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    PACING ACTIVITY DATA OF A USER
    1.
    发明申请
    PACING ACTIVITY DATA OF A USER 审中-公开
    用户的活动数据

    公开(公告)号：WO2016036486A1

    公开(公告)日：2016-03-10

    申请号：PCT/US2015/044882

    申请日：2015-08-12

    Applicant: APPLE INC.

    Inventor： KEEN, Daniel S. ,  BLAHNIK, Jay C. ,  KAPOOR, Gaurav ,  SIRACUSA, Michael R.

    IPC: G06F19/00

    CPC classification number: G06F19/3481 ,  G06F19/00

    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.

    Abstract translation: 可以管理用户的步行者活动数据。 例如，可以接收对应于当天之前一天的特定时间的用户的历史活动数据。 此外，可以生成被配置为显示用户的活动目标的用户界面，并且可以提供用户界面用于呈现。 在一些方面，用户界面可以被配置为显示识别针对活动目标的累积进展的第一指示符和识别针对活动目标的预测累积进展的第二指示符。 可以基于从当天开始到当天的特定时间的监视的活动来计算累积进展，并且可以基于接收到的与之前一天的特定时间相对应的历史活动数据来计算预测累积进度 当天。

    INTEGRATION OF LEARNING MODELS INTO A SOFTWARE DEVELOPMENT SYSTEM
    2.
    发明申请
    INTEGRATION OF LEARNING MODELS INTO A SOFTWARE DEVELOPMENT SYSTEM 审中-公开

    公开(公告)号：WO2020242809A1

    公开(公告)日：2020-12-03

    申请号：PCT/US2020/033482

    申请日：2020-05-18

    Applicant: APPLE INC.

    Inventor： SIRACUSA, Michael R. ,  KATTI, Anil ,  FARHADI, Mohammad Reza ,  WADHWA, Aseem ,  BRENNAN, Michael Ryan ,  RACHWALSKI, Andrew Joseph

    IPC: G06F8/35 ,  G06N20/00 ,  G06F8/36 ,  G06F8/71 ,  G06F8/60 ,  G06F8/10

    Abstract: The subject technology provides for determining that a machine learning model in a first format includes sufficient data to conform to a particular model specification in a second format, the second format corresponding to an object oriented programming language), wherein the machine learning model includes a model parameter of the machine learning model. The subject technology transforms the machine learning model into a transformed machine learning model that is compatible with the particular model specification. The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model and the object includes an interface to update the model parameter. Further, the subject technology provides the generated code interface and the code for display in an integrated development environment (IDE), the IDE enabling modifying of the generated code interface and the code.

    DYNAMIC TASK ALLOCATION FOR NEURAL NETWORKS
    3.
    发明申请
    DYNAMIC TASK ALLOCATION FOR NEURAL NETWORKS 审中-公开

    公开(公告)号：WO2018222299A1

    公开(公告)日：2018-12-06

    申请号：PCT/US2018/029201

    申请日：2018-04-24

    Applicant: APPLE INC.

    Inventor： ROSSI, Francesco ,  KAPOOR, Gaurav ,  SIRACUSA, Michael R. ,  MARCH, William B.

    IPC: G06F9/50 ,  G06N3/063

    Abstract: The subject technology provides for dynamic task allocation for neural network models. The subject technology determines an operation performed at a node of a neural network model. The subject technology assigns an annotation to indicate whether the operation is better performed on a CPU or a GPU based at least in part on hardware capabilities of a target platform. The subject technology determines whether the neural network model includes a second layer. The subject technology, in response to determining that the neural network model includes a second layer, for each node of the second layer of the neural network model, determines a second operation performed at the node. Further the subject technology assigns a second annotation to indicate whether the second operation is better performed on the CPU or the GPU based at least in part on the hardware capabilities of the target platform.

    USER INTERFACE FOR MACHINE LANGUAGE MODEL CREATION
    6.
    发明申请
    USER INTERFACE FOR MACHINE LANGUAGE MODEL CREATION 审中-公开

    公开(公告)号：WO2020247282A1

    公开(公告)日：2020-12-10

    申请号：PCT/US2020/035444

    申请日：2020-05-29

    Applicant: APPLE INC.

    Inventor： SIRACUSA, Michael R. ,  BROWN, Alexander B. ,  GOSWAMI, Dheeraj ,  WERTMAN, Nathan C. ,  SAWYER, Jacob T. ,  FIRLIK, Donald M.

    IPC: G06N20/00 ,  G06F3/0482

    Abstract: The present disclosure presents devices, methods, and computer readable medium for user interfaces for creating machine learning models. Application developers can select a machine learning template (304) from a plurality of templates appropriate for the type of data used in their application. Templates can include multiple templates for classification of images, text, sound, motion, and tabular data. A graphical user interface (300) allows for intuitive selection of training data (316), validation data (318), and integration of the trained model (314) into the application. The user interface further display a numerical score for both the training accuracy (508) and validation accuracy (510) using the test data. The application provides a live mode that allows for execution of the machine learning model on a mobile device to allow for testing the model from data from one or more of the sensors (i.e., camera or microphone) on the mobile device.

    PREDICTION AND NOTIFICATION OF CHANGES IN THE OPERATING CONTEXT OF A COMPUTING DEVICE
    9.
    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。

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