Patent search and analysis supporting technology innovation

Bin LIU; Ling FENG; Fei WANG; Zhi-yong PENG

doi:10.11959/j.issn.1000-436x.2016055

您当前的位置：

首页 >

文章列表页 >

Patent search and analysis supporting technology innovation

Academic paper | 更新时间：2024-06-05

- Patent search and analysis supporting technology innovation
- Journal on Communications Vol. 37, Issue 3, Pages: 79-89(2016)
- 作者机构：
  
  1. 武汉大学计算机学院，湖北武汉430072
  2. 武汉大学软件工程国家重点实验室，湖北武汉430072
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61232002);The Science and Technology Support Pro-gram of Hubei Province(2015BAA127);The Wuhan Innovation Team Project(2014070504020237)
- DOI：10.11959/j.issn.1000-436x.2016055
  CLC： TP391.1
- Online First：2016-03，
  
  Published：25 March 2016
- 稿件说明：
移动端阅览
Bin LIU, Ling FENG, Fei WANG, et al. Patent search and analysis supporting technology innovation[J]. Journal on Communications, 2016, 37(3): 79-89.
DOI：

Bin LIU, Ling FENG, Fei WANG, et al. Patent search and analysis supporting technology innovation[J]. Journal on Communications, 2016, 37(3): 79-89. DOI： 10.11959/j.issn.1000-436x.2016055.

摘要

介绍了目前专利检索和分析的主要研究工作，包括专利的可检索性、技术现状检索和相关性检索方法等，以及专利地图分析、新颖度分析和PatentDom专利分析框架等分析方法。最后基于深度学习的思想，讨论了新一代的支持技术创新的专利检索方法、专利论文检索方法以及专利趋势分析方法。

Abstract

The main research work of patent search and analysis were summarizes.The patent search includes patentabil-ity search

prior art search

and query expansion.And the patent analysis includes patent map

novelty analysis

and a new analysis framework named PatentDom.Finally

based on the idea of deep learning

three new methods of patent search and analysis are put forward.

关键词

Keywords

references

State Intellectual Property Office of PRC.2014 key IP5 statistical data [EB/OL ] . http://www.sipo.gov.cn/tjxx/wjndbg/201507/P020150707534432342721.pdf http://www.sipo.gov.cn/tjxx/wjndbg/201507/P020150707534432342721.pdf .

State Intellectual Property Office of PRC.2013 key IP5 statistical data [EB/OL ] . http://www.sipo.gov.cn/tjxx/wjndbg/201509/P020150901583608432123.pdf http://www.sipo.gov.cn/tjxx/wjndbg/201509/P020150901583608432123.pdf .

State Intellectual Property Office of PRC.2012 key IP5 statistical data [EB/OL ] . http://www.sipo.gov.cn/tjxx/2012tjbgen.pdf http://www.sipo.gov.cn/tjxx/2012tjbgen.pdf .

CHEN C . Searching for intellectual turning points:progressive know-ledge domain visualization [J ] . PNAS , 2004 , 1011 ( Suppll ): 5303 - 5310 .

ERDI P , MAKOVI M , SOMOGYVARI Z , et al . Prediction of Emerg-ing technologies based on analysis of the US patent citation network [J ] . Scientometrics , 2013 , 95 ( l ): 225 - 242 .

FUJII A , ISHIKAWA T . NTCIR-3 patent retrieval experiments at ULIS [C ] // NII Test Collection for IR Systems-3 . c 2002 : 1 - 6 .

FUJII A , ISHIKAWA T , KANDO N . Test collections for ptent-to-ptent rtrieval and ptent map generation in NTCIR-4 workshop [C ] // The 4th International Conference on Language Resources and Eva uation . c 2004 : 1643 - 1646 .

FUJII A , ISHIKAWA T , KANDO N . Overview of the patent retrieval task at the NTCIR-6 workshop [C ] // NII Test Collection for IR Sys-tems-6 . Tokyo,Japan , c 2007 : 359 - 365 .

WU S , SUN J , TANG J . Patent partner recommendation in enterprise social networks [C ] // WSDM . Rome,Italy , c 2013 : 43 - 52 .

JIN X , SPANGLER S , CHEN Y , et al . Patent maintenance recom-mendation with patent information network model [C ] // ICDM . Vancouver,Canada , c 2011 : 280 - 289 .

MANNING C , RAGAVAN P , SCHUTZE H . An introduction to in-formation retrieval [M ] . London : Cambridge University Press , 2009 .

MAGDY W , JONES G . PRES:a score metric for evaluating re-call-oriented information retrieval applications [C ] // SIGIR . Geneva,Switzerland , c 2010 : 611 - 618 .

HIDEO I , HIROKO M , YASUSHI O . Term distillation in patent re-trieval [C ] // The ACL-2003 Workshop on Patent Corpus . c 2013 : 41 - 45 .

VERBERNE S , HONDT E D . Prior art retrieval using the claims section as a bag of words [C ] // The Cross-language Evaluation Forum Conference on Multilingual Information Access Evaluation:Text Re-trieval Experiments . c 2009 : 497 - 501 .

VARMA M , VARMA V . Applying key phrase extraction to aid inva-lidity search [C ] // International Conference on Artificial Intelligence and Law . Pittsburgh,PA , c 2011 : 249 - 255 .

KONISHI K . Query terms extraction from patent document for inva-lidity search [C ] // NTCIR-5 Workshop Meeting . Tokyo,Japan , c 2005 .

MAHDABI P , ANDERSSON L , Keikha M , et al . Automatic refine-ment of patent queries using concept importance predictors [C ] // SIGIR . Portland,USA , c 2012 : 505 - 514 .

TAKAKI T , FUJII A , ISHIKAWA T . Associative document retrieval by query subtopic analysis and its application to inva idity patent search [C ] // CIKM , Washington,USA , c 2004 : 399 - 405 .

ADAMS S . Comparing the IPC and the US classification systems for the patent searcher [J ] . World Patent Information , 2001 , 23 ( l ): 15 - 23 .

MAHDABI P , GERANI S , HUANG J X , et al . Leveraging conceptual lexicon:query disambiguation using proximity informat n for patent retrieval [C ] // SIGIR . Dublin,Ireland , c 2013 : 113 - 122 .

GANGULY D , LEVELING L , MAGDY W , et al . Patent query reduc-tion based on pseudo-relevant documents [C ] // CIKM . Glasgow,Scot-land,UK , c 2011 : 1953 - 1956 .

MAGDY W , JONES G . A study on query expansion methods for pa-tent retrieval [C ] // PAIR . c 2011 : 19 - 24 .

KRESTEL R , SMYTH P . Recommending patents based on latent topics [C ] // Recommender Systems . c 2013 : 395 - 398 .

MAHDABI P , CRESTANI F . Query-driven mining of citation net-works for patent citation retrieval and recommendation [C ] // CIKM . Shanghai,China , c 2014 : 1659 - 1668 .

HIRONORI D , YOHEI S , et al . A patent retrieval method using a hierarchy of clusters at TUT [C ] // NTCIR-5 Workshop Meeting . Tokyo,Japan , c 2006 .

BHATIA S , HE B , HE Q , et al . A scalable approach for performing proximal search for verbose patent search queries [C ] // CIKM . Maui,HI,USA , c 2012 : 2603 - 2606 .

BASHIR S , AUBER A . Analyzing document retrievability in patent retrieval settings [C ] // DEXA . c 2009 : 753 - 760 .

BASHIR S , AUBER A . Improving retrievability of patents in prior-art search [C ] // ECIR . Dublin,Ireland , c 2010 : 457 - 450 .

SOOYOUNG O , ZHEN L , LEE W C , et al . CV-PCR:a context-guided value-driven framework for patent citation recommendation [C ] // CIKM . San Francisco,CA,USA , c 2013 : 2291 - 2296 .

HUANG W , KATARIA S , CARAGEA C , et al . Recommending cita-tions:translating papers into references [C ] // CIKM . Maui,HI,USA , c 2012 : 1910 - 1914 .

XUE X , CROFT W . Automatic query generation for patent search [C ] // CIKM . Hong Kong,China , c 2009 : 2037 - 2040 .

JUN S H , PARK S , SIK J D . Technology forecasting using matrix map and patent clustering [J ] // Industrial Management ＆ Data Systems . 2012 , 112 ( 5 ): 786 - 806 .

CHEN X , PENG Z , ZENG C . A co-training based method for chinese patent semantic annotation [C ] // CIKM . Maui,HI,USA , c 2012 : 2379 - 2382 .

LIU D , PENG Z , LIU B . Technology effect phrase extraction in Chi-nese patent abstracts [C ] // APWeb . Changsha,China , c 2014 : 141 - 152 .

DRAZIC M , KUKOLJ D , VITAS M , et al . Technology matchin of the patent documents using clustering algorithms [C ] // The 14th IEEE International Symposium on Computational Intelligence and Infor-matics . c 2013 : 405 - 408 .

HASAN M A , SPANGLER S , GRIFFIN T , et al . COA:finding vel patents through text analysis [C ] // SIGKDD . Paris,France , c 2009 : 1175 - 1184 .

ZHANG L H , LI L , LI T , et al . PatentLine:analyzing technology evolution on multi-view patent graphs [C ] // SIGIR . Boston,Massachu-setts,USA , c 2014 : 1095 - 1098 .

ZHANG L H , LI L , LI T , et al . PatentDom:analyzing patent relation-ships on multi-view patent graphs [C ] // CIKM . Shanghai,China , c 2014 : 1369 - 1378 .

TADURI S , YU H , LAU G , et al . Developing a comprehensive patent related information retrieval tool [J ] . Journal of Theoretical and Ap-plied Electronic Commerce Research , 2001 , 6 ( 2 ): 1 - 16 .

BNEGIO Y , DUCHARME R , VINCENT P , et al . A neural probabilis-tic language model [J ] . Journal of Machine Learning Research , c 2009 : 1137 - 1155 .

WANG M X , LU Z D , LI H , et al . GenCNN:a convolutional architec-ture for word sequence prediction [C ] // ACL . c 2015 .

Views

4175

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Target visibility prediction method based on cost-sensitive CNN-BiLSTM network

Multi-modal semantics aided mmWave beam prediction method in industrial haze environment

Robust RF fingerprint identification under temporal domain shifts via confidence discrepancy and entropy minimization

Malware detection method based on API temporal relationship graph

Research on multi-dimensional resource dynamic allocation algorithm for UAV assisted 6G air-ground integrated network

Related Author

Liao Xi

Lin Ruijia

Zheng Xiangquan

Wen Kai

Xie Xin

Yang Dihan

Wang Yifan

Wang Heng

Related Institution

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications

PLA Unit

Key Laboratory of Industrial Internet of Things and Networked Control, Chongqing University of Posts and Telecommunications

College of Telecommunications & Information Engineering, Nanjing University of Posts and Telecommunications

Department of Information and Computer Science, Keio University, Yokohama

AI问答

⁰