• Journal of Information Management
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• v.30 no.3
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• pp.44-54
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• 1999

A description of SGML(Standard Generalized Markup Language) is given together with a detailed description of WIPO Standard ST.32. The benefits of the use of SGML are highlighted-its system Independence and flexibility in building publication systems and full-text databases. A structure of WIPO Standard ST,32 based patent content is defined by DTD(document type definition) written in ST.32, and full-text itself is described with generalized markup depending on DTD. This article explains how to represent a document structure : a hierarchy structure like a entire document, a specific, sub-document, a paragraph, or non-hirarchy structure like a table drawings, or chemical structures. Merits of SGML In patent document processing are also discussed.

• Proceedings of the Korea Contents Association Conference
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• 2003.05a
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• pp.319-322
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• 2003

This Paper reviews various formats of patent numbers that unique identifier of patent document according to country. And it describes standard DOI model that can unify each country＇s serveral patent numbers. Based on this patent DOI, it describes implementation method to link digitalized patent document to patent bibliographic record at a time.

• 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.

• Information Systems Review
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• v.22 no.1
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• pp.17-31
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• 2020

With the start of the fourth industrial revolution era, technologies of various fields are merged and new types of technologies and products are being developed. In addition, the importance of the registration of intellectual property rights and patent registration to gain market dominance of them is increasing in oversea as well as in domestic. Accordingly, the number of patents to be processed per examiner is increasing every year, so time and cost for prior art research are increasing. Therefore, a number of researches have been carried out to reduce examination time and cost for patent-pending technology. This paper proposes a method to calculate the degree of similarity among patent documents of the same priority claim when a plurality of patent rights priority claims are filed and to provide them to the examiner and the patent applicant. To this end, we preprocessed the data of the existing irregular patent documents, used Word2vec to obtain similarity between patent documents, and then proposed recommendation model that recommends a similar patent document in descending order of score. This makes it possible to promptly refer to the examination history of patent documents judged to be similar at the time of examination by the examiner, thereby reducing the burden of work and enabling efficient search in the applicant's prior art research. We expect it will contribute greatly.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.4
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• pp.1458-1467
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• 2010

Recently, imitation and infringement rights of an intellectual property are being recognized as impediments to nation's industrial growth. To prevent the huge loss which comes from theses impediments, many researchers are studying protection and efficient management of an intellectual property in various ways. Especially, the prediction of patent registration is very important part to protect and assert intellectual property rights. In this study, we propose the patent document analysis method by using text mining to predict whether the patent is registered or rejected. In the first instance, the proposed method builds the database by using the word frequencies of the rejected patent documents. And comparing the builded database with another patent documents draws the similarity value between each patent document and the database. In this study, we used k-means which is partitioning clustering algorithm to select criteria value of patent rejection. In result, we found conclusion that some patent which similar to rejected patent have strong possibility of rejection. We used U.S.A patent documents about bluetooth technology, solar battery technology and display technology for experiment data.

• International Journal of Contents
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• v.13 no.4
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• pp.70-79
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• 2017

Images are an important element in patents and many experts use images to analyze a patent or to check differences between patents. However, there is little research on image analysis for patents partly because image processing is an advanced technology and typically patent images consist of visual parts as well as of text and numbers. This study suggests two methods for using image processing; the Scale Invariant Feature Transform(SIFT) algorithm and Optical Character Recognition(OCR). The first method which works with SIFT uses image feature points. Through feature matching, it can be applied to calculate the similarity between documents containing these images. And in the second method, OCR is used to extract text from the images. By using numbers which are extracted from an image, it is possible to extract the corresponding related text within the text passages. Subsequently, document similarity can be calculated based on the extracted text. Through comparing the suggested methods and an existing method based only on text for calculating the similarity, the feasibility is achieved. Additionally, the correlation between both the similarity measures is low which shows that they capture different aspects of the patent content.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.301-302
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• 2014

• Journal of the Korean Society for information Management
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• v.27 no.4
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• pp.239-258
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• 2010

The purpose of this study is to develop a prediction model of patent citation counts based on major factors which affect patent citation. To this end, we performed multiple regression analysis between the patent citation counts and five explanatory variables such as the number of pages, the number of claims, the reference-average-citation rate, the strength of bibliographic coupling, and the document similarity proved as having 5% or more standardized variances($r^2$) with patent citation counts, with a test dataset of U.S. patents in five subject fields. As a result, our prediction models showed 58.3% to 89.6% predictability depending on subject fields and revealed the document similarity has the highest impact on citation counts among the five predictive variables in all the subject fields. The result of comparison between the predicted citation counts and the actual ones confirmed the usefulness of the citation prediction models built for each subject field.

• Journal of the Korean Society for information Management
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• v.27 no.1
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• pp.103-118
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• 2010

Recently, the valuation of patented technology has been greatly emphasized, and patent citation has been accepted as a very useful index of this technology. In this study, we performed correlation analyses between the patent citation counts and 17 explanatory variables of morphological, technological, and conceptual factors with a test dataset of U.S. patents in five subject fields. Seven variables having 5% or more standardized variances($r^2$) with patent citation counts were identified; number of pages, number of claims, reference-average-citation rate, patent increase/decrease rate, strength of bibliographic coupling, co-citation counts and document similarity. The result of the ANOVA test shows that the mean values of these variables vary among most subject fields.

• Journal of Korean Society for Quality Management
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• v.48 no.1
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• pp.227-239
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• 2020

Purpose: The purpose of this study is to introduce the improving attorney system. It was confirmed by document writing that the staff in the field in Korea had the most difficulty in the Quality circle activities. The reform improving attorney is a quality expert at the Quality Secretariat and assists them in their documentation. Methods: Just as a patent attorney helps to get a patent, an improving attorney system helps document the improvement results of the field Quality circle and even standardizes the company. The process of introducing an improving attorney includes the declaration of an improving attorney are 11 levels of work. Results: An improving attorney was conducted in 600 organizations for 18 months. As a result of the comparison of the number of cases before and after the introduction of the improving attorney, the number of solution problem was lowered to 9 before 2016 and 8 in 2017, but after the introduction, it increased exponentially to 181 in 2018 and 162 in August 2019. In 2016 and 2017, only one standard was registered, but 64 standards were registered in 2018 and 71 in 2019. Conclusion: In this case, the improving attorney system was found to be helpful in activating the Quality circle. It is expected to revitalize Korea's Quality circle by spreading the improving attorney system to many companies.