会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 2. 发明授权
    • Assisted clustering
    • 辅助聚类
    • US08359279B2
    • 2013-01-22
    • US12787945
    • 2010-05-26
    • Danyel A. FisherSumit BasuSteven DruckerGonzalo A. RamosHao Lu
    • Danyel A. FisherSumit BasuSteven DruckerGonzalo A. RamosHao Lu
    • G06N5/00
    • G06F17/30994G06Q30/0631
    • Assisted clustering systems and methods are described herein that provide a user interface by which a user can easily create clusters and selectively associate data items with such clusters. Information regarding data item-cluster associations made by the user is processed by a recommendation engine to learn a clustering model. The clustering model is then be used to generate recommendations for the user regarding which unassociated data items should be associated with which clusters. In certain embodiments, after the user has determined that the clustering model is performing at a satisfactory level based on the quality of the recommendations, the user can cause the system to automatically cluster a large quantity of remaining unassociated data items. In accordance with further embodiments, a user can specify arbitrary data item types for clustering as well as features of such data types that should be considered in generating the clustering model.
    • 本文描述了辅助集群系统和方法,其提供用户界面,通过该界面,用户可以容易地创建集群并且选择性地将数据项与这些集群相关联。 关于由用户进行的数据项集群关联的信息由推荐引擎处理以学习聚类模型。 然后,聚类模型用于为用户生成与哪些不相关的数据项应与哪个集群相关联的建议。 在某些实施例中,在用户基于建议的质量确定聚类模型正在令人满意的级别之后,用户可以使系统自动聚集大量剩余的未关联的数据项。 根据进一步的实施例,用户可以指定用于聚类的任意数据项类型以及在生成聚类模型时应考虑的这些数据类型的特征。
    • 4. 发明申请
    • ASSISTED CLUSTERING
    • 辅助集群
    • US20110295773A1
    • 2011-12-01
    • US12787945
    • 2010-05-26
    • Danyel A. FisherSumit BasuSteven DruckerGonzalo A. RamosHao Lu
    • Danyel A. FisherSumit BasuSteven DruckerGonzalo A. RamosHao Lu
    • G06F15/18G06F17/30
    • G06F17/30994G06Q30/0631
    • Assisted clustering systems and methods are described herein that provide a user interface by which a user can easily create clusters and selectively associate data items with such clusters. Information regarding data item-cluster associations made by the user is processed by a recommendation engine to learn a clustering model. The clustering model is then be used to generate recommendations for the user regarding which unassociated data items should be associated with which clusters. In certain embodiments, after the user has determined that the clustering model is performing at a satisfactory level based on the quality of the recommendations, the user can cause the system to automatically cluster a large quantity of remaining unassociated data items. In accordance with further embodiments, a user can specify arbitrary data item types for clustering as well as features of such data types that should be considered in generating the clustering model.
    • 本文描述了辅助集群系统和方法,其提供用户界面,通过该界面,用户可以容易地创建集群并且选择性地将数据项与这些集群相关联。 关于由用户进行的数据项集群关联的信息由推荐引擎处理以学习聚类模型。 然后,聚类模型用于为用户生成与哪些不相关的数据项应与哪个集群相关联的建议。 在某些实施例中,在用户基于建议的质量确定聚类模型正在令人满意的级别之后,用户可以使系统自动聚集大量剩余的未关联的数据项。 根据进一步的实施例,用户可以指定用于聚类的任意数据项类型以及在生成聚类模型时应考虑的这些数据类型的特征。
    • 8. 发明申请
    • POPULARITY BASED GEOGRAPHICAL NAVIGATION
    • 基于人气的地理导航
    • US20080086264A1
    • 2008-04-10
    • US11539184
    • 2006-10-06
    • Danyel A. Fisher
    • Danyel A. Fisher
    • G01C21/30
    • G01C21/30
    • Typically, users click or otherwise show interest in a particular geographic region on a map to indicate their desire to center and/or zoom in on that location. However, it is more likely that a user has a higher interest in popular points of interest in a region than with a point of selection on a map. By employing popularity map data, the map navigation can be biased towards popular points of interest relative to a user's selection. This allows users to more effectively navigate through maps, rather than through trial and error selections based solely on geography. Some instances utilize weighting of popularity points to determine an offset distance from a user's selection. Other instances can employ a most popular point nearest a user's selection as a bias point. Included popularity points for biasing can be determined based on a percentage of a visualized map image presented to a user.
    • 通常,用户在地图上点击或以其他方式显示特定地理区域的兴趣,以指示他们希望在该位置居中和/或放大。 然而,用户对区域中的受欢迎兴趣点的兴趣比在地图上的选择点更有可能。 通过使用人气地图数据,相对于用户的选择,地图导航可以偏向流行的兴趣点。 这允许用户更有效地浏览地图,而不是通过仅基于地理位置的试用和错误选择。 一些实例利用人气点的加权来确定与用户选择的偏移距离。 其他实例可以使用最接近用户选择的最流行点作为偏见点。 可以基于呈现给用户的可视化地图图像的百分比来确定包括偏好的人气点。