• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.227-240
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• 2011

New concepts and ideas often result from extensive recombination of existing concepts or ideas. Both researchers and developers build on existing concepts and ideas in published papers or registered patents to develop new theories and technologies that in turn serve as a basis for further development. As the importance of patent increases, so does that of patent analysis. Patent analysis is largely divided into network-based and keyword-based analyses. The former lacks its ability to analyze information technology in details while the letter is unable to identify the relationship between such technologies. In order to overcome the limitations of network-based and keyword-based analyses, this study, which blends those two methods, suggests the keyword network based analysis methodology. In this study, we collected significant technology information in each patent that is related to Light Emitting Diode (LED) through text mining, built a keyword network, and then executed a community network analysis on the collected data. The results of analysis are as the following. First, the patent keyword network indicated very low density and exceptionally high clustering coefficient. Technically, density is obtained by dividing the number of ties in a network by the number of all possible ties. The value ranges between 0 and 1, with higher values indicating denser networks and lower values indicating sparser networks. In real-world networks, the density varies depending on the size of a network; increasing the size of a network generally leads to a decrease in the density. The clustering coefficient is a network-level measure that illustrates the tendency of nodes to cluster in densely interconnected modules. This measure is to show the small-world property in which a network can be highly clustered even though it has a small average distance between nodes in spite of the large number of nodes. Therefore, high density in patent keyword network means that nodes in the patent keyword network are connected sporadically, and high clustering coefficient shows that nodes in the network are closely connected one another. Second, the cumulative degree distribution of the patent keyword network, as any other knowledge network like citation network or collaboration network, followed a clear power-law distribution. A well-known mechanism of this pattern is the preferential attachment mechanism, whereby a node with more links is likely to attain further new links in the evolution of the corresponding network. Unlike general normal distributions, the power-law distribution does not have a representative scale. This means that one cannot pick a representative or an average because there is always a considerable probability of finding much larger values. Networks with power-law distributions are therefore often referred to as scale-free networks. The presence of heavy-tailed scale-free distribution represents the fundamental signature of an emergent collective behavior of the actors who contribute to forming the network. In our context, the more frequently a patent keyword is used, the more often it is selected by researchers and is associated with other keywords or concepts to constitute and convey new patents or technologies. The evidence of power-law distribution implies that the preferential attachment mechanism suggests the origin of heavy-tailed distributions in a wide range of growing patent keyword network. Third, we found that among keywords that flew into a particular field, the vast majority of keywords with new links join existing keywords in the associated community in forming the concept of a new patent. This finding resulted in the same outcomes for both the short-term period (4-year) and long-term period (10-year) analyses. Furthermore, using the keyword combination information that was derived from the methodology suggested by our study enables one to forecast which concepts combine to form a new patent dimension and refer to those concepts when developing a new patent.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1273-1284
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• 2016

In this paper, we analyzed the patents on machine learning using keyword network analysis and clique analysis. To construct a keyword network, important keywords were extracted based on the TF-IDF weight and their association, and network structure analysis and clique analysis was performed. Density and clustering coefficient of the patent keyword network are low, which shows that patent keywords on machine learning are weakly connected with each other. It is because the important patents on machine learning are mainly registered in the application system of machine learning rather thant machine learning techniques. Also, our results of clique analysis showed that the keywords found by cliques in 2005 patents are the subjects such as newsmaker verification, product forecasting, virus detection, biomarkers, and workflow management, while those in 2015 patents contain the subjects such as digital imaging, payment card, calling system, mammogram system, price prediction, etc. The clique analysis can be used not only for identifying specialized subjects, but also for search keywords in patent search systems.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.719-728
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• 2016

In this paper, we analyzed IoT patent data using the social network analysis of keyword community network in patents related to Internet of Things technology. To identify the difference of IoT patent trends between Korea and USA, 100 Korea patents and 100 USA patents were collected, respectively. First, we first extracted important keywords from IoT patent abstracts using the TF-IDF weight and their correlation and then constructed the keyword network based on the selected keywords. Second, we constructed a keyword community network based on the keyword community and performed social network analysis. Our experimental results showed while Korea patents focus on the core technologies of IoT (such as security, semiconductors and image process areas), USA patents focus on the applications of IoT (such as the smart home, interactive media and telecommunications).

• 발명특허
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• v.28 no.7 s.325
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• pp.28-33
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• 2003

도메인에 대한 분쟁들은 이미 인터넷이 보편화되면서부터 시작되어 법적으로나 행정적으로나 일정한 방향으로 해결되어 나가고 있습니다. 도메인 분쟁과 관련된 분쟁이 2000년도부터 시작되어 2003년 5월 현재 좀더 구체화되고 있습니다. 이 분쟁이 일명 '한글키워드 서비스'에 대한 분쟁입니다. 이 분쟁은 도메인과 유사한 표지로써의 분쟁과 함께 특허분쟁이 포함되어 있습니다. 이하, '한글 키워드 서비스'에 대한 간단한 설명과 함께 표지로써의 분쟁과 특허분쟁에 대한 경과 및 그 문제점을 간단히 설명하도록 하겠습니다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1219-1222
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• 2015

금융과 IT가 융합되는 핀테크(Fintech)가 IT산업과 금융산업에 새로운 패러다임으로 급부상하고 있다. 핀테크 기술에 대한 기술동향을 파악하고 유사한 연관 기술을 도출하는 것은 관련 사업자가 시장 경쟁에서 우위를 차지하기 위해 필요한 전략적 방향을 제시해 준다. 하지만 핀테크와 같이 단 기간 내에 기술에 대한 파급 속도가 빠르게 일어나며 산업전반에서 기술선점의 필요성이 크게 대두되는 경우 특허 데이터베이스만으로 유사기술을 검색을 위한 키워드를 선정하는 것이 어렵다는 단점이 있다. 본 논문에서는 새롭게 이슈화되는 기술 중 그 성장세가 급격하게 변화하여 등록된 특허만으로는 연관 기술 영역을 파악하는 일이 번거로운 상황에서 기사 분석을 통해 연관 기술 키워드를 추출 할 수 있는 방법을 제안하고자 한다. 특히 핀테크에서 중요하게 인식되는 결제, 보안, 사용자환경에 대한 연관 기술 키워드를 기사 내용에 포함되는 단어의 빈도 분석을 통해 추출하고자 하였다. 최종적으로 추출된 기술 키워드를 이용하여 실제 특허 검색 데이터베이스에서 관련 특허를 수집하고 분석하여 핀테크와 관련성이 매우 높은 연관 핵심 기술 특허를 도출하였다.

• Journal of the Korea Convergence Society
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• v.9 no.2
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• pp.251-257
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• 2018

As the use of big data is necessary for increasing business value, the size of the big data market is getting bigger. Accordingly, it is important to apply competitive patents in order to gain the big data market. In this study, we conducted the patent analysis based keyword network to analyze the trend of big data patents. The analysis procedure consists of big data collection and preprocessing, network construction, and network analysis. The results of the study are as follows. Most of big data patents are related to data processing and analysis, and the keywords with high degree centrality and between centrality are "analysis", "process", "information", "data", "prediction", "server", "service", and "construction". we expect that the results of this study will offer useful information in applying big data patent.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.143-149
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• 2022

Since patent documents contain detailed results of research and development technologies, many studies on various patent analysis methods for effective technology analysis have been conducted. In particular, research on quantitative patent analysis by statistics and machine learning algorithms has been actively conducted recently. The most used patent data in quantitative patent analysis is technology keywords. Most of the existing methods for analyzing the keyword data were models based on the Gaussian probability distribution with random variable on real space from negative infinity to positive infinity. In this paper, we propose a model using gamma probability distribution to analyze the frequency data of patent keywords that can theoretically have values from zero to positive infinity. In addition, in order to determine the regression equation of the gamma-based regression model, two-mode network is constructed to visualize the technological association between keywords. Practical patent data is collected and analyzed for performance evaluation between the proposed method and the existing Gaussian-based analysis models.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2015.07a
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• pp.50-52
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• 2015

특허는 기술에 대한 광범위한 정보를 포함하고 있다. 기존의 기술예측은 정량적분석으로 시도되었지만 특허분석을 활용하여 정성적분석을 실시하였다. 특허분석을 시행하기 위하여 R 프로그램을 이용하여 주성분분석과 다중선형회귀분석을 실행하였다. 주성분분석과 다중선형회귀분석을 통하여 키워드를 추출하고 추출된 키워드를 통해 기술예측을 실시한다.

• Journal of Information Technology and Architecture
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• v.9 no.3
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• pp.323-331
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• 2012

With the advancement of the area of knowledge and information, Intellectual Property, especially, patents have captured attention more and more emergent. The increasing need for efficient way of patent information search has been essential, but the prevailing patent search engines have included too many noises for the results due to the Boolean models. This has occasioned too much time for the professional experts to investigate the results manually. In this paper, we reveal the differences between the conventional document search and patent search and analyze the limitations of existing patent search. Furthermore, we propose a specialized in patent search, so that the relationship between the keywords within each document and their significance within each patent document search keyword can be identified. Which in turn, the keywords and the relationships have been appointed a ranking for this patent in the upper ranks and the noise in the data sub-ranked. Therefore this approach is proposed to significantly reduce noise ratio of the data from the search results. Finally, in, we demonstrate the superiority of the proposed methodology by comparing the Kipris dataset.

• The Inventors News
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• no.12
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• pp.2-2
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• 2003

텔레비전에서 고급 향수의 향기가 솔솔$\~$ - BM특허권 보호위한 대책 마련 시급하다 - KRNIC, 넷피아 키워드 검색 특허에 제동 - 칼로리 분할표기 미국 특허 취득 - 벤처농업인, 마늘가공방법 특허 출원