Developments in interactive programming guides that utilize a confluence of users/viewers from data that may be block chain and/or data collection from IoT and other devices, proximity sensing of viewership, and both machine and guided learning over a large dataset to produce rules for AI selection of content, format, and features presented to currently active or a nearby set of television users/viewers. Blockchain may be implemented separately for accounting, verification, billing, and/or fees/royalty payments owed to content owners/copyright holders. Playback may be initiated by a remote device and played on a same or yet another or a plurality of remote devices.

Aspects of the subject disclosure may include, for example, a device, that has a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, where the operations include detecting each individual of an audience viewing media content on user equipment; retrieving a user profile for each individual of the audience resulting in user profiles; creating a group profile from the user profiles; determining, based on the group profile, a recommendation for viewing a candidate media content; and providing the recommendation to the user equipment for the audience. Other embodiments are disclosed.

A method and system for content display. The method includes: capturing, by an imaging device, a plurality of images of an environment; generating, by a computing device, first user profiles based on appearance of users recognized in the images; detecting devices in the environment and associating the detected devices to the users to obtain associated devices that are held by the users; retrieving, by the computing device, second user profiles of the users based on identifications of the associated devices; selecting at least one content based on the first user profiles and the second user profiles; and displaying the at least one selected content.

Systems and methods are provided herein for determining reduction of interest in a content series and to increasing the interest upon such determination. This may be accomplished by a device monitoring consumption of a content series to determine a pattern of consumption. The device may identify a change in the pattern of consumption indicative of a reduction of interest and determine a reason for the reduction in interest. Based on the reason for the reduction of interest, the device may provide an operation, such as a spoiler, to increase interest in the content series.

Systems and techniques are provided for automation and recommendation based on device control protocols. HDMI-CEC device control data may be received from a connected electronic device that may be connected to an electronic display device. The HDMI-CEC device control data may be based on a HDMI-CEC device control signal from the electronic display device. Signals may be received from sensor devices located in a same structure as the electronic display device. The HDMI-CEC device control data and signals from the sensor devices may be input to the machine learning system. The machine learning system may generate a control signal for a device. The control signal may be sent to the device to be implemented by the device.

A method for comment information display includes steps described below: Comment information of a plurality of target videos in a video collection is acquired; a bullet comment file is generated according to the comment information; and in response to any target video in the video collection being played, the bullet comment file is loaded in segments according to a preset playing rule, and each piece of the comment information is displayed in a form of scrolling display of the bullet comment file.

Systems and methods for recommending a media asset relating to a character unknown to a user are provided herein. The systems and methods may receive a first media asset viewed by a user, determine a first character in the first media asset, and include the first character in the character viewing profile for the user. Further, the systems and methods may receive a second media asset, including the first character, determine that a second character appears in the second media asset, and determine that the second character is not in the character viewing profile. The systems and methods may recommend a third media asset to the user for the second character.

In some embodiments, a method receives a first set of video files at a node that delivers video files to client devices and receives a second set of video files. The second set of video files are predicted to be delivered by the node to a client device during a time period. The node receives a request for a video file from a client device and determines whether the video file is stored in the first set of video files and the second set of video files. When the video file is stored at the node, the node sends the video file from the first set of video files and the second set of video files. When the video file is not stored in the first set of video files and the second set of video files, the node sends a request for the video file to another node.

This application describes methods, systems, and apparatus for allowing a user to easily determine whether the user and/or other user(s) have viewed content. For example, a program guide may display one or more graphics associated with the content (e.g., a movie poster), and in response to determining whether the user has viewed the content, the program guide may include an indication of whether the user has viewed the content. The program guide may include indications associated with multiple users (e.g., different colors, icons, graphics, or text). The program guide may filter the content based on whether the user has viewed the content (e.g., only display content the user has not viewed). A computing device storing the content may manage recording of the content based on whether the user has viewed the content (e.g., delete the recording if the user has viewed the content). A viewing history of the user may be maintained.

Advertisements are delivered with videos, the advertisements being cued to begin based on an analysis of metadata associated with the videos such as moments identified from user-submitted comments relative to the video. Credit earned by users based on their viewing and engagement activities may be gifted to other users or applied towards baseline fees assessed in connection with videos delivered to the user.