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    • 1. 发明专利
    • Verifying human interaction to computer entity by way of trusted component on computing device
    • 通过计算机设备上的有害元件的方式验证人机对计算机实体的相互作用
    • JP2006005921A
    • 2006-01-05
    • JP2005161388
    • 2005-06-01
    • Microsoft Corpマイクロソフト コーポレーション
    • MEEK CHRISTOPHER AHECKERMAN DAVID EARLBENALOH JOSH DGOODMAN JOSHUA THEODOREPEINADO MARCUS
    • H04L9/32G06F1/00G06F12/06G06F21/00
    • G06F21/31
    • PROBLEM TO BE SOLVED: To describe user interaction in combination with a fact sending a send item from an application of a computing device to a recipient. SOLUTION: The computing device has an attestation unit thereon for attesting to trustworthiness. The application supports a user in constructing the send item, and predetermined indicia are monitored that can be employed to detect that the user is in fact expending effort to construct the send item. The attestation unit authenticates the application to impart trust thereto, and when the user commands the application to send, a send attestation is constructed to accompany the send item. The send attestation is based on the monitored indicia and the authentication of the application and thereby the user interaction is described. The constructed send attestation is packaged with the constructed send item and the package is sent to the recipient. COPYRIGHT: (C)2006,JPO&NCIPI
    • 要解决的问题:结合从计算设备的应用向接收者发送发送项目的事实来描述用户交互。

      解决方案:计算设备在其上具有证明可信赖性的认证单元。 应用程序支持用户构建发送项目,并且监视可用于检测用户实际上花费构建发送项目的努力的预定标记。 认证单元对应用进行认证以赋予其信任,并且当用户命令应用发送时,构建发送认证以伴随发送项目。 发送证明是基于被监视的标记和应用的认证,从而描述了用户交互。 构建的发送证明与构建的发送项目一起打包,并将包发送给收件人。 版权所有(C)2006,JPO&NCIPI

    • 3. 发明申请
    • MIXTURES OF BAYESIAN NETWORKS
    • BAYESIAN网络的混合
    • WO9928832A2
    • 1999-06-10
    • PCT/US9825535
    • 1998-12-03
    • MICROSOFT CORP
    • THIESSON BOMEEK CHRISTOPHER ACHICKERING DAVID MAXWELLHECKERMAN DAVID EARL
    • G06N5/02G06F17/00
    • G06K9/6296G06N5/025Y10S707/99945Y10S707/99948
    • One aspect of the invention is the construction of mixtures of Bayesian networks. Another aspect of the invention is the use of such mixtures of Bayesian networks to perform inferencing. A mixture of Bayesian networks (MBN) consists of plural hypothesis-specific Bayesian networks (HSBNs) having possibly hidden and observed variables. A common external hidden variable is associated with the MBN, but is not included in any of the HSBNs. The number of HSBNs in the MBN corresponds to the number of states of the common external hidden variable, and each HSBN is based upon the hypothesis that the common external hidden variable is in a corresponding one of those states. In one mode of the invention, the MBN having the highest MBN score is selected for use in performing inferencing. In another mode of the invention, some or all of the MBNs are retained as a collection of MBNs which perform inferencing in parallel, their outputs being weighted in accordance with the corresponding MBN scores and the MBN collection output being the weighted sum of all the MBN outputs. In one application of the invention, collaborative filtering may be performed by defining the observed variables to be choices made among a sample of users and the hidden variables to be the preferences of those users.
    • 本发明的一个方面是构建贝叶斯网络的混合物。 本发明的另一方面是使用贝叶斯网络的这种混合来执行推理。 贝叶斯网络(MBN)的混合由多个具有隐藏和观察变量的假设特定贝叶斯网络(HSBN)组成。 常见的外部隐藏变量与MBN相关联,但不包括在任何HSBN中。 MBN中的HSBN的数量对应于公共外部隐藏变量的状态数,并且每个HSBN基于公共外部隐藏变量在这些状态中的相应一个状态中的假设。 在本发明的一种模式中,选择具有最高MBN分数的MBN用于执行推定。 在本发明的另一模式中,一些或所有MBN被保留为并行执行推论的MBN的集合,其输出根据相应的MBN分数加权,并且MBN收集输出是所有MBN的加权和 输出。 在本发明的一个应用中,可以通过将观察到的变量定义为在用户样本中作出的选择和作为这些用户的偏好的隐藏变量来执行协同过滤。