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.

Abstract: Methods and systems for monitoring a communication network using machine-learning techniques are disclosed. In some implementations, a forecasted amount of traffic for a communication network is determined using one or more network traffic forecasting models being configured to generate the forecasted amount of traffic based on data indicating one or more previous amounts of traffic for the communication network. A measure of network health is generated based on a measured amount of traffic and the forecasted amount of traffic. Data indicating one or more characteristics of the communication network is processed using one or more machine learning models to generate a predicted measure of network health for a future time period. An indication of the predicted measure of network health for the future time period is provided.

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.

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.

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.

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.

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.

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.

Abstract: Methods and systems for monitoring a communication network using machine-learning techniques are disclosed. In some implementations, a forecasted amount of traffic for a communication network is determined using one or more network traffic forecasting models being configured to generate the forecasted amount of traffic based on data indicating one or more previous amounts of traffic for the communication network. A measure of network health is generated based on a measured amount of traffic and the forecasted amount of traffic. Data indicating one or more characteristics of the communication network is processed using one or more machine learning models to generate a predicted measure of network health for a future time period. An indication of the predicted measure of network health for the future time period is provided.

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.

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.

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 terminal in a set of multiple satellite terminals 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 satellite beam capable of providing at least a minimum level of efficiency for communication at the geographic location. After training the machine learning model, an indication of a predicted satellite beam for a particular location is generated for a particular geographic location. A determination is then made whether to change the current satellite beam for a terminal at the particular geographic location based on the predicted satellite beam.

Abstract: Methods and systems for monitoring a communication network using machine-learning techniques are disclosed. In some implementations, a forecasted amount of traffic for a communication network is determined using one or more network traffic forecasting models being configured to generate the forecasted amount of traffic based on data indicating one or more previous amounts of traffic for the communication network. A measure of network health is generated based on a measured amount of traffic and the forecasted amount of traffic. Data indicating one or more characteristics of the communication network is processed using one or more machine learning models to generate a predicted measure of network health for a future time period. An indication of the predicted measure of network health for the future time period is provided.

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.

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.

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.

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.

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.

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.

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.