Abstract: A method that incorporates teachings of the subject disclosure may include, for example, determining, by a system comprising a processor, more common features of a plurality of images according to similarity matrices indicating relative similarities between instances of common features occurring within multiple images of the plurality of images, defining, by the system, cluster groups associated with the more common features, where each cluster group comprises cluster images of the plurality of images, and where the more common features are present in each the cluster images, and performing, by the system, quality-based filtering on the cluster images to identify a target cluster image to represent the cluster images for each of the cluster groups. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, modifying a user profile associated with a user associated with a content service to generate an updated user profile according to consumption of media content by the user and user feedback information associated with the consumption of the media content, determining a user context according to information associated with user device, where the user context includes current activity of the user, modifying a set of media content according to the user context that is determined to generate an updated set of media content, where a type of media content is eliminated from the set of media content in the updated set of media content according to the user context, and presenting the updated set of content at a presentation device of the user via a personal media channel of the user associated with the content service. Other embodiments are disclosed.

Abstract: A method includes receiving, by a device, a first image of a scene and a second image of at least a portion of the scene. The method includes identifying a first plurality of features from the first image and comparing the first plurality of features to a second plurality of features from the second image to identify a common feature. The method includes determining a particular subset of pixels that corresponds to the common feature, the particular subset of pixels corresponding to a first subset of pixels of the first image and a second subset of pixels of the second image. The method also includes generating a first image quality estimate of the first image based on a comparison of a first degree of variation within the first subset of pixels and a second degree of variation within the second subset of pixels.

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: A wireless communication device includes a processor, a display device coupled to the processor, and a memory accessible to the processor. The memory includes instructions executable by the processor to perform operations. The operations include receiving a user input to play a media content item. The operations include sending a request for a summary of the media content item to a network device in response to the user input in lieu of sending a particular request for the media content item. The operations also include, in response to the reply including media content, sending the media content to the display device for display.

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: A method that incorporates teachings of the subject disclosure may include, for example, determining, by a system comprising a processor, more common features of a plurality of images according to similarity matrices indicating relative similarities between instances of common features occurring within multiple images of the plurality of images, defining, by the system, cluster groups associated with the more common features, where each cluster group comprises cluster images of the plurality of images, and where the more common features are present in each the cluster images, and performing, by the system, quality-based filtering on the cluster images to identify a target cluster image to represent the cluster images for each of the cluster groups. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, modifying a user profile associated with a user associated with a content service to generate an updated user profile according to consumption of media content by the user and user feedback information associated with the consumption of the media content, determining a user context according to information associated with user device, where the user context includes current activity of the user, modifying a set of media content according to the user context that is determined to generate an updated set of media content, where a type of media content is eliminated from the set of media content in the updated set of media content according to the user context, and presenting the updated set of content at a presentation device of the user via a personal media channel of the user associated with the content service. 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: 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: 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: In one example, the present disclosure describes a device, computer-readable medium, and method for monitoring a user's food intake. For instance, in one example, a user's food intake is monitored based on data collected from a sensor. The user's current nutrient consumption is estimated based on the monitoring. A recommendation is presented to the user based on the estimating, where the recommendation is designed to help the user achieve a target nutrient consumption.

Abstract: Concepts and technologies disclosed herein are directed to image prediction. According to one aspect disclosed herein, an image prediction system can receive a training data set that includes a plurality of training images. The image prediction system can define N-dimensional feature vectors corresponding to the plurality of training images in the training data set, parameterize the N-dimensional feature vectors to obtain a plurality of parameterized curves corresponding the plurality of training images in the training data set, obtain a square root velocity representation for each parameterized curve of the plurality of parameterized curves, rescale the plurality of parameterized curves to remove scaling variability among the plurality of parameterized curves, define a pre-shape space for the plurality of parameterized curves, and obtain shape space points pertaining to each parameterized curve of the plurality of parameterized curves on a shape space that inherits a structure from the pre-shape space.

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: 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: 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: A wireless communication device includes a processor, a display device coupled to the processor, and a memory accessible to the processor. The memory includes instructions executable by the processor to perform operations. The operations include receiving a user input to play a media content item. The operations include sending a request for a summary of the media content item to a network device in response to the user input in lieu of sending a particular request for the media content item. The operations also include, in response to the reply including media content, sending the media content to the display device for display.

Abstract: Aspects of the subject disclosure may include, for example, facilitating access to a synopsis of a missed portion of a media program, identifying subject matter in a segment of content in the media program that has not been presented, determining a particular time for presenting the summary according to the subject matter in the segment of content, and initiating a presentation of the summary at the particular time. 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 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.