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    Remote execution of machine-learned models
    61.
    发明授权
    Remote execution of machine-learned models 有权

    公开(公告)号：US11496600B2

    公开(公告)日：2022-11-08

    申请号：US16554518

    申请日：2019-08-28

    Applicant: Apple Inc.

    Inventor： Umesh S. Vaishampayan ,  Gaurav Kapoor ,  Kit-man Wan

    IPC: H04L67/59 ,  H04L67/2895 ,  G06F3/04883 ,  G06N20/00 ,  H04W4/021 ,  H04L67/00

    Abstract: In an exemplary process for remote execution of machine-learned models, one or more signals from a second electronic device is detected by a first electronic device. The second electronic device includes a machine-learned model associated with an application implemented on the first electronic device. Based on the one or more signals, a communication connection is established with the second electronic device and a proxy to the machine-learned model is generated. Input data is obtained via a sensor of the first electronic device. A representation of the input data is sent to the second electronic device via the proxy and the established communication connection. The representation of the input data is processed through the machine-learned model to generate an output. A result derived from the output is received via the communication connection and a representation of the result is outputted.

    Methods and system for managing predictive models
    63.
    发明授权
    Methods and system for managing predictive models 有权

    公开(公告)号：US10380488B2

    公开(公告)日：2019-08-13

    申请号：US14500985

    申请日：2014-09-29

    Applicant: Apple Inc.

    Inventor： Binu K. Mathew ,  Kit-Man Wan ,  Gaurav Kapoor

    IPC: G06N5/04 ,  H04L29/08 ,  G06N20/00

    Abstract: Disclosed herein is a technique for implementing a framework that enables application developers to enhance their applications with dynamic adjustment capabilities. Specifically, the framework, when utilized by an application on a mobile computing device that implements the framework, can enable the application to establish predictive models that can be used to identify meaningful behavioral patterns of an individual who uses the application. In turn, the predictive models can be used to preempt the individual's actions and provide an enhanced overall user experience. The framework is configured to interface with other software entities on the mobile computing device that conduct various analyses to identify appropriate times for the application to manage and update its predictive models. Such appropriate times can include, for example, identified periods of time where the individual is not operating the mobile computing device, as well as recognized conditions where power consumption is not a concern.

    Systems and methods for providing predictions to applications executing on a computing device
    64.
    发明授权
    Systems and methods for providing predictions to applications executing on a computing device 有权

    公开(公告)号：US10346441B2

    公开(公告)日：2019-07-09

    申请号：US14866786

    申请日：2015-09-25

    Applicant: Apple Inc.

    Inventor： Joao Pedro Lacerda ,  Gaurav Kapoor

    IPC: G06F16/28 ,  G06N20/00 ,  G06F16/903

    Abstract: The embodiments set forth techniques for implementing various “prediction engines” that can be configured to provide different kinds of predictions within a mobile computing device. According to some embodiments, each prediction engine can assign itself as an “expert” on one or more “prediction categories” within the mobile computing device. When a software application issues a request for a prediction for a particular category, and two or more prediction engines respond with their respective prediction(s), a “prediction center” can be configured to receive and process the predictions prior to responding to the request. Processing the predictions can involve removing duplicate information that exists across the predictions, sorting the predictions in accordance with confidence levels advertised by the prediction engines, and the like. In this manner, the prediction center can distill multiple predictions down into an optimized prediction and provide the optimized prediction to the software application.

    LEARNING NEW WORDS
    65.
    发明申请
    LEARNING NEW WORDS 审中-公开

    公开(公告)号：US20190097978A1

    公开(公告)日：2019-03-28

    申请号：US16159473

    申请日：2018-10-12

    Applicant: Apple Inc.

    Inventor： Abhradeep Guha Thakurta ,  Andrew H. Vyrros ,  Umesh S. Vaishampayan ,  Gaurav Kapoor ,  Julien Freudiger ,  Vivek Rangarajan Sridhar ,  Doug Davidson

    IPC: H04L29/06 ,  G06F17/30 ,  G06F17/27 ,  G06F7/58 ,  G06F9/30

    Abstract: Systems and methods are disclosed for a server learning new words generated by user client devices in a crowdsourced manner while maintaining local differential privacy of client devices. A client device can determine that a word typed on the client device is a new word that is not contained in a dictionary or asset catalog on the client device. New words can be grouped in classifications such as entertainment, health, finance, etc. A differential privacy system on the client device can comprise a privacy budget for each classification of new words. If there is privacy budget available for the classification, then one or more new terms in a classification can be sent to new term learning server, and the privacy budget for the classification reduced. The privacy budget can be periodically replenished.

    Pacing activity data of a user
    68.
    发明授权
    Pacing activity data of a user 有权

    公开(公告)号：US10117600B2

    公开(公告)日：2018-11-06

    申请号：US14475417

    申请日：2014-09-02

    Applicant: APPLE INC.

    Inventor： Daniel S. Keen ,  Jay C. Blahnik ,  Gaurav Kapoor ,  Michael R. Siracusa

    IPC: A61B5/103 ,  A61B5/117 ,  A61B5/11 ,  G06F9/54 ,  G06F9/48 ,  A61B5/00 ,  G06F8/65

    Abstract: Pacer activity data of a user may be managed. For example, historical activity data of a user corresponding to a particular time of a day prior to a current day may be received. Additionally, a user interface configured to display an activity goal of the user may be generated and the user interface may be provided for presentation. In some aspects, the user interface may be configured to display a first indicator that identifies cumulative progress towards the activity goal and a second indicator that identifies predicted cumulative progress towards the activity goal. The cumulative progress may be calculated based on monitored activity from a start of the current day to the particular time of the current day and the predicted cumulative progress may be calculated based on the received historical activity data corresponding to the particular time of the day prior to the current day.

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