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    DYNAMIC ADJUSTMENT OF MOBILE DEVICE BASED ON THERMAL CONDITIONS
    1.
    发明申请
    DYNAMIC ADJUSTMENT OF MOBILE DEVICE BASED ON THERMAL CONDITIONS 审中-公开
    基于热条件的移动设备的动态调整

    公开(公告)号：WO2015183514A1

    公开(公告)日：2015-12-03

    申请号：PCT/US2015/029768

    申请日：2015-05-07

    Applicant: APPLE INC.

    Inventor： STANLEY-MARBELL, Phillip ,  KAPOOR, Gaurav ,  VAISHAMPAYAN, Umesh S.

    IPC: G06F9/54 ,  G06F1/32 ,  H04M1/725

    CPC classification number: H04W8/22 ,  G06F1/206 ,  G06F1/3206 ,  G06F1/329 ,  G06F9/542 ,  H04M1/72569 ,  Y02D10/24

    Abstract: In some implementations, a mobile device can be configured to monitor environmental, system and user events associated with the mobile device and/or a peer device. The occurrence of one or more events can trigger adjustments to system settings. The mobile device can be configured to keep frequently invoked applications up to date based on a forecast of predicted invocations by the user. In some implementations, the mobile device can receive push notifications associated with applications that indicate that new content is available for the applications to download. The mobile device can launch the applications associated with the push notifications in the background and download the new content. Before running an application or communicating with a peer device, the mobile device can be configured to check energy and data budgets and environmental conditions of the mobile device and/or a peer device to ensure a high quality user experience.

    Abstract translation: 在一些实现中，移动设备可以被配置为监视与移动设备和/或对等设备相关联的环境，系统和用户事件。 一个或多个事件的发生可以触发对系统设置的调整。 移动设备可以被配置为基于用户预测的调用的预测来保持频繁被调用的应用程序的最新。 在一些实现中，移动设备可以接收与指示新内容可用于应用下载的应用相关联的推送通知。 移动设备可以在后台启动与推送通知相关联的应用，并下载新的内容。 在运行应用程序或与对等设备通信之前，移动设备可被配置为检查移动设备和/或对等设备的能量和数据预算以及环境状况，以确保高质量的用户体验。

    DISTRIBUTED LABELING FOR SUPERVISED LEARNING
    4.
    发明申请
    DISTRIBUTED LABELING FOR SUPERVISED LEARNING 审中-公开

    公开(公告)号：WO2020068360A1

    公开(公告)日：2020-04-02

    申请号：PCT/US2019/048924

    申请日：2019-08-29

    Applicant: APPLE INC.

    Inventor： BHOWMICK, Abhishek ,  ROGERS, Ryan M. ,  VAISHAMPAYAN, Umesh S. ,  VYRROS, Andrew H.

    IPC: G06N3/04

    Abstract: Embodiments described herein provide a technique to crowdsource labeling of training data for a machine learning model while maintaining the privacy of the data provided by crowdsourcing participants. Client devices can be used to generate proposed labels for a unit of data to be used in a training dataset. One or more privacy mechanisms are used to protect user data when transmitting the data to a server. The server can aggregate the proposed labels and use the most frequently proposed labels for an element as the label for the element when generating training data for the machine learning model. The machine learning model is then trained using the crowdsourced labels to improve the accuracy of the model.

    PRIVATIZED APRIORI ALGORITHM FOR SEQUENTIAL DATA DISCOVERY
    5.
    发明申请
    PRIVATIZED APRIORI ALGORITHM FOR SEQUENTIAL DATA DISCOVERY 审中-公开

    公开(公告)号：WO2019231627A2

    公开(公告)日：2019-12-05

    申请号：PCT/US2019/031319

    申请日：2019-05-08

    Applicant: APPLE INC.

    Inventor： BHOWMICK, Abhishek ,  ROGERS, Ryan M. ,  VAISHAMPAYAN, Umesh S. ,  VENKATRAMAN, Kartik R.

    IPC: G06F17/27 ,  G06N20/00

    Abstract: Embodiments described herein provide techniques to encode sequential data in a privacy preserving manner before the data is sent to a sequence learning server. The server can then determine aggregate trends within an overall set of users, without having any specific knowledge about the contributions of individual users. The server can be used to learn new words generated by user client devices in a crowdsourced manner while maintaining local differential privacy of client devices. The server can also learn other sequential data including typed, autocorrected, revised text sequences, sequences of application launches, sequences of purchases on an application store, or other sequences of activities that can be performed on an electronic device.

    PREDICTION AND NOTIFICATION OF CHANGES IN THE OPERATING CONTEXT OF A COMPUTING DEVICE
    6.
    发明申请
    PREDICTION AND NOTIFICATION OF CHANGES IN THE OPERATING CONTEXT OF A COMPUTING DEVICE 审中-公开
    计算机操作中的变化预测与通知

    公开(公告)号：WO2016196497A1

    公开(公告)日：2016-12-08

    申请号：PCT/US2016/035069

    申请日：2016-05-31

    Applicant: APPLE INC.

    Inventor： KAPOOR, Gaurav ,  DE LACERDA, Joao Pedro Forjaz ,  WAN, Kit-Man ,  SIRACUSA, Michael R. ,  VAISHAMPAYAN, Umesh S. ,  BROWN, Alexander Barraclough ,  LI, Song ,  POLLACK, Daniel Ben ,  CHAN, David M. ,  LINGUTLA, Varaprasad V.

    IPC: G06F9/54 ,  H04W4/02 ,  H04L29/08 ,  G06F9/445

    CPC classification number: G06F9/542 ,  H04L67/22 ,  H04W4/029

    Abstract: Disclosed are systems, methods, and non-transitory computer-readable storage media for predicting a future context of a computing device. In some implementations, a context daemon can use historical context information to predict future events and/or context changes. For example, the context daemon can analyze historical context information to predict user sleep patterns, user exercise patterns, and/or other user activity. In some implementations, a context client can register a callback for a predicted future context. For example, the context client can request to be notified ten minutes in advance of a predicted event and/or context change. The context daemon can use the prediction to notify a context client in advance of the predicted event.

    Abstract translation: 公开了用于预测计算设备的未来上下文的系统，方法和非暂时的计算机可读存储介质。 在一些实现中，上下文守护进程可以使用历史上下文信息来预测未来的事件和/或上下文的变化。 例如，上下文守护进程可以分析历史上下文信息以预测用户睡眠模式，用户锻炼模式和/或其他用户活动。 在一些实现中，上下文客户端可以为预测的未来上下文注册回调。 例如，上下文客户端可以在预测事件和/或上下文变化之前十分钟请求通知。 上下文守护进程可以使用该预测来事先通知上下文客户机。

    METHOD FOR TRACKING MEMORY USAGES OF A DATA PROCESSING SYSTEM
    7.
    发明申请
    METHOD FOR TRACKING MEMORY USAGES OF A DATA PROCESSING SYSTEM 审中-公开
    跟踪数据处理系统的存储器使用的方法

    公开(公告)号：WO2013074201A1

    公开(公告)日：2013-05-23

    申请号：PCT/US2012/056902

    申请日：2012-09-24

    Applicant: APPLE INC. ,  VAISHAMPAYAN, Umesh S. ,  CHIMENE, Daniel A. ,  HELLER, Daniel S.

    Inventor： VAISHAMPAYAN, Umesh S. ,  CHIMENE, Daniel A. ,  HELLER, Daniel S.

    IPC: G06F12/02

    CPC classification number: G06F12/0292 ,  G06F11/073 ,  G06F11/076 ,  G06F11/079 ,  G06F11/3466 ,  G06F12/0253

    Abstract: Techniques for tracking memory usages of a data processing system are described herein. According to one embodiment, a memory manager is to perform a first lookup operation in a memory allocation table to identify an allocation entry based on a handle representing a memory address of a memory block allocated to a client and to retrieve a trace entry pointer from the allocation entry. The memory manager is then to perform a second lookup operation in a memory trace table to identify a trace entry based on the trace entry pointer and to increment a memory allocation count of the trace entry. The memory allocation count is utilized to indicate a likelihood of the client causing a memory leak.

    Abstract translation: 本文描述了用于跟踪数据处理系统的存储器使用的技术。 根据一个实施例，存储器管理器将在存储器分配表中执行第一查找操作以基于表示分配给客户端的存储器块的存储器地址的句柄来识别分配条目，并且从所述存储器分配表中检索跟踪条目指针 分配条目。 然后，存储器管理器在存储器跟踪表中执行第二查找操作，以基于跟踪条目指针识别跟踪条目，并增加跟踪条目的存储器分配计数。 存储器分配计数用于指示客户端造成内存泄漏的可能性。

    PRIVATIZED APRIORI ALGORITHM FOR SEQUENTIAL DATA DISCOVERY
    8.
    发明申请
    PRIVATIZED APRIORI ALGORITHM FOR SEQUENTIAL DATA DISCOVERY 审中-公开

    公开(公告)号：WO2019231627A3

    公开(公告)日：2019-12-05

    申请号：PCT/US2019/031319

    申请日：2019-05-08

    Applicant: APPLE INC.

    Inventor： BHOWMICK, Abhishek ,  ROGERS, Ryan M. ,  VAISHAMPAYAN, Umesh S. ,  VENKATRAMAN, Kartik R.

    IPC: G06F17/27 ,  G06N20/00

    Abstract: Embodiments described herein provide techniques to encode sequential data in a privacy preserving manner before the data is sent to a sequence learning server. The server can then determine aggregate trends within an overall set of users, without having any specific knowledge about the contributions of individual users. The server can be used to learn new words generated by user client devices in a crowdsourced manner while maintaining local differential privacy of client devices. The server can also learn other sequential data including typed, autocorrected, revised text sequences, sequences of application launches, sequences of purchases on an application store, or other sequences of activities that can be performed on an electronic device.

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