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公开(公告)号：US20150347908A1
公开(公告)日：2015-12-03
申请号：US14500990
申请日：2014-09-29
Applicant: Apple Inc.
Inventor： Binu K. MATHEW , Kit-Man WAN , Gaurav KAPOOR
Abstract: Disclosed herein is a technique for implementing a framework that enables application developers to enhance their applications with dynamic adjustment capabilities. Specifically, the framework, when utilized by an application on a mobile computing device that implements the framework, can enable the application to establish predictive models that can be used to identify meaningful behavioral patterns of an individual who uses the application. In turn, the predictive models can be used to preempt the individual's actions and provide an enhanced overall user experience. The framework is configured to interface with other software entities on the mobile computing device that conduct various analyses to identify appropriate times for the application to manage and update its predictive models. Such appropriate times can include, for example, identified periods of time where the individual is not operating the mobile computing device, as well as recognized conditions where power consumption is not a concern.
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22.发明公开公开(公告)号：US20230267133A1
公开(公告)日：2023-08-24
申请号：US18299684
申请日：2023-04-12
Applicant: Apple Inc.
Inventor： Joao Pedro LACERDA , Gaurav KAPOOR
IPC: G06F16/28 , G06N20/00 , G06F16/903
CPC classification number: G06F16/285 , G06N20/00 , G06F16/90335
Abstract: The embodiments set forth techniques for implementing various “prediction engines” that can be configured to provide different kinds of predictions within a mobile computing device. According to some embodiments, each prediction engine can assign itself as an “expert” on one or more “prediction categories” within the mobile computing device. When a software application issues a request for a prediction for a particular category, and two or more prediction engines respond with their respective prediction(s), a “prediction center” can be configured to receive and process the predictions prior to responding to the request. Processing the predictions can involve removing duplicate information that exists across the predictions, sorting the predictions in accordance with confidence levels advertised by the prediction engines, and the like. In this manner, the prediction center can distill multiple predictions down into an optimized prediction and provide the optimized prediction to the software application.
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公开(公告)号：US20210398021A1
公开(公告)日：2021-12-23
申请号：US17347563
申请日：2021-06-14
Applicant: Apple Inc.
Inventor： Umesh S. VAISHAMPAYAN , Gaurav KAPOOR , Kit-Man WAN
IPC: G06N20/00
Abstract: A device implementing a system to execute machine learning models from memory includes at least one processor configured to receive a request to provide an input to one or more machine learning (ML) models arranged into a graph of connected layers, the one or more ML models stored in the first type of memory. The at least one processor is further configured to divide the graph of connected layers into a plurality of segments such that at least two of the plurality of segments concurrently fits within allocated space of the second type of memory. The at least one processor is further configured to cause the input to be processed through the first segment of the plurality of segments using the second type of memory while a second segment of the plurality of segments is concurrently loaded from the first type of memory into the second type of memory.
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公开(公告)号：US20210306812A1
公开(公告)日：2021-09-30
申请号：US17345737
申请日：2021-06-11
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 P. PAGALLO , Xin WANG , Jun HATORI , Alexandre R. MOHA , Kevin D. CLARK , Karl Christian KOHLSCHUETTER , Jesper A. ANDERSEN , Hafid ARRAS , Alexandre CARLHIAN , Thomas DENIAU , Mathieu J. MARTEL , Sofiane TOUDJI
Abstract: Systems and methods for proactively identifying and surfacing relevant content on an electronic device with a touch-sensitive display are disclosed herein. In one aspect, a method includes presenting content in a first application. At least a portion of the content is presented without requiring input from a user. The method further includes receiving a request to open a second application. In response to receiving the request, the second application is presented with an input-receiving field. Before receiving any user input at the input-receiving field, a selectable user interface object is displayed with an indication that the portion of the content was viewed in the first application, allowing the user to paste at least the portion of the content into the input-receiving field. In response to detecting a selection of the selectable user interface object, the portion of the content is pasted into the input-receiving field.
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公开(公告)号：US20210006943A1
公开(公告)日：2021-01-07
申请号：US17030300
申请日：2020-09-23
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 P. PAGALLO , Xin WANG , Jun HATORI , Alexandre R. MOHA , Kevin D. CLARK , Karl Christian KOHLSCHUETTER , Jesper A. ANDERSEN , Hafid ARRAS , Alexandre CARLHIAN , Thomas DENIAU , Mathieu J. MARTEL , Sofiane TOUDJI
Abstract: Systems and methods for proactively identifying and surfacing relevant content are disclosed herein. An example method includes: detecting, via the touch-sensitive display, a search activation gesture from a user of the electronic device. The method also includes: in response to detecting only the search activation gesture, displaying a search interface on substantially all of the touch-sensitive display, the search interface including: (i) a search entry portion; and (ii) a predictions portion with one or more user interface objects each associated with a respective locally-installed application. Each respective locally-installed application is selected from among a plurality of locally-installed applications for inclusion in the predictions portion based on an application usage history associated with the user of the electronic device.
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Patent Agency Ranking
公开(公告)号：US20190286424A1
公开(公告)日：2019-09-19
申请号：US16430351
申请日：2019-06-03
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：US20190057007A1
公开(公告)日：2019-02-21
申请号：US16121400
申请日：2018-09-04
Applicant: Apple Inc.
Inventor： Cyril DE LA CROPTE DE CHANTERAC , Phillip STANLEY-MARBELL , Kartik VENKATRAMAN , Gaurav KAPOOR
Abstract: Systems and methods are disclosed for advising a user when an energy storage device in a computing system needs charging. State of charge data of the energy storage device can be measured and stored at regular intervals. The historic state of charge data can be queried over a plurality of intervals and a state of charge curve generated that is representative of a user's charging habits over time. The state of charge curve can be used to generate a rate of charge histogram and an acceleration of charge histogram. These can be used to predict when a user will charge next, and whether the energy storage device will have an amount of energy below a predetermined threshold amount before the next predicted charging time. A first device can determine when a second device typically charges and whether the energy storage device in the second device will have an amount of energy below the predetermined threshold amount before the next predicted charge time for the second device. The first device can generate an advice to charge notification to the user on either, or both, devices.
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公开(公告)号：US20180349109A1
公开(公告)日：2018-12-06
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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公开(公告)号：US20160357654A1
公开(公告)日：2016-12-08
申请号：US14871856
申请日：2015-09-30
Applicant: Apple Inc.
Inventor： Cyril de la CROPTE de CHANTÉRAC , Phillip STANLEY-MARBELL , Kartik VENKATRAMAN , Gaurav KAPOOR
CPC classification number: G06F11/327 , G06F1/3203 , G06F1/3212 , Y02D10/174
Abstract: Systems and methods are disclosed for advising a user when an energy storage device in a computing system needs charging. State of charge data of the energy storage device can be measured and stored at regular intervals. The historic state of charge data can be queried over a plurality of intervals and a state of charge curve generated that is representative of a user's charging habits over time. The state of charge curve can be used to generate a rate of charge histogram and an acceleration of charge histogram. These can be used to predict when a user will charge next, and whether the energy storage device will have an amount of energy below a predetermined threshold amount before the next predicted charging time. A first device can determine when a second device typically charges and whether the energy storage device in the second device will have an amount of energy below the predetermined threshold amount before the next predicted charge time for the second device. The first device can generate an advice to charge notification to the user on either, or both, devices.
Abstract translation: 公开了系统和方法，用于在计算系统中的能量存储装置需要充电时向用户提供建议。 能量储存装置的充电状态数据可以以规则的间隔进行测量和存储。 可以在多个间隔中查询历史状态的电荷数据，并且生成代表用户充电习惯的充电状态曲线。 充电状态曲线可用于产生电荷直方图和充电直方图的加速度。 这些可以用于预测用户何时将要充电，以及能量存储装置在下一预测充电时间之前是否具有低于预定阈值量的能量。 第一设备可以确定第二设备何时通常充电，以及第二设备中的能量存储设备是否将在第二设备的下一预测充电时间之前具有低于预定阈值量的能量量。 第一个设备可以产生建议，以在设备中的一个或两者上向用户收取通知。
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公开(公告)号：US20150347907A1
公开(公告)日：2015-12-03
申请号：US14500985
申请日：2014-09-29
Applicant: Apple Inc.
Inventor： Binu K. MATHEW , Kit-Man WAN , Gaurav KAPOOR
Abstract: Disclosed herein is a technique for implementing a framework that enables application developers to enhance their applications with dynamic adjustment capabilities. Specifically, the framework, when utilized by an application on a mobile computing device that implements the framework, can enable the application to establish predictive models that can be used to identify meaningful behavioral patterns of an individual who uses the application. In turn, the predictive models can be used to preempt the individual's actions and provide an enhanced overall user experience. The framework is configured to interface with other software entities on the mobile computing device that conduct various analyses to identify appropriate times for the application to manage and update its predictive models. Such appropriate times can include, for example, identified periods of time where the individual is not operating the mobile computing device, as well as recognized conditions where power consumption is not a concern.
Abstract translation: 这里公开了一种用于实现框架的技术，其使应用开发者能够通过动态调整功能来增强其应用。 具体地说，该框架当被实施框架的移动计算设备上的应用程序使用时，可以使应用程序能够建立可用于识别使用该应用程序的个人的有意义的行为模式的预测模型。 反过来，预测模型可以用于抢占个人的行为并提供增强的整体用户体验。 该框架被配置为与移动计算设备上的其他软件实体进行接口，进行各种分析以确定应用程序管理和更新其预测模型的适当时间。 这种适当的时间可以包括例如个体不操作移动计算设备的识别的时间段以及功率消耗不是关注的公认条件。
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