Abstract: Task delegation and cooperation for automated assistants is presented. A method comprises 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: 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: 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.

Abstract: Methods, computer-readable media, and devices are disclosed for providing a notification of an anomaly in a media content that is associated with an event type. For example, a method may include a processing system including at least one processor for detecting a first anomaly from a first media content, generating a first anomaly signature for the first anomaly, obtaining a notification of a first event, the notification including an event type, time information, and location information of the first event, correlating the first anomaly to the notification of the first event, and labeling the first anomaly signature with the event type. The processing system may further detect a second anomaly from a second media content that matches the first anomaly signature and transmit a notification of a second event of the event type when it is detected that the second anomaly matches the first anomaly signature.

Abstract: A method may include a processor training a machine learning model with a training data set, computing a data distribution of the training data set, processing a stream of new data to determine a likelihood of the new data from the data distribution that is computed, incrementing a counter when the likelihood of the new data is less than a first threshold, and retraining the machine learning model when the counter exceeds a second threshold.

Abstract: Methods, computer-readable media, and devices are disclosed for improving an object model based upon measurements of physical properties of an object via an unmanned vehicle using adversarial examples. For example, a method may include a processing system capturing measurements of physical properties of an object via at least one unmanned vehicle, updating an object model for the object to include the measurements of the physical properties of the object, where the object model is associated with a feature space, and generating an example from the feature space, where the example comprises an adversarial example. The processing system may further apply the object model to the example to generate a prediction, capture additional measurements of the physical properties of the object via the at least one unmanned vehicle when the prediction fails to identify that the example is an adversarial example, and update the object model to include the additional measurements.

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: One example of a method includes receiving a plurality of video streams depicting a scene, wherein the plurality of video streams provides images of the scene from a plurality of different viewpoints, identifying a target that is present in the scene, wherein the target is identified based on a determination of a likelihood of being of interest to a viewer of the scene, determining a trajectory of the target through the plurality of video streams, wherein the determining is based in part on an automated visual analysis of the plurality of video streams, wherein the determining is based in part on a visual analysis of the plurality of video streams, rendering a volumetric video traversal that follows the target through the scene, wherein the rendering comprises compositing the plurality of video streams, receiving viewer feedback regarding the volumetric video traversal, and adjusting the rendering in response to the viewer feedback.

Abstract: Methods, computer-readable media, and devices are disclosed for providing a notification of an anomaly in a media content that is associated with an event type. For example, a method may include a processing system including at least one processor for detecting a first anomaly from a first media content, generating a first anomaly signature for the first anomaly, obtaining a notification of a first event, the notification including an event type, time information, and location information of the first event, correlating the first anomaly to the notification of the first event, and labeling the first anomaly signature with the event type. The processing system may further detect a second anomaly from a second media content that matches the first anomaly signature and transmit a notification of a second event of the event type when it is detected that the second anomaly matches the first anomaly signature.

Abstract: A method may include a processor training a machine learning model with a training data set, computing a data distribution of the training data set, processing a stream of new data to determine a likelihood of the new data from the data distribution that is computed, incrementing a counter when the likelihood of the new data is less than a first threshold, and retraining the machine learning model when the counter exceeds a second threshold.

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: Methods, computer-readable media, and devices are disclosed for improving an object model based upon measurements of physical properties of an object via an unmanned vehicle using adversarial examples. For example, a method may include a processing system capturing measurements of physical properties of an object via at least one unmanned vehicle, updating an object model for the object to include the measurements of the physical properties of the object, where the object model is associated with a feature space, and generating an example from the feature space, where the example comprises an adversarial example. The processing system may further apply the object model to the example to generate a prediction, capture additional measurements of the physical properties of the object via the at least one unmanned vehicle when the prediction fails to identify that the example is an adversarial example, and update the object model to include the additional measurements.

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: One example of a method includes receiving a plurality of video streams depicting a scene, wherein the plurality of video streams provides images of the scene from a plurality of different viewpoints, identifying a target that is present in the scene, wherein the target is identified based on a determination of a likelihood of being of interest to a viewer of the scene, determining a trajectory of the target through the plurality of video streams, wherein the determining is based in part on an automated visual analysis of the plurality of video streams, wherein the determining is based in part on a visual analysis of the plurality of video streams, rendering a volumetric video traversal that follows the target through the scene, wherein the rendering comprises compositing the plurality of video streams, receiving viewer feedback regarding the volumetric video traversal, and adjusting the rendering in response to the viewer feedback.

Abstract: Aspects of the subject disclosure may include, for example, embodiments provisioning a neural network comprising a plurality of layers. Further embodiments include provisioning a plurality of Markov logic state machines among the plurality of layers of the neural network resulting in a machine learning application. Additional embodiments include training the machine learning application using historical network video traffic resulting in a trained machine learning application. Also, embodiments include receiving current network video traffic. Embodiments include provisioning network resources to route the current network video traffic according to the trained machine learning application. Other embodiments are disclosed.

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: 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 includes receiving, at a server from a user device, a request for media content. The request identifies a first personal quality profile. The method includes identifying, at the server, a version of the media content to send to the user device based on first media quality preferences indicated by the first personal quality profile. The method includes, in response to a determination of unavailability of the version from a content source: retrieving, at the server from the content source, a particular version of the media content; and modifying, at the server, a visual attribute of the particular version to generate the version. The method further includes sending the version from the server to the user device.

Abstract: A processor may generate initial subspaces by reducing data of a plurality of domains to a same dimensionality, cluster data within each initial subspace into clusters, create an input data matrix by projecting data from each cluster to an associated one of a plurality of generative subspaces associated with the plurality of clusters, perform a parallel transport between first and second sets of generative subspaces to identify a set of directions between pairs of subspaces of the first and second sets, sample points comprising a plurality of intermediate representations along the directions via an exponential mapping, create an expanded set of prior information by projecting the data from the first and second sets to the plurality of intermediate representations, perform a sparse coding with the input data matrix and the expanded set of prior information, and perform a cross domain inference using the output of the sparse coding.

Abstract: Aspects of the subject disclosure may include, for example, generating narrative descriptions corresponding to visual features, visual events, and interactions there between for media content, where the narrative descriptions are associated with time stamps of the media content, and presenting the media content and an audio reproduction of the narrative descriptions, wherein the audio reproduction is synchronized to video of the media content according to the time stamps. Other embodiments are disclosed.