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公开(公告)号：US20230163607A1
公开(公告)日：2023-05-25
申请号：US17530630
申请日：2021-11-19
发明人： Mounika Vanka , Saba Shah
CPC分类号： H02J7/0044 , H02J7/005 , H02J50/80 , H02J50/10 , G06F1/1635 , H02J2300/24
摘要： An auxiliary power case can include a frame; a panel coupled to the frame, where the panel defines at least a portion of a recess; a rechargeable battery disposed at least in part in the recess; and a power interface operatively coupled to the rechargeable battery.
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公开(公告)号：US20220237044A1
公开(公告)日：2022-07-28
申请号：US17157896
申请日：2021-01-25
发明人： Saba Shah , Xiaohua Xu , Rod D. Waltermann
摘要： Apparatuses, methods, systems, and program products are disclosed for dynamic client/server selection for machine learning execution. An apparatus includes a processor and a memory that stores code executable by the processor. The code is executable by the processor to receive a request at a first device to execute a machine learning workload for the first device, dynamically determine at least one characteristic of the first device that is related to execution of the machine learning workload, dynamically determine at least one characteristic of a second device that is related to execution of the machine learning workload, and select one of the first and second devices to execute the machine learning workload in response to the at least one characteristic of the selected one of the first and second devices being more suitable for execution of the machine learning workload than another of the first and second devices.
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公开(公告)号：US12106151B2
公开(公告)日：2024-10-01
申请号：US17157896
申请日：2021-01-25
发明人： Saba Shah , Xiaohua Xu , Rod D. Waltermann
CPC分类号： G06F9/5038 , G06F9/5044 , G06F9/505 , G06F11/3409 , G06F11/3433 , G06N20/00 , G06F11/3003 , G06F2209/501 , G06F2209/509
摘要： An apparatus includes a processor and a memory that stores code executable by the processor. The code is executable by the processor to receive a request at a first device to execute a machine learning workload for the first device, dynamically determine at least one characteristic of the first device that is related to execution of the machine learning workload, dynamically determine at least one characteristic of a second device that is related to execution of the machine learning workload, and select one of the first and second devices to execute the machine learning workload in response to the at least one characteristic of the selected one of the first and second devices being more suitable for execution of the machine learning workload than another of the first and second devices.
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公开(公告)号：US11460936B2
公开(公告)日：2022-10-04
申请号：US17137913
申请日：2020-12-30
发明人： Mounika Vanka , Saba Shah
IPC分类号： G06F3/038 , G06F1/16 , G06F3/0354
摘要： A computing device can include a processor; memory accessible by the processor; a housing that includes a surface; and a deployable attachment mechanism for releasable attachment of an object to the surface.
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公开(公告)号：US11610141B2
公开(公告)日：2023-03-21
申请号：US16368999
申请日：2019-03-29
发明人： Rod D. Waltermann , Sidney Rhodes , Saba Shah
摘要： One embodiment provides a method, including: obtaining a dataset for generation of an outcome using a plurality of artificial intelligence models; classifying, using another artificial intelligence model and before employing the plurality of artificial intelligence models, the dataset into a feature-space; and employing a subset of the plurality of artificial intelligence models on the dataset, wherein the subset is selected based upon the classification of the dataset. Other aspects are described and claimed.
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公开(公告)号：US20220206592A1
公开(公告)日：2022-06-30
申请号：US17137913
申请日：2020-12-30
发明人： Mounika Vanka , Saba Shah
摘要： A computing device can include a processor; memory accessible by the processor; a housing that includes a surface; and a deployable attachment mechanism for releasable attachment of an object to the surface.
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公开(公告)号：US20200311570A1
公开(公告)日：2020-10-01
申请号：US16368999
申请日：2019-03-29
发明人： Rod D. Waltermann , Sidney Rhodes , Saba Shah
摘要： One embodiment provides a method, including: obtaining a dataset for generation of an outcome using a plurality of artificial intelligence models; classifying, using another artificial intelligence model and before employing the plurality of artificial intelligence models, the dataset into a feature-space; and employing a subset of the plurality of artificial intelligence models on the dataset, wherein the subset is selected based upon the classification of the dataset. Other aspects are described and claimed.
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