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1.发明授权公开(公告)号：US11120343B2
公开(公告)日：2021-09-14
申请号：US15152379
申请日：2016-05-11
Applicant: CISCO TECHNOLOGY, INC.
Inventor： Aparupa Das Gupta , Rahul Ramakrishna , Yathiraj B. Udupi , Debojyoti Dutta , Manoj Sharma
Abstract: A method for ranking detected anomalies is disclosed. The method includes generating a graph based on a plurality of rules, wherein the graph comprises nodes representing metrics identified in the rules, edges connecting nodes where metrics associated with connected nodes are identified in a given rule, and edge weights of the edges each representing a severity level assigned to the given rule. The method further includes ranking nodes of the graph based on the edge weights. The method further includes ranking detected anomalies based on the ranking of the nodes corresponding to the metrics associated with the detected anomalies.
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2.发明申请公开(公告)号：US20210397982A1
公开(公告)日：2021-12-23
申请号：US17464530
申请日：2021-09-01
Applicant: Cisco Technology, Inc.
Inventor： Aparupa Das Gupta , Rahul Ramakrishna , Yathiraj B. Udupi , Debojyoti Dutta , Manoj Sharma
Abstract: A method for ranking detected anomalies is disclosed. The method includes generating a graph based on a plurality of rules, wherein the graph comprises nodes representing metrics identified in the rules, edges connecting nodes where metrics associated with connected nodes are identified in a given rule, and edge weights of the edges each representing a severity level assigned to the given rule. The method further includes ranking nodes of the graph based on the edge weights. The method further includes ranking detected anomalies based on the ranking of the nodes corresponding to the metrics associated with the detected anomalies.
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公开(公告)号：US10361935B2
公开(公告)日：2019-07-23
申请号：US15420248
申请日：2017-01-31
Applicant: Cisco Technology, Inc.
Inventor： Yathiraj B. Udupi , Aparupa Das Gupta , Rahul Ramakrishna
Abstract: In one embodiment, a device in a network aggregates values for a set of key performance indicators (KPIs) for a system the network to form a plurality of KPI states. The device associates a plurality of observed performance metric values from the system with the KPI states. The device constructs a machine learning-based decision tree. Internal vertices of the decision tree represent conditions for the plurality of observed performance metric values and leaves of the tree represent the KPI states. The device predicts a KPI state by using the machine learning-based decision tree to analyze live performance metric values streamed from the system. The device generates a proactive alert based on the predicted KPI state.
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公开(公告)号：US10884807B2
公开(公告)日：2021-01-05
申请号：US15485910
申请日：2017-04-12
Applicant: CISCO TECHNOLOGY, INC.
Inventor： Komei Shimamura , Timothy Okwii , Debojyoti Dutta , Yathiraj B. Udupi , Rahul Ramakrishna , Xinyuan Huang
IPC: G06F9/50
Abstract: In one embodiment, a method for serverless computing comprises: receiving a task definition, wherein the task definition comprises a first task and a second task chained to the first task; adding the first task and the second task to a task queue; executing the first task from the task queue using hardware computing resources in a first serverless environment associated with a first serverless environment provider; and executing the second task from the task queue using hardware computing resources in a second serverless environment selected based on a condition on an output of the first task.
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公开(公告)号：US10733037B2
公开(公告)日：2020-08-04
申请号：US15342340
申请日：2016-11-03
Applicant: Cisco Technology, Inc.
Inventor： Rahul Ramakrishna , Yathiraj B. Udupi , Debojyoti Dutta
Abstract: In one embodiment, a server in a network reports one or more symptoms of a monitored device that is malfunctioning to a user interface via a particular chatbot session. The server receives, via the particular chatbot session, a triage request to enter a triage mode regarding the one or more reported symptoms. The server predicts a corrective action using the one or more reported symptoms as input to a machine learning model. The machine learning model is trained using a history of observed symptoms in the network, a history of corrective actions initiated via chatbot sessions and associated with the observed symptoms, and a history of feedback regarding the corrective actions received via the chatbot sessions. The server provides the predicted corrective action to the user interface via the particular chatbot session as a suggested corrective action, in response to the received triage request.
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Patent Agency Ranking
公开(公告)号：US20180091376A1
公开(公告)日：2018-03-29
申请号：US15278740
申请日：2016-09-28
Applicant: CISCO TECHNOLOGY, INC.
Inventor： Rahul Ramakrishna , Yathiraj B. Udupi , Ralf Rantzau
IPC: H04L12/24 , H04L12/813
Abstract: In an example, there is disclosed a logging server computing apparatus, having: a processor; a memory; and a logging engine to: analyze a network; build an entity-state matrix M from an entity vector e and a state vector s; determine that there is a strong correlation between an entity ec and a state sc; and report the strong correlation.
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公开(公告)号：US10419294B2
公开(公告)日：2019-09-17
申请号：US15278740
申请日：2016-09-28
Applicant: CISCO TECHNOLOGY, INC.
Inventor： Rahul Ramakrishna , Yathiraj B. Udupi , Ralf Rantzau
IPC: H04L12/24 , H04L12/26 , H04L29/08 , H04L12/801
Abstract: In an example, there is disclosed a logging server computing apparatus, having: a processor; a memory; and a logging engine to: analyze a network; build an entity-state matrix M from an entity vector e and a state vector s; determine that there is a strong correlation between an entity ec and a state sc; and report the strong correlation.
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公开(公告)号：US20180300173A1
公开(公告)日：2018-10-18
申请号：US15485910
申请日：2017-04-12
Applicant: CISCO TECHNOLOGY, INC.
Inventor： Komei Shimamura , Timothy Okwii , Debojyoti Dutta , Yathiraj B. Udupi , Rahul Ramakrishna , Xinyuan Huang
Abstract: In one embodiment, a method for serverless computing comprises: receiving a task definition, wherein the task definition comprises a first task and a second task chained to the first task; adding the first task and the second task to a task queue; executing the first task from the task queue using hardware computing resources in a first serverless environment associated with a first serverless environment provider; and executing the second task from the task queue using hardware computing resources in a second serverless environment selected based on a condition on an output of the first task.
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公开(公告)号：US20180219754A1
公开(公告)日：2018-08-02
申请号：US15420248
申请日：2017-01-31
Applicant: Cisco Technology, Inc.
Inventor： Yathiraj B. Udupi , Aparupa Das Gupta , Rahul Ramakrishna
CPC classification number: H04L43/08 , H04L41/147 , H04L41/16 , H04L41/5009
Abstract: In one embodiment, a device in a network aggregates values for a set of key performance indicators (KPIs) for a system the network to form a plurality of KPI states. The device associates a plurality of observed performance metric values from the system with the KPI states. The device constructs a machine learning-based decision tree. Internal vertices of the decision tree represent conditions for the plurality of observed performance metric values and leaves of the tree represent the KPI states. The device predicts a KPI state by using the machine learning-based decision tree to analyze live performance metric values streamed from the system. The device generates a proactive alert based on the predicted KPI state.
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公开(公告)号：US20180121808A1
公开(公告)日：2018-05-03
申请号：US15342340
申请日：2016-11-03
Applicant: Cisco Technology, Inc.
Inventor： Rahul Ramakrishna , Yathiraj Udupi , Debojyoti Dutta
CPC classification number: G06F11/0709 , G06F11/0787 , G06F11/0793 , G06N20/00 , H04L51/02
Abstract: In one embodiment, a server in a network reports one or more symptoms of a monitored device that is malfunctioning to a user interface via a particular chatbot session. The server receives, via the particular chatbot session, a triage request to enter a triage mode regarding the one or more reported symptoms. The server predicts a corrective action using the one or more reported symptoms as input to a machine learning model. The machine learning model is trained using a history of observed symptoms in the network, a history of corrective actions initiated via chatbot sessions and associated with the observed symptoms, and a history of feedback regarding the corrective actions received via the chatbot sessions. The server provides the predicted corrective action to the user interface via the particular chatbot session as a suggested corrective action, in response to the received triage request.
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