公开(公告)号：US11686803B2

公开(公告)日：2023-06-27

申请号：US17521622

申请日：2021-11-08

Applicant: Intel Corporation

Inventor： Yatish Mishra ,  Mats Agerstam ,  Mateo Guzman ,  Sindhu Pandian ,  Shubhangi Rajasekhar ,  Pranav Sanghadia ,  Troy Willes

IPC: H04W52/22 ,  G01R35/00 ,  G06N3/08 ,  H04L67/125 ,  G06Q10/04 ,  G06F18/214 ,  G06F18/2413 ,  G06N20/00 ,  G06F16/903 ,  H04W4/50 ,  G06N3/04 ,  H04W4/70 ,  G06V10/764 ,  H04L67/12

CPC classification number: G01R35/005 ,  G06F16/90335 ,  G06F18/214 ,  G06F18/24143 ,  G06N3/04 ,  G06N3/08 ,  G06N20/00 ,  G06Q10/04 ,  G06V10/764 ,  H04L67/125 ,  H04W4/50 ,  H04W4/70 ,  H04W52/223 ,  H04L67/12

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.

公开(公告)号：US20190138295A1

公开(公告)日：2019-05-09

申请号：US16235842

申请日：2018-12-28

Applicant: Intel Corporation

Inventor： Mats Agerstam ,  Sindhu Pandian ,  Shubhangi Rajasekhar ,  Mateo Guzman ,  Yatish Mishra ,  Pranav Sanghadia ,  Troy Willes ,  Cesar Martinez-Spessot ,  Lakshmi Talluru

IPC: G06F8/65 ,  H04L29/08 ,  H04L12/66 ,  H04L12/751 ,  H04L12/733 ,  H04L12/24

Abstract: In embodiments, an apparatus for selectively delivering software updates to nodes in a network includes a receiver to receive a software update and a list of nodes of the network scheduled to receive the software update. In embodiments, the apparatus further includes a device management agent (DMA) to: identify a set of traversals to leaf nodes of the list of nodes necessary to traverse all nodes on the list, and distribute the software updates to the nodes on the list using the set of traversals.

公开(公告)号：US20230266419A1

公开(公告)日：2023-08-24

申请号：US18309366

申请日：2023-04-28

Applicant: Intel Corporation

Inventor： Yatish Mishra ,  Mats Agerstam ,  Mateo Guzman ,  Sindhu Pandian ,  Shubhangi Rajasekhar ,  Pranav Sanghadia ,  Troy Willes

IPC: G01R35/00 ,  G06N3/08 ,  H04L67/125 ,  G06Q10/04 ,  G06N20/00 ,  G06F16/903 ,  H04W4/50 ,  G06N3/04 ,  H04W4/70 ,  G06F18/214 ,  G06F18/2413 ,  G06V10/764 ,  H04W52/22

CPC classification number: G01R35/005 ,  G06N3/08 ,  H04L67/125 ,  G06Q10/04 ,  G06N20/00 ,  G06F16/90335 ,  H04W4/50 ,  G06N3/04 ,  H04W4/70 ,  G06F18/214 ,  G06F18/24143 ,  G06V10/764 ,  H04W52/223 ,  H04L67/12

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.

公开(公告)号：US20220244336A1

公开(公告)日：2022-08-04

申请号：US17521622

申请日：2021-11-08

Applicant: Intel Corporation

Inventor： Yatish Mishra ,  Mats Agerstam ,  Mateo Guzman ,  Sindhu Pandian ,  Shubhangi Rajasekhar ,  Pranav Sanghadia ,  Troy Willes

IPC: G01R35/00 ,  G06N3/08 ,  H04L67/125 ,  G06K9/62 ,  G06Q10/04 ,  G06N20/00 ,  G06F16/903 ,  H04W4/50 ,  G06N3/04 ,  H04W4/70

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.

公开(公告)号：US11169239B2

公开(公告)日：2021-11-09

申请号：US16146893

申请日：2018-09-28

Applicant: Intel Corporation

Inventor： Yatish Mishra ,  Mats Agerstam ,  Mateo Guzman ,  Sindhu Pandian ,  Shubhangi Rajasekhar ,  Pranav Sanghadia ,  Troy Willes

IPC: G01R35/00 ,  G06N3/08 ,  H04L29/08 ,  G06N20/00 ,  H04W4/50 ,  H04W4/70 ,  G06K9/62 ,  G06Q10/04 ,  G06F16/903 ,  G06N3/04

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.