Abstract: Aspects of the subject disclosure may include, for example, a method including obtaining media content and an identification of a plurality of points of interest in the media content, receiving a request from a user to view the media content, obtaining information about the user, identifying one or more highlight and/or degrade points based on the information about the user, modifying the media content to create highlighted content, the highlighted content being the media content modified to attract attention of the user to the highlight point and drive the attention of the user away from the degrade point, presenting the highlighted content to the user, and monitoring the user's consumption of the content. Other embodiments are disclosed.

Abstract: 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.

Abstract: A method includes receiving media data corresponding to a media content item. The method includes analyzing the media data to determine characteristics of the media content item based on first visual information of the media content item. The method includes analyzing the characteristics of the media content item based on a media popularity data structure to generate a prediction of whether the media content item is likely to exceed a popularity threshold within a particular period of time. The method further includes determining a location to store the media data based on the prediction.

Abstract: Aspects of the subject disclosure may include, for example, a method comprising obtaining, by a processing system including a processor, first and second models for a structure of an object, based respectively on ground-level and aerial observations of the object. Model parameters are determined for a three-dimensional (3D) third model of the object based on the first and second models; the determining comprises a transfer learning procedure. Data representing observations of the object is captured at an airborne unmanned aircraft system (UAS) operating at an altitude between that of the ground-level observations and the aerial observations. The method also comprises dynamically adjusting the third model in accordance with the operating altitude of the UAS; updating the adjusted third model in accordance with the data; and determining a 3D representation of the structure of the object, based on the updated adjusted third model. Other embodiments are disclosed.

Abstract: 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.

Abstract: Aspects of the subject disclosure may include, for example, a method including obtaining media content and an identification of a plurality of points of interest in the media content, receiving a request from a user to view the media content, obtaining information about the user, identifying one or more highlight and/or degrade points based on the information about the user, modifying the media content to create highlighted content, the highlighted content being the media content modified to attract attention of the user to the highlight point and drive the attention of the user away from the degrade point, presenting the highlighted content to the user, and monitoring the user's consumption of the content. Other embodiments are disclosed.

Abstract: 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.

Abstract: 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.

Abstract: Aspects of the subject disclosure may include, for example, a method for determining a first set of features in first images of first media content, generating a similarity score by processing the first set of features with a favorability model derived by identifying generative features and discriminative features of second media content that is favored by a viewer, and providing the similarity score to a network for predicting a response by the viewer to the first media content. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving a plurality of media items, each including an object having a plurality of variables, performing adaptive analysis and transfer analysis on the media items, generating a model based on both the adaptive analysis and the transfer analysis and recognizing, using the model, the object in media content. The model may also provide a description of at least one of the variables of the object in the media content, and/or how that variable relates to the object. The variables may be known in the media items and unknown in the media content. The adaptive analysis and/or the transfer analysis may be performed iteratively and/or simultaneously. The variables may include an intensity of the object, a viewpoint from which the object is observed, an illumination of the object, a blur of the object, and/or other variables. Other embodiments are disclosed.

Abstract: 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.

Abstract: A method, computer-readable medium, and apparatus for configuring a software defined network via machine learning are disclosed. For example, a method may generate a first policy for a first service by a first policy model using machine learning for processing first data of the first service, the first service being implement by at least one first virtual component of a software defined network, determine whether the first policy is to be applied to a second service, the second service being implement by at least one second virtual component of the software defined network, apply the first policy to the second service when the first policy is deemed to be applicable to the second service, generate a second policy for the second service, and implement the second policy in the second service, wherein the first service and the second service are provided by a single service provider.

Abstract: In one example, the present disclosure describes a device, computer-readable medium, and method for enabling secure video sharing by exploiting data sparsity. In one example, the method includes applying a transformation to a video dataset containing a plurality of video samples, to produce a plurality of sparse vectors in a first dimensional space, wherein each sparse vector of the plurality of sparse vectors corresponds to one video sample of the plurality of video samples, and multiplying each sparse vector of the plurality of sparse vectors by a transformation matrix to produce a plurality of reduced vectors in a second dimensional space, wherein the dimension of the second dimensional space is smaller than a dimension of the first dimensional space, and wherein the plurality of reduced vectors in the second dimensional space hides information about the video dataset while preserving relational properties between the plurality of video samples.

Abstract: 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.

Abstract: The present disclosure provides task delegation and cooperation for automated assistants. In one example, a method includes receiving, at a centralized support center that is in contact with a plurality of automated assistants including a first automated assistant and a second automated assistant, a request to perform a task on behalf of an individual, formulating, at the centralized support center, the task as a plurality of sub-tasks including a first sub-task and a second sub-task, delegating, at the centralized support center, the first sub-task to the first automated assistant, based on a determination at the centralized support center that the first automated assistant is capable of performing the first sub-task, and delegating, at the centralized support center, the second sub-task to the second automated assistant, based on a determination at the centralized support center that the second automated assistant is capable of performing the second sub-task.

Abstract: In one example, the present disclosure describes a device, computer-readable medium, and method for inferring the presence of an occluded entity in a video captured via drone. For instance, in one example, a video is obtained. The video is captured by a drone monitoring a field of view of a scene. It is determined, based on a combination of statistical reasoning and contextual modeling of the video, that an occluded entity is likely to be present, but not entirely visible, in the field of view. A signal is sent to the drone to instruct the drone to adjust its orientation to make the occluded entity more visible.

Abstract: 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.

Abstract: Concepts and technologies directed to network aggregation of streaming data interactions from distinct user interfaces are disclosed herein. Embodiments can include a network server that includes a processor and a memory that has computer-executable instructions. Upon execution, the processor can cause the network server to perform operations. The operations can include retrieving a content package from a content data store. The operations also can include splicing video content of the content package into a plurality of content segments and assigning a boundary time marker to each of the plurality of content segments. The operations can include creating a segment overlay field for each of the plurality of content segments, where each segment overlay field can be configured to receive a client generated metric corresponding to the boundary time marker for each of the content segments. The operations can include providing the content package to a plurality of user equipment.

Abstract: Methods, computer-readable media, and apparatuses for adjusting a network-controllable physical resource of a sensory service in response to biometric data for a group of users are disclosed. In one example, a processing system including at least one processor may gather biometric data of each user of a group of users of a sensory service. The sensory service may comprise at least one network-controllable physical resource. The processing system may then aggregate the biometric data for the group of users and tailor the sensory service for the group of users based upon the biometric data for the group of users that is aggregated. In one example, the tailoring of the sensory service may include adjusting the at least one network-controllable physical resource in response to the biometric data that is aggregated for the group of users.

Abstract: 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.