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    DEVICE PERFORMANCE ISSUE OPTIMIZATION
    1.
    发明申请
    DEVICE PERFORMANCE ISSUE OPTIMIZATION 有权

    公开(公告)号：US20250138984A1

    公开(公告)日：2025-05-01

    申请号：US18384412

    申请日：2023-10-27

    Applicant: Apple Inc.

    Inventor： Abhinav Pathak ,  Amit K. Vyas ,  Anand Ramadurai ,  Anca A. Chandra ,  Anshul Dawra ,  Ashish Patro ,  David S. Choi ,  Jonathan Y. Hanna ,  Nehal Bhandari ,  Ning Ding ,  Rohit Mundra ,  Ryan A. Carlson ,  Xiaomeng Chen ,  Yue Li ,  Zoe S. Bohn

    IPC: G06F11/36

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing performance issues. One of the methods includes maintaining, for a plurality of devices at least some of which have different contexts, metric data for an application that executed on each of the plurality of devices; determining, for a metric attribute from a plurality of metric attributes and a subset of the plurality of devices each of which have at least one common context, a potential performance issue for the subset of the plurality of devices using aggregated metric data for the metric attribute; determining, using at least a portion of the aggregated metric data, a portion of a code base or a hardware subcomponent that likely caused the potential performance issue; and providing data for the portion of the code base or the hardware subcomponent that likely caused the potential performance issue.

    Visualization Techniques for Third Party Application Data
    4.
    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
    5.
    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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