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    • 1. 发明授权
    • Content object indexing using domain knowledge
    • 使用领域知识的内容对象索引
    • US07698294B2
    • 2010-04-13
    • US11275509
    • 2006-01-11
    • Wei-Ying MaLie LuJi-Rong WenZhiwei LiZaiqing NieHsiao-Wuen Hon
    • Wei-Ying MaLie LuJi-Rong WenZhiwei LiZaiqing NieHsiao-Wuen Hon
    • G06F17/30
    • G06F17/30613
    • A content object indexing process including creating a content object knowledge index, calculating a description vector of a target content object, and indexing the target content object by searching for the description vector in the content object knowledge database. It may be difficult to search for an exact content object such as a music file or academic researcher as a conventional search index may not include related hierarchical information. A content object indexing process may add hierarchical information taken from a content object knowledge index and incorporate the hierarchical information to the index entry for a specific content object. An application of such a content object indexing process may be a world wide web search engine.
    • 内容对象索引处理包括创建内容对象知识索引,计算目标内容对象的描述向量,并通过搜索内容对象知识库中的描述向量来索引目标内容对象。 可能难以搜索诸如音乐文件或学术研究者的确切内容对象,因为传统的搜索索引可能不包括相关的分层信息。 内容对象索引处理可以添加从内容对象知识索引获取的分层信息,并且将分层信息并入特定内容对象的索引条目。 这样的内容对象索引处理的应用可以是万维网搜索引擎。
    • 2. 发明申请
    • Content Object Indexing Using Domain Knowledge
    • 使用域知识的内容对象索引
    • US20070162408A1
    • 2007-07-12
    • US11275509
    • 2006-01-11
    • Wei-Ying MaLie LuJi-Rong WenZhiwei LiZaiqing NieHsiao-Wuen Hon
    • Wei-Ying MaLie LuJi-Rong WenZhiwei LiZaiqing NieHsiao-Wuen Hon
    • G06N5/02
    • G06F17/30613
    • A content object indexing process including creating a content object knowledge index, calculating a description vector of a target content object, and indexing the target content object by searching for the description vector in the content object knowledge database. It may be difficult to search for an exact content object such as a music file or academic researcher as a conventional search index may not include related hierarchical information. A content object indexing process may add hierarchical information taken from a content object knowledge index and incorporate the hierarchical information to the index entry for a specific content object. An application of such a content object indexing process may be a world wide web search engine.
    • 内容对象索引处理包括创建内容对象知识索引,计算目标内容对象的描述向量,并通过搜索内容对象知识库中的描述向量来索引目标内容对象。 可能难以搜索诸如音乐文件或学术研究者的确切内容对象,因为传统的搜索索引可能不包括相关的分层信息。 内容对象索引处理可以添加从内容对象知识索引获取的分层信息,并且将分层信息并入特定内容对象的索引条目。 这样的内容对象索引处理的应用可以是万维网搜索引擎。
    • 4. 发明申请
    • Cost-Per-Action Model Based on Advertiser-Reported Actions
    • 基于广告商报告的动作的每次操作费用模型
    • US20130246167A1
    • 2013-09-19
    • US13421626
    • 2012-03-15
    • Tao QinTie-Yan LiuWenkui DingWei-Ying MaHsiao-Wuen Hon
    • Tao QinTie-Yan LiuWenkui DingWei-Ying MaHsiao-Wuen Hon
    • G06Q30/02
    • G06Q30/0256
    • According to a cost-per-action advertising model, advertisers submit ads with cost-per-action bids. Ad auctions are conducted and winning ads are returned with contextually relevant search results. Each time a winning ad is selected by a user, resulting in the user being redirected to a website associated with the advertiser, a selected impression and a price is recorded for the winning ad. Periodically, an advertiser submits a report indicating a number of actions attributed to the ads that have occurred through the advertiser website. The advertiser is then charged a fee for each reported action based on the recorded prices for the winning ads and based on the number of selected impressions recorded for the winning ads.
    • 根据每次操作费用广告模式,广告客户会按照每次操作费用出价提交广告。 进行广告拍卖,并返回具有内容相关搜索结果的获胜广告。 每当用户选择获胜广告时,导致用户被重定向到与广告商相关联的网站,则为获胜广告记录所选择的展示和价格。 定期地,广告客户会提交一份报告,指示通过广告客户网站发生的广告归因的一些操作。 然后,根据获胜广告的记录价格并根据为获胜广告记录的所选曝光次数,为每个报告的动作收取费用。
    • 9. 发明申请
    • AIR QUALITY INFERENCE USING MULTIPLE DATA SOURCES
    • 使用多个数据源的空气质量控制
    • US20160125307A1
    • 2016-05-05
    • US14896344
    • 2013-06-05
    • Yu ZHENGXing XIEWei-Ying MAHsiao-Wuen HONEric I-Chao CHANGMICROSOFT TECHNOLOGY LICENSING, LLC
    • Yu ZhengXing XieWei-Ying MaHsiao-Wuen HonEric I-Chao Chang
    • G06N7/00G06N3/08G06N99/00
    • G06N7/005G06N3/08G06N20/00
    • The use of data from multiple data source provides inferred air quality indices with respect to a particular pollutant for multiple areas without the addition of air quality monitor stations to those areas. Labeled air quality index data for a pollutant in a region may be obtained from one or more air quality monitor stations. Spatial features for the region may be extracted from spatially-related data for the region. The spatially-related data may include information on fixed infrastructures in the region. Likewise, temporal features for the region may be extracted from temporally-related data for the region that changes over time. A co-training based learning framework may be further applied to co-train a spatial classifier and a temporal classifier based at least on the labeled air quality index data, the spatial features for the region, and the temporal features for the region.
    • 使用多个数据源的数据可以为多个地区的特定污染物提供推测的空气质量指标,而无需向这些地区添加空气质量监测站。 可以从一个或多个空气质量监测站获得区域中污染物的标签空气质量指数数据。 该区域的空间特征可以从该区域的空间相关数据中提取。 与空间有关的数据可能包括有关该地区固定基础设施的信息。 类似地,可以从随时间变化的区域的时间相关数据中提取该区域的时间特征。 基于共同训练的学习框架可以进一步应用于至少基于标记的空气质量指数数据,该区域的空间特征和该区域的时间特征来共同训练空间分类器和时间分类器。
    • 10. 发明授权
    • Joint ranking model for multilingual web search
    • 多语言网络搜索的联合排名模型
    • US08326785B2
    • 2012-12-04
    • US12241078
    • 2008-09-30
    • Cheng NiuMing ZhouHsiao-Wuen Hon
    • Cheng NiuMing ZhouHsiao-Wuen Hon
    • G06F17/00G06F17/20G06F7/00G06F17/30G06N5/00
    • G06F17/30675
    • A classifier is built to rank documents of different languages found in a query based at least in part on similarity to other documents and the relevance of those other documents to the query. A joint ranking model, e.g., based upon a Boltzmann machine, is used to represent the content similarity among documents, and to help determine joint relevance probability for a set of documents. The relevant documents of one language are thus leveraged to improve the relevance estimation for documents of different languages. In one aspect, a hidden layer of units (neurons) represents clusters (corresponding to relevant topics) among the retrieved documents, with an output layer representing the relevant documents and their features, and edges representing a relationship between clusters and documents.
    • 构建分类器至少部分地基于与其他文档的相似性以及这些其他文档与查询的相关性来对查询中发现的不同语言的文档进行排序。 联合排名模型,例如基于玻尔兹曼(Boltzmann)机器,用于表示文档之间的内容相似性,并且帮助确定一组文档的联合相关概率。 因此,利用一种语言的相关文件来改进不同语言文件的相关性估计。 在一个方面,隐藏的单位(神经元)表示检索的文档中的集群(对应于相关主题),输出层表示相关文档及其特征,边缘表示集群和文档之间的关系。