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    • 1. 发明申请
    • SOCIAL INCENTIVES PLATFORM
    • 社会激励平台
    • US20120158477A1
    • 2012-06-21
    • US12970968
    • 2010-12-17
    • Moshe TennenholtzRoy VarshavskyRon KaridiAviv ZoharYuval EmekKira Radinsky
    • Moshe TennenholtzRoy VarshavskyRon KaridiAviv ZoharYuval EmekKira Radinsky
    • G06Q30/00
    • G06Q30/0217G06Q30/0214G06Q50/01
    • A social incentive system is described herein that formalizes information propagation through social networks in a structured and systematic way. The system provides incentives and rewards to entities who participate in propagating the information, allowing heavy influencers to gain from their influence while the marketer rewards them. The system provides one or more tools for creation and design of social incentive plans with rewards for socially distributing information, including marketing campaigns. In particular, the system introduces a semantic framework where marketers can create structured incentive plans for rewarding consumers and distribution platforms for distributing information through social networks. As users complete specified activities they earn points, and the points can be redeemed for various incentives, such as cash, debit cards, prizes, merchandise, subscriptions, and so forth. The framework is robustly designed to avoid abuse and allow for fraud detection.
    • 本文描述了一种社会激励制度,其通过社会网络以组织和系统的方式形式化信息传播。 该系统为参与传播信息的实体提供激励和奖励,让重度影响者从营销人员的奖励中获益。 该系统提供一个或多个工具,用于创建和设计社会激励计划,并提供社会分发信息(包括营销活动)的奖励。 特别是,系统引入了一个语义框架,营销人员可以创建结构化的激励计划,以奖励消费者和通过社交网络分发信息的分发平台。 当用户完成指定的活动时,他们可以获得积分,积分可以兑换现金,借记卡,奖品,商品,订阅等各种奖励。 该框架设计强大,以避免滥用并允许欺诈检测。
    • 6. 发明申请
    • REALTIME MULTIPLE ENGINE SELECTION AND COMBINING
    • 实时多发动机选择和组合
    • US20120084859A1
    • 2012-04-05
    • US12894185
    • 2010-09-30
    • Kira RadinskyRoy VarshavskyJack W. StokesVladimir HolostovEdward Schaefer
    • Kira RadinskyRoy VarshavskyJack W. StokesVladimir HolostovEdward Schaefer
    • G06F21/00G06F17/30
    • G06F21/563G06F21/56G06Q10/06G06Q30/00
    • Architecture that selects a classification engine based on the expertise of the engine to process a given entity (e.g., a file). Selection of an engine is based on a probability that the engine will detect an unknown entity classification using properties of the entity. One or more of the highest ranked engines are activated in order to achieve the desired performance. A statistical, performance-light module is employed to skip or select several performance-demanding processes. Methods and algorithms are utilized for learning based on matching the best classification engine(s) to detect the entity class based on the entity properties. A user selection option is provided for specifying a maximum number of ranked, classification engines to consider for each state of the machine. A user can also select the minimum probability of detection for a specific entity (e.g., unknown file). The best classifications are re-evaluated over time as the classification engines are updated.
    • 基于引擎的专长来选择分类引擎以处理给定实体(例如,文件)的架构。 引擎的选择是基于引擎将使用实体的属性来检测未知实体分类的概率。 一个或多个最高排名的引擎被激活以实现期望的性能。 采用统计的性能灯模块来跳过或选择若干性能要求高的过程。 基于匹配最佳分类引擎的方法和算法用于学习,以根据实体属性检测实体类。 提供用户选择选项,用于指定针对机器的每个状态考虑的排名最大的分类引擎。 用户还可以选择特定实体(例如,未知文件)的最小检测概率。 随着分类引擎的更新,最好的分类会随着时间的推移重新评估。
    • 7. 发明授权
    • Realtime multiple engine selection and combining
    • 实时多引擎选择和组合
    • US08869277B2
    • 2014-10-21
    • US12894185
    • 2010-09-30
    • Kira RadinskyRoy VarshavskyJack W. StokesVladimir HolostovEdward Schaefer
    • Kira RadinskyRoy VarshavskyJack W. StokesVladimir HolostovEdward Schaefer
    • G06F21/00G06Q30/00G06F21/56
    • G06F21/563G06F21/56G06Q10/06G06Q30/00
    • Architecture that selects a classification engine based on the expertise of the engine to process a given entity (e.g., a file). Selection of an engine is based on a probability that the engine will detect an unknown entity classification using properties of the entity. One or more of the highest ranked engines are activated in order to achieve the desired performance. A statistical, performance-light module is employed to skip or select several performance-demanding processes. Methods and algorithms are utilized for learning based on matching the best classification engine(s) to detect the entity class based on the entity properties. A user selection option is provided for specifying a maximum number of ranked, classification engines to consider for each state of the machine. A user can also select the minimum probability of detection for a specific entity (e.g., unknown file). The best classifications are re-evaluated over time as the classification engines are updated.
    • 基于引擎的专长来选择分类引擎以处理给定实体(例如,文件)的架构。 引擎的选择是基于引擎将使用实体的属性来检测未知实体分类的概率。 一个或多个最高排名的引擎被激活以实现期望的性能。 采用统计的性能灯模块来跳过或选择若干性能要求高的过程。 基于匹配最佳分类引擎的方法和算法用于学习,以根据实体属性检测实体类。 提供用户选择选项,用于指定针对机器的每个状态考虑的排名最大的分类引擎。 用户还可以选择特定实体(例如,未知文件)的最小检测概率。 随着分类引擎的更新,最好的分类会随着时间的推移重新评估。
    • 10. 发明申请
    • Statistical Network Traffic Signature Analyzer
    • 统计网络交通签名分析仪
    • US20120317306A1
    • 2012-12-13
    • US13157316
    • 2011-06-10
    • Kira RadinskyEvgeney RyzhykMoshe Golan
    • Kira RadinskyEvgeney RyzhykMoshe Golan
    • G06F15/16
    • H04L43/028H04L63/1408
    • A network traffic analyzer may identify applications transmitting information across a network by analyzing various protocol attributes of the communication. A set of signatures may be created by training a machine learning system using network traffic with and without a specific application. The machine learning system may generate a signature for the specific application, and the signature may be analyzed using a monitoring system to identify the presence of the application's traffic on the network. In some embodiments, a decision tree may be used to detect the application within a statistical confidence. The monitoring system may be used for malware detection as well as other applications.
    • 网络流量分析器可以通过分析通信的各种协议属性来识别通过网络传输信息的应用。 可以通过使用具有和不具有特定应用的网络流量训练机器学习系统来创建一组签名。 机器学习系统可以为特定应用生成签名,并且可以使用监视系统分析签名,以识别应用在网络上的流量的存在。 在一些实施例中,可以使用决策树来在统计置信度内检测应用。 监控系统可用于恶意软件检测以及其他应用。