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    LOGGING TECHNIQUES FOR THIRD PARTY APPLICATION DATA
    2.
    发明申请
    LOGGING TECHNIQUES FOR THIRD PARTY APPLICATION DATA 审中-公开

    公开(公告)号：US20200379878A1

    公开(公告)日：2020-12-03

    申请号：US16671093

    申请日：2019-10-31

    Applicant: Apple Inc.

    Inventor： Amit K. Vyas ,  Abhinav Pathak ,  Anthony R. Newnam ,  Phillip J. Azar ,  Ashish Patro ,  Prajakta S. Karandikar ,  Daniel J. Etter ,  Conor J. O'Reilly ,  Andreas C. Bergen ,  Nehal Bhandari ,  Jeffrey S. Lale ,  Andrew P. Sakai ,  Terrence R. Long ,  Soren C. Spies

    IPC: G06F11/36

    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.

    AUTOMATIC DIAGNOSTICS AND MITIGATION FOR IMPROVING APPLICATION RESPONSIVENESS
    5.
    发明公开
    AUTOMATIC DIAGNOSTICS AND MITIGATION FOR IMPROVING APPLICATION RESPONSIVENESS 审中-公开

    公开(公告)号：US20230393962A1

    公开(公告)日：2023-12-07

    申请号：US18204784

    申请日：2023-06-01

    Applicant: Apple Inc.

    Inventor： Sonia Mannan ,  Anshul Dawra ,  John T. Crowson ,  Akshay Salpekar ,  Phillip J. Azar ,  Anthony R. Newnam

    IPC: G06F11/34 ,  G06F11/32

    CPC classification number: G06F11/3476 ,  G06F11/324

    Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium related to identifying, generating, and presenting diagnostic data corresponding to devices from which the diagnostic data was obtained. In some embodiments, the diagnostic data may include log file data associated with a common error, operational metrics, or the like. Commonality may be identified based on call path signatures. Call path signatures may be generated for log files and compared to one another to determine matches. Matched log files may be grouped or otherwise associated with a common error (e.g., a hanging error). A user interface is provided to view the diagnostic data associated with a common error. The disclosed techniques provide an intelligent method for visualizing performance changes and/or identifying errors in applications.

    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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