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公开(公告)号：US20230176907A1
公开(公告)日：2023-06-08
申请号：US18074440
申请日：2022-12-02
Applicant: Apple Inc.
Inventor： Francesco ROSSI , Gaurav KAPOOR , Michael R. SIRACUSA , William B. MARCH
CPC classification number: G06F9/50 , G06F9/5044 , G06N3/063 , G06F8/451 , G06F9/485 , G06F9/5038 , G06N3/02 , G06F2209/509
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.
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公开(公告)号：US20200034725A1
公开(公告)日：2020-01-30
申请号：US16538706
申请日：2019-08-12
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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公开(公告)号：US20210390087A1
公开(公告)日：2021-12-16
申请号：US17356475
申请日：2021-06-23
Applicant: Apple Inc.
Inventor： Stephen C. PETERS , Kit-Man WAN , Gaurav KAPOOR
IPC: G06F16/23 , G06F3/0482 , G06F3/0488 , G06F3/0481 , G06F3/0484
Abstract: This application relates to features for a mobile device that allow the mobile device to assign utility values to applications and thereafter suggest applications for a user to execute. The suggested application can be derived from a list of applications that have been assigned a utility by software in the mobile device. The utility assignment of the individual applications from the list of applications can be performed based on the occurrence of an event, an environmental change, or a period of frequent application usage. A feedback mechanism is provided in some embodiments for more accurately assigning a utility to particular applications. The feedback mechanism can track what a user does during a period of suggestion for certain applications and thereafter modify the utility of applications based on what applications a user selects during the period of suggestion.
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公开(公告)号：US20170156111A1
公开(公告)日：2017-06-01
申请号：US15431628
申请日：2017-02-13
Applicant: Apple Inc.
Inventor： Patrick Y. LAW , Keith COX , John M. ANANNY , Stephen D. STERZ , Derek J. DICARLO , Gaurav KAPOOR , Jason L. PANG , Alex J. CRUMLIN
CPC classification number: H04W52/028 , H04B1/385 , H04L43/16 , H04W52/0261 , H04W52/027 , H04W52/0277 , H04W52/0287 , Y02D70/00 , Y02D70/142 , Y02D70/144 , Y02D70/146 , Y02D70/166 , Y02D70/26
Abstract: The subject matter of the disclosure relates to low temperature power throttling at a mobile device to reduce the likelihood of an unexpected power down event in cold weather environments. A mobile device employing a power management solution may be configured to determine that a monitored temperature at the mobile device (at the battery of the mobile device) is below a first threshold level, and whether a hardware component (such as a camera) is active or inactive. Then, based on these determinations, the mobile device can select a throttle setting from a first set of throttle settings when the hardware component is active, and a second set of throttle settings when the hardware component is inactive. Subsequently the mobile device can throttle power consumption for one or more components of the mobile device according to the selected throttle setting.
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5.发明申请SYSTEMS AND METHODS FOR PROVIDING PREDICTIONS TO APPLICATIONS EXECUTING ON A COMPUTING DEVICE 审中-公开用于在计算机上执行应用的预测的系统和方法公开(公告)号：US20160358078A1
公开(公告)日：2016-12-08
申请号：US14866786
申请日：2015-09-25
Applicant: Apple Inc.
Inventor： Joao Pedro LACERDA , Gaurav KAPOOR
CPC classification number: G06F16/285 , G06F16/90335 , G06N20/00
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.
Abstract translation: 实施例阐述了用于实现可以被配置为在移动计算设备内提供不同种类的预测的各种“预测引擎”的技术。 根据一些实施例，每个预测引擎可以在移动计算设备内的一个或多个“预测类别”上将自身分配为“专家”。 当软件应用程序发出对特定类别的预测的请求时，并且两个或更多个预测引擎用它们各自的预测进行响应时，“预测中心”可被配置为在响应请求之前接收和处理预测 。 处理预测可以涉及删除存在于预测中的重复信息，根据由预测引擎公布的置信度等对预测进行排序。 以这种方式，预测中心可以将多个预测分解成优化的预测，并向软件应用提供优化的预测。
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Patent Agency Ranking
公开(公告)号：US20150347519A1
公开(公告)日：2015-12-03
申请号：US14721945
申请日：2015-05-26
Applicant: Apple Inc.
Inventor： John M. HORNKVIST , Gaurav KAPOOR
CPC classification number: G06F16/2457 , G06F16/9535 , G06F16/9537 , G06N20/00
Abstract: Systems and methods are disclosed for improving search results returned to a user from one or more search domains, utilizing query features learned locally on the user's device. A search engine can receive, analyze and forward query results from multiple search domains and pass the query results to a client device. A search engine can determine a feature by analyzing query results, generate a predictor for the feature, instruct a client device to use the predictor to train on the feature, and report back to the search engine on training progress. A search engine can instruct a first and second set of client devices to train on set A and B of predictors, respectively, and report back training progress to the search engine. A client device can store search session context and share the context with a search engine between sessions with one or more search engines. A synchronization system can synchronize local predictors between multiple client devices of a user.
Abstract translation: 公开了系统和方法，以利用在用户设备上本地学习的查询特征来改进从一个或多个搜索域返回给用户的搜索结果。 搜索引擎可以从多个搜索域接收，分析和转发查询结果，并将查询结果传递给客户端设备。 搜索引擎可以通过分析查询结果来确定特征，生成特征的预测器，指示客户端设备使用预测器来训练该特征，并向搜索引擎报告训练进度。 搜索引擎可以指示第一和第二组客户端设备分别在集合A和B上进行训练，并将训练进度报告给搜索引擎。 客户端设备可以存储搜索会话环境并且在与一个或多个搜索引擎的会话之间与搜索引擎共享上下文。 同步系统可以在用户的多个客户端设备之间同步本地预测器。
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公开(公告)号：US20150347488A1
公开(公告)日：2015-12-03
申请号：US14501000
申请日：2014-09-29
Applicant: Apple Inc.
Inventor： Stephen C. PETERS , Kit-Man WAN , Gaurav KAPOOR
IPC: G06F17/30 , G06F3/0484 , G06F3/0481
CPC classification number: G06F17/30345 , G06F3/04817 , G06F3/0482 , G06F3/04842 , G06F3/04847 , G06F3/04883
Abstract: This application relates to features for a mobile device that allow the mobile device to assign utility values to applications and thereafter suggest applications for a user to execute. The suggested application can be derived from a list of applications that have been assigned a utility by software in the mobile device. The utility assignment of the individual applications from the list of applications can be performed based on the occurrence of an event, an environmental change, or a period of frequent application usage. A feedback mechanism is provided in some embodiments for more accurately assigning a utility to particular applications. The feedback mechanism can track what a user does during a period of suggestion for certain applications and thereafter modify the utility of applications based on what applications a user selects during the period of suggestion.
Abstract translation: 该应用涉及移动设备的特征，其允许移动设备将应用值分配给应用，然后向用户建议应用以执行。 建议的应用程序可以从已经由移动设备中的软件分配实用程序的应用程序列表中导出。 可以根据事件发生，环境变化或频繁使用应用的周期来执行应用列表中各个应用的实用程序分配。 在一些实施例中提供反馈机制以更准确地将实用程序分配给特定应用。 反馈机制可以跟踪用户在特定应用的建议期间所做的工作，然后根据用户在建议期间选择的应用来修改应用的实用性。
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公开(公告)号：US20240202092A1
公开(公告)日：2024-06-20
申请号：US18590290
申请日：2024-02-28
Applicant: Apple Inc.
Inventor： Cyril DE LA CROPTE DE CHANTERAC , Phillip STANLEY-MARBELL , Katrik VENKATRAMAN , Gaurav KAPOOR
IPC: G06F11/32 , G06F1/3203 , G06F1/3212
CPC classification number: G06F11/327 , G06F1/3203 , G06F1/3212 , Y02D10/00
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：US20230177350A1
公开(公告)日：2023-06-08
申请号：US17903991
申请日：2022-09-06
Applicant: Apple Inc.
Inventor： Gaurav KAPOOR , Cecile M. FORET , Francesco ROSSI , Kit-Man WAN , Umesh S. VAISHAMPAYAN , Etienne BELANGER , Albert ANTONY , Alexey MARINICHEV , Marco ZULIANI , Xiaojin SHI
CPC classification number: G06N3/10 , G06N3/08 , G06N3/04 , G06F8/443 , G06F8/41 , G06F8/4441 , G06N3/063 , G06F9/50
Abstract: The subject technology provides receiving a neural network (NN) model to be executed on a target platform, the NN model including multiple layers that include operations and some of the operations being executable on multiple processors of the target platform. The subject technology further sorts the operations from the multiple layers in a particular order based at least in part on grouping the operations that are executable by a particular processor of the multiple processors. The subject technology determines, based at least in part on a cost of transferring the operations between the multiple processors, an assignment of one of the multiple processors for each of the sorted operations of each of the layers in a manner that minimizes a total cost of executing the operations. Further, for each layer of the NN model, the subject technology includes an annotation to indicate the processor assigned for each of the operations.
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公开(公告)号：US20210109718A1
公开(公告)日：2021-04-15
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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