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15 条记录 (耗时 0.019 秒)

    DELIVERY OF CONTEXTUALLY RELEVANT WEB DATA
    11.
    发明申请
    DELIVERY OF CONTEXTUALLY RELEVANT WEB DATA 审中-公开
    交付相关的WEB数据

    公开(公告)号：US20080195954A1

    公开(公告)日：2008-08-14

    申请号：US11673125

    申请日：2007-02-09

    申请人： Baskaran Dharmarajan ,  Dennis Takchi Cheung ,  Eliot Spencer Savarese ,  Mukund Narasimhan ,  Imran Khan ,  Denise K. Ho

    发明人： Baskaran Dharmarajan ,  Dennis Takchi Cheung ,  Eliot Spencer Savarese ,  Mukund Narasimhan ,  Imran Khan ,  Denise K. Ho

    IPC分类号： G06F3/00

    CPC分类号： G06F3/0482 ,  G06F16/9535

    摘要： A web-browser plug-in is described herein that detects the type of content a user selects on a web page and allows the user to retrieve additional information about selected web content or initiate a communication application. The plug-in analyzes the user's selection to determine what type of web content was selected. A smart menu is created and presented to the user with options relating to the type of web content selected. The user can then either download additional information about the web content or initiate a communication application without having to navigate to another web page or request information from a web service. Without having to navigate to a second web page, the user can select an option and either view the additional web information or initiate the communication application.

    摘要翻译： 本文描述了一种网络浏览器插件，其检测用户在网页上选择的内容的类型，并允许用户检索关于所选web内容的附加信息或发起通信应用。 插件分析用户的选择以确定选择了哪种类型的网页内容。 创建智能菜单并向用户呈现与所选择的网络内容的类型相关的选项。 然后，用户可以下载关于web内容的附加信息或发起通信应用，而不必导航到另一个网页或从Web服务请求信息。 无需浏览到第二个网页，用户可以选择一个选项，并查看附加的网页信息或启动通信应用程序。

    Metadata Prediction of Objects in a Social Networking System Using Crowd Sourcing
    14.
    发明申请
    Metadata Prediction of Objects in a Social Networking System Using Crowd Sourcing 有权
    使用人群采购的社交网络系统中对象的元数据预测

    公开(公告)号：US20130151612A1

    公开(公告)日：2013-06-13

    申请号：US13324776

    申请日：2011-12-13

    申请人： Jeffrey Scott Dunn ,  Robert William Cathcart ,  Cameron Alexander Marlow ,  Mukund Narasimhan ,  Yuankai Ge ,  Venkataramanan Iyer Nandagopal ,  Chad Little

    发明人： Jeffrey Scott Dunn ,  Robert William Cathcart ,  Cameron Alexander Marlow ,  Mukund Narasimhan ,  Yuankai Ge ,  Venkataramanan Iyer Nandagopal ,  Chad Little

    IPC分类号： G06F15/16

    CPC分类号： H04L67/306 ,  G06Q10/00 ,  H04L67/22

    摘要： A social networking system leverages user's social information to evaluate content submitted for inclusion in objects. If the evaluated submission is accepted, the submission is added to the content of an object. Accepted submissions are also used to predict associations between metadata and objects. Metadata is used to predict which objects will match user searches for information. The social networking system also provides a user interface configured to prompt users to submit information to objects. When a user completes a submission to an object, the user is provided with other options for groups of objects to contribute to. The objects offered are chosen to increase the likelihood that the user will choose to provide submissions to one of the provided objects.

    摘要翻译： 社交网络系统利用用户的社交信息来评估提交给对象的内容。 如果评估的提交被接受，则将提交添加到对象的内容。 接受的提交也用于预测元数据和对象之间的关联。 元数据用于预测哪些对象将匹配用户搜索的信息。 社交网络系统还提供用户界面，用于提示用户将信息提交给对象。 当用户完成对对象的提交时，向用户提供用于贡献的对象组的其他选项。 所提供的对象被选择以增加用户将选择向所提供的对象之一提供提交的可能性。

    Learning A* priority function from unlabeled data
    15.
    发明授权
    Learning A* priority function from unlabeled data 有权
    从未标记的数据学习A *优先级功能

    公开(公告)号：US07840503B2

    公开(公告)日：2010-11-23

    申请号：US11786006

    申请日：2007-04-10

    申请人： Mukund Narasimhan ,  Paul A. Viola ,  Gregory Druck

    发明人： Mukund Narasimhan ,  Paul A. Viola ,  Gregory Druck

    IPC分类号： G06F15/18

    CPC分类号： G06K9/6297 ,  G06N99/005

    摘要： A technique for increasing efficiency of inference of structure variables (e.g., an inference problem) using a priority-driven algorithm rather than conventional dynamic programming. The technique employs a probable approximate underestimate which can be used to compute a probable approximate solution to the inference problem when used as a priority function (“a probable approximate underestimate function”) for a more computationally complex classification function. The probable approximate underestimate function can have a functional form of a simpler, easier to decode model. The model can be learned from unlabeled data by solving a linear/quadratic optimization problem. The priority function can be computed quickly, and can result in solutions that are substantially optimal. Using the priority function, computation efficiency of a classification function (e.g., discriminative classifier) can be increased using a generalization of the A* algorithm.

    摘要翻译： 一种使用优先级驱动算法而不是常规动态规划来提高结构变量推理效率（例如，推理问题）的技术。 该技术采用可能的近似低估，可用于计算对于更为计算复杂的分类函数的用作优先级函数（“可能的近似低估函数”）的推理问题的可能近似解。 可能的近似低估函数可以具有更简单，更容易解码模型的功能形式。 通过求解线性/二次优化问题，可以从未标记的数据中学习该模型。 可以快速计算优先级函数，并且可以产生基本上最优的解。 使用优先功能，可以使用A *算法的泛化来增加分类函数（例如，辨别分类器）的计算效率。