公开(公告)号：US10769549B2

公开(公告)日：2020-09-08

申请号：US15357559

申请日：2016-11-21

Applicant: Google Inc.

Inventor： Keith Bonawitz ,  Daniel Ramage

IPC: G06N20/00 ,  G06N5/02

Abstract: The present disclosure provides systems and methods for the management and/or evaluation of machine-learned models based on locally logged data. In one example, a user computing device can obtain a machine-learned model (e.g., from a server computing device) and can evaluate at least one performance metric for the machine-learned model. In particular, the at least one performance metric for the machine-learned model can be evaluated relative to data that is stored locally at the user computing device. The user computing device and/or the server computing device can determine whether to activate the machine-learned model on the user computing device based at least in part on the at least one performance metric. In another example, the user computing device can evaluate a plurality of machine-learned models against locally stored data. At least one of the models can be selected based on the evaluated performance metrics.

公开(公告)号：US20180144265A1

公开(公告)日：2018-05-24

申请号：US15357559

申请日：2016-11-21

Applicant: Google Inc.

Inventor： Keith Bonawitz ,  Daniel Ramage

IPC: G06N99/00 ,  G06N5/02

Abstract: The present disclosure provides systems and methods for the management and/or evaluation of machine-learned models based on locally logged data. In one example, a user computing device can obtain a machine-learned model (e.g., from a server computing device) and can evaluate at least one performance metric for the machine-learned model. In particular, the at least one performance metric for the machine-learned model can be evaluated relative to data that is stored locally at the user computing device. The user computing device and/or the server computing device can determine whether to activate the machine-learned model on the user computing device based at least in part on the at least one performance metric. In another example, the user computing device can evaluate a plurality of machine-learned models against locally stored data. At least one of the models can be selected based on the evaluated performance metrics.

公开(公告)号：US20160173324A1

公开(公告)日：2016-06-16

申请号：US15048010

申请日：2016-02-19

Applicant: Google Inc.

Inventor： Keith Bonawitz ,  Joshua Weaver ,  Richard DeVaul

IPC: H04L12/24 ,  H04W24/02

CPC classification number: H04W24/04 ,  H04B7/18504 ,  H04L41/0677 ,  H04W24/02

Abstract: Example methods and systems for assigning tasks to balloons within a balloon network are described. One example system includes a first sub-fleet of balloons assigned a first set of one or more tasks within a balloon network, a second sub-fleet of balloons assigned a second set of one or more tasks within the balloon network, and a control system configured to determine that a first balloon in the first sub-fleet of balloons initially has a predicted failure mode that corresponds to the first set of tasks, subsequently determine that the first balloon has a predicted failure mode that corresponds to the second set of tasks, and reassign the first balloon from the first sub-fleet of balloons to the second sub-fleet of balloons.

公开(公告)号：US20150063159A1

公开(公告)日：2015-03-05

申请号：US14014822

申请日：2013-08-30

Applicant: Google Inc.

Inventor： Keith Bonawitz ,  Joshua Weaver ,  Richard DeVaul

IPC: H04W24/02

CPC classification number: H04L41/0677 ,  H04B7/18504 ,  H04W24/02 ,  H04W24/04

Abstract: Example methods and systems for assigning tasks to balloons within a balloon network are described. One example system includes a first sub-fleet of balloons assigned a first set of one or more tasks within a balloon network, a second sub-fleet of balloons assigned a second set of one or more tasks within the balloon network, and a control system configured to determine that a first balloon in the first sub-fleet of balloons initially has a predicted failure mode that corresponds to the first set of tasks, subsequently determine that the first balloon has a predicted failure mode that corresponds to the second set of tasks, and reassign the first balloon from the first sub-fleet of balloons to the second sub-fleet of balloons.

Abstract translation: 描述用于将任务分配给气球网络内的气球的示例方法和系统。 一个示例性系统包括：气球网络中分配有第一组一个或多个任务的气球的第一子队，分配有气球网络内的一个或多个任务的第二组气球的第二子队，以及控制系统 被配置为确定所述第一子集队中的第一气球最初具有对应于所述第一组任务的预测故障模式，随后确定所述第一气球具有对应于所述第二组任务的预测故障模式， 并将第一个气球从第一个气球子队重新分配到第二个气球子队。

公开(公告)号：US09319905B2

公开(公告)日：2016-04-19

申请号：US14014822

申请日：2013-08-30

Applicant: Google Inc.

Inventor： Keith Bonawitz ,  Joshua Weaver ,  Richard DeVaul

IPC: H04L12/28 ,  H04W24/04

CPC classification number: H04L41/0677 ,  H04B7/18504 ,  H04W24/02 ,  H04W24/04

Abstract: Example methods and systems for assigning tasks to balloons within a balloon network are described. One example system includes a first sub-fleet of balloons assigned a first set of one or more tasks within a balloon network, a second sub-fleet of balloons assigned a second set of one or more tasks within the balloon network, and a control system configured to determine that a first balloon in the first sub-fleet of balloons initially has a predicted failure mode that corresponds to the first set of tasks, subsequently determine that the first balloon has a predicted failure mode that corresponds to the second set of tasks, and reassign the first balloon from the first sub-fleet of balloons to the second sub-fleet of balloons.

Abstract translation: 描述用于将任务分配给气球网络内的气球的示例方法和系统。 一个示例性系统包括：气球网络中分配有第一组一个或多个任务的气球的第一子队，分配有气球网络内的一个或多个任务的第二组气球的第二子队，以及控制系统 被配置为确定所述第一子集队中的第一气球最初具有对应于所述第一组任务的预测故障模式，随后确定所述第一气球具有对应于所述第二组任务的预测故障模式， 并将第一个气球从第一个气球子队重新分配到第二个气球子队。