NSL OpenIR  > 中国科学院成都文献情报中心  > 信息技术部
Automatic classification of patents oriented to TRIZ: a case study on large aperture optical elements
Hu ZY(胡正银); Fang S(方曙); Wen Y(文奕); Zhang X(张娴); Liang T(梁田)
Conference Name4th Global Tech Mining Conference
Source Publication4th Global Tech Mining Conference
Conference Date2014.9.2
Conference PlaceLeiden,Netherland
AbstractMost of the current available patent classification systems are too general to TRIZ users who pay more attention to find analogous patents in other fields that have solved the similar technical problems by using the same solutions. In this paper, we propose an approach to automatically classify patents oriented to TRIZ applications based on a personalized classification schema. Firstly, we construct a personalized classification schema in micro-meso-macro levels. Micro-level is composed of Subject-Action-Object (SAO) extracted from patent text, meso-level Problems solved and Solutions used (P&S) topics generated by Latent Dirichlet Allocation (LDA) based on SAO and macro-level technology domain generated by LDA based on P&S topics. Then, we choose an appropriate feature and classifier to preliminarily classify patents according to the personalized classification schema. Finally, the classifier is optimized by smoothing imbalanced data and reducing features dimensions of SAO. We evaluated the approach with Large Aperture Optical Elements (LAOE) patent documents set as a case study. The results of case study showed that this approach can classify patents with high accuracy and speed and facilitate TRIZ users to better utilize patents in medium size data set.
KeywordTopic Model Automatic Classification Sao Clumping Semantic Knowledge Representation Patent Analysis
Subject Area信息组织与服务 ; 信息内容分析 ; 知识组织 ; 自然语言处理 ; 情报研究理论与方法
Document Type会议论文
Recommended Citation
GB/T 7714
Hu ZY,Fang S,Wen Y,et al. Automatic classification of patents oriented to TRIZ: a case study on large aperture optical elements[C],2014.
Files in This Item: Download All
File Name/Size DocType Version Access License
gtm20140_submission_(107KB) 开放获取View Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Hu ZY(胡正银)]'s Articles
[Fang S(方曙)]'s Articles
[Wen Y(文奕)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Hu ZY(胡正银)]'s Articles
[Fang S(方曙)]'s Articles
[Wen Y(文奕)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Hu ZY(胡正银)]'s Articles
[Fang S(方曙)]'s Articles
[Wen Y(文奕)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: gtm20140_submission_13.docx
Format: Microsoft Word
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.