John A Kenyon

US Patent:

20230004867, Jan 5, 2023

Filed:

Jul 15, 2022

Appl. No.:

17/865492

Inventors:

- Germantown MD, US
John Kenyon - Germantown MD, US

International Classification:

G06N 20/00
G06N 5/02

Abstract:

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining availability of network service. In some implementations, a request indicating a location and a communication service level is received. A first subset of service providers or communication technologies is determined based on outputs generated by multiple first machine learning models each trained to predict service availability for different service providers or communication technologies. A second subset is selected from the first subset based on outputs generated by multiple second machine learning models trained to predict availability of different communication service levels for different service providers or communication technologies. At least one service provider or communication technology is selected from the second subset based on output generated by a third machine learning model. A response to the request indicating the selected service provider or communication technology is provided.

US Patent:

20210399794, Dec 23, 2021

Filed:

Sep 3, 2021

Appl. No.:

17/466719

Inventors:

- Germantown MD, US
Archana Gharpuray - Germantown MD, US
John Kenyon - Germantown MD, US

International Classification:

H04B 7/185
H04W 4/02
G06N 20/00
H04W 72/04

Abstract:

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for machine learning models for adjusting communication parameters. In some implementations, data for each device in a set of multiple communication devices is obtained. A machine learning model is trained based on the obtained data. The model can be trained to receive an indication of a geographic location and predict a communication setting capable of providing at least a minimum level of efficiency. After training the machine learning model, an indication of a predicted communication setting for a particular communication device is generated. A determination is then made whether to change a current communication setting for the particular communication device based on the predicted communication setting.

US Patent:

20210336857, Oct 28, 2021

Filed:

Jul 8, 2021

Appl. No.:

17/370529

Inventors:

- Germantown MD, US
Satyajit Roy - Germantown MD, US
John Kenyon - Germantown MD, US

International Classification:

H04L 12/24
G06N 20/00
G06N 5/04
H04L 12/851
H04L 29/08

Abstract:

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training and using machine learning models to classify network traffic as IoT traffic or non-IoT traffic and managing the traffic based on the classification. In some implementations, machine learning parameters of a local machine learning model trained by the edge device is received each of at least a subset of a set of edge devices. The machine learning parameters received from an edge device are parameters of the local machine learning model trained by the edge device based on local network traffic processed by the edge device and to classify the network traffic as Internet of Things (IoT) traffic or non-IoT traffic. A global machine learning model is generated, using the machine learning parameters, to classify network traffic processed by edge devices as IoT traffic or non-IoT traffic.

US Patent:

20210143532, May 13, 2021

Filed:

Jan 22, 2021

Appl. No.:

17/155359

Inventors:

- Germantown MD, US
Archana Gharpuray - Germantown MD, US
John Kenyon - Germantown MD, US

International Classification:

H01Q 1/28
G01C 21/20
G06K 9/46
H04B 7/185

Abstract:

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training and using machine learning models to detect problems in a satellite communication system. In some implementations, one or more feature vectors that respectively correspond to different times are obtained. The feature vector(s) are provided as input to one or more machine learning models trained to receive at least one feature vector that includes feature values representing properties of the satellite communication system and output an indication of potential causes of a condition of the satellite communication system based on the properties of the satellite communication system. A particular cause that is indicated as being a most likely cause of the condition of the satellite communication system is determined based on one or more machine learning model outputs received from each of the one or more machine learning models.

US Patent:

20200410300, Dec 31, 2020

Filed:

Aug 6, 2020

Appl. No.:

16/987028

Inventors:

- Germantown MD, US
Archana Gharpuray - Germantown MD, US
John Kenyon - Germantown MD, US

International Classification:

G06K 9/62
G06N 20/00
G06F 9/54

Abstract:

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training and using machine learning clustering models to determine conditions a satellite communication system. In some implementations, feature vectors for a time period are obtained. Each feature vector includes feature values that represent properties of a satellite communication system at a respective time during the time period. Each feature vector is provided as input to a machine learning model that assigns the feature vector to a based on the properties of the satellite communication system represented by the feature vector. Each cluster corresponds to a respective potential operating condition of the satellite communication system. Data is generated that indicates a likelihood that each potential operating condition is the actual operating condition based on a quantity of the feature vectors that have been assigned to the cluster corresponding to the potential operating condition during the time period.

US Patent:

20200210885, Jul 2, 2020

Filed:

Dec 31, 2018

Appl. No.:

16/236739

Inventors:

- Germantown MD, US
John Kenyon - Germantown MD, US

International Classification:

G06N 20/00
G06N 5/02

Abstract:

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining availability of network service. In some implementations, a request indicating a location and a communication service level is received. A first subset of service providers or communication technologies is determined based on outputs generated by multiple first machine learning models each trained to predict service availability for different service providers or communication technologies. A second subset is selected from the first subset based on outputs generated by multiple second machine learning models trained to predict availability of different communication service levels for different service providers or communication technologies. At least one service provider or communication technology is selected from the second subset based on output generated by a third machine learning model. A response to the request indicating the selected service provider or communication technology is provided.

US Patent:

20200194876, Jun 18, 2020

Filed:

Dec 16, 2019

Appl. No.:

16/715411

Inventors:

- Germantown MD, US
Archana Gharpuray - Germantown MD, US
John Kenyon - Germantown MD, US

International Classification:

H01Q 1/28
H04B 7/185
G06K 9/46
G01C 21/20

Abstract:

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training and using machine learning models to detect problems in a satellite communication system. In some implementations, one or more feature vectors that respectively correspond to different times are obtained. The feature vector(s) are provided as input to one or more machine learning models trained to receive at least one feature vector that includes feature values representing properties of the satellite communication system and output an indication of potential causes of a condition of the satellite communication system based on the properties of the satellite communication system. A particular cause that is indicated as being a most likely cause of the condition of the satellite communication system is determined based on one or more machine learning model outputs received from each of the one or more machine learning models.

US Patent:

20200175971, Jun 4, 2020

Filed:

Nov 29, 2018

Appl. No.:

16/204287

Inventors:

- Germantown MD, US
Archana Gharpuray - Germantown MD, US
John Kenyon - Germantown MD, US

International Classification:

G10L 15/22
G10L 15/26
G10L 13/04
G10L 15/30
H04B 7/185

Abstract:

Methods and systems for spoken language interface for network management are disclosed. In some implementations, data indicating a transcription of a spoken request from a user of a voice-response interface is received. Status information for a communication system is received. The request is interpreted based on the transcription and the status information for the communication system. A response to the request is generated based on the status information for the communication system, and data for a synthesized speech utterance of the response is provided in response to the spoken request.