At&t Intellectual Property I, L.p.

Yadong Mu

Zhu Liu

Eric Martin Zavesky

David Crawford Gibbon

Behzad Shahraray

Lee Begeja

Bernard S Renger

Raghuraman Gopalan

Aspects of the subject disclosure may include, for example, a system and method for selecting media content for a group of persons located at a venue. The system and method identify a plurality of viewers in a vicinity of a venue that has one or more display devices from location data and extracts a content viewing preference of each viewer from their profile data. The process includes aggregating the content viewing preference of each of the plurality of viewers to generate an aggregated content profile. Based on the aggregated content profile, a list of content is generated. Next, a first group of viewers approaching a viewing range of a display device are sensed by imaging data. Any conflicts between content viewing preferences of the first group of viewers are detected and resolved based on weighting the viewing preferences of the group. Other embodiments are disclosed.

Automated content selection for groups

Aspects of the subject disclosure may include, for example, systems and methods aggregating video content and adjusting the aggregate video content according to a training model. The adjusted aggregate video content comprises a first subset of the images and does not comprise a second subset of the images. The first subset of the images is determined by the training model based on a plurality of categories corresponding to a plurality of events. The illustrative embodiments also include presenting the adjusted aggregate video content and receiving identifications for the first subset of the images in the aggregate video content. Further, the illustrative embodiments include adjusting the training model according to the identifications and providing the adjusted training model to a network device. Other embodiments are disclosed.

Method and system for aggregating video content

A method and apparatus for providing a recommended action are disclosed. For example, the method receives first data that is measured via at least one sensor deployed at a venue, receives second data from at least one user endpoint device present at the venue, wherein the second data comprises data that is measured via a sensor associated with the at least one user endpoint device, applies a perception model of the venue to the first data and the second data that are received, generates at least one recommended action when a threshold is reached, wherein the threshold is established for generating the at least one recommended action, and provides an update to the perception model of the venue based on the at least one recommended action that is generated.

Method and apparatus for providing a recommended action for a venue via a network

A method, computer-readable medium, and apparatus for modeling data of a service for providing a policy are disclosed. For example, a method may include a processor for generating a first policy for a first service by a first policy model using machine learning for processing first data of the first service, determining whether the first policy is to be applied to a second service, applying the first policy to the second service when the first policy is deemed to be applicable to the second service, wherein the applying the first policy provides the first policy to a second policy model using machine learning for processing second data of the second service, generating a second policy for the second service, and implementing the second policy in the second service, wherein the first service and the second service are provided by a single service provider.

Optimization of multiple services via machine learning

Devices, computer-readable media, and methods are disclosed for assigning a service to a vehicle-based mobile node. For example, a processor deployed in a communication network may receive a request for the service from a mobile endpoint device, determine a route of the mobile endpoint device, determine a route of the vehicle-based mobile node, and assign the service to the vehicle-based mobile node when the route of the mobile endpoint device and the route of the vehicle-based mobile node coincide.

Vehicle-based mobile node fleet for network service deployment

A method includes accessing, at a base station of a wireless communications network, information identifying a plurality of user devices in a coverage area of the base station. The method includes determining a forecasted content request for media content at a particular time for delivery to a first user device of the plurality of user devices based on media consumption data for the first user device. The method includes receiving, from a second user device of the plurality of user devices, a request for the media content at a time before the particular time. The method includes sending an instruction to the first user device that directs the first user device store the media content received via a multicast transmission of the media content. The method also includes sending the media content via the multicast transmission to the first user device and to the second user device responsive to the request.

Generating a pre-caching schedule based on forecasted content requests

Aspects of the subject disclosure may include, for example, analyzing content flow data in a communication network to obtain flow statistics; the statistics are aggregated to construct a model of the content flow based on the aggregated flow statistics. Operations of network nodes are predicted based on the model; a content delivery path in the communication network is identified. A quality of service is detected for network nodes on the content delivery path. The detected quality of service is compared with a predicted quality of service based on the model; a network anomaly is detected based on the comparing. The content flow on the content delivery path is adjusted responsive to detection of the network anomaly. Other embodiments are disclosed.

Content quality assessment and prediction via flows

A method for classifying images using a neural network is described. The method utilizes the neural network to generate a deep hash data structure. In one embodiment, the method includes receiving, at a neural network, first data corresponding to a plurality of images of a training image set. The method includes adjusting parameters of the neural network based on concurrent application at the neural network of multiple loss functions to second data corresponding to the plurality of images to generate adjusted parameters. The method includes generating a deep hash data structure based on the adjusted parameters of the neural network. The method further includes sending, via a transmitter to a device, third data corresponding to the deep hash data structure.

Image hash codes generated by a neural network

A method, computer-readable medium, and apparatus for modeling data of a plurality of services for providing a new service are disclosed. For example, a method may include a processor for generating a first policy from a first service by a first policy model using machine learning for processing first data of the first service, generating a second policy from a second service by a second policy model using machine learning for processing second data of the second service, wherein the first service and the second service are different, and implementing one or more functions for a new service using the first policy and the second policy.

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Middletown, NJ, United States of America

Yadong Mu