효율적인 특허정보 조사를 위한 분류 모형A Novel Classification Model for Efficient Patent Information Research
- Other Titles
- A Novel Classification Model for Efficient Patent Information Research
- Authors
- 김영호; 박상성; 장동식
- Issue Date
- 2019
- Publisher
- (사)디지털산업정보학회
- Keywords
- Patent Information Research; Noise Patent Classification; Word2Vec; Random Fores
- Citation
- (사)디지털산업정보학회 논문지, v.15, no.4, pp.103 - 110
- Indexed
- KCI
- Journal Title
- (사)디지털산업정보학회 논문지
- Volume
- 15
- Number
- 4
- Start Page
- 103
- End Page
- 110
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/70620
- ISSN
- 1738-6667
- Abstract
- A patent contains detailed information of the developed technology and is published to the public. Thus, patents can be used to overcome the limitations of traditional technology trend research and prediction techniques. Recently, due to the advantages of patented analytical methodology, IP R&D is carried out worldwide. The patent is big data and has a huge amount, various domains, and structured and unstructured data characteristics. For this reason, there are many difficulties in collecting and researching patent information. Patent research generally writes the Search formula to collect patent documents from DB. The collected patent documents contain some noise patents that are irrelevant to the purpose of analysis, so they are removed. However, eliminating noise patents is a manual task of reading and classifying technology, which is time consuming and expensive. In this study, we propose a model that automatically classifies The Noise patent for efficient patent information research. The proposed method performs Patent Embedding using Word2Vec and generates Noise seed label. In addition, noise patent classification is performed using the Random forest. The experimental data is published and registered with the USPTO among the patents related to Ocean Surveillance & Tracking Network technology. As a result of experimenting with the proposed model, it showed 73% accuracy with the label actually given by experts.
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Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
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