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    Optimized scheduling of calendar events
    2.
    发明授权
    Optimized scheduling of calendar events 有权

    公开(公告)号：US11250386B2

    公开(公告)日：2022-02-15

    申请号：US15837751

    申请日：2017-12-11

    申请人： Microsoft Technology Licensing, LLC

    发明人： Byungki Byun ,  Chenlei Guo ,  Divya Jetley ,  Pavel Metrikov ,  Ye-yi Wang

    IPC分类号： G06Q10/10

    摘要： Systems and methods are disclosed to provide optimized scheduling of calendar events based on flexibility scores of calendar events. A flexibility score may be representative of a probability or likelihood that a calendar event can or will be rescheduled in response to a conflicting calendar event. Flexibility scores of calendar events may be calculated based on one or more factors, which may be weighted, using one or more machine-learning models. Factors may include: event densities of invitees' calendars, organizational rankings of respective invitees, the remaining time before an event start time, an urgency of respective calendar events, etc. In this way, if open time slots are not available for all invitees to a proposed calendar request, an event organizer may identify time slots occupied by existing calendar events with the highest likelihood of being rescheduled in view of the proposed calendar event, thereby facilitating scheduling of the proposed calendar event.

    CONTEXTUAL PEOPLE RECOMMENDATIONS
    4.
    发明申请
    CONTEXTUAL PEOPLE RECOMMENDATIONS 审中-公开
    相关人士建议

    公开(公告)号：US20160323398A1

    公开(公告)日：2016-11-03

    申请号：US14806281

    申请日：2015-07-22

    申请人： Microsoft Technology Licensing, LLC

    发明人： Chenlei Guo ,  Jianfeng Gao ,  Xinying Song ,  Byungki Byun ,  Yelong Shen ,  Ye-Yi Wang ,  Brian D. Remick ,  Edward Thiele ,  Mohammed Aatif Ali ,  Marcus Gois ,  Xiaodong He ,  Jianshu Chen ,  Divya Jetley ,  Stephen Friesen

    IPC分类号： H04L29/08 ,  G06F17/30 ,  H04L12/58 ,  G06N99/00

    CPC分类号： H04L67/22 ,  G06F17/3053 ,  G06F17/30699 ,  G06N99/005 ,  G06Q10/06311 ,  G06Q10/06313 ,  G06Q10/06315 ,  H04L51/20 ,  H04L67/306

    摘要： Techniques for providing a people recommendation system for predicting and recommending relevant people (or other entities) to include in a conversation based on contextual indicators. In an exemplary embodiment, email recipient recommendations may be suggested based on contextual signals, e.g., project names, body text, existing recipients, current date and time, etc. In an aspect, a plurality of properties including ranked key phrases are associated with profiles corresponding to personal entities. Aggregated profiles are analyzed using first- and second-layer processing techniques. The recommendations may be provided to the user reactively, e.g., in response to a specific query by the user to the people recommendation system, or proactively, e.g., based on the context of what the user is currently working on, in the absence of a specific query by the user.

    摘要翻译： 提供人员推荐系统的技术，用于根据情境指标预测和推荐相关人员（或其他实体）包括在对话中。 在示例性实施例中，可以基于上下文信号（例如项目名称，正文，现有收件人，当前日期和时间等）来建议电子邮件接收者建议。在一方面，包括排序关键短语的多个属性与简档相关联 对应个人实体。 使用第一层和第二层处理技术分析聚集的轮廓。 可以例如响应于用户对人们推荐系统的特定查询，或主动地，例如，基于用户当前正在工作的上下文，在没有 由用户进行具体查询。

    RELEVANCE GROUP SUGGESTIONS
    5.
    发明申请
    RELEVANCE GROUP SUGGESTIONS 审中-公开
    相关小组建议

    公开(公告)号：US20160321283A1

    公开(公告)日：2016-11-03

    申请号：US14811397

    申请日：2015-07-28

    申请人： Microsoft Technology Licensing, LLC

    发明人： Yelong Shen ,  Xinying Song ,  Jianfeng Gao ,  Chenlei Guo ,  Byungki Byun ,  Ye-Yi Wang ,  Brian D. Remick ,  Edward Thiele ,  Mohammed Aatif Ali ,  Marcus Gois ,  Yang Zou ,  Mariana Stepp ,  Divya Jetley ,  Stephen Friesen

    IPC分类号： G06F17/30 ,  H04L12/58

    CPC分类号： G06F17/3097 ,  G06F17/3053 ,  G06F17/30598 ,  G06F17/30699 ,  G06Q10/06311 ,  G06Q10/06313 ,  G06Q10/06315 ,  H04L51/04

    摘要： Techniques for providing a people recommendation system for predicting and recommending relevant people (or other entities) to include in a conversation. In an exemplary embodiment, a plurality of conversation boxes associated with communications between a user and target recipients, or between other users and recipients, are collected and stored as user history. During a training phase, the user history is used to train encoder and decoder blocks in a de-noising auto-encoder model. During a prediction phase, the trained encoder and decoder are used to predict one or more recipients for a current conversation box composed by the user, based on contextual and other signals extracted from the current conversation box. The predicted recipients are ranked using a scoring function, and the top-ranked individuals or entities may be recommended to the user.

    摘要翻译： 提供用于预测和推荐相关人（或其他实体）包括在对话中的人推荐系统的技术。 在示例性实施例中，与用户和目标接收者之间或其他用户和接收者之间的通信相关联的多个会话框被收集并存储为用户历史。 在训练阶段，用户历史用于在去噪自动编码器模型中训练编码器和解码器块。 在预测阶段期间，经训练的编码器和解码器用于基于从当前会话框提取的上下文和其他信号来预测用户组成的当前会话框的一个或多个接收者。 使用评分功能对预测的收件者进行排名，并且可以向用户推荐排名最高的个人或实体。