• Proceedings of the Korea Technology Innovation Society Conference
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• 2005.10a
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• pp.689-701
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• 2005

최근 특허청의 특허정보 활용 확산 정책으로 인하여 특허정보에 대한 관심이 집중되고 있다. 또한 특허정보를 연구하는 분야에서도 기술혁신 추세 분석, 기술혁신과 기술개발 주체에 대한 관계 분석을 수행하는 특허정보, 전산과학, 문헌정보학을 융합하는 특허정보학 (patinformatics)이라는 분야가 등장하였다. 특허정보를 이용한 최근의 연구동향은 특허 인용정보를 이용하여, 기술 파급효과 분석, 특허 인용지수, 기술영향력 지수, 특허가치 평가등 대부분의 연구가 인용정보를 기반으로 진행퇴고 있다. 기존의 연구 및 분석에 사용된 데이터는 미국특허의 인용정보를 이용하여 수행되고 있다. 한국특허 정보에는 인용정보가 존재하지 않기 때문이다. 한국특허의 분석을 위해서는 미국에 출원된 미국특허 데이터를 수집하여 한국의 연구개발 동향을 분석해야하는 문제점을 내포하고 있다 본 연구는 한국특허청 심사관의 인용정보 생성에 관한 업무 부하를 최소화하는 것을 목적으로 미국특허의 비 특허 문헌정보가 가지고 있는 문제점을 살펴봄으로써 국내에 구축되어 있는 과학기술문헌의 연계를 통한 효과적인 특허인용정보생성 방안을 제시하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.500-503
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• 2020

특허를 통해 기술의 권리를 정의하고 보호하는 일이 매우 중요해짐에 따라 특허 문서를 분석하는 연구 또한 중요해지고 있다. 특히 특허의 청구항을 종속항과 독립항을 구분하고, 관련된 인용을 찾아내는 일은 관련 특허들을 분석하는데 매우 중요하다. 본 연구는 최근 텍스트 분석 분야에 획기적 성능 개선을 이끈 BERT(Bidirectional Encoder Representations From Transformers) 언어 모델을 사용하고 Neural Network 의 파인 튜닝 과정을 통해 청구항의 독립과 종속을 구분하였고, 인용하는 항의 번호와 인용 문구로 이루어진 인용 패턴을 통해 종속항의 인용 항을 찾아내었다. 이 방법을 2003 년 이후의 xml 형식의 미국 특허 데이터에 사용한 결과, 정확도 99% 의 성능을 확보하였다.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.657-661
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• 2006

오늘날 지식기반사회에서 새로운 과학의 출현이 새로운 산업으로 확산되어 국가경쟁력에 이바지하고 있다. 일반적으로 논문을 통한 연구는 과학을 대변하고, 기술은 특허로 대체되는 것으로 인지하고 있다. 현재 과학은 SCI 의 인용색인 데이터를 바탕으로 각 문헌에 대한 인용통계정보를 제공하는 저널평가 테이터베이스인 JCR을 활용하여 분석하고 평가, 파악하고 있는 실정이다. 과학기술 분야의 누적된 논문수, 인용횟수, 연구자 분석 등 다양하게 적용할 수 있다. 반면 특허는 전체 출원, 등록건수 등 국가의 거시적인 흐름을 평가하는 수단으로는 주로 활용되어지고 있으나 정보소스의 한계나 분석시의 한계로 인하여 다양한 분석이 활용되기 어려운 실정이다. 인용정보로서 활용시 이를 위한 과도한 전처리 작업이 요구되며, 분석이 번거롭고 용이하지 않는 현실이므로 다양하게 분석시 사용하기가 어렵다. 이를 활용하여 문헌에 적용된 분석을 활용하여 새로운 고부가 정보를 제안해 보고자 한다.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.1-15
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• 2022

This study analyzes the relationship of citations appearing in the patent data to understand knowledge transfers and impacts between patent documents in the field of pharmaceutical research. Patent data were collected from a website, Google Patents. The top 25 assignees were selected by searching for patent documents related to pharmaceutical research. We identify the citation relationships between assignees, then calculate and compare the values of h-index and derived indicators by using the number of citations and rank for each document of each assignee. As a result, in the case of pharmaceutical research, the assignee, such as 'Pfizer, MIT, and Abbott' shows a high impact. Among the five bibliometric indicators, the g-index and hS-index show similar results, and the indicators are the most related to the rankings of Total Citation Frequency, Cites per Patents, and Maximum Citation Frequency. In addition, it is highly related to the five indicators in the order of Total Citation Frequency, Cites per Patents, and Maximum Citation Frequency. In some cases, it is difficult to make an accurate comparison with Cites per Patents alone, which is previously known to indicate the technological influence of patent assignees.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.1
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• pp.143-151
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• 2015

Technology convergence, recently accelerated in various technical fields, can be achieved by discovering a new technology while exchanging the knowledge among the different technologies and utilizing such knowledge into the existing fields of technology. In particular, technology convergence actively occurs in knowledge-intensive ICT. However, limited research is available on the routes of ICT technical knowledge diffusion because of insufficient data. Therefore, this study built a database on the citations of patent data applied from 2006 to 2013 in Korea and their cited patents. We drew a patent citation network, a technology citation network and an applicant citation network, after which we analyzed the routes of technical knowledge diffusion. Results showed that ICT played a leading role in knowledge citation among technologies and that such diffusion took a shorter time in technology citation when it occurred more frequently and when the citation occurred between ICTs. In addition, most of the ICT showed a strong citation relationship with the other ICTs or such technologies in the field of physics or electricity, whereas electric elements (H01) showed various citation relationships with technologies other than ICT. Furthermore, we found a strong technology diffusion relationship between domestic corporations and domestic natural persons. National organizations often cited the patents of other applicants, whereas the patents of domestic corporations were actively cited between domestic corporations or by other applicants. Thus, this study is expected to be useful in measuring the performance of technologies, including the diffusion to other technologies. As well as in considering the routes of technical knowledge diffusion in Korea.

• Patent21
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• s.62
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• pp.8-15
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• 2005

본 보고서는 일본의 경제연구소(RIETI)에서 발간한 보고서로서 특허인용문에 쓰이는 일반적인 과학저널의 데이터를 대상으로 분석을 실시한 것이다.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2006.11b
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• pp.323-337
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• 2006

Recently, several studies have been carried on knowledge flow of science, technology, industry to establish STI policy. Still it is in need of studies on relationship of science-industry although there have been studied only in aspect of science-technology relationship or technology-industry relationship. This paper's purpose is to propose method to measure knowledge flow among science, technology, and industry by means of patent citation by USPTO. After gathering knowledge flow data between science and technology through mapping citing patent and cited pater, it gets knowledge flows data between technology-industry by using OTC (OECD Technology Concordance), technology-industry mapping program. Based on these knowledge flow data, it examines knowledge flow science to industry by applying overlap function that is a network link weight function.

• The Journal of the Convergence on Culture Technology
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• v.7 no.2
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• pp.351-357
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• 2021

At the time when interest in the use of big data is rising in the face of the technological paradigm shift of the 4th industrial revolution, interest in the use of patented big data is increasing, especially as the proportion of intangible assets of companies increases. In addition to quantitative information, patent data contains various information such as unstructured text such as title, abstract, claim, citation and citation relations, drawings, and technology classification. It is judged that the use of treatment is important. Therefore, in this study, in order to systematically operate the innovative patent strategy framework and to secure a competitive advantage by strengthening the fundamental technological competitiveness of the company, we propose a method of using patent big data centering on the case of Company A, and verify its validity. I would like to suggest some implications. Through this, it is intended to raise awareness of the use of patent big data, and to suggest ways to use patent big data in connection with the company's company-wide strategy, business strategy, and functional strategy.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1343-1351
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• 2012

In this study, trends for five research areas of LED(Light Emitting Diode), OLED(Organic Light Emitting Diode), LCD(Liquid Crystal Display), PDP(Plasma Display Panel) and CRT(Cathode Ray Tube) are investigated using bibliometric analysis. The papers and patents citation data were extracted from Scopus and USPTO databases, respectively. We could figure out the research trends by the number of publications and citation information. We prospect the current interests and future trends by investigating the development process of the 5 research areas as function of time.

• Journal of the Korean Data and Information Science Society
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• v.24 no.5
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• pp.999-1011
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• 2013

In this research, we propose a document recommendation method which can find documents that are relatively important to a specific document based on citation information. The key idea is parameter tuning in the Neumann kernal which is an intermediate between a measure of importance (HITS) and of relatedness (co-citation). Our method properly selects the tuning parameter ${\gamma}$ in the Neumann kernal minimizing the prediction error in future citation. We also discuss some comutational issues needed for analysing large citation data. Finally, results of analyzing patents data from the US Patent Office are given.