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    • 3. 发明申请
    • TECHNOLOGIES FOR A SEAMLESS DATA STREAMING EXPERIENCE
    • 无缝数据流的经验技术
    • US20160285938A1
    • 2016-09-29
    • US14670952
    • 2015-03-27
    • Tomer RiderIgor Tatourian
    • Tomer RiderIgor Tatourian
    • H04L29/06H04L29/08
    • Technologies for seamless data streaming include a control server and one or more client computing devices. A client computing device receives user presence data indicative of whether a user is nearby from one or more sensors. The client computing device may receive user interest data indicative of the user's interest level in the current data stream from one or more sensors. The control server identifies available client computing devices based on the user presence data, selects a target client computing device, and causes the data stream to transition from the current client computing device to the target client computing device. The target client computing device may be selected based on proximity of the user or the user's interest level in the data stream. The volume or balance of the data stream may be adjusted to provide a smooth transition between client computing devices. Other embodiments are described and claimed.
    • 用于无缝数据流的技术包括控制服务器和一个或多个客户端计算设备。 客户端计算设备从一个或多个传感器接收指示用户是否在附近的用户存在数据。 客户端计算设备可以从一个或多个传感器接收指示用户在当前数据流中的兴趣水平的用户兴趣数据。 控制服务器基于用户存在数据来识别可用的客户端计算设备,选择目标客户端计算设备,并使数据流从当前客户端计算设备转移到目标客户端计算设备。 可以基于用户的接近度或用户在数据流中的兴趣等级来选择目标客户端计算设备。 可以调整数据流的容量或平衡以提供客户端计算设备之间的平滑过渡。 描述和要求保护其他实施例。
    • 6. 发明申请
    • PROTECTION SYSTEM INCLUDING MACHINE LEARNING SNAPSHOT EVALUATION
    • 保护系统,包括机器学习快速评估
    • US20150178496A1
    • 2015-06-25
    • US14360333
    • 2013-12-19
    • Tobias M. KohlenbergIgor Tatourian
    • Tobias M. KohlenbergIgor Tatourian
    • G06F21/55G06N99/00
    • H04L63/1425G06F21/316G06F21/552G06F21/566G06N99/005H04L63/1416H04L63/1433
    • This disclosure is directed to a protection system including machine learning snapshot evaluation. A device may comprise a machine learning engine (MLE) to generate snapshots of device operation. The MLE may use active or planned operations in the snapshot to learn user behavior. Once normal user behavior is established for the device, the MLE may be able to determine when snapshots include unusual behavior that may signify a threat to the device. Snapshots determined to include unusual behavior may be transmitted to a remote resource for evaluation. The remote resource may include at least a user behavior classification engine (UBCE) to classify the user behavior by characterizing it as at least one type of use. The snapshot may be analyzed by the UBCE to determine if potential threats exist in the device, and the threat analysis may be provided to the device for evaluation and/or corrective action.
    • 本公开涉及包括机器学习快照评估的保护系统。 设备可以包括机器学习引擎(MLE)以产生设备操作的快照。 快照中的MLE可以使用活动或计划的操作来学习用户行为。 一旦为设备建立了正常的用户行为,MLE可能能够确定何时快照包括可能意味着对设备的威胁的异常行为。 确定包含异常行为的快照可能会传输到远程资源进行评估。 远程资源可以至少包括用户行为分类引擎(UBCE),以通过将用户行为表征为至少一种类型的用途来对用户行为进行分类。 UBCE可以分析快照,以确定设备中是否存在潜在威胁,并且威胁分析可以提供给设备进行评估和/或纠正措施。