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    APPLICATION RELAUNCH OPTIMIZATION
    1.
    发明公开
    APPLICATION RELAUNCH OPTIMIZATION 审中-公开

    公开(公告)号：US20230393910A1

    公开(公告)日：2023-12-07

    申请号：US17898118

    申请日：2022-08-29

    Applicant: Apple Inc.

    Inventor： Jeffrey D. Curless ,  Rohit Mundra

    IPC: G06F9/54 ,  G06F9/451

    CPC classification number: G06F9/542 ,  G06F9/451

    Abstract: Certain embodiments disclosed herein provide application relaunch optimization. Aspects of the present disclosure include deferring the termination of at least some applications notwithstanding an action taken by the user to terminate such applications. For example, a trigger event such as a deferral time can be set, prior to which the termination will take place if an additional user action occurs, and after which the application will be kept suspended in volatile memory indefinitely. As another example, the termination can take place, or not, depending on the type of application or the nature of its usage. An application that has not been terminated despite the termination action being taken by the user and the application's removal from the application management interface can be invoked quickly and efficiently, improving the user experience.

    Visualization Techniques for Third Party Application Data
    7.
    发明申请
    Visualization Techniques for Third Party Application Data 审中-公开

    公开(公告)号：US20200380033A1

    公开(公告)日：2020-12-03

    申请号：US16671144

    申请日：2019-10-31

    Applicant: Apple Inc.

    Inventor： Amit K. Vyas ,  Abhinav Pathak ,  Anthony R. Newnam ,  Anshul Dawra ,  Matthew Smith ,  Scott C. Tooker ,  Tianhe Wu ,  Aditya Bhushan ,  Hina Dixit ,  Conor O'Reilly ,  Rohit Mundra ,  Eduardo Sanchez ,  Anca A. Chandra ,  Yue Li

    IPC: G06F16/904 ,  G06K9/62 ,  G06F17/18

    Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for measuring operational performance metrics, and presenting these metrics through an application programming interface (API) for developers to access for optimizing their applications. Exemplary metrics can include central processing unit or graphics processing unit time, foreground/background time, networking bytes (per application), location activity, display average picture luminance, cellular networking condition, peak memory, number of logical writes, launch and resume time, frame rates, and hang time. Regional markers can also be used to measure specific metrics for in application tasks. The techniques provide multiple user interfaces to help developers recognize the important metrics to optimize the performance of their applications. The data can be normalized over various different devices having different battery size, screen size, and processing requirements. The user interfaces can provide an intelligent method for visualizing performance changes for significant changes in application versions.

    Clustering techniques for third party application data
    8.
    发明授权
    Clustering techniques for third party application data 有权

    公开(公告)号：US10824536B1

    公开(公告)日：2020-11-03

    申请号：US16671128

    申请日：2019-10-31

    Applicant: Apple Inc.

    Inventor： Amit K. Vyas ,  Abhinav Pathak ,  Anthony R. Newnam ,  Anshul Dawra ,  Vincent B. Yeh ,  Steve Brashear ,  Andreas C. Bergen ,  Yue Li ,  Rohit Mundra ,  Ritika Kalia ,  Anca A. Chandra ,  Sanket Bedare

    IPC: G06F15/173 ,  G06F11/34 ,  G06F9/54 ,  G06F16/906

    Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for measuring operational performance metrics, and presenting these metrics through an application programming interface (API) for developers to access for optimizing their applications. Exemplary metrics can include central processing unit or graphics processing unit time, foreground/background time, networking bytes (per application), location activity, display average picture luminance, cellular networking condition, peak memory, number of logical writes, launch and resume time, frame rates, and hang time. Regional markers can also be used to measure specific metrics for in application tasks. The techniques provide multiple user interfaces to help developers recognize the important metrics to optimize the performance of their applications. The data can be normalized over various different devices having different battery size, screen size, and processing requirements. The user interfaces can provide an intelligent method for visualizing performance changes for significant changes in application versions.

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