{"created":"2025-01-18T23:36:10.600437+00:00","updated":"2025-01-20T06:51:14.824298+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00082250","sets":["581:6644:6781"]},"path":["6781"],"owner":"11","recid":"82250","title":["Modeling Patent Quality: A System for Large-scale Patentability Analysis using Text Mining"],"pubdate":{"attribute_name":"公開日","attribute_value":"2012-05-15"},"_buckets":{"deposit":"27fe9c93-aba5-4807-ace0-638bee678a6b"},"_deposit":{"id":"82250","pid":{"type":"depid","value":"82250","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"Modeling Patent Quality: A System for Large-scale Patentability Analysis using Text Mining","author_link":["357993","358012","358008","357991","358006","358009","358000","357990","358001","358005","357998","357992","358011","358004","358007","357995","357997","358003","357994","358010","357989","357999","357996","358002"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Modeling Patent Quality: A System for Large-scale Patentability Analysis using Text Mining"},{"subitem_title":"Modeling Patent Quality: A System for Large-scale Patentability Analysis using Text Mining","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"[Special Issue on Theory and Application of Intelligent Information Technology] patent quality index, patentability, document classification","subitem_subject_scheme":"Other"}]},"item_type_id":"2","publish_date":"2012-05-15","item_2_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Analytics & Intelligence, IBM Research - Tokyo/Presently with Preferred Infrastructure, Inc."},{"subitem_text_value":"Analytics & Intelligence, IBM Research - Tokyo"},{"subitem_text_value":"Analytics & Intelligence, IBM Research - Tokyo"},{"subitem_text_value":"Analytics & Intelligence, IBM Research - Tokyo"},{"subitem_text_value":"Analytics & Intelligence, IBM Research - Tokyo"},{"subitem_text_value":"Analytics & Intelligence, IBM Research - Tokyo"},{"subitem_text_value":"Analytics & Intelligence, IBM Research - Tokyo"},{"subitem_text_value":"IP Law Department, IBM Japan"},{"subitem_text_value":"IP Law Department, IBM Japan"},{"subitem_text_value":"IP Law Department, IBM Japan"},{"subitem_text_value":"Analytics & Intelligence, IBM Research - Tokyo/Presently with Yahoo! Japan"},{"subitem_text_value":"Research Center for Advanced Science and Technology, The University of Tokyo"}]},"item_2_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Analytics & Intelligence, IBM Research - Tokyo / Presently with Preferred Infrastructure, Inc.","subitem_text_language":"en"},{"subitem_text_value":"Analytics & Intelligence, IBM Research - Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Analytics & Intelligence, IBM Research - Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Analytics & Intelligence, IBM Research - Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Analytics & Intelligence, IBM Research - Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Analytics & Intelligence, IBM Research - Tokyo","subitem_text_language":"en"},{"subitem_text_value":"Analytics & Intelligence, IBM Research - Tokyo","subitem_text_language":"en"},{"subitem_text_value":"IP Law Department, IBM Japan","subitem_text_language":"en"},{"subitem_text_value":"IP Law Department, IBM Japan","subitem_text_language":"en"},{"subitem_text_value":"IP Law Department, IBM Japan","subitem_text_language":"en"},{"subitem_text_value":"Analytics & Intelligence, IBM Research - Tokyo / Presently with Yahoo! Japan","subitem_text_language":"en"},{"subitem_text_value":"Research Center for Advanced Science and Technology, The University of Tokyo","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"publish_status":"0","weko_shared_id":11,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/82250/files/IPSJ-JNL5305018.pdf","label":"IPSJ-JNL5305018"},"date":[{"dateType":"Available","dateValue":"2014-05-15"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-JNL5305018.pdf","filesize":[{"value":"900.9 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"6aea6f3b-33d9-4388-aa62-5114860c9a22","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2012 by the Information Processing Society of Japan"}]},"item_2_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Shohei, Hido"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shoko, Suzuki"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Risa, Nishiyama"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takashi, Imamichi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Rikiya, Takahashi"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tetsuya, Nasukawa"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tsuyoshi, Idé"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yusuke, Kanehira"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Rinju, Yohda"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takeshi, Ueno"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Akira, Tajima"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Toshiya, Watanabe"}],"nameIdentifiers":[{}]}]},"item_2_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Shohei, Hido","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shoko, Suzuki","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Risa, Nishiyama","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takashi, Imamichi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Rikiya, Takahashi","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tetsuya, Nasukawa","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tsuyoshi, Idé","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yusuke, Kanehira","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Rinju, Yohda","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Takeshi, Ueno","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Akira, Tajima","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Toshiya, Watanabe","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_2_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00116647","subitem_source_identifier_type":"NCID"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_6501","resourcetype":"journal article"}]},"item_2_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1882-7764","subitem_source_identifier_type":"ISSN"}]},"item_2_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Current patent systems face a serious problem of declining quality of patents as the larger number of applications make it difficult for patent officers to spend enough time for evaluating each application. For building a better patent system, it is necessary to define a public consensus on the quality of patent applications in a quantitative way. In this article, we tackle the problem of assessing the quality of patent applications based on machine learning and text mining techniques. For each patent application, our tool automatically computes a score called patentability, which indicates how likely it is that the application will be approved by the patent office. We employ a new statistical prediction model to estimate examination results (approval or rejection) based on a large data set including 0.3 million patent applications. The model computes the patentability score based on a set of feature variables including the text contents of the specification documents. Experimental results showed that our model outperforms a conventional method which uses only the structural properties of the documents. Since users can access the estimated result through a Web-browser-based GUI, this system allows both patent examiners and applicants to quickly detect weak applications and to find their specific flaws.\n\n------------------------------ \nThis is a preprint of an article intended for publication Journal of \nInformation Processing(JIP). This preprint should not be cited. This \narticle should be cited as: Journal of Information Processing Vol.20(2012) No.3 (online) \nDOI http://dx.doi.org/10.2197/ipsjjip.20.655\n------------------------------ \n","subitem_description_type":"Other"}]},"item_2_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Current patent systems face a serious problem of declining quality of patents as the larger number of applications make it difficult for patent officers to spend enough time for evaluating each application. For building a better patent system, it is necessary to define a public consensus on the quality of patent applications in a quantitative way. In this article, we tackle the problem of assessing the quality of patent applications based on machine learning and text mining techniques. For each patent application, our tool automatically computes a score called patentability, which indicates how likely it is that the application will be approved by the patent office. We employ a new statistical prediction model to estimate examination results (approval or rejection) based on a large data set including 0.3 million patent applications. The model computes the patentability score based on a set of feature variables including the text contents of the specification documents. Experimental results showed that our model outperforms a conventional method which uses only the structural properties of the documents. Since users can access the estimated result through a Web-browser-based GUI, this system allows both patent examiners and applicants to quickly detect weak applications and to find their specific flaws.\n\n------------------------------ \nThis is a preprint of an article intended for publication Journal of \nInformation Processing(JIP). This preprint should not be cited. This \narticle should be cited as: Journal of Information Processing Vol.20(2012) No.3 (online) \nDOI http://dx.doi.org/10.2197/ipsjjip.20.655\n------------------------------ \n","subitem_description_type":"Other"}]},"item_2_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographic_titles":[{"bibliographic_title":"情報処理学会論文誌"}],"bibliographicIssueDates":{"bibliographicIssueDate":"2012-05-15","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"5","bibliographicVolumeNumber":"53"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":82250,"links":{}}