公开(公告)号：US20160360007A1

公开(公告)日：2016-12-08

申请号：US15151306

申请日：2016-05-10

Applicant: Apple Inc.

Inventor： Michael R. Siracusa ,  Joao Pedro Forjaz de Lacerda ,  Kit-Man Wan ,  Gaurav Kapoor ,  Umesh S. Vaishampayan

IPC: H04L29/08

CPC classification number: H04L67/327 ,  G06F8/61 ,  G06F9/445 ,  G06F9/461 ,  G06F9/542 ,  G06F9/546 ,  G06F11/30 ,  G06N7/005 ,  H04L67/14 ,  H04L67/26 ,  H04W24/02 ,  H04W52/0264 ,  Y02D10/43 ,  Y02D70/00 ,  Y02D70/142 ,  Y02D70/144 ,  Y02D70/164 ,  Y02D70/26

Abstract: Disclosed are systems, methods, and non-transitory computer-readable storage media for notifying context clients of changes to the current context of a computing device. In some implementations, a context client can register to be called back when the context daemon detects specified context. For example, the context client can specify a context in which the context client is interested. When the context daemon detects that the current context of the computing device corresponds to the registered context, the context daemon can notify the context client that the current context matches the context in which the context client is interested. Thus, context clients do not require the programming necessary to independently obtain context updates and detect changes in context that are relevant or of interest to the context client.

Abstract translation: 公开了用于通知上下文客户机对计算设备的当前上下文的改变的系统，方法和非暂时的计算机可读存储介质。 在一些实现中，当上下文守护程序检测到指定的上下文时，上下文客户端可以注册以被调用。 例如，上下文客户端可以指定上下文客户端感兴趣的上下文。 当上下文守护程序检测到计算设备的当前上下文对应于注册的上下文时，上下文守护进程可以通知上下文客户端当前上下文与上下文客户端感兴趣的上下文匹配。 因此，上下文客户端不需要必要的编程来独立地获取上下文更新并检测上下文中与上下文客户端相关或感兴趣的上下文中的变化。

公开(公告)号：US20240211805A1

公开(公告)日：2024-06-27

申请号：US18378068

申请日：2023-10-09

Applicant: APPLE INC.

Inventor： Michael R. Siracusa ,  Alexander B. Brown ,  Dheeraj Goswami ,  Nathan C. Wertman ,  Jacob T. Sawyer ,  Donald M. Firlik

IPC: G06N20/00 ,  G06F3/048 ,  G06F3/0486 ,  G06F8/34 ,  G06F18/21 ,  G06F18/214 ,  G06F18/2431 ,  G06V10/776

CPC classification number: G06N20/00 ,  G06F3/048 ,  G06F3/0486 ,  G06F8/34 ,  G06F18/2148 ,  G06F18/2193 ,  G06F18/2431 ,  G06V10/776

Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for creating machine learning models. Application developers can select a machine learning template 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 allows for intuitive selection of training data, validation data, and integration of the trained model into the application. The techniques further display a numerical score for both the training accuracy and validation accuracy 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.

公开(公告)号：US11783223B2

公开(公告)日：2023-10-10

申请号：US16670914

申请日：2019-10-31

Applicant: Apple Inc.

Inventor： Michael R. Siracusa ,  Alexander B. Brown ,  Dheeraj Goswami ,  Nathan C. Wertman ,  Jacob T. Sawyer ,  Donald M. Firlik

IPC: G06F3/048 ,  G06N20/00 ,  G06F3/0486 ,  G06F8/34 ,  G06F18/214 ,  G06F18/21 ,  G06F18/2431 ,  G06V10/776

CPC classification number: G06N20/00 ,  G06F3/048 ,  G06F3/0486 ,  G06F8/34 ,  G06F18/2148 ,  G06F18/2193 ,  G06F18/2431 ,  G06V10/776

Abstract: Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for creating machine learning models. Application developers can select a machine learning template 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 allows for intuitive selection of training data, validation data, and integration of the trained model into the application. The techniques further display a numerical score for both the training accuracy and validation accuracy 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.

公开(公告)号：US11687830B2

公开(公告)日：2023-06-27

申请号：US16875565

申请日：2020-05-15

Applicant: Apple Inc.

Inventor： Michael R. Siracusa ,  Anil Kumar Katti ,  Mohammad Reza Farhadi ,  Aseem Wadhwa ,  Michael Ryan Brennan ,  Andrew Joseph Rachwalski

IPC: G06N20/00 ,  G06F8/10 ,  G06F8/33

CPC classification number: G06N20/00 ,  G06F8/10 ,  G06F8/33

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.

公开(公告)号：US11614922B2

公开(公告)日：2023-03-28

申请号：US17129782

申请日：2020-12-21

Applicant: Apple Inc.

Inventor： Alexander B. Brown ,  Michael R. Siracusa ,  Gaurav Kapoor ,  Elizabeth Ottens ,  Christopher M. Hanson ,  Zachary A. Nation ,  Vrushali Mundhe ,  Srikrishna Sridhar

IPC: G06F8/30 ,  G06N20/00 ,  G06F8/35 ,  G06F8/10 ,  G06F8/36 ,  G06F8/60 ,  G06F8/71

Abstract: The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). 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. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.

公开(公告)号：US10606566B2

公开(公告)日：2020-03-31

申请号：US15721701

申请日：2017-09-29

Applicant: Apple Inc.

Inventor： Alexander B. Brown ,  Michael R. Siracusa ,  Gaurav Kapoor ,  Elizabeth A. Ottens ,  Christopher M. Hanson ,  Zachary A. Nation ,  Vrushali H. Mundhe ,  Srikrishna Sridhar

IPC: G06F8/35 ,  G06N20/00 ,  G06F8/41 ,  G06F8/10 ,  G06F8/30 ,  G06F8/71 ,  G06F8/60 ,  G06F8/36

Abstract: The subject technology provides for generating machine learning (ML) model code from a ML document file, the ML document file being in a first data format, the ML document file being converted to code in an object oriented programming language different than the first data format. The subject technology further provides for receiving additional code that calls a function provided by the ML model code. The subject technology compiles the ML model code and the additional code, the compiled ML model code including object code corresponding to the compiled ML model code and the compiled additional code including object code corresponding to the additional code. The subject technology generates a package including the compiled ML model code and the compiled additional code. Further, the subject technology sends the package to a runtime environment on a target device for execution.

公开(公告)号：US20180349114A1

公开(公告)日：2018-12-06

申请号：US15721722

申请日：2017-09-29

Applicant: Apple Inc.

Inventor： Alexander B. Brown ,  Michael R. Siracusa ,  Norman N. Wang

IPC: G06F9/45 ,  G06N99/00

Abstract: The subject technology provides for parsing a line of code in a project of an integrated development environment (IDE). The subject technology executes indirectly, using the interpreter, the parsed line of code. The interpreter references a translated source code document generated by a source code translation component from a machine learning (ML) document written in a particular data format. The translated source code document includes code in a chosen programming language specific to the IDE, and the code of the translated source code document is executable by the interpreter. Further the subject technology provides, by the interpreter, an output of the executed parsed line of code.

公开(公告)号：US20160360336A1

公开(公告)日：2016-12-08

申请号：US15167713

申请日：2016-05-27

Applicant: Apple Inc.

Inventor： Daniel C. Gross ,  Patrick L. Coffman ,  Richard R. Dellinger ,  Christopher P. Foss ,  Jason J. Gauci ,  Aria D. Haghighi ,  Cyrus D. Irani ,  Bronwyn A. Jones ,  Gaurav Kapoor ,  Stephen O. Lemay ,  Colin C. Morris ,  Michael R. Siracusa ,  Lawrence Y. Yang ,  Brent D. Ramerth ,  Jerome R. Bellegarda ,  Jannes G.A. Dolfing ,  Giulia M. Pagallo ,  Xin Wang ,  Jun Hatori ,  Alexandre R. Moha ,  Sofiane Toudji ,  Kevin D. Clark ,  Karl Christian Kohlschuetter ,  Jesper S. Andersen ,  Hafid Arras ,  Alexandre Carlhian ,  Thomas Deniau ,  Mathieu J. Martel

IPC: H04W4/00 ,  H04L29/08 ,  H04M1/725 ,  H04W4/02

CPC classification number: H04W4/50 ,  G06F9/543 ,  H04L67/125 ,  H04L67/18 ,  H04M1/72522 ,  H04M1/72583 ,  H04M2250/10 ,  H04M2250/12 ,  H04M2250/22 ,  H04W4/025

Abstract: Systems and methods for proactively populating an application with information that was previously viewed by a user in a different application are disclosed herein. An example method includes: while displaying a first application, obtaining information identifying a first physical location viewed by a user in the first application. The method also includes exiting the first application and, after exiting the first application, receiving a request from the user to open a second application that is distinct from the first application. In response to receiving the request and in accordance with a determination that the second application is capable of accepting geographic location information, the method includes presenting the second application so that the second application is populated with information that is based at least in part on the information identifying the first physical location.

Abstract translation: 本文公开了用于主动填充应用程序的系统和方法，该应用程序以前由用户在不同应用程序中查看的信息。 示例性方法包括：在显示第一应用程序的同时，获得识别用户在第一应用中查看的第一物理位置的信息。 该方法还包括退出第一应用程序，并且在退出第一应用程序之后，接收来自用户的打开与第一应用程序不同的第二应用程序的请求。 响应于接收到请求并且根据第二应用能够接受地理位置信息的确定，该方法包括呈现第二应用，使得第二应用被填充至少部分基于该信息的信息 识别第一个物理位置。

公开(公告)号：US11520629B2

公开(公告)日：2022-12-06

申请号：US16776338

申请日：2020-01-29

Applicant: Apple Inc.

Inventor： Francesco Rossi ,  Gaurav Kapoor ,  Michael R. Siracusa ,  William B. March

IPC: G06F9/46 ,  G06F9/50 ,  G06N3/063 ,  G06F8/41 ,  G06F9/48 ,  G06N3/02

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.

公开(公告)号：US11116425B2

公开(公告)日：2021-09-14

申请号：US16180483

申请日：2018-11-05

Applicant: APPLE INC.

Inventor： Daniel S. Keen ,  Jay C. Blahnik ,  Gaurav Kapoor ,  Michael R. Siracusa

IPC: A61B5/00 ,  A61B5/11 ,  G06F9/54 ,  G06F9/48 ,  G06F8/65

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.