Abstract: Embodiments herein provide a method for adaptively traversing conversation states using conversational AI to extract contextual information. The method includes (i) loading states that define a logical flow of the automated conversation and comprises a content boundary, (ii) dynamically generating a first question associated with the first conversation state by obtaining a prompt, (iii) determine whether a first response is inside or outside of the content boundary, (iv) generating in real-time a first follow-up question by (a) determining a missing content, or (b) analyzing the resume of the user, job description, (v) monitoring a second response to extract a skill level of the user, (vi) automatically computing possible paths of the conversation to obtain an updated N subsequent conversation states of the conversation, (vii) generating a second follow-up question; and (viii) repeating generating follow-up questions for adaptively traversing the N updated conversation states.

Abstract: The technologies described herein are generally directed to modeling radio wave propagation in a fifth generation (5G) network or other next generation networks. For example, a method described herein can include, for a network application, identifying, by a system comprising a processor, a characteristic value of a performance characteristic associated with an uplink connection enabled via a network of a user equipment to application server equipment hosting the network application. The method can further include, based on the characteristic value and a criterion, selecting, by the system, a first packet size for the uplink connection. The method can further include communicating, by the system, to the user equipment, the first packet size for use with the uplink connection.

Abstract: The technologies described herein are generally directed to modeling radio wave propagation in a fifth generation (5G) network or other next generation networks. For example, a method described herein can include, for a network application, identifying, by a system comprising a processor, a characteristic value of a performance characteristic associated with an uplink connection enabled via a network of a user equipment to application server equipment hosting the network application. The method can further include, based on the characteristic value and a criterion, selecting, by the system, a first packet size for the uplink connection. The method can further include communicating, by the system, to the user equipment, the first packet size for use with the uplink connection.

Abstract: The described technology is generally directed towards reducing latency in a wireless communications network. Radio access network latency data corresponding to a measured latency impact criterion is obtained by a network device of a wireless network. Based on the radio access network latency data, latency guidance data usable by the radio network device to achieve a reduction in communication latency that is experienced by a user equipment is predicted, e.g., by a learned model. The latency guidance data can be used to facilitate a reduction in the communication latency that is experienced by a user equipment.

Abstract: An apparatus comprising an infotainment system including a proactive automotive assistant that executes a first action and a second action, wherein the first action is that of permitting spontaneous communication to an occupant in a vehicle and the second action is that of providing information indicating that spontaneous communication with the occupant is impermissible. The automotive assistant is configured to receive information selected from the group consisting of vehicle-status information concerning operation of the vehicle and occupant-status information concerning the occupant and to base the first and second actions at least in part on the information.

Abstract: Aspects of the subject disclosure may include, for example, receiving and over a first interface of a radio access network (RAN), first data corresponding to a plurality of user equipment (UEs), wherein the first data is associated with first network parameters, and wherein the first network parameters include scheduled UEs, UE spatial separability, or a combination thereof, transmitting the first data to an artificial intelligence (AI) model, responsive to the transmitting the first data to the AI model, obtaining second data from the AI model, wherein the second data is associated with second network parameters, and wherein the second network parameters include downlink (DL) transmit power allocation, and causing the second data to be provided over a second interface to a control unit to enable use of the second data for the plurality of UEs. Other embodiments are disclosed.

Abstract: The technologies described herein are generally directed to modeling radio wave propagation in a fifth generation (5G) network or other next generation networks. For example, a method described herein can include, for a network application, identifying, by a system comprising a processor, a characteristic value of a performance characteristic associated with an uplink connection enabled via a network of a user equipment to application server equipment hosting the network application. The method can further include, based on the characteristic value and a criterion, selecting, by the system, a first packet size for the uplink connection. The method can further include communicating, by the system, to the user equipment, the first packet size for use with the uplink connection.

Abstract: The described technology is generally directed towards generating and deploying radio access network parameters by a management platform. The management platform can use a defined data structure as a carrier for parameters within the management platform. Newly generated parameters can be placed in the defined data structure. The newly generated parameters can be approved at the management platform for deployment to radio access network devices. A software defined networking function of the management platform can convert a parameter into a specific format utilized at a radio access network, and the software defined networking function can deploy the converted parameter to the radio access network.

Abstract: The described technology is generally directed towards network traffic splitting for dual connectivity user equipment. A network controller can monitor communications transmitted between radio access network node devices. The radio access network node devices can be associated with different generation communication protocols. The inter-node communications can be used assess relative performance of the radio access network node devices. For example, buffer growth rates can be estimated for the radio access network node devices, and buffer growth rates can be correlated with network node device performance. The relative performance of the radio access network node devices can then be used to adjust network traffic splits between the radio access network node devices.

Abstract: The described technology is generally directed towards generating and deploying radio access network parameters by a management platform. The management platform can use a defined data structure as a carrier for parameters within the management platform. Newly generated parameters can be placed in the defined data structure. The newly generated parameters can be approved at the management platform for deployment to radio access network devices. A software defined networking function of the management platform can convert a parameter into a specific format utilized at a radio access network, and the software defined networking function can deploy the converted parameter to the radio access network.

Abstract: The described technology is generally directed towards machine learning deployment in radio access networks. A machine learning deployment pipeline can comprise a machine learning model design platform, a network automation platform, and a radio access network. Machine learning models can be designed at the machine learning model design platform, trained at the network automation platform, and deployed and used at the radio access network. The technology includes operations performed at each stage of the deployment pipeline in order to deploy machine learning models.

Abstract: The described technology is generally directed towards reducing latency in a wireless communications network. Radio access network latency data corresponding to a measured latency impact criterion is obtained by a network device of a wireless network. Based on the radio access network latency data, latency guidance data usable by the radio network device to achieve a reduction in communication latency that is experienced by a user equipment is predicted, e.g., by a learned model. The latency guidance data can be used to facilitate a reduction in the communication latency that is experienced by a user equipment.

Abstract: The described technology is generally directed towards network slice management. A mobile communications network can comprise multiple sub-networks, namely, as an access network, a transport network, and a core network. Access network resources can be managed according to the techniques provided herein to meet service level agreement (SLA) commitments associated with network slices. Furthermore, resources of any sub-network can be managed in a manner that accounts for constraints imposed by the other sub-networks.

Abstract: The described technology is generally directed towards reducing latency in a wireless communications network. Radio access network latency data corresponding to a measured latency impact criterion is obtained by a network device of a wireless network. Based on the radio access network latency data, latency guidance data usable by the radio network device to achieve a reduction in communication latency that is experienced by a user equipment is predicted, e.g., by a learned model. The latency guidance data can be used to facilitate a reduction in the communication latency that is experienced by a user equipment.

Abstract: The described technology is generally directed towards generating and deploying radio access network parameters by a management platform. The management platform can use a defined data structure as a carrier for parameters within the management platform. Newly generated parameters can be placed in the defined data structure. The newly generated parameters can be approved at the management platform for deployment to radio access network devices. A software defined networking function of the management platform can convert a parameter into a specific format utilized at a radio access network, and the software defined networking function can deploy the converted parameter to the radio access network.

Abstract: The described technology is generally directed towards machine learning deployment in radio access networks. A machine learning deployment pipeline can comprise a machine learning model design platform, a network automation platform, and a radio access network. Machine learning models can be designed at the machine learning model design platform, trained at the network automation platform, and deployed and used at the radio access network. The technology includes operations performed at each stage of the deployment pipeline in order to deploy machine learning models.

Abstract: The described technology is generally directed towards network slice management. A mobile communications network can comprise multiple sub-networks, namely, as an access network, a transport network, and a core network. Access network resources can be managed according to the techniques provided herein to meet service level agreement (SLA) commitments associated with network slices. Furthermore, resources of any sub-network can be managed in a manner that accounts for constraints imposed by the other sub-networks.

Abstract: The described technology is generally directed towards network slice management. A mobile communications network can comprise multiple sub-networks, namely, as an access network, a transport network, and a core network. Access network resources can be managed according to the techniques provided herein to meet service level agreement (SLA) commitments associated with network slices. Furthermore, resources of any sub-network can be managed in a manner that accounts for constraints imposed by the other sub-networks.

Abstract: The described technology is generally directed towards machine learning deployment in radio access networks. A machine learning deployment pipeline can comprise a machine learning model design platform, a network automation platform, and a radio access network. Machine learning models can be designed at the machine learning model design platform, trained at the network automation platform, and deployed and used at the radio access network. The technology includes operations performed at each stage of the deployment pipeline in order to deploy machine learning models.

Abstract: Aspects of the subject disclosure may include, for example, a system that provides for obtaining, by a server comprising a processor, network parameter data from an eNodeB of a wireless network. The server can determine a predicted bandwidth for a group of end user devices in a coverage area of the eNodeB according to the network parameter data. The server can receive, from an end user device of the group of end user devices, a request for the predicted bandwidth. The server can provide the predicted bandwidth to the end user device. The providing of the predicted bandwidth enables the end user device to provide a video chunk request to a content server that is based on a selected chunk delivery schedule and facilitates streaming of video content to the end user device from the content server. Other embodiments are disclosed.