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    • 5. 发明申请
    • MULTI-TIERED APPROACH TO E-MAIL PRIORITIZATION
    • 电子邮件优先的多层次的方法
    • US20130339276A1
    • 2013-12-19
    • US13528598
    • 2012-06-20
    • Jennifer C. LaiJie LuShimei PanZhen Wen
    • Jennifer C. LaiJie LuShimei PanZhen Wen
    • G06F15/18
    • G06Q10/107
    • A method of automating incoming message prioritization. The method including training a global classifier of a computer system using training data. Dynamically training a user-specific classifier of the computer system based on a plurality of feedback instances. Inferring a topic of the incoming message received by the computer system based on a topic-based user model. Computing a plurality of contextual features of the incoming message. Determining a priority classification strategy for assigning a priority level to the incoming message based on the computed contextual features of the incoming message and a weighted combination of the global classifier and the user specific classifier. Classifying the incoming message based on the priority classification strategy.
    • 一种自动传入消息优先级的方法。 该方法包括使用训练数据训练计算机系统的全局分类器。 基于多个反馈实例动态训练计算机系统的用户专用分类器。 基于基于主题的用户模型推断计算机系统接收到的传入消息的主题。 计算传入消息的多个上下文特征。 确定用于基于输入消息的计算的上下文特征以及全局分类器和用户特定分类器的加权组合来为入局消息分配优先级的优先级分类策略。 根据优先级分类策略对传入的消息进行分类。
    • 6. 发明申请
    • MULTI-TIERED APPROACH TO E-MAIL PRIORITIZATION
    • 电子邮件优先的多层次的方法
    • US20130212047A1
    • 2013-08-15
    • US13525173
    • 2012-06-15
    • Jennifer C. LaiJie LuShimei PanZhen Wen
    • Jennifer C. LaiJie LuShimei PanZhen Wen
    • G06F15/18
    • G06Q10/107G06N5/00
    • An apparatus for automating a prioritization of an incoming message, including a batch learning module that generates a global classifier based on training data that is input to the batch learning module. A feedback learning module that generates a user-specific classifier based on a plurality of feedback instances. A feature extraction module that receives the incoming message and a topic-based user model, infers a topic of the incoming message based on the topic-based user model, and computes a plurality of contextual features of the incoming message. A classification module that dynamically determines a priority classification strategy for assigning a priority level to the incoming message based on the plurality of contextual features of the incoming message and a weighted combination of the global classifier and the user-specific classifier, and classifies the incoming message based on the priority classification strategy.
    • 一种用于自动化进入消息的优先级的装置,包括基于输入到批量学习模块的训练数据生成全局分类器的批量学习模块。 反馈学习模块,其基于多个反馈实例生成用户特定的分类器。 一种特征提取模块,其接收所述传入消息和基于主题的用户模型,基于所述基于所述主题的用户模型推断所述传入消息的主题,并且计算所述传入消息的多个上下文特征。 一种分类模块,其基于所述输入消息的所述多个上下文特征以及所述全局分类器和所述用户特定分类器的加权组合来动态地确定用于向所述传入消息分配优先级的优先级分类策略,并且对所述传入消息进行分类 基于优先级分类策略。
    • 7. 发明授权
    • Multi-tiered approach to E-mail prioritization
    • 多层次的电子邮件优先排序方法
    • US09152953B2
    • 2015-10-06
    • US13525173
    • 2012-06-15
    • Jennifer C. LaiJie LuShimei PanZhen Wen
    • Jennifer C. LaiJie LuShimei PanZhen Wen
    • G06F15/18G06Q10/10G06N5/00
    • G06Q10/107G06N5/00
    • An apparatus for automating a prioritization of an incoming message, including a batch learning module that generates a global classifier based on training data that is input to the batch learning module. A feedback learning module that generates a user-specific classifier based on a plurality of feedback instances. A feature extraction module that receives the incoming message and a topic-based user model, infers a topic of the incoming message based on the topic-based user model, and computes a plurality of contextual features of the incoming message. A classification module that dynamically determines a priority classification strategy for assigning a priority level to the incoming message based on the plurality of contextual features of the incoming message and a weighted combination of the global classifier and the user-specific classifier, and classifies the incoming message based on the priority classification strategy.
    • 一种用于自动化进入消息的优先级的装置,包括基于输入到批量学习模块的训练数据生成全局分类器的批量学习模块。 反馈学习模块,其基于多个反馈实例生成用户特定的分类器。 一种特征提取模块,其接收所述传入消息和基于主题的用户模型,基于所述基于所述主题的用户模型推断所述传入消息的主题,并且计算所述传入消息的多个上下文特征。 一种分类模块,其基于所述输入消息的所述多个上下文特征以及所述全局分类器和所述用户特定分类器的加权组合来动态地确定用于向所述传入消息分配优先级的优先级分类策略,并且对所述传入消息进行分类 基于优先级分类策略。
    • 10. 发明授权
    • Multi-tiered approach to E-mail prioritization
    • 多层次的电子邮件优先排序方法
    • US09256862B2
    • 2016-02-09
    • US13528598
    • 2012-06-20
    • Jennifer C. LaiJie LuShimei PanZhen Wen
    • Jennifer C. LaiJie LuShimei PanZhen Wen
    • G06F15/18G06Q10/10
    • G06Q10/107
    • A method of automating incoming message prioritization. The method including training a global classifier of a computer system using training data. Dynamically training a user-specific classifier of the computer system based on a plurality of feedback instances. Inferring a topic of the incoming message received by the computer system based on a topic-based user model. Computing a plurality of contextual features of the incoming message. Determining a priority classification strategy for assigning a priority level to the incoming message based on the computed contextual features of the incoming message and a weighted combination of the global classifier and the user specific classifier. Classifying the incoming message based on the priority classification strategy.
    • 一种自动传入消息优先级的方法。 该方法包括使用训练数据训练计算机系统的全局分类器。 基于多个反馈实例动态训练计算机系统的用户专用分类器。 基于基于主题的用户模型推断计算机系统接收到的传入消息的主题。 计算传入消息的多个上下文特征。 确定用于基于输入消息的计算的上下文特征以及全局分类器和用户特定分类器的加权组合来为入局消息分配优先级的优先级分类策略。 根据优先级分类策略对传入的消息进行分类。