SELECTING REPRESENTATIVE MODELS
    5.
    发明申请
    SELECTING REPRESENTATIVE MODELS 审中-公开
    选择代表性模型

    公开(公告)号:US20150100369A1

    公开(公告)日:2015-04-09

    申请号:US14046861

    申请日:2013-10-04

    CPC classification number: G06Q10/06315

    Abstract: Embodiments of the present invention provide a system, method and computer program product for selecting representative models. A method comprises generating a first data model representing a first aggregation level, and generating multiple additional data models. Each additional data model represents a lower aggregation level than the first data model. For each additional data model, a corresponding score is determined. For each lower aggregation level, a corresponding combined score is determined based on two or more highest scoring additional data models representing the lower aggregation level. The method further comprises reporting a second aggregation level and a set of data models. The second aggregation level is a lower aggregation level having the highest combined score over all other lower aggregation levels. The set of data models comprises two, or more, highest scoring additional data models representing the second aggregation level.

    Abstract translation: 本发明的实施例提供一种用于选择代表性模型的系统,方法和计算机程序产品。 一种方法包括生成表示第一聚合级别的第一数据模型,以及生成多个附加数据模型。 每个附加数据模型表示比第一个数据模型更低的聚合级别。 对于每个附加数据模型,确定相应的分数。 对于每个较低的聚合级别,基于表示较低聚合级别的两个或更多个最高得分的附加数据模型来确定相应的组合分数。 该方法还包括报告第二聚合级别和一组数据模型。 第二个聚合级别是在所有其他较低聚合级别上具有最高组合得分的较低聚合级别。 该组数据模型包括表示第二聚合级别的两个或更多个最高得分的附加数据模型。

    Meeting room reservation system
    6.
    发明授权

    公开(公告)号:US11188878B2

    公开(公告)日:2021-11-30

    申请号:US14861959

    申请日:2015-09-22

    Abstract: Embodiments of the present invention provide a method comprising maintaining historical meeting information, receiving an event data stream corresponding to a meeting, and delaying confirmation of an assignment of a meeting room for the meeting for a period of delay defined by a confirmation condition to predict a number of in-person attendees at the meeting based on the event data stream and the historical meeting information. The meeting room is tentatively assigned to the meeting based on the predicted number of in-person attendees. The method further comprises sending confirmation of the assignment of the meeting room for the meeting to at least one invitee only after the period of delay has elapsed.

    Drone air traffic control and flight plan management

    公开(公告)号:US10540900B2

    公开(公告)日:2020-01-21

    申请号:US15799826

    申请日:2017-10-31

    Abstract: One embodiment provides a method comprising receiving a flight plan request for a drone. The flight plan request comprises a drone identity, departure information, and arrival information. The method further comprises constructing a modified flight plan for the drone based on the flight plan request, wherein the modified flight plan represents an approved, congestion reducing, and executable flight plan for the drone, and the modified flight plan comprises a sequence of four-dimensional (4D) cells representing a planned flight path for the drone. For each 4D cell of the modified flight plan, the method further comprises attempting to place an exclusive lock on behalf of the drone on the 4D cell, and in response to a failure to place the exclusive lock on behalf of the drone on the 4D cell, rerouting the modified flight plan around the 4D cell to a random neighboring 4D cell.

    QUANTITATIVE DISCOVERY OF NAME CHANGES
    8.
    发明申请

    公开(公告)号:US20190220780A1

    公开(公告)日:2019-07-18

    申请号:US16367046

    申请日:2019-03-27

    CPC classification number: G06N20/00 G06Q10/06375

    Abstract: Embodiments of the present invention provide a method for detecting a temporal change of name associated with performance data. The method comprises receiving at least one candidate name replacement pair comprising a pair of names. The method further comprises, in a training stage, for each known name replacement pair included in the performance data, determining a window of time covering a most recent appearance of a first name of the known name replacement pair. The window of time is determined based on quantitative features of a time series model comprising performance data for the first name and a second name of the known name replacement pair. The method further comprises, in the training stage, training a machine learning classifier based on quantitative features computed using a portion of the performance data for the first name and the second name, where the portion is within the window of time determined.

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