{"title":"潜在客户驱动的技术推荐","authors":"Qi He, W. Spangler, Bin He, Ying Chen, Linda Kato","doi":"10.1109/SRII.2012.23","DOIUrl":null,"url":null,"abstract":"Helping locate the patents of the right technologies for licensing to prospective clients is more than one billion USD business annually to IBM. However, searching for right technologies from multiple massive data sources for a value presentation to customers is a typical human labor intensive task in the past. In this paper, we design a prospective client driven technology recommendation system to enable the automatic search of technologies for patent licensing. The idea has been to make use of knowledges from the large-scale encyclopedia Wikipedia in conjunction with 11 millions patent documents to develop an online technology recommendation system for prospective clients of IBM. The live system demands not only the fast response time but also a set of highly relevant patent documents which are technically interesting to a query prospective client.","PeriodicalId":110778,"journal":{"name":"2012 Annual SRII Global Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Prospective Client Driven Technology Recommendation\",\"authors\":\"Qi He, W. Spangler, Bin He, Ying Chen, Linda Kato\",\"doi\":\"10.1109/SRII.2012.23\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Helping locate the patents of the right technologies for licensing to prospective clients is more than one billion USD business annually to IBM. However, searching for right technologies from multiple massive data sources for a value presentation to customers is a typical human labor intensive task in the past. In this paper, we design a prospective client driven technology recommendation system to enable the automatic search of technologies for patent licensing. The idea has been to make use of knowledges from the large-scale encyclopedia Wikipedia in conjunction with 11 millions patent documents to develop an online technology recommendation system for prospective clients of IBM. The live system demands not only the fast response time but also a set of highly relevant patent documents which are technically interesting to a query prospective client.\",\"PeriodicalId\":110778,\"journal\":{\"name\":\"2012 Annual SRII Global Conference\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Annual SRII Global Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SRII.2012.23\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Annual SRII Global Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SRII.2012.23","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Helping locate the patents of the right technologies for licensing to prospective clients is more than one billion USD business annually to IBM. However, searching for right technologies from multiple massive data sources for a value presentation to customers is a typical human labor intensive task in the past. In this paper, we design a prospective client driven technology recommendation system to enable the automatic search of technologies for patent licensing. The idea has been to make use of knowledges from the large-scale encyclopedia Wikipedia in conjunction with 11 millions patent documents to develop an online technology recommendation system for prospective clients of IBM. The live system demands not only the fast response time but also a set of highly relevant patent documents which are technically interesting to a query prospective client.
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