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    • 5. 发明申请
    • Clustered search processing
    • 集群搜索处理
    • US20080168052A1
    • 2008-07-10
    • US11651102
    • 2007-01-05
    • Edward Stanley OttKeith David SaftMarco BoerriesMeher TendjoukianPaul Yiu
    • Edward Stanley OttKeith David SaftMarco BoerriesMeher TendjoukianPaul Yiu
    • G06F17/30
    • G06F17/30864G06F17/30867Y10S707/99933
    • Methods and apparatus for searching data and grouping search results into clusters that are ordered according to search relevance. Each cluster comprises one or more data type, such as images, web pages, local information, news, advertisements, and the like. In one embodiment, a search term is evaluated for related concepts indicating categories of data sources to search. Data sources may also be identified by context information such as a location of a client device, a currently running application, and the like. Search results in each cluster are ordered by relevance and each cluster is given a score based on an aggregate of the relevance within the cluster. Each cluster score may be modified based on one or more corresponding concepts and/or context information. The clusters are ordered based on the modified scores. Content, including advertisements, may also be added to the ordered list to appear as another cluster.
    • 用于搜索数据并将搜索结果分组为根据搜索相关性排序的群集的方法和装置。 每个群集包括一个或多个数据类型,诸如图像,网页,本地信息,新闻,广告等。 在一个实施例中,针对指示要搜索的数据源的类别的相关概念评估搜索项。 数据源也可以通过诸如客户端设备的位置,当前正在运行的应用等的上下文信息来识别。 每个集群中的搜索结果按照相关性排序,并且每个集群都基于集群内相关性的总和给出分数。 可以基于一个或多个对应的概念和/或上下文信息来修改每个聚类分数。 集群根据修改的分数进行排序。 内容,包括广告,也可以添加到有序列表中,以显示为另一个群集。
    • 6. 发明申请
    • TALENT IDENTIFICATION SYSTEM AND METHOD
    • 人员识别系统和方法
    • US20080077568A1
    • 2008-03-27
    • US11535248
    • 2006-09-26
    • Edward Stanley Ott
    • Edward Stanley Ott
    • G06F17/30
    • G06F16/951
    • Systems and methods are disclosed for automatically identifying talent from quality and popularity data available on a computing network. The computing network is monitored and new content items and their associated publishers are identified. In addition, quality and popularity data associated with each content item are retrieved from one or more locations on the network. The quality and popularity data are then analyzed to identify popular content items within a particular scope and create a popularity measure of each content item. The popularity measure of each content item is then used to create a popularity measure of each publisher.
    • 公开了用于根据计算网络上可用的质量和流行度数据自动识别人才的系统和方法。 监视计算网络,并识别新的内容项目及其相关联的发布者。 此外,从网络上的一个或多个位置检索与每个内容项相关联的质量和流行度数据。 然后分析质量和流行度数据以识别特定范围内的流行内容项目,并创建每个内容项目的受欢迎程度。 然后,每个内容项目的受欢迎程度用于创建每个发布者的受欢迎程度。
    • 8. 发明申请
    • CLUSTERED SEARCH PROCESSING
    • 集群搜索处理
    • US20120102044A1
    • 2012-04-26
    • US13271198
    • 2011-10-11
    • Edward Stanley Ott, IVKeith David SaftMarco BoerriesMeher TendjoukianPaul Yiu
    • Edward Stanley Ott, IVKeith David SaftMarco BoerriesMeher TendjoukianPaul Yiu
    • G06F17/30
    • G06F17/30864G06F17/30867Y10S707/99933
    • Methods and apparatus for searching data and grouping search results into clusters that are ordered according to search relevance. Each cluster comprises one or more data type, such as images, web pages, local information, news, advertisements, and the like. In one embodiment, a search term is evaluated for related concepts indicating categories of data sources to search. Data sources may also be identified by context information such as a location of a client device, a currently running application, and the like. Search results in each cluster are ordered by relevance and each cluster is given a score based on an aggregate of the relevance within the cluster. Each cluster score may be modified based on one or more corresponding concepts and/or context information. The clusters are ordered based on the modified scores. Content, including advertisements, may also be added to the ordered list to appear as another cluster.
    • 用于搜索数据并将搜索结果分组为根据搜索相关性排序的群集的方法和装置。 每个群集包括一个或多个数据类型,诸如图像,网页,本地信息,新闻,广告等。 在一个实施例中,针对指示要搜索的数据源的类别的相关概念评估搜索项。 数据源也可以通过诸如客户端设备的位置,当前正在运行的应用等的上下文信息来识别。 每个集群中的搜索结果按照相关性排序,并且每个集群都基于集群内相关性的总和给出分数。 可以基于一个或多个对应的概念和/或上下文信息来修改每个聚类分数。 集群根据修改的分数进行排序。 内容,包括广告,也可以添加到有序列表中,以显示为另一个集群。