公开(公告)号：US20240095012A1

公开(公告)日：2024-03-21

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

CPC classification number: G06F8/65 ,  H04L41/082 ,  H04L41/16

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：US20230029760A1

公开(公告)日：2023-02-02

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 ,  H04L12/24 ,  H04W12/041 ,  H04W88/08 ,  G06N20/00

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.

公开(公告)号：US20220158927A1

公开(公告)日：2022-05-19

申请号：US17508879

申请日：2021-10-22

Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP

Inventor： ABTIN ANSARI ,  BERND BANDEMER ,  JOSE TELLADO

IPC: H04L12/26 ,  H04L12/24

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：US20200067919A1

公开(公告)日：2020-02-27

申请号：US16108304

申请日：2018-08-22

Applicant: Hewlett Packard Enterprise Development LP

Inventor： GAURAV PATWARDHAN ,  SACHIN GANU ,  JOSE TELLADO

IPC: H04L29/06 ,  H04L12/26 ,  H04L1/00

Abstract: Systems and methods are disclosed for generating a client device fingerprint model and identifying client devices using the model. Identifying client devices includes monitoring traffic transmitted over a wireless network to an unknown first client device, the traffic using at least one value of at least one wireless network operational parameter; determining, for each at least one value of the at least one wireless network operational parameter, a respective probability of successful packet reception at the first client device; comparing each probability to a data model representing probabilities of successful packet reception at each of a plurality of known second client devices for each of a plurality of values of the wireless network operational parameter; and associating at least one of the labels of the second client devices with the first client device based on the compare.