公开(公告)号：US12166629B2

公开(公告)日：2024-12-10

申请号：US17949106

申请日：2022-09-20

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Sargam Jain ,  Charles Hogg ,  David Fehling, Jr. ,  Bernd Bandemer ,  Jose Tellado

IPC: G06F8/65 ,  H04L41/082 ,  H04L41/16 ,  G06F11/00 ,  G06F11/34 ,  G06N20/10 ,  H04L41/0631

Abstract: Examples of the presently disclosed technology provide automated firmware recommendation systems that inject the intelligence of machine learning into the firmware recommendation process. To accomplish this, examples train a machine learning model on troves of historical customer firmware update data on a dynamic basis (e.g., examples may train the machine learning model on weekly basis to predict accepted firmware updates made by a vendor's customers across the most recent 6 months). From this dynamic training, the machine learning model can learn to predict/recommend an optimal firmware version for a customer/network device cluster based on firmware-related features, recent customer preferences, and other customer-specific factors. Once trained, examples can deploy the machine learning model to make highly tailored firmware recommendations for individual network device clusters of individual customers taking the above described factors into account.

公开(公告)号：US20220078632A1

公开(公告)日：2022-03-10

申请号：US17530828

申请日：2021-11-19

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Jose Tellado ,  Bernd Bandemer ,  Sachin N. Ganu ,  Sai Pradeep Venkatraman

IPC: H04W16/22 ,  H04W36/14 ,  H04W36/32 ,  H04W36/18 ,  H04W36/00 ,  H04W36/26 ,  H04W40/18

Abstract: A method for identifying a client device in a network, and a first radio in the network that is coupled with the client device is provided. The method includes determining one or more sequences of roaming events for multiple client devices in the network, evaluating a performance metric for a roaming event and evaluating an interaction between the client device and one or more radios involved in the roaming events for the plurality of client devices. The method also includes selecting a second radio in the network based at least in part on (1) the one or more sequences of roaming events, (2) the performance metric, and (3) the interaction between the client device and the one or more radios, and recommending switching the client device from the first radio to the second radio. A system and a predictive tool to perform the above method are also provided.

公开(公告)号：US11234197B2

公开(公告)日：2022-01-25

申请号：US16427840

申请日：2019-05-31

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Jose Tellado ,  Bernd Bandemer ,  Alexander Mendez ,  Hilmi Gunes Kayacik ,  Charlie Hogg

IPC: H04W52/22 ,  H04W88/08 ,  H04W52/02 ,  H04B17/391 ,  H04W24/02 ,  H04L5/00 ,  H04W52/24

Abstract: Example implementations relate to predicting power states of access points (APs). A non-transitory computer readable medium may store instructions executable by a processing resource to: in response to a first client device being associated with a first access point (AP) of a group of APs, determine a first degree of performance a second AP of the group is to provide if the first client device is associated with the second AP; and predict, based on the determined degree of performance, a second degree of performance the group of AP is to provide to a second client device if the second client device is associated with the group of APs during a first time interval at a location.

公开(公告)号：US11206550B2

公开(公告)日：2021-12-21

申请号：US16219334

申请日：2018-12-13

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Jose Tellado ,  Bernd Bandemer ,  Sachin N. Ganu ,  Sai Pradeep Venkatraman

IPC: H04W16/22 ,  H04W36/14 ,  H04W40/18 ,  H04W36/32 ,  H04W36/18 ,  H04W36/00 ,  H04W36/26

Abstract: A method for identifying a client device in a network, and a first radio in the network that is coupled with the client device is provided. The method includes determining one or more sequences of roaming events for multiple client devices in the network, evaluating a performance metric for a roaming event and evaluating an interaction between the client device and one or more radios involved in the roaming events for the plurality of client devices. The method also includes selecting a second radio in the network based at least in part on (1) the one or more sequences of roaming events, (2) the performance metric, and (3) the interaction between the client device and the one or more radios, and recommending switching the client device from the first radio to the second radio. A system and a predictive tool to perform the above method are also provided.

公开(公告)号：US20210007046A1

公开(公告)日：2021-01-07

申请号：US17024976

申请日：2020-09-18

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Jose Tellado ,  Bernd Bandemer ,  Yusuke Sakamoto

IPC: H04W48/20 ,  H04L12/857 ,  H04W24/02

Abstract: Some of examples disclosed herein relate to classifying access points (APs). Some of the example may enable automatically classifying a set of APs based on at least one AP attribute of each AP in the set of APs, determining, based on the automatic classification, that a subset of APs in the set of APs are classified into a same class; and automatically determining a recommended configuration setting for the subset of APs.

公开(公告)号：US20200037149A1

公开(公告)日：2020-01-30

申请号：US16046296

申请日：2018-07-26

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Sachin Ganu ,  Bernd Bandemer ,  Jose Tellado ,  Gaurav Patwardhan

IPC: H04W8/14 ,  G06N99/00 ,  H04B17/391 ,  H04W60/04 ,  H04L12/24

Abstract: An example network information aggregator is disclosed. The network information aggregator includes a network interface, a memory, and processing circuitry. The processing circuitry is to receive a machine learning model trained using initialization data from a model creation device. The processing circuitry is also to generate a device steering rule to steer a client device from a first radio to a second radio using the machine learning model. The processing circuitry is also to send a steer command to the first radio.

公开(公告)号：US20190268215A1

公开(公告)日：2019-08-29

申请号：US15904489

申请日：2018-02-26

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Jose Tellado ,  Bernd Bandemer ,  Susmita Ghose ,  Gunes Kayacik

IPC: H04L12/24 ,  H04W24/04

Abstract: In some examples, a system generates event records representing anomalous events associated with respective devices in a network, each respective anomalous event of the anomalous events being identified as anomalous for a respective context of a device of the devices. The system clusters the event records to produce a plurality of clusters of the event records. The system outputs the plurality of clusters for application of a remediation of an issue in the network.

公开(公告)号：US12101643B2

公开(公告)日：2024-09-24

申请号：US17530828

申请日：2021-11-19

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Jose Tellado ,  Bernd Bandemer ,  Sachin N. Ganu ,  Sai Pradeep Venkatraman

IPC: H04W16/22 ,  H04W36/00 ,  H04W36/14 ,  H04W36/18 ,  H04W36/26 ,  H04W36/32 ,  H04W40/18

CPC classification number: H04W16/225 ,  H04W36/0085 ,  H04W36/14 ,  H04W36/18 ,  H04W36/26 ,  H04W36/32 ,  H04W40/18

Abstract: A method for identifying a client device in a network, and a first radio in the network that is coupled with the client device is provided. The method includes determining one or more sequences of roaming events for multiple client devices in the network, evaluating a performance metric for a roaming event and evaluating an interaction between the client device and one or more radios involved in the roaming events for the plurality of client devices. The method also includes selecting a second radio in the network based at least in part on (1) the one or more sequences of roaming events, (2) the performance metric, and (3) the interaction between the client device and the one or more radios, and recommending switching the client device from the first radio to the second radio. A system and a predictive tool to perform the above method are also provided.

公开(公告)号：US11968109B2

公开(公告)日：2024-04-23

申请号：US18052843

申请日：2022-11-04

Applicant: Hewlett Packard Enterprise Development LP

Inventor： Abtin Ansari ,  Bernd Bandemer ,  Jose Tellado

IPC: H04L43/50 ,  H04L41/06

CPC classification number: H04L43/50 ,  H04L41/06

Abstract: Systems and methods are provided for receiving a set of feature vectors. Each feature vector in the set may comprise feature values for a plurality of features associated with network communications. A first score for a first subset of the feature vectors that have at least one common feature value for a first feature of the plurality of features may be determined. A second score for a second subset of the feature vectors may be determined. The second subset may comprise the first subset and other feature vectors that have a different feature value for the first feature. Based on a change between the first score and the second score, whether to group the common feature value and the different feature value together may be determined.

公开(公告)号：US11910249B2

公开(公告)日：2024-02-20

申请号：US17391559

申请日：2021-08-02

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： Gopal Gupta ,  Abhinesh Mishra ,  Isaac Theogaraj ,  Sachin Ganu ,  Bernd Bandemer ,  Jose Tellado

IPC: H04W36/00 ,  H04L41/16 ,  G06N20/00 ,  H04W12/041 ,  H04W88/08

CPC classification number: H04W36/0038 ,  G06N20/00 ,  H04L41/16 ,  H04W12/041 ,  H04W36/0061 ,  H04W88/08

Abstract: Systems and methods are provided for optimizing resource consumption by bringing intelligence to the key allocation process for fast roaming. Specifically, embodiments of the disclosed technology use machine learning to predict which AP a wireless client device will migrate to next. In some embodiments, machine learning may also be used to select a subset of top neighbors from a neighborhood list. Thus, instead of allocating keys for each of the APs on the neighborhood list, key allocation may be limited to the predicted next AP, and the subset of top neighbors. In some embodiments, a reinforcement learning model may be used to dynamically adjust the size of the subset in order to optimize resources while satisfying variable client demand.