Methods, systems, and apparatuses for determining viewership of a content item are described herein. Machine learning techniques may be used to determine which user(s) among a user group at a multi-user location is consuming a content item. A machine learning model may be trained using demographic attributes and content attributes associated with a plurality of single-user locations. A probability engine may train a machine learning model using the demographic attributes and content attributes and one or more machine learning algorithms. The trained machine learning model may be used to determine which user(s) among at least two users is consuming a content item at a multi-user location at which multiple people reside.

Accommodation for color or visual impairments may be implemented by selective color substitution. A color accommodation module receives an image frame from a host system and generates a color-adapted version of the image frame. The color accommodation module may include a rule based filter that substitutes one or more colors within the image frame with one or more corresponding alternative colors.

Methods, apparatus, systems, and articles of manufacture are disclosed. An example apparatus includes a thermal image detector to determine a heat blob count based on a frame of thermal image data, the frame of thermal image data captured in the media environment, a comparator to compare the heat blob count to a prompted people count, the prompted people count based on one or more responses to a prompting message, and when the heat blob count and the prompted people count match, cause a timer that is to trigger generation of the prompting message to be reset.

Systems and methods for audience interaction in real-time multimedia applications are provided. In one embodiment, a method for a real-time video conference comprises, during the real-time video conference, receiving a media stream including audio and video from a remote computing system over a network, acquiring, video and audio of a user, detecting an event with a machine learning model in at least one of the video and the audio of the user, and transmitting an event detection message indicating the detected event and/or audio and video of the user to the remote computing system over the network. In this way, natural audience reactions may be automatically detected and shared.

A system and method for correlating video frames in a computing environment. The method includes receiving first video data and second video data from one or more data sources. The method further includes encoding the received first video data and the second video data using machine learning network. Further, the method includes generating first embedding video data and second embedding video data corresponding to the received first video data and the received second video data. Additionally, the method includes determining a contrastive IDM temporal regularization value for the first video data and the second video data. The method further includes determining temporal alignment loss between the first video data and the second video data. Also, the method includes determining correlated video frames between the first video data and the second video databased on the determined temporal alignment loss and the determined contrastive IDM temporal regularization value.

A display device is disclosed. According to the present invention, a display device comprises: a sensor; a display; a storage unit in which history information on content provided by the display device is stored; and a processor for acquiring, through the sensor, information on the distance between a user and the display device if a preset event occurs, displaying a background screen in the display if the user is identified, on the basis of the acquired information, as existing in a first region among a plurality of regions classified according to the distance from the display device, providing content on the basis of first history information if the user is identified, on the basis of the acquired information, as existing in a second region among the plurality of regions, and providing content on the basis of second history information if the user is identified, on the basis of the acquired information, as existing in a third region among the plurality of regions, wherein the first history information can include information on content provided by the display device during the existence of the user in the second region, and the second history information can include information on content provided by the display device during the existence of the user in the third region. The display device can provide content by using an artificial intelligence (AI) model having been taught according to at least one of machine learning, neural network and deep learning algorithms, in the providing the content.

The disclosed computer-implemented method may include accessing media segments that correspond to respective media items. At least one of the media segments may be divided into discrete video shots. The method may also include matching the discrete video shots in the media segments to corresponding video shots in the corresponding media items according to various matching factors. The method may further include generating a relative similarity score between the matched video shots in the media segments and the corresponding video shots in the media items, and training a machine learning model to automatically identify video shots in the media items according to the generated relative similarity score between matched video shots. Various other methods, systems, and computer-readable media are also disclosed.

The present disclosure relates to a computer-implemented process for evaluating user activity, user preference, and/or user habit via one or more personal devices and providing precisely timed and situationally targeted content recommendations. It is an object of the present disclosure to provide a technological solution to the long felt need in small scale content recommendation systems caused by the technical problem of generating situationally targeted and user preference targeted content recommendations for users of an interactive electronic system.

Provided herein are methods and systems for improved accessibility scoring for content items. A predicted accessibility score may be based on a plurality of multimodal features present within a content item. The plurality of multimodal features may include video features (e.g., based on video/image analysis), audio features (e.g., based on audio analysis), text-based features (e.g., based on closed-captioning analysis), features indicated by metadata (e.g., duration, genre, etc.), a combination thereof, and/or the like. A predicted accessibility score for a content item may indicate how accessible the content item may be for persons who are visually impaired, hearing impaired, cognitively impaired, etc., as well as for persons who desire to view content that requires less visual attention and/or audio attention as the case may be.

Systems and methods are described herein for curating content that follows a narrative structure. A narrative structure comprises narrative portions that have a defined order. Signature analysis of known content that follows the narrative structure is used to train machine learning models for the narrative structure and the narrative portions that make up the narrative structure. Signature analysis of candidate content segments, along with machine learning models for the narrative portions, are used to identify candidate content segments that match the respective narrative portions. A candidate playlist is generated of the identified candidate content segments in the defined order. In one embodiment, the machine learning model for the narrative structure is used to validate the generated playlist.