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
    • 7. 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
    • US20110004574A1
    • 2011-01-06
    • US12710204
    • 2010-02-22
    • Sangoh JEONGSimon J. GIBBSXinwen ZHANGAnugeetha KUNJITHAPATHAM
    • Sangoh JEONGSimon J. GIBBSXinwen ZHANGAnugeetha KUNJITHAPATHAM
    • G06F9/50G06F15/18
    • 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。
    • 8. 发明申请
    • SYSTEM AND METHOD FOR AUTOMATICALLY RATING VIDEO CONTENT
    • 用于自动评估视频内容的系统和方法
    • US20090133048A1
    • 2009-05-21
    • US12120217
    • 2008-05-13
    • Simon J. GibbsBottyan NemethPriyang RathodAnugeetha KunjithapathamMithun SheshagiriPhuong Nguyen
    • Simon J. GibbsBottyan NemethPriyang RathodAnugeetha KunjithapathamMithun SheshagiriPhuong Nguyen
    • H04H60/32
    • 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。
    • 9. 发明申请
    • COMBINATION OF COLLABORATIVE FILTERING AND CLIPRANK FOR PERSONALIZED MEDIA CONTENT RECOMMENDATION
    • 合作过滤与个人化媒体内容推荐组合
    • US20090132520A1
    • 2009-05-21
    • US12120211
    • 2008-05-13
    • Bottyan NemethSimon J. GibbsMithun SheshagiriPriyang Rathod
    • Bottyan NemethSimon J. GibbsMithun SheshagiriPriyang Rathod
    • G06F17/30
    • G06F17/30038
    • Various methods for combining ClipRank and Collaborative Filtering are provided. According to one embodiment, the ClipRank weights associated with a plurality of pieces of media content are calculated based on the relationships among the plurality of pieces of media content and a plurality of users. Those pieces having ClipRank weights greater than or equal to a predefined weight threshold are selected from the plurality of pieces of media content to obtain a plurality of selected pieces of media content. Collaborative Filtering is then performed on the plurality of selected pieces of media content and the plurality of users. According to another embodiment, Collaborative Filtering on a plurality of pieces of media content and a plurality of users is performed for one of the plurality of users. Personalized ClipRank weights associated with the plurality of pieces of media content is calculated for the user based on Collaborative Filtering ratings obtained for the plurality of pieces of media content for the user.
    • 提供了组合ClipRank和协同过滤的各种方法。 根据一个实施例,基于多个媒体内容和多个用户之间的关系来计算与多个媒体内容相关联的ClipRank权重。 从多个媒体内容中选择具有大于或等于预定权重阈值的ClipRank权重的片段,以获得多个所选择的媒体内容。 然后对多个选定的媒体内容和多个用户执行协作过滤。 根据另一个实施例,对多个用户中的一个执行对多个媒体内容和多个用户的协作过滤。 基于针对用户的多个媒体内容获得的协作过滤等级,为用户计算与多个媒体内容相关联的个性化ClipRank权重。