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    • 1. 发明授权
    • Execution allocation cost assessment for computing systems and environments including elastic computing systems and environments
    • 包括弹性计算系统和环境在内的计算系统和环境的执行分配成本评估
    • US08239538B2
    • 2012-08-07
    • US12609970
    • 2009-10-30
    • Xinwen ZhangOnur AciicmezSimon J. GibbsAnugeetha KunjithapathamSangoh JeongDoreen Cheng
    • Xinwen ZhangOnur AciicmezSimon J. GibbsAnugeetha KunjithapathamSangoh JeongDoreen Cheng
    • G06F15/173
    • H04L29/08144G06F9/5044G06F9/5094H04L29/08306H04N21/23103Y02D10/22
    • Techniques for assessing the cost of allocation of execution and affecting the allocation of execution are disclosed. The cost of allocation of execution to or between a first computing device (e.g., a mobile device) and one or more computing resource providers (e.g., one or more Clouds) can be determined during runtime of the executable code. It will be appreciated that a computing system can operate independently of the first computing device and one or more computing resource providers and provide execution allocation cost assessment as a service to the first computing device and/or one or more computing resource providers. Execution allocation cost can be assessed (or determined) based on execution allocation data pertaining to the first computing device and/or one or more computing resource providers. By way of example, power consumption of a mobile device can be used as a factor in determining how to allocate individual components of an application program (e.g., weblets) between a mobile phone and a Cloud. The invention is especially suited for Elastic computing environment and systems. In an Elastic computing environment, scalable and dynamic external computing resources can be used in order to effectively extend the computing capabilities beyond that which can be provided by internal computing resources of a computing system or environment.
    • 披露了评估分配成本和影响执行分配的技术。 可以在可执行代码的运行时间期间确定向第一计算设备(例如,移动设备)和一个或多个计算资源提供者(例如,一个或多个云)分配执行的成本。 应当理解,计算系统可以独立于第一计算设备和一个或多个计算资源提供者操作,并且将作为服务的执行分配成本评估提供给第一计算设备和/或一个或多个计算资源提供者。 可以基于与第一计算设备和/或一个或多个计算资源提供者有关的执行分配数据来评估(或确定)执行分配成本。 作为示例,可以使用移动设备的功率消耗作为确定如何在移动电话和云之间分配应用程序(例如,冒号)的各个组件的因素。 本发明特别适用于弹性计算环境和系统。 在弹性计算环境中,可以使用可扩展和动态的外部计算资源,以便有效地将计算能力扩展到可以由计算系统或环境的内部计算资源提供的能力。
    • 2. 发明申请
    • EXECUTION ALLOCATION COST ASSESSMENT FOR COMPUTING SYSTEMS AND ENVIRONMENTS INCLUDING ELASTIC COMPUTING SYSTEMS AND ENVIRONMENTS
    • 包括弹性计算系统和环境在内的计算系统和环境的执行成本分摊费用评估
    • US20100131592A1
    • 2010-05-27
    • US12609970
    • 2009-10-30
    • Xinwen ZhangOnur AciicmezSimon J. GibbsAnugeetha KunjithapathamSangoh JeongDoreen Cheng
    • Xinwen ZhangOnur AciicmezSimon J. GibbsAnugeetha KunjithapathamSangoh JeongDoreen Cheng
    • G06F9/50G06F15/16G06F15/173
    • H04L29/08144G06F9/5044G06F9/5094H04L29/08306H04N21/23103Y02D10/22
    • Techniques for assessing the cost of allocation of execution and affecting the allocation of execution are disclosed. The cost of allocation of execution to or between a first computing device (e.g., a mobile device) and one or more computing resource providers (e.g., one or more Clouds) can be determined during runtime of the executable code. It will be appreciated that a computing system can operate independently of the first computing device and one or more computing resource providers and provide execution allocation cost assessment as a service to the first computing device and/or one or more computing resource providers. Execution allocation cost can be assessed (or determined) based on execution allocation data pertaining to the first computing device and/or one or more computing resource providers. By way of example, power consumption of a mobile device can be used as a factor in determining how to allocate individual components of an application program (e.g., weblets) between a mobile phone and a Cloud. The invention is especially suited for Elastic computing environment and systems. In an Elastic computing environment, scalable and dynamic external computing resources can be used in order to effectively extend the computing capabilities beyond that which can be provided by internal computing resources of a computing system or environment.
    • 披露了评估分配成本和影响执行分配的技术。 可以在可执行代码的运行时间期间确定向第一计算设备(例如,移动设备)和一个或多个计算资源提供者(例如,一个或多个云)分配执行的成本。 应当理解,计算系统可以独立于第一计算设备和一个或多个计算资源提供者操作,并且将作为服务的执行分配成本评估提供给第一计算设备和/或一个或多个计算资源提供者。 可以基于与第一计算设备和/或一个或多个计算资源提供者有关的执行分配数据来评估(或确定)执行分配成本。 作为示例,可以使用移动设备的功率消耗作为确定如何在移动电话和云之间分配应用程序(例如,冒号)的各个组件的因素。 本发明特别适用于弹性计算环境和系统。 在弹性计算环境中,可以使用可扩展和动态的外部计算资源,以便有效地将计算能力扩展到可以由计算系统或环境的内部计算资源提供的能力。
    • 6. 发明授权
    • Execution allocation cost assessment for computing systems and environments including elastic computing systems and environments
    • 包括弹性计算系统和环境在内的计算系统和环境的执行分配成本评估
    • US08560465B2
    • 2013-10-15
    • US12710204
    • 2010-02-22
    • Sangoh JeongSimon J. GibbsXinwen ZhangAnugeetha Kunjithapatham
    • Sangoh JeongSimon J. GibbsXinwen ZhangAnugeetha Kunjithapatham
    • G06N5/00
    • G06N5/02G06F9/5066
    • Techniques for allocating individually executable portions of executable code for execution in an Elastic computing environment are disclosed. In an Elastic computing environment, scalable and dynamic external computing resources can be used in order to effectively extend the computing capabilities beyond that which can be provided by internal computing resources of a computing system or environment. Machine learning can be used to automatically determine whether to allocate each individual portion of executable code (e.g., a Weblet) for execution to either internal computing resources of a computing system (e.g., a computing device) or external resources of an dynamically scalable computing resource (e.g., a Cloud). By way of example, status and preference data can be used to train a supervised learning mechanism to allow a computing device to automatically allocate executable code to internal and external computing resources of an Elastic computing environment.
    • 公开了用于在弹性计算环境中分配用于执行的可执行代码的单独可执行部分的技术。 在弹性计算环境中,可以使用可扩展和动态的外部计算资源,以便有效地将计算能力扩展到可以由计算系统或环境的内部计算资源提供的能力。 机器学习可用于自动确定是否将可执行代码(例如,Weblet)的每个单独部分分配给计算系统(例如,计算设备)的内部计算资源或动态可扩展计算资源的外部资源 (例如,云)。 作为示例,状态和偏好数据可以用于训练监督学习机制,以允许计算设备自动地将可执行代码分配给弹性计算环境的内部和外部计算资源。
    • 9. 发明授权
    • System and method for automatically rating video content
    • 自动对视频内容进行评分的系统和方法
    • US08001561B2
    • 2011-08-16
    • US12120217
    • 2008-05-13
    • Simon J. GibbsBottyán NémethPriyang RathodAnugeetha KunjithapathamMithun SheshagiriPhuong Nguyen
    • Simon J. GibbsBottyán NémethPriyang RathodAnugeetha KunjithapathamMithun SheshagiriPhuong Nguyen
    • H04N7/16H04N5/455
    • G06F17/30038
    • System and method for automatically rating the content of video media based on video operations performed on a media device and in reference to a plurality of rating rules are provided. Usage of the media device is continuously monitored and user actions with respect to operating the video media on the media device are automatically logged. Each rating rule includes a device usage pattern with respect to operating videos on the media device and a rating action indicating adjustments to content ratings of the videos based upon characteristics described by the device usage pattern that are inferred from the recorded user inputted video control operations. When the device usage pattern of a rating rule is inferred from one or more user actions operating a piece of video media directly on the media device, the content rating of the piece of video media is adjusted based on the rating rule.
    • 提供了基于在媒体设备上执行的视频操作并参考多个评级规则来自动对视频媒体的内容进行评级的系统和方法。 持续监控媒体设备的使用情况,并自动记录用户在媒体设备上操作视频媒体的操作。 每个评级规则包括关于媒体设备上的操作视频的设备使用模式,以及基于从记录的用户输入的视频控制操作推断出的设备使用模式所描述的特性的指示对视频的内容评级进行调整的评级动作。 当通过直接在媒体设备上操作一段视频媒体的一个或多个用户动作来推断评级规则的设备使用模式时,基于评级规则来调整该片视频媒体的内容分级。
    • 10. 发明申请
    • DETERMINING THE INTEREST OF INDIVIDUAL ENTITIES BASED ON A GENERAL INTEREST
    • 基于一般利益确定个人实体的利益
    • US20100205041A1
    • 2010-08-12
    • US12370414
    • 2009-02-12
    • Priyang RathodSimon J. GibbsAnugeetha KunjithapathamMithun SheshagiriPhuong Nguyen
    • Priyang RathodSimon J. GibbsAnugeetha KunjithapathamMithun SheshagiriPhuong Nguyen
    • G06Q10/00
    • G06Q10/00G06Q30/0204
    • An interest value indicative of the interest of a particular entity in one or more items can be determined based on a general interest value (e.g., a group interest/preference value) associated with a plurality of entities (e.g., persons, members of a group) that include that particular entity. The interest value can be determined based on Collaborative Filtering (CF) data and/or individual (or non-collaborative) data. In contrast to the Collaborative Filtering (CF) data which may include data associated with various entities, the individual (or non-collaborative) data typically pertains to one entity, namely, the entity whose interest is to be determined. It will be appreciated that both collaborative and non-collaborative data pertaining to individuals can be considered, thereby allowing for a better estimation of individual interests. The interest of a particular entity can be determined, for example, by considering the difference between a predicted CF interest value (determined based on CF data) and a group interest value and/or by considering the difference between a predicted individual interest value (determined based on non-collaborative data) and the group interest value.
    • 可以基于与多个实体(例如,个人,组的成员)相关联的一般兴趣值(例如,组兴趣/偏好值)来确定指示一个或多个项目中特定实体的兴趣的兴趣值 )包括该特定实体。 可以基于协同过滤(CF)数据和/或单个(或非协作)数据来确定兴趣值。 与可以包括与各种实体相关联的数据的协作过滤(CF)数据相反,个体(或非协作)数据通常涉及一个实体,即其利益将被确定的实体。 应当理解,可以考虑与个人有关的协作和非协作数据,从而允许更好地估计个人兴趣。 特定实体的兴趣可以例如通过考虑预测的CF利息值(基于CF数据确定)和组合利息价值之间的差异和/或通过考虑预测的个人利益价值(确定的)之间的差异来确定 基于非协作数据)和组利益价值。