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91.发明申请公开(公告)号：US20140222728A1
公开(公告)日：2014-08-07
申请号：US13937705
申请日：2013-07-09
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Grégory Mermoud , Sukrit Dasgupta
IPC: G06N99/00
CPC classification number: G06N99/005 , H04L67/12 , H04W4/70
Abstract: In one embodiment, network data is received at a Learning Machine (LM) in a network. It is determined whether the LM recognizes the received network data based on information available to the LM. When the LM fails to recognize the received network data: a connection to a central management node is established, a request is sent for information relating to the unrecognized network data to the central management node, and information is received from the central management node in response to the request. The received information assists the LM in recognizing the unrecognized network data.
Abstract translation: 在一个实施例中，在网络中的学习机器（LM）处接收网络数据。 基于LM可用的信息确定LM是否识别接收到的网络数据。 当LM无法识别接收到的网络数据时：建立到中央管理节点的连接，向中央管理节点发送与无法识别的网络数据相关的信息的请求，响应于从中央管理节点接收信息 要求。 接收到的信息有助于LM识别无法识别的网络数据。
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公开(公告)号：US20140222727A1
公开(公告)日：2014-08-07
申请号：US13937664
申请日：2013-07-09
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Grégory Mermoud , Sukrit Dasgupta
CPC classification number: G06N20/00
Abstract: In one embodiment, network data is processed using a Learning Machine (LM) algorithm in a network, and results of the processing of network data are determined. A reliability checking algorithm is selected to determine a reliability level of the results. The reliability checking algorithm may be a local reliability checking algorithm or an external reliability checking algorithm. The reliability level of the results is determined using the reliability checking algorithm. Then, the LM algorithm is adjusted based on the determined reliability level.
Abstract translation: 在一个实施例中，使用网络中的学习机器（LM）算法处理网络数据，并确定网络数据的处理结果。 选择可靠性检查算法来确定结果的可靠性水平。 可靠性检查算法可以是局部可靠性检查算法或外部可靠性检查算法。 使用可靠性检查算法确定结果的可靠性水平。 然后，基于确定的可靠性水平来调整LM算法。
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93.发明申请ACCELERATING LEARNING BY SHARING INFORMATION BETWEEN MULTIPLE LEARNING MACHINES 有权通过多种学习机器之间的共享信息来加速学习公开(公告)号：US20140222726A1
公开(公告)日：2014-08-07
申请号：US13937631
申请日：2013-07-09
Applicant: Cisco Technology, Inc.
Inventor： Grégory Mermoud , Jean-Philippe Vasseur , Sukrit Dasgupta
IPC: G06N99/00
CPC classification number: G06N99/005 , H04L29/12 , H04L61/00
Abstract: In one embodiment, variables maintained by each of a plurality of Learning Machines (LMs) are determined. The LMs are hosted on a plurality of Field Area Routers (FARs) in a network, and the variables are sharable between the FARs. A plurality of correlation values defining a correlation between the variables is calculated. Then, a cluster of FARs is computed based on the plurality of correlation values, such that the clustered FARs are associated with correlated variables, and the cluster allows the clustered FARs to share their respective variables.
Abstract translation: 在一个实施例中，确定由多个学习机器（LM）中的每一个维护的变量。 LM在网络中托管在多个场区域路由器（FAR）上，变量在FAR之间可共享。 计算定义变量之间的相关性的多个相关值。 然后，基于多个相关值计算一组FAR，使得聚类FAR与相关变量相关联，并且该群集允许群集FAR共享其各自的变量。
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94.发明申请公开(公告)号：US20240397441A1
公开(公告)日：2024-11-28
申请号：US18324528
申请日：2023-05-26
Applicant: Cisco Technology, Inc.
Inventor： Sukrit Dasgupta
Abstract: Association and mobility patterns corresponding to client devices within a 5G network are tracked in real-time. A machine learning model is trained to identify, based on these patterns, periods of time for powering down one or more 5G nodes within the 5G network. The machine learning model, based on these periods of time, generates a set of power saving profiles that are used to automatically define power saving modes for the one or more 5G nodes. The machine learning model is updated according to changes to the association and mobility patterns resulting from the power saving modes.
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公开(公告)号：US20240373264A1
公开(公告)日：2024-11-07
申请号：US18310592
申请日：2023-05-02
Applicant: Cisco Technology, Inc.
Inventor： Federico Lovison , Sukrit Dasgupta , Mohit Dubey
IPC: H04W24/08 , H04L41/0631 , H04L41/16 , H04L41/5009
Abstract: A system and method are provided for tracking the quality of telemetry data in a wireless network, and to provide profiling of the telemetry to prevent erroneous radio resource management (RRM) computations. Statistical profiles are generated from the telemetry data that includes both computation data, which is used in RRM computations, and other network data. A data-quality score is generated based on the other data and statistical profiles of the computation data. The data-quality score represents whether the telemetry data is of sufficient quality to be used in RRM computations. The data-quality score can be based, at least in part, on detecting changes in the statistical profiles relative to a baseline statistical profile of the telemetry data and using the second network data to assess a likelihood that the detected changes arise from a degradation in a quality of the first network data.
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Patent Agency Ranking
公开(公告)号：US20240314578A1
公开(公告)日：2024-09-19
申请号：US18603956
申请日：2024-03-13
Applicant: Cisco Technology, Inc.
Inventor： Jerome Henry , Federico Lovison , Robert E. Barton , Sukrit Dasgupta
Abstract: Cell edge prediction for optimized roaming may be provided. Cell edge prediction can include predicting cell edges for a plurality of APs including a connected AP and one or more additional APs. A cell edge prediction can be for a client connected to the connected AP. The cell edge prediction may comprise an indication of one or more candidate APs for the client to roam to of the one or more additional APs and an estimated time the client will reach the cell edge of the connected AP. After generating the cell edge prediction, the cell edge prediction can be transmitted to the client.
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公开(公告)号：US20200162344A1
公开(公告)日：2020-05-21
申请号：US16393767
申请日：2019-04-24
Applicant: Cisco Technology, Inc.
Inventor： Carlo Zapponi , Sukrit Dasgupta
IPC: H04L12/24
Abstract: The technology provides for providing an interactive user interface to explore a complete network, see relationships with various aspects of the network, and drill down to details in an instinctive manner. In some embodiments, network component data is received that identifies metrics associated with network components. A graphical user interface made up of representations of network components of a network is presented, where the network components are selectable. Relevant network components are displayed at varying network scales by receiving an input selecting a first representation of a first network component at a first network level. Based on a network component relationship between the first representation of the first network component and a second relationship of a second network component, second network component data is received that identifies one or more metrics associated with the second network component. The second network component is at a second network level. The one or more metrics associated with the second network component are presented within a context of the second network level.
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公开(公告)号：US10484406B2
公开(公告)日：2019-11-19
申请号：US14990064
申请日：2016-01-07
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Grégory Mermoud , Sukrit Dasgupta , Xav Laumonier
Abstract: In one embodiment, a first device in a network maintains raw traffic flow information for the network. The first device provides a compressed summary of the raw traffic flow information to a second device in the network. The second device is configured to transform the compressed summary for presentation to a user interface. The first device detects an anomalous traffic flow based on an analysis of the raw traffic flow information using a machine learning-based anomaly detector. The first device provides at least a portion of the raw traffic flow information related to the anomalous traffic flow to the second device for presentation to the user interface.
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99.发明授权公开(公告)号：US10484255B2
公开(公告)日：2019-11-19
申请号：US15626412
申请日：2017-06-19
Applicant: Cisco Technology, Inc.
Inventor： Andrea Di Pietro , Grégory Mermoud , Jean-Philippe Vasseur , Sukrit Dasgupta
IPC: H04L12/26 , H04L29/06 , G06F16/2457 , H04L12/24 , G06N20/00
Abstract: In one embodiment, a device receives health status data indicative of a health status of a data source in a network that provides collected telemetry data from the network for analysis by a machine learning-based network analyzer. The device maintains a performance model for the data source that models the health of the data source. The device computes a trustworthiness index for the telemetry data provided by the data source based on the received health status data and the performance model for the data source. The device adjusts, based on the computed trustworthiness index for the telemetry data provided by the data source, one or more parameters used by the machine learning-based network analyzer to analyze the telemetry data provided by the data source.
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公开(公告)号：US10182066B2
公开(公告)日：2019-01-15
申请号：US15801807
申请日：2017-11-02
Applicant: Cisco Technology, Inc.
Inventor： Fabien Flacher , Grégory Mermoud , Jean-Philippe Vasseur , Sukrit Dasgupta
IPC: H04L29/06
Abstract: In one embodiment, a device in a network analyzes data indicative of a behavior of a network using a supervised anomaly detection model. The device determines whether the supervised anomaly detection model detected an anomaly in the network from the analyzed data. The device trains an unsupervised anomaly detection model, based on a determination that no anomalies were detected by the supervised anomaly detection model.
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