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公开(公告)号：US11516139B2
公开(公告)日：2022-11-29
申请号：US17110196
申请日：2020-12-02
Applicant: Cisco Technology, Inc.
IPC: H04L12/851 , H04L12/801 , H04L12/807 , H04L12/815 , H04L12/841 , H04L47/2441 , H04L47/22 , H04L47/27 , H04L47/12 , H04L47/283 , H04L47/10
Abstract: Systems and methods provide for generating traffic class-specific congestion signatures and other machine learning models for improving network performance. In some embodiments, a network controller can receive historical traffic data captured by a plurality of network devices within a first period of time that the network devices apply one or more traffic shaping policies for a predetermined traffic class and a predetermined congestion state. The controller can generate training data sets including flows of the historical traffic data labeled as corresponding to the predetermined traffic class and predetermined congestion state. The controller can generate, based on the training data sets, traffic class-specific congestion signatures that receive input traffic data determined to correspond to the predetermined traffic class and output an indication whether the input traffic data corresponds to the predetermined congestion state. The controller can adjust, based on the congestion signatures, traffic shaping operations of the plurality of network devices.
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公开(公告)号：US11190956B2
公开(公告)日：2021-11-30
申请号：US16752313
申请日：2020-01-24
Applicant: Cisco Technology, Inc.
Inventor： Sebastian Jeuk , Gonzalo A. Salgueiro , M. David Hanes
Abstract: Embodiments herein describe association rules (e.g., affinity and anti-affinity rules) that a wireless device can use to optimize its performance in a Wi-Fi network. While BSS coloring is typically used to eliminate color collisions, the embodiments herein use BSS coloring to define what BSS colors should be on the same channel and which should not. For example, an affinity rule can indicate that a wireless device assigned a first BSS color (e.g., red) can share the same channel with wireless devices (or BSSs) assigned a second BSS color (e.g., green). In contrast, an anti-affinity rule can indicate that a wireless device in the red BSS color cannot share a channel with a wireless device assigned to a third BSS color (e.g., blue). The embodiments herein permit the wireless devices to be grouped with, or separated from, wireless devices having different BSS colors.
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公开(公告)号：US20200267543A1
公开(公告)日：2020-08-20
申请号：US16278430
申请日：2019-02-18
Applicant: Cisco Technology, Inc.
Inventor： Charles Calvin Byers , M. David Hanes , Gonzalo Salgueiro , Dmitri Goloubev , Joseph Michael Clarke
Abstract: Presented herein are methodologies to on-board and monitor Internet of Things (IoT) devices on a network. The methodology includes receiving at a server, from a plurality of IoT devices communicating over a network, data representative of external environmental factors being experienced by individual ones of the plurality of IoT devices at a predetermined location; generating, using machine learning, an aggregated model of the external environmental factors at the predetermined location; receiving, at the server, a communication indicative that a new IoT device seeks to join the network at the predetermined location; receiving, from the new IoT device, data representative of external environmental factors being experienced by the new IoT device; determining whether there is a discrepancy between the external environmental factors of the new IoT device and the aggregated model; and when there is such a discrepancy, prohibiting the new IoT device from joining the network.
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公开(公告)号：US10735271B2
公开(公告)日：2020-08-04
申请号：US15829139
申请日：2017-12-01
Applicant: Cisco Technology, Inc.
Inventor： Charles Calvin Byers , Joseph Michael Clarke , Gonzalo Salgueiro , M. David Hanes
Abstract: Automatic, adaptive stimulus generation includes receiving, at a network device that is associated with a network or system, analytics data that provides an indication of how the network or system is responding to a set of test stimuli introduced into the network or system to facilitate an analysis operation. The network device analyzes the analytics data based on an intended objective for the analysis operation and generates control settings based on the analyzing. The control settings control creation of a subsequent stimulus to be introduced into the network or system during subsequent execution of the analysis operation.
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公开(公告)号：US10433400B1
公开(公告)日：2019-10-01
申请号：US16124093
申请日：2018-09-06
Applicant: Cisco Technology, Inc.
Inventor： Charles Calvin Byers , Joseph M. Clarke , Gonzalo A. Salgueiro , M. David Hanes
Abstract: Techniques relating to a geographic lighting controller. A controller determines a target lighting pattern based on an instruction for a smart lighting effect. The controller retrieves from a database, based on the target geographic location, information identifying a first plurality of smart lights to activate as part of the smart lighting effect. The controller determines a plurality of network addresses for the first plurality of smart lights, based on the retrieved information, generates a lighting effect command relating to the first plurality of smart lights, and transmits the lighting effect command to create the smart lighting effect.
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Patent Agency Ranking
公开(公告)号：US20190253319A1
公开(公告)日：2019-08-15
申请号：US15892951
申请日：2018-02-09
Applicant: Cisco Technology, Inc.
Inventor： Panagiotis Theodorou Kampanakis , Blake Harrell Anderson , Brian E. Weis , Charles Calvin Byers , M. David Hanes , Joseph Michael Clarke , Gonzalo Salgueiro
CPC classification number: H04L41/0893 , G06N5/025 , H04L41/0816 , H04L43/08
Abstract: In one embodiment, a classification device in a computer network analyzes data from a given device in the computer network, and classifies the given device as a particular type of device based on the data. The classification device may then determine whether a manufacturer usage description (MUD) policy exists for the particular type of device. In response to there being no existing MUD policy for the particular type of device, the classification device may then determine patterns of the analyzed data, classify the patterns into context-based policies, and generate a derived MUD policy for the particular type of device based on the context-based policies. The classification device may then apply one of either the existing or derived MUD policy for the given device within the computer network.
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公开(公告)号：US20190173761A1
公开(公告)日：2019-06-06
申请号：US15829139
申请日：2017-12-01
Applicant: Cisco Technology, Inc.
Inventor： Charles Calvin Byers , Joseph Michael Clarke , Gonzalo Salgueiro , M. David Hanes
Abstract: Automatic, adaptive stimulus generation includes receiving, at a network device that is associated with a network or system, analytics data that provides an indication of how the network or system is responding to a set of test stimuli introduced into the network or system to facilitate an analysis operation. The network device analyzes the analytics data based on an intended objective for the analysis operation and generates control settings based on the analyzing. The control settings control creation of a subsequent stimulus to be introduced into the network or system during subsequent execution of the analysis operation.
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公开(公告)号：US20250039235A1
公开(公告)日：2025-01-30
申请号：US18360124
申请日：2023-07-27
Applicant: Cisco Technology, Inc.
Inventor： M. David Hanes , Amanda L. Holst , Sudha Katgeri , Ana M Montenegro , Ishita Maheshkumar Thanki
IPC: H04L9/40
Abstract: A method includes creating, via a server, a plurality of virtualized human personalities associated with respective human users; receiving, via the server, a cyberattack message; determining, via the server, the cyberattack message targets a human user of the respective human users; selecting, via the server, a virtualized human personality of the plurality of virtualized human personalities based on the virtualized human personality being associated with the human user targeted by the cyberattack message; and responding, via the server, to the cyberattack message using the virtualized human personality selected from the plurality of virtualized human personalities.
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公开(公告)号：US20250036674A1
公开(公告)日：2025-01-30
申请号：US18458739
申请日：2023-08-30
Applicant: Cisco Technology, Inc.
Inventor： M. David Hanes , Vivek Kumar Singh , Gonzalo A. Salgueiro , Derek William Engi
IPC: G06F16/335 , G06F16/332
Abstract: A method comprises: receiving a query on a topic from a user associated with user attributes indicative of a user comprehension level on the topic; providing the query to an AI model; receiving from the AI model a response to the query that has a response comprehension level on the topic that is less than the user comprehension level; iteratively adding, to the query, topically-relevant user attributes of the user attributes to produce iterative queries that increase in technical detail on the topic; providing the iterative queries to the AI model; responsive to providing the iterative queries, receiving, from the AI model, iterative responses that increase in technical detail on the topic and have response comprehension levels that increase on the topic; and determining, among the iterative responses, a final response having a response comprehension level that most nearly matches the user comprehension level.
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公开(公告)号：US11562176B2
公开(公告)日：2023-01-24
申请号：US16282781
申请日：2019-02-22
Applicant: Cisco Technology, Inc.
Inventor： Volodymyr Iashyn , Gonzalo Salgueiro , M. David Hanes
Abstract: Systems, methods, and computer-readable mediums for distributing machine learning model training to network edge devices, while centrally monitoring training of the models and controlling deployment of the models. A machine learning model architecture can be generated at a machine learning structure controller. The machine learning model architecture can be deployed to network edge devices in a network environment to instantiate and train a machine learning model at the network edge devices. Performance reports indicating performance of the machine learning model at the network edge devices can be received by the machine learning structure controller from the network edge devices. The machine learning structure controller can determine whether to deploy another machine learning model architecture to the network edge devices based on the performance reports and subsequently deploy the another architecture to the network edge devices if it is determined to deploy the architecture based on the performance reports.
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