A system (100) for context driven processing of multimedia content (402) on an edge device (104) is presented. The system (100) includes an acquisition subsystem (404). Furthermore, the system (100) includes a processing subsystem (406) that includes a context aware artificial intelligence platform (408) configured to generate context characteristics based on user characteristics, edge device characteristics, and multimedia characteristics, retrieve a model (324, 412) based on the context characteristics, identify processing steps based on the model (324, 412), the context characteristics, or both, where the processing steps are used to perform context driven processing of input multimedia content (402) on the edge device (104), select, based on the model (324, 412), the context characteristics, or both, one or more target processing units (100) to perform the processing steps, and execute the processing steps on the selected target processing units (418, 420, 422, 424, 426) to generate improved output multimedia content. The system (100) includes an interface unit (428, 430) configured to provide, on the edge device (104), the improved output multimedia content.

This disclosure describes techniques that enable a categorization controller to detect activation of a portable recording device that is configured to capture a real-time multimedia stream of the current event. The categorization controller may further identify a set of categories that are likely associated with the real-time multimedia stream, determine an ordered ranking of the set of categories, and generate a ranked category dataset for delivery to the portable recording device. In doing so, the portable recording device may present the ordered ranking of the set of categories at a user interface.

Disclosed herein are system, apparatus, article of manufacture, method, and/or computer program product embodiments for a crowd sourced indexing and/or searching of content. An embodiment operates by receiving one or more requests for content from one or more media devices, each request comprising content identifier information that identifies the content, determining whether crowd sourced content index information has been generated for the content, transmitting a response to the one or more media devices of the one or more media devices, in response to the one or more requests, the response comprising content location information and a content indexing request, and receiving content index information for the content identified by the content identifier information from the one or more media devices.

A hardware media items scheduling and packaging system, which schedules and distributes channels to be viewed on a plurality of consumer devices, extracts contextual data from program-specific information associated with programming media content of a channel received from a distribution source device. A plurality of potential non-programming media items is determined for a plurality of users based on a match between a sentiment type of each of a plurality of non-programming media items and the extracted contextual data. Based on at least the extracted contextual data and the sentiment type of each of the plurality of potential non-programming media items, a plurality of candidate spots in the programming media content is determined. Based on at least a set of constraints and user estimation data associated with the plurality of users, a schedule of non-programming media item(s) is dynamically generated for at least one candidate spot in the programming media content.

Data on a user interacting with a media presentation is collected at a client device. The data includes facial image data of the user. The facial image data is analyzed to extract cognitive state content of the user. One or more emotional intensity metrics are generated. The metrics are based on the cognitive state content. The media presentation is manipulated, based on the emotional intensity metrics and the cognitive state content. An engagement score for the media presentation is provided. The engagement score is based on the emotional intensity metric. A facial expression metric and a cognitive state metric are generated for the user. The manipulating includes optimization of the previously viewed media presentation. The optimization changes various aspects of the media presentation, including the length of different portions of the media presentation, the overall length of the media presentation, character selection, music selection, advertisement placement, and brand reveal time.

A detection device detecting a scene related to a sponsor credit included in a commercial message from a target video is provided. The detection device comprises a detection unit that associates, from a preliminary video, a still image related to the sponsor credit with an audio signal related to the sponsor credit included other than in a frame or an audio signal configuring the commercial message so as to detect the scene related to the sponsor credit from the target video.

A multi-media presentation system includes an input subsystem operable to collect data including an unassisted data input of a viewer from within a natural, unaided sensing range of the viewer; a storage subsystem with a multiple of storage subsystems operable to store the data including the unassisted data input in a storage subsystem in a manner associated with a preference; and a server subsystem operable to selectively display a multi-media presentation in accordance with the preference.

A display device comprising a memory in which a first application corresponding to a first content provision server and a second application corresponding to a second content provision server are stored; a display; and a processor to control, by executing the first application, the display to display a recommendation content list of content of the first content provision server based on a content use history of a user so that the content included in the recommended content list is aligned in a preset alignment. The preset alignment can be the same as an alignment of a recommendation content list of content of the second content provision server to be provided through the second application.

A system and method for automated generation of control signals for sexual stimulation devices from usage history and other data. The system and method involve analyzing historical usage and other data for a user for a device or devices, processing the data through machine learning algorithms, and generating new or recombined patterns of stimulation based on the outputs from the machine learning algorithms. The resulting automated control signals represent partially or fully customized stimulation for a given user which evolve over time as the user continues to use the device or devices.

A system and method for voice-based control of sexual stimulation devices. In some configurations, the system and method involve receiving voice data, analyzing the voice data to detect spoken commands, and generating control signals based on the commands. In some configurations, the system and method involve receiving voice data, analyzing the voice data for non-speech vocalizations, detecting voice stress patterns, and generating control signals based on the detected patterns. In some configurations, the analyses of the voice data are performed by machine learning algorithms which may be trained on associations between speech and non-speech vocalizations of a user while the user engages in one or more voice-based training tasks, associating speech and non-speech vocalizations with controls of the sexual stimulation device. In some configurations, machine learning algorithms are used to make the associations. In some configurations, data from other biometric sensors is included in the associations.