• Journal of KIISE
• /
• v.42 no.1
• /
• pp.33-43
• /
• 2015

Recently, patent images are growing importance and thus patent image retrieval is a growing area of research. However, most existing patent image retrieval systems use edges extracted in the images, whose performance is affected by the quality of edge detection in the image pre-processing step. To overcome this disadvantage, we propose a SURF-based patent image retrieval method which uses the morphological characteristics of the images. The proposed method detects SURF interest points with directions and computes regional histograms. We apply the proposed method to a patent image database with 2000 binary images and we show the proposed retrieval system achieves excellent results, even when the images have some loss or degradation.

• Journal of the Korean Society for information Management
• /
• v.23 no.2
• /
• pp.61-79
• /
• 2006

To come up with the recommendations to improve the patent & trademark retrieval efficiency, 100,016 patent & trademark search requests by 17,559 unique users over a period of 193 days were analyzed. By analyzing 2,202 multi-query sessions, where one user issuing two or more queries consecutively, we discovered a number of retrieval efficiency improvements clues. The session analysis result also led to suggestions for new system features to help users reformulating queries. The patent & trademark retrieval users were found to be similar to the typical web users in certain aspects especially in issuing short queries. However, we also found that the patent & trademark retrieval users used Boolean operators more than the typical web search users. By analyzing the multi-query sessions, we found that the users had five intentions in reformulating queries such as paraphrasing, specialization, generalization, alternation, and interruption, which were also used by the web search engine users.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.8
• /
• pp.3677-3688
• /
• 2011

The patent information retrieval system can serve a variety of purposes. In general, the patent information is retrieved using limited key words. To identify earlier technology and priority rights repeated effort is needed. This study proposes a method of content-based retrieval using text mining. Using the proposed algorithm, each of the documents is invested with characteristic value. The characteristic values are used to compare similarities between query documents and database documents. Text analysis is composed of 3 steps: stop-word, keyword analysis and weighted value calculation. In the test results, the general retrieval and the proposed algorithm were compared by using accuracy measurements. As the study arranges the result documents as similarities of the query documents, the surfer can improve the efficiency by reviewing the similar documents first. Also because of being able to input the full-text of patent documents, the users unacquainted with surfing can use it easily and quickly. It can reduce the amount of displayed missing data through the use of content based retrieval instead of keyword based retrieval for extending the scope of the search.

• Proceedings of the Korean Society for Information Management Conference
• /
• 1999.08a
• /
• pp.29-32
• /
• 1999

특허정보는 일반 과학기술정보와 다른 특성을 가지고 있어 정확성과 최신성이 절대적으로 필요하다. 이와 같은 특허정보의 특성을 고려하여 이용자의 정보요구를 충족시키고 효과적으로 검색할 수 있는 특허정보검색시스템 구축을 위한 기초자료로서 특허문헌구조를 고찰하였다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2011.06a
• /
• pp.369-371
• /
• 2011

본 연구에서는 특허문서에 사용된 핵심키워드 찾아내고 추출된 핵심키워드에 가중치를 부여하여 특허데이터DB에서 질의문서와 유사한 특허기술문서를 찾고 유사도 순으로 우선 배치하여 검색에 효율을 높일 수 있는 알고리즘을 제안한다. 본 연구는 제안한 알고리즘은 검색결과에 대하여 질의한 문서와 유사한 문서 순으로 랭크가 가능하기 때문에 검색의 효율을 높이는 효과를 가지고 온다.

• International Journal of Contents
• /
• v.13 no.2
• /
• pp.66-74
• /
• 2017

Patent classification is becoming more critical as patent filings have been increasing over the years. Despite comprehensive studies in the area, there remain several issues in classifying patents on IPC hierarchical levels. Not only structural complexity but also shortage of patents in the lower level of the hierarchy causes the decline in classification performance. Therefore, we propose a new method of classification based on different criteria that are categories defined by the domain's experts mentioned in trend analysis reports, i.e. Patent Landscape Report (PLR). Several experiments were conducted with the purpose of identifying type of features and weighting methods that lead to the best classification performance using Support Vector Machine (SVM). Two types of features (noun and noun phrases) and five different weighting schemes (TF-idf, TF-rf, TF-icf, TF-icf-based, and TF-idcef-based) were experimented on.

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

• Journal of dental hygiene science
• /
• v.24 no.1
• /
• pp.9-21
• /
• 2024

Background: The purpose of this study was to utilize text network analysis and topic modeling to identify interconnected relationships among keywords present in patent information related to oral health, and subsequently extract latent topics and visualize them. By examining key keywords and specific subjects, this study sought to comprehend the technological trends in oral health-related innovations. Furthermore, it aims to serve as foundational material, suggesting directions for technological advancement in dentistry and dental hygiene. Methods: The data utilized in this study consisted of information registered over a 20-year period until July 31st, 2023, obtained from the patent information retrieval service, KIPRIS. A total of 6,865 patent titles related to keywords, such as "dentistry," "teeth," and "oral health," were collected through the searches. The research tools included a custom-designed program coded specifically for the research objectives based on Python 3.10. This program was used for keyword frequency analysis, semantic network analysis, and implementation of Latent Dirichlet Allocation for topic modeling. Results: Upon analyzing the centrality of connections among the top 50 frequently occurring words, "method," "tooth," and "manufacturing" displayed the highest centrality, while "active ingredient" had the lowest. Regarding topic modeling outcomes, the "implant" topic constituted the largest share at 22.0%, while topics concerning "devices and materials for oral health" and "toothbrushes and oral care" exhibited the lowest proportions at 5.5% each. Conclusion: Technologies concerning methods and implants are continually being researched in patents related to oral health, while there is comparatively less technological development in devices and materials for oral health. This study is expected to be a valuable resource for uncovering potential themes from a large volume of patent titles and suggesting research directions.

• Journal of Advanced Information Technology and Convergence
• /
• v.10 no.2
• /
• pp.139-151
• /
• 2020

Entity name recognition is a part of information extraction that extracts entity names from documents and classifies the types of extracted entity names. Entity name recognition technologies are widely used in natural language processing, such as information retrieval, machine translation, and query response systems. Various deep learning-based models exist to improve entity name recognition performance, but studies that compared and analyzed these models on Korean data are insufficient. In this paper, we compare and analyze the performance of CRF, LSTM-CRF, BiLSTM-CRF, and BERT, which are actively used to identify entity names using Korean data. Also, we compare and evaluate whether embedding models, which are variously used in recent natural language processing tasks, can affect the entity name recognition model's performance improvement. As a result of experiments on patent data and Korean corpus, it was confirmed that the BiLSTM-CRF using FastText method showed the highest performance.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.04b
• /
• pp.895-897
• /
• 2004

문서 검색 시스템에서 특정 주지에 관한 문서를 검색하기 위한 색인어의 가중치 부여 방법으로 단순빈도와 역문헌빈도에 의한 가중치 부여 방법을 주로 이용한다 하지만 빈도 정보만을 이용한 방법은 성능 및 정확도의 향상에 한계가 있다. 이에 본 논문에서는 특허 문헌 검색 시스템의 검색 효율을 높이기 위해 자주 출현하는 복합명사의 재출현 양상과 복합명사의 역할변화에 따른 가중치 부여 방법을 제안한다 본 연구에서 제안한 가중치 부여 방법을 이용하여 실험한 결과 단순빈도와 역문헌빈도 정보를 이용한 방법보다 더 나은 성능을 보였다 .