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    • 3. 发明申请
    • ADAPTIVE DISTRIBUTION OF THE PROCESSING OF HIGHLY INTERACTIVE APPLICATIONS
    • 高度互动应用程序的自适应分配
    • US20110246549A1
    • 2011-10-06
    • US12752961
    • 2010-04-01
    • Gary Shon KatzenbergerVijay MitalOlivier ColleBrian C. BeckmanDarryl Ellis Rubin
    • Gary Shon KatzenbergerVijay MitalOlivier ColleBrian C. BeckmanDarryl Ellis Rubin
    • G06F15/16
    • G06F9/505
    • An application defined by expressions whose execution may be dynamically distributed over multiple devices. An application defined by expressions may include a number of expressions that provide a symbolic representation of computations to be performed. The application defined by expressions may have input variables and output variables and provide a solution for the output variables using the input variables and the expressions that define the application. In providing a solution for the output variables, an expression engine may determine dependencies for the expressions to be solved and distribute the solution of some of those expressions dynamically at runtime to other devices based on the capabilities of the devices, privacy and security concerns, communications bandwidth and latency, the resources available to devices, and commercial or cost implications of where the processing is done.
    • 由表达式定义的应用程序,其执行可以在多个设备上动态分布。 由表达式定义的应用可以包括提供要执行的计算的符号表示的多个表达式。 由表达式定义的应用程序可能具有输入变量和输出变量,并使用输入变量和定义应用程序的表达式为输出变量提供解决方案。 在为输出变量提供解决方案时,表达式引擎可以确定要解决的表达式的依赖性,并且基于设备的能力,隐私和安全性关注,通信,将运行时动态地将某些表达式的解释分布到其他设备 带宽和延迟,设备可用的资源以及处理完成的商业或成本影响。
    • 10. 发明授权
    • Interactive recommendations
    • 互动推荐
    • US08566261B2
    • 2013-10-22
    • US12971914
    • 2010-12-17
    • Vijay MitalR. Donald Thompson, IIIRobert PoveyGary Shon Katzenberger
    • Vijay MitalR. Donald Thompson, IIIRobert PoveyGary Shon Katzenberger
    • G06F15/18
    • G06F17/30867G06Q30/0201G06Q30/0631G06Q50/01
    • An interactive recommendation system generates one or more recommendations (e.g., recommended products, travel destinations, etc.) for a user based on a recommendation model. The recommendation model includes one or more criteria that are used to analyze a datastore of user characteristics (e.g., a user's age, location, past online behavior, etc.) and generate one or more recommendations based thereon. The interactive recommendation system further presents a user interface that allows the user to interactively modify the criteria of the recommendation model and to apply the modified recommendation model to the datastore in order to generate one or more modified recommendations. In this manner, for example, the user can customize the recommendations he or she receives by interacting with the recommendation system to modify the recommendation model used to generate such recommendations.
    • 交互式推荐系统基于推荐模型生成针对用户的一个或多个建议(例如,推荐产品,旅行目的地等)。 推荐模型包括用于分析用户特征的数据存储(例如,用户的年龄,位置,过去的在线行为等)的一个或多个标准,并基于此产生一个或多个建议。 交互式推荐系统进一步呈现用户界面,其允许用户交互地修改推荐模型的标准并且将修改的推荐模型应用于数据存储区以便生成一个或多个修改的建议。 以这种方式,例如,用户可以通过与推荐系统交互来定制他或她所接收的建议,以修改用于产生这样的建议的推荐模型。