• Annual Conference on Human and Language Technology
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• 2005.10a
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• pp.28-34
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• 2005

특허 검색은 수많은 특허 문서 중에서 특정 해당분야의 문서 집합 내에서 검색을 수행하기 때문에 정확한 특허 분류에 크게 의존하게 된다. 이러한 특허 분류의 중요성에 덧붙여, 특허 문서의 수가 빠르게 증가하게 되면서 특허를 자동으로 분류하려는 요구가 더욱 필요하게 되었다. 특허문서는 일반문서와는 달리 구조화되어 있기 때문에 특허분류를 하기 위해서는 이러한 점이 고려되어야 한다. 본 논문에서는 k-NN 방법을 이용하여 일본어 특허 문서를 자동으로 분류하는 방법을 제안한다. 훈련집합으로부터 유사문서를 검색할 때, 구조화되어 있는 특허 문서의 특징을 이용한다. 문서 전체가 아닌 (기존 기술), (응용 분야), (해결하고자 하는 문제), (문제를 해결하려는 방법) 등의 세분화된 요소끼리 비교하여 유사성을 계산한다. 특허 문서에는 사용자가 정의한 많은 의미 요소가 있기 때문에 먼저 이들을 군집화한 후에 이용한다. 실험 결과 제안한 방법이 특허문서를 그대로 이용하는 것보다는 74%, 특허문서에 나타난 <요약>, <청구항>, <상세한 설명>의 큰 구조 정보를 이용하는 것보다는 4%의 성능 향상을 가져왔다.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.191-196
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• 2010

표준특허란 표준문서의 규격을 기술적으로 구현하는 과정에서 필수적으로 적용해야 하는 특허로서, 최근 선진국은 "R&D-특허-표준화의 연계"를 강화하고 있고 글로벌 기업 또한 표준과 연계한 특허 획득에 주력하고 있다. 우리나라 또한 기술선점, 시장지배력 및 경제적 파급효과 등의 표준특허 확보의 중요성을 인식하고 있고, 정부와 정부부처의 산하 연구기관을 중심으로 표준특허 관련 법 제도 등의 관련연구가 진행되고 있다. 그러나 표준특허 분석결과만이 연구결과로 공개되고 있을 뿐, 실제로 표준특허 선별을 위한 정형화된 기법은 전무한 상태이며 분석방법론 관련연구 또한 매우 미미한 상태이다. 따라서 본 논문은 형태학적 특성에 기반을 두어 표준과 특허문서 간 유사도를 측정하고, 측정된 유사도를 분석하여 신뢰성 있게 표준특허를 선별하는 방법을 제안하고 그 적용사례를 분석한다.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.101-115
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• 2020

In this paper, big data analysis method was used to find out global trends in autonomous driving and to derive activate spatial information services. The applied big data was used in conjunction with news articles and patent document in order to analysis trend in news article and patents document data in spatial information. In this paper, big data was created and key words were extracted by using LDA (Latent Dirichlet Allocation) based on the topic model in major news on autonomous driving. In addition, Analysis of spatial information and connectivity, global technology trend analysis, and trend analysis and prediction in the spatial information field were conducted by using WordNet applied based on key words of patent information. This paper was proposed a big data analysis method for predicting a trend and future through the analysis of the connection between the autonomous driving field and spatial information. In future, as a global trend of spatial information in autonomous driving, platform alliances, business partnerships, mergers and acquisitions, joint venture establishment, standardization and technology development were derived through big data analysis.

• Journal of Korean Public Health Nursing
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• v.6 no.2
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• pp.58-69
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• 1992

The purposes of this study were 1) to assess the currunt documentation system 2) to identify the problems in communication regarding to documentation 3) to develop new documentation system 4) to suggest effective communication channel using new documentation system Research was conducted by direct observation, chart review, staffs interview and servey. Results were as follows: 1) nursing care plans were not used in ongoing care 2) documentation format was primarily narrative and charting was time consuming 3) documentation did not reflect the nursing process 4) patient records were not used as effective communication tool between case manager and part time nurse 5) difficult access to patient record for nurse manager created inefficiency in coordinating 6) documentation of patient education did not describe the precise contents of education, and the responses of the patients and evaluation To solve these problems, new documentation format was developed. With new formats nurses : 1) use standardized care plan which contains nursing diagnosis, ecpected outcome, time frame for evaluation, flow sheet for updating the plans 2) leave one copy of care plan at patient home for mutual agreement with patent and communication among nursing staffs 3) carry one copy of care plan for updating 4) document and evaluate the patient education using education check list keeping in patient's home 5) document nursing process in focus charting visit report 6) carry one copy of visit report 7) have one copy of visit report which was deligated to part time nurses 8) use documentation in direct communication with part time nurse 9) use beeper and memo to promote communication

• Journal of Digital Convergence
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• v.14 no.3
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• pp.251-260
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• 2016

Knowledge map is widely used to represent knowledge in many domains. This paper presents a method of integrating the national R&D data and assists of users to navigate the integrated data via using a knowledge map service. The knowledge map service is built by using a lightweight ontology modeling method. The national R&D data is integrated with the research project as its center, i.e., the other R&D data such as research papers, patent, and project reports are connected with the research project as its outputs. The lightweight ontology is used to represent the simple relationships between the integrated data such as project-outputs relationships, document-author relationships, and document-topic relationships. Knowledge map enables us to infer the further relationships such as co-author and co-topic relationships. To extract the relationships between the integrated data, a RDB-to-Triples transformer is implemented. Lastly, we show an experiment on R&D data integration using the lightweight ontology, triples generation, and visualization and navigation of the knowledge map.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.249-265
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• 2023

Recently, the satellite industry has been paying attention to the private-led 'New Space' paradigm, which is a departure from the traditional government-led industry. The space industry, which is considered to be the next food industry, is still receiving relatively little attention in Korea compared to the global market. Therefore, the purpose of this study is to explore future signals that can help determine the market entry strategies of private companies in the domestic satellite industry. To this end, this study utilizes the theoretical background of future signal theory and the Keyword Portfolio Map method to analyze keyword potential in patent document data based on keyword growth rate and keyword occurrence frequency. In addition, news data was collected to categorize future signals into first symptom and early information, respectively. This is utilized as an interpretive indicator of how the keywords reveal their actual potential outside of patent documents. This study describes the process of data collection and analysis to explore future signals and traces the evolution of each keyword in the collected documents from a weak signal to a strong signal by specifically visualizing how it can be used through the visualization of keyword maps. The process of this research can contribute to the methodological contribution and expansion of the scope of existing research on future signals, and the results can contribute to the establishment of new industry planning and research directions in the satellite industry.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.1-9
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• 2024

This paper analyzes the trends in generative AI technology based on patent application documents. To achieve this, we selected 5,433 generative AI-related patents filed in South Korea, the United States, and Europe from 2003 to 2023, and analyzed the data by country, technology category, year, and applicant, presenting it visually to find insights and understand the flow of technology. The analysis shows that patents in the image category account for 36.9%, the largest share, with a continuous increase in filings, while filings in the text/document and music/speech categories have either decreased or remained stable since 2019. Although the company with the highest number of filings is a South Korean company, four out of the top five filers are U.S. companies, and all companies have filed the majority of their patents in the U.S., indicating that generative AI is growing and competing centered around the U.S. market. The findings of this paper are expected to be useful for future research and development in generative AI, as well as for formulating strategies for acquiring intellectual property.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.1-8
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• 2020

The use of text data in big data analytics has been increased. So, much research on methods for text data analysis has been performed. In this paper, we study Bayesian learning based on conjugate prior for analyzing keyword data extracted from text big data. Bayesian statistics provides learning process for updating parameters when new data is added to existing data. This is an efficient process in big data environment, because a large amount of data is created and added over time in big data platform. In order to show the performance and applicability of proposed method, we carry out a case study by analyzing the keyword data from real patent document data.

• Journal of Information Science Theory and Practice
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• v.4 no.2
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• pp.53-65
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• 2016

The authors in this paper aim to identify the growth of literature on Intellectual Property Rights (IPRs). The research publications on IPRs were downloaded from the Scopus online citation database and the authors found that there were 1,513,138 records contributed globally over a period of 10 years from 2005 to 2014. The distribution of publications based on the year, country, and document type were studied. Relative growth rate (RGR) of the publications and doubling time (T_d) were calculated. Most productive organizations, source titles, and the productive authors on IPR research were studied. Most cited articles in the study area were identified. The results show that a number of publications under the subjects Medicine and Engineering were produced. The developed countries are very active in IPR research and producing publications. It is found that one institution which holds the sixth place among the top 10 most productive institutions belongs to Brazil, a developing country. Two developing countries such as China and India hold second and tenth positions respectively in the top 10 countries contributing literature on IPRs.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.369-372
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• 2018

최근 지식경영에 있어 특허를 통한 지식재산권 확보는 기업 운영에 큰 영향을 주는 요소이다. 성공적인 특허 확보를 위해서, 먼저 변화하는 특허 분류 제계를 이해하고, 방대한 특허 정보 데이터를 빠르고 신속하게 특허 분류 체계에 따라 분류화 시킬 필요가 있다. 본 연구에서는 머신 러닝 기술 중에서도 계층적 주의 네트워크를 활용하여 특허 자료의 초록을 학습시켜 분류를 할 수 있는 방법을 제안한다. 그리고 본 연구에서는 제안된 계층적 주의 네트워크의 성능을 검증하기 위해 수정된 입력데이터와 다른 워드 임베딩을 활용하여 진행하였다. 이를 통하여 특허 문서 분류에 활용하려는 계층적 주의 네트워크의 성능과 특허 문서 분류 활용화 방안을 보여주고자 한다. 본 연구의 결과는 많은 기업 지식경영에서 실용적으로 활용할 수 있도록 지식경영 연구자, 기업의 관리자 및 실무자에게 유용한 특허분류기법에 관한 이론적 실무적 활용 방안을 제시한다.