System and method to operate a drone
    12.
    发明授权
    System and method to operate a drone 有权
    操作无人机的系统和方法

    公开(公告)号:US09471064B1

    公开(公告)日:2016-10-18

    申请号:US14962147

    申请日:2015-12-08

    Abstract: A method for controlling a drone includes receiving a natural language request for information about a spatial location, parsing the natural language request into data requests, configuring a flight plan and controlling one or more drones to fly over the spatial location to obtain data types based on the data requests, and extracting and analyzing data to answer the request. The method can include extracting data points from the data types, obtaining labels from a user for one or more of the data points, predicting labels for unlabeled data points from a learning algorithm using the labels obtained from the user, determining the predicted labels are true labels for the unlabeled data points and combining the extracted data, the user labeled data points and the true labeled data points to answer the request for information. The learning algorithm may be active learning using a support vector machine.

    Abstract translation: 用于控制无人机的方法包括接收关于空间位置的信息的自然语言请求,将自然语言请求解析为数据请求,配置飞行计划并且控制一个或多个无人机在空间位置上飞行以获得基于 数据请求,以及提取和分析数据以应答请求。 该方法可以包括从数据类型提取数据点,从一个或多个数据点的用户获得标签,使用从用户获得的标签从学习算法预测未标记数据点的标签,确定预测标签是真实的 用于未标记数据点的标签,并组合提取的数据,用户标记的数据点和真实标记的数据点以回答信息请求。 学习算法可以是使用支持向量机的主动学习。

    Predicting user attentiveness to electronic notifications

    公开(公告)号:US10832160B2

    公开(公告)日:2020-11-10

    申请号:US15139716

    申请日:2016-04-27

    Abstract: A database comprises historical information of a user's response to previous notifications. The database is accessed to determine a time at which to provide a (new) notification to the user, utilizing at least: a) current user activity status (e.g., determined from measurement information collected from one or more personal devices and/or user calendar events; b) time/day; and c) context information about the notification (e.g., geo-location, indoors/outdoors) including notification type (e.g., calendar entry, email, IM). The user gets the notification via a portable device at the determined time. A machine learning model can select the determined time by discriminating features of the previous notifications for which the user immediately attended versus those that were deferred and/or ignored. Content of the notification can also be altered in view of such discriminating features so as to increase a likelihood the user will immediately attend to the provided notification.

    Predicting User Attentiveness to Electronic Notifications

    公开(公告)号:US20170316320A1

    公开(公告)日:2017-11-02

    申请号:US15139716

    申请日:2016-04-27

    Abstract: A database comprises historical information of a user's response to previous notifications. The database is accessed to determine a time at which to provide a (new) notification to the user, utilizing at least: a) current user activity status (e.g., determined from measurement information collected from one or more personal devices and/or user calendar events; b) time/day; and c) context information about the notification (e.g., geo-location, indoors/outdoors) including notification type (e.g., calendar entry, email, IM). The user gets the notification via a portable device at the determined time. A machine learning model can select the determined time by discriminating features of the previous notifications for which the user immediately attended versus those that were deferred and/or ignored. Content of the notification can also be altered in view of such discriminating features so as to increase a likelihood the user will immediately attend to the provided notification.

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