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公开(公告)号：US20210049020A1
公开(公告)日：2021-02-18
申请号：US16543219
申请日：2019-08-16
摘要： Regulating a flow of data from an electronic device comprises generating a user profile associated with a user. Further, a resource profile is generated and is associated with one or more resources external to the electronic device. Additionally, a context profile is generated and describes an outcome of one or more previous interactions between the user of the electronic device and one or more resources. Rules are generated based on a comparison the user profile, the context profile and the site profile. Further, data sent from an electronic device is modified based on the set of rules.
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公开(公告)号：US10387409B2
公开(公告)日：2019-08-20
申请号：US13912147
申请日：2013-06-06
IPC分类号： G06F16/242
摘要： A common infrastructure collects data from a plurality of mobile devices and traditional sensors at Internet scale to respond to natural language queries received at different applications. The infrastructure includes a semantic interpreter to translate the natural language query to a data request specification that is processed by the data collection system. The data collection system includes a phenomenon layer that expresses data and information needs in a declarative fashion and coordinates data collection and processing for queries. An edge layer manages devices, receives collection requirements from the backend layer, configures and instructs devices for data collection, and conducts aggregation and primitive processing of data. This layer contains network edge nodes, such as base stations in a cellular network. Each node manages a set of local data generating networked devices. The device agent data layer using common agents on the networked devices receives data collection instructions and performs data collection.
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3.发明申请公开(公告)号：US20140365517A1
公开(公告)日：2014-12-11
申请号：US13912058
申请日：2013-06-06
IPC分类号： G06F17/30
CPC分类号： G06F16/243
摘要： A common infrastructure collects data from a plurality of mobile devices and traditional sensors at Internet scale to respond to natural language queries received at different applications. The infrastructure includes a semantic interpreter to translate the natural language query to a data request specification that is processed by the data collection system. The data collection system includes a phenomenon layer that expresses data and information needs in a declarative fashion and coordinates data collection and processing for queries. An edge layer manages devices, receives collection requirements from the backend layer, configures and instructs devices for data collection, and conducts aggregation and primitive processing of data. This layer contains network edge nodes, such as base stations in a cellular network. Each node manages a set of local data generating networked devices. The device agent data layer using common agents on the networked devices receives data collection instructions and performs data collection.
摘要翻译： 公共基础设施从互联网规模的多个移动设备和传统传感器收集数据，以响应在不同应用处接收到的自然语言查询。 基础设施包括语义解释器，用于将自然语言查询转换为由数据收集系统处理的数据请求规范。 数据收集系统包括以声明方式表达数据和信息需求并协调查询的数据收集和处理的现象层。 边缘层管理设备，从后端层接收收集要求，配置和指示设备进行数据收集，并进行数据的聚合和原始处理。 该层包含网络边缘节点，例如蜂窝网络中的基站。 每个节点管理一组生成网络设备的本地数据。 使用联网设备上的公用代理的设备代理数据层接收数据收集指令并执行数据收集。
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公开(公告)号：US20200372380A1
公开(公告)日：2020-11-26
申请号：US16419315
申请日：2019-05-22
摘要： A computer-implemented method, a computer program product, and a computer system for enhanced distributed machine learning. A fusion server in a distributed machine learning system determines correlation relationships across agents in the distributed machine learning system, based on auxiliary information. The fusion server clusters the agents to form one or more communities, based on the correlation relationships. The fusion server selects, from the one or more communities, participating agents that participate in the enhanced distributed machine learning.
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公开(公告)号：US20140365518A1
公开(公告)日：2014-12-11
申请号：US13912147
申请日：2013-06-06
IPC分类号： G06F17/30
CPC分类号： G06F16/243
摘要： A common infrastructure collects data from a plurality of mobile devices and traditional sensors at Internet scale to respond to natural language queries received at different applications. The infrastructure includes a semantic interpreter to translate the natural language query to a data request specification that is processed by the data collection system. The data collection system includes a phenomenon layer that expresses data and information needs in a declarative fashion and coordinates data collection and processing for queries. An edge layer manages devices, receives collection requirements from the backend layer, configures and instructs devices for data collection, and conducts aggregation and primitive processing of data. This layer contains network edge nodes, such as base stations in a cellular network. Each node manages a set of local data generating networked devices. The device agent data layer using common agents on the networked devices receives data collection instructions and performs data collection.
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公开(公告)号：US20240107330A1
公开(公告)日：2024-03-28
申请号：US17934612
申请日：2022-09-23
IPC分类号： H04W16/18 , H04B17/318 , H04W4/021 , H04W24/08
CPC分类号： H04W16/18 , H04B17/318 , H04W4/021 , H04W24/08
摘要： Computer-implemented methods for drone-assisted communications networks are provided. Aspects include collecting, from one or more sensing drones, signal strength information for a communications network in a geographic area and creating, based at least in part on the signal strength information, a signal strength map for the geographic area. Aspects also include deploying one or more networking drones into the geographic area, wherein a location of the one or more drones in the geographic area is determined based at least in part on the signal strength map. The one or more networking drones are configured to provide access to the communications network.
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公开(公告)号：US11521091B2
公开(公告)日：2022-12-06
申请号：US16419315
申请日：2019-05-22
摘要： A computer-implemented method, a computer program product, and a computer system for enhanced distributed machine learning. A fusion server in a distributed machine learning system determines correlation relationships across agents in the distributed machine learning system, based on auxiliary information. The fusion server clusters the agents to form one or more communities, based on the correlation relationships. The fusion server selects, from the one or more communities, participating agents that participate in the enhanced distributed machine learning.
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公开(公告)号：US20200167677A1
公开(公告)日：2020-05-28
申请号：US16201393
申请日：2018-11-27
摘要： A method includes training, using a first set of training data, to produce a machine learning model to generate an output based on an input. In an embodiment, the method includes training, using a second set of training data, to produce a second model to generate the output based on the input. In an embodiment, the method includes receiving a query to explain a decision-making process of the machine learning model. In an embodiment, the method includes producing, in response to the query, an explanation of the decision-making process of the second model.
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公开(公告)号：US20240354132A1
公开(公告)日：2024-10-24
申请号：US18302237
申请日：2023-04-18
发明人： Nirmit V. Desai , Jae-Wook Ahn , Tova Roth , Dinesh C. Verma , Douglas M. Freimuth , Seraphin Bernard Calo , Anshu Kak , Steven A. Waite , Roger Hollander
IPC分类号： G06F9/455
CPC分类号： G06F9/45508
摘要： Methods and systems for emulating an application include generating a log template to match one or more patterns in a set of application logs collected from an original application. Semantic state representations are learned for the original application from the log templates. A classifier is trained to predict a next action template based on a sequence of prior action templates. A regressor is trained to generate a parameter value for a template based on a sequence of prior action templates and particular semantic state of the original application.
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公开(公告)号：US20220067450A1
公开(公告)日：2022-03-03
申请号：US17008747
申请日：2020-09-01
摘要： Techniques for determining system performance without ground truth include receiving a trained model and one or more generator models, the trained model having been trained on training data. The trained model is used on testing data to produce labeled testing data, and the labeled testing data is used to train a proxy model. The one or more generator models are used to produce synthetic training data that is representative of the training data. The proxy model is used on the synthetic training data to produce predictions, and performance of the trained model is determined based on the predictions by the proxy model.
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