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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.
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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
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.
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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
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.
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Patent Agency Ranking
14.发明申请公开(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
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.
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公开(公告)号：US20150351037A1
公开(公告)日：2015-12-03
申请号：US14290795
申请日：2014-05-29
Applicant: Apple Inc.
Inventor： Alexander B. Brown , Gaurav Kapoor
CPC classification number: H04W52/0261 , G01R31/3651 , G01R31/3693 , G06F1/32 , G06F1/3203 , G06F1/3212 , G06N5/04 , G06N99/005 , H01M6/5044 , H01M10/4207 , H01M10/48 , H01M2010/4271 , H04M1/0202 , H04M2001/0204 , Y02D10/174 , Y02D70/142 , Y02D70/144 , Y02D70/146 , Y02D70/164
Abstract: According to one embodiment, a first battery number is determined representing a battery condition of a battery of a mobile device using a predictive model, where the predictive model is configured to predict future battery conditions based on a past battery usage of the battery. A second battery number is determined representing the battery condition using a drain model, where the drain model is configured to predict a future battery discharge rate based on a past battery discharge rate. A third battery number is determined representing the battery condition based on a current battery level corresponding to a remaining life of the battery at the point in time. Power management logic performs a power management action based on the battery condition derived from at least one of the first battery number, the second battery number and the third battery number.
Abstract translation: 根据一个实施例，确定代表使用预测模型的移动设备的电池的电池状况的第一电池电量，其中预测模型被配置为基于电池的过去的电池使用来预测未来的电池状况。 使用排水模型来确定表示电池状况的第二电池号码，其中排水模型被配置为基于过去的电池放电速率预测未来的电池放电率。 根据与该电池在该时间点的剩余寿命相对应的当前电池电量，确定代表电池状况的第三电池号码。 电源管理逻辑基于从第一电池号码，第二电池号码和第三电池号码中的至少一个导出的电池状况来执行电源管理动作。
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公开(公告)号：US11256479B2
公开(公告)日：2022-02-22
申请号：US16855067
申请日：2020-04-22
Applicant: Apple Inc.
Inventor： Alexander B. Brown
Abstract: An interactive software development environment in one embodiment can interactively provide outputs of execution or evaluation of software entered into the environment prior to compilation of the software and can automatically add one or more error handling expressions that isolate errors from effecting future software. The environment can automatically add the one or more error handling expressions for each line for software or for a set of software to wrap the set to catch and isolate errors. The execution or evaluation of software in the environment can be, for example, by read-evaluate-print-loop functionality provided by the environment.
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公开(公告)号：US20200380301A1
公开(公告)日：2020-12-03
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: G06K9/62 , G06N20/00 , G06F8/34 , G06F3/0486
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.
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公开(公告)号：US10310821B2
公开(公告)日：2019-06-04
申请号：US15721712
申请日：2017-09-29
Applicant: Apple Inc.
Inventor： Alexander B. Brown , Michael R. Siracusa , Gaurav Kapoor , Elizabeth Ottens , Christopher M. Hanson , Zachary A. Nation , Vrushali Mundhe , Srikrishna Sridhar
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. 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. 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.
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