• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.2
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• pp.99-107
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• 2024

The recent digital transformation in many industries has led to an explosion of data, which has exponentially increased the amount of data that companies need to generate and process. As a result, enterprises are leveraging ERP systems to manage and analyze large amounts of data in real time. However, due to cost and time issues in processing large amounts of data in existing ERP systems, it is essential to apply data archiving technology that can compress and store data in real time in existing systems. Therefore, this paper aims to identify the trends of the target technology by utilizing patent data on ERP-based real-time data archiving technology, analyze the core patents, and create new core patents based on them.

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1025-1039
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• 2016

We introduce various methods to investigate the relations between innovation of technology and social changes by analyzing more than 4 millions of patents registered at United States Patent and Trademark Office(USPTO) from year 1985 to 2015. First, we review the history of patent law and its relation with the quantitative changes of registered patents. Second, we investigate the differences of technical innovations of several countries by use of cluster analysis based on the numbers of registered patents at several technical sectors. Third, we introduce the PageRank algorithm to define important nodes in network type data and apply the PageRank algorithm to find important technical sectors based on citation information between registered patents. Finally, we explain how to use the canonical correlation analysis to study relationship between technical innovation and social changes.

• 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.

• Patent21
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• s.72
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• pp.20-27
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• 2007

• Annual Conference of KIPS
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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.

• Journal of Labour Economics
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• v.41 no.3
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• pp.161-186
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• 2018

Albeit numerous endeavors in matching names and surveying inventors, inventor-level studies of patent data have been scarce because unique inventors can not be identified across patents. Using the Korean patent data with inventor IDs, birth year, and gender available, we construct unique inventor-level panel data. As the first undertaking with our data, we investigate the age profile of patent productivity among inventors. We find an inverted U-shaped profile with the peak at age 31. We also find an increasing productivity for younger cohorts of inventors. These findings are robust after we control for the calendar year effects and the quality of patents.

• Journal of Digital Convergence
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• v.15 no.2
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• pp.165-172
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• 2017

In this paper, we analyze the research / technology trends of electric vehicles from 2001 to 2014, through keyword analysis using paper data published in SCIE or SSCI Journal on electric vehicles, time series analysis using patent data by IPC, and network analysis using nodeXL. also we predicted promising technologies of electric vehicles using one of the prediction methods, weighted moving average method. As a result of this study, battery technology among the electric vehicle component technologies appeared as a promising technology.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.341-346
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• 2011

A vacant technology forecasting is an important issue in management of technology. The forecast of vacant technology leads to the growth of nation and company. So, we need the results of technology developments until now to predict the vacant technology. Patent is an objective thing of the results in research and development of technology. We study a predictive method for forecasting the vacant technology quantitatively using patent data in this paper. We propose an ensemble model that is to vote some clustering criteria because we can't guarantee a model is optimal. Therefore, an objective and accurate forecasting model of vacant technology is researched in our paper. This model combines statistical analysis methods with machine learning algorithms. To verify our performance evaluation objectively, we make experiments using patent documents of diverse technology fields.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.9
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• pp.419-424
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• 2016

Named entity recognition is required to improve the retrieval accuracy of patent documents or similar patents in the claims and patent descriptions. In this paper, we proposed an automatic named entity recognition for patents by using a conditional random field that is one of the best methods in machine learning research. Named entity recognition system has been constructed from the training set of tagged corpus with 660,000 words and 70,000 words are used as a test set for evaluation. The experiment shows that the accuracy is 93.6% and the Kappa coefficient is 0.67 between manual tagging and automatic tagging system. This figure is better than the Kappa coefficient 0.6 for manually tagged results and it shows that automatic named entity tagging system can be used as a practical tagging for patent documents in replacement of a manual tagging.