A61;182, A23;46 in IPC analysis 3. In classification of content, 'application-food' is 67 cases, 'application-functional cosmetic' is 18 cases, 'application-clothes' is 5 cases. 4. Korea Institute of Oriental Medicine have 14 patent cases, that is the most patent agency in possession. Main applicants are person > company > university > laboratory > state or local government. Conclusion : This study shows that it has high frequency of daily necessity in patent cases about oriental pediatrics. The results are more distributed 'applied' field than 'treatment' field. The patent information should be connected research achievement results in Korean Oriental Medicine.

• The Korean Journal of Community Living Science
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• v.17 no.4
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• pp.15-27
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• 2006

Most countries recognize traditional knowledge as an economical resource in recent years, and so are actively participating in WIPO discussions for making sure of their intellectual property rights. In this study, the definition of traditional knowledge was discussed for making clear its categories and relative subjects. A tool for Korean Traditional Knowledge Resource Classification(KTKRC) was developed for putting the data of the resources in order, and was indispensable for searching for and examining cultural artifacts within the system of international intellectual property rights. KTKRC covers comprehensively our various traditional knowledge resources and has a similar structure to IPC for international searching, examining, and information exchange. KTKRC consists of a section of traditional knowledge(A), and three subsections: production technology(A0), living technology(A2) and creative technology(A4). The subsections include 8 classes, 28 subclasses, 105 groups, and a great number of subgroups.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.4
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• pp.332-339
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• 2004

A technical analysis was conducted to predict the development trend for the highly efficient absorption type refrigeration system. The study was based on a submitted patent during January 1981 and December 2000 in Korea, Japan and America. The total number of extruded patents from the registered database was 24,822 and the filtering process makes the reduction of the data number to 3,510. Technical development of Japan for the absorption type refrigeration system is prominent compared to the other country due to approximately 75％ of the patents coming from Japan. When the patent is divided into two categories, the patent for component technology for the refrigeration system makes up 75％ and the refrigerating type technology 25％ of the patents. This shows technical development for the system component is advanced compared to the technology development for the system type. When the patents are classified by nationality of patent applicants, foreigners contribute up to 33％ of the patents in Korea. However, Japan's case shows the 99％ of the patents are invented by the Japanese. If the patents are classified to the International Patent Classification, most of the data for the absorption type refrigeration system belongs to IPC F25B.

• Journal of the Korean Society of Industry Convergence
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• v.7 no.2
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• pp.199-205
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• 2004

This paper describes the fault diagnosis method to order to construct CIM in complex system with hierarchical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement a special neural network. Fault diagnosis system can forecast faults in a system and decide from the signal information of current machine state. Comparing with other diagnosis system for a single fault, the developed system deals with multiple fault diagnosis, comprising hierarchical neural network (HNN). HNN consists of four level neural network, i.e. first is fault symptom classification and second fault diagnosis for item, third is symptom classification and forth fault diagnosis for component. UNIX IPC is used for implementing HNN with multitasking and message transfer between processes in SUN workstation with X-Windows (Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural network represents a separate process in UNIX operating system, information exchanging and cooperating between each neural network was done by message queue.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.135-146
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• 1997

This paper describes fault diagnosis method in complex system with hierachical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. Fault diagnosis system can forecast faults in a system and decide from current machine state signal information. Comparing with other diagnosis system for single fault, the developed system deals with multiple fault diagnosis comprising Hierarchical Neural Network(HNN). HNN consists of four level neural network, first level for item fault symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. UNIX IPC(Inter Process Communication) is used for implementing HNN wiht multitasking and message transfer between processes in SUN workstation with X-Windows(Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural newtork operate as a separate process in HNN. The message queue take charge of information exdhange and cooperation between each neural network.