A television system with a network function is disclosed. The television system includes several client devices and a server device. The several client devices are configured to store and transmit the client information. The client information includes channel information and several positions of the several client devices. The server device is communicatively connected to the several client devices and is configured to receive the client information, to integrate the client information so as to generate integrated information, and to transmit the integrated information to the several client devices. The several client devices are further configured to confirm the several integrated information so as to update the several client information.

A system and method for dynamically training a system to determine an age rating for media content. An exemplary method includes obtaining age rating data for a plurality of territories; determining, based on the age rating data, a similarity vector relating to the target territory; determining, for the similarity vector, a territory associated with a highest prediction score; in response to determining that the territory associated with the highest prediction score is not the source territory, generating a training dataset comprising the age rating data for the target territory, the source territory, and the territory associated with the highest prediction score; and executing a machine learning model, trained by the training dataset, to output an age rating for a content item in the target territory based on an age rating for the content item in the source territory.

The disclosed technology includes systems and methods for time-shifted prefetching of predicted content for wireless users. The disclosed technology can include a method of prefetching video data. The method can include retrieving video data and feature data and generating a video candidate set comprising selected videos. The method can include determining principal components of each video in the video candidate set by performing a principal component analysis. Furthermore, the method can include determining predicted videos using a k-nearest neighbor classifier. The predicted videos can be videos of the video candidate set that are likely to be viewed by a user at a future time. The method can include outputting instructions to the user device to prefetch the predicted videos by downloading the predicted videos to the user device.

Systems and methods are provided herein for identifying a playlist of highlights to use for refreshing a user on a plot related to a media asset the user has requested to access based on how long it has been since the user last saw related programming. The media guidance application may receive a request from a user to access a media asset and may determine whether the user previously consumed a related media asset to the media asset. The media guidance application may determine whether a period of time between receiving the request and a time when the user previously consumed the related media asset exceeds a threshold period of time. If the period of time does not exceed the threshold, the media guidance application may play back a predefined playlist of highlights, and if it exceeds the threshold, the media guidance application may play back a customized playlist of highlights.

Various approaches relate to user defined content filtering in media playing devices of undesirable content represented in stored and real-time content from content providers. For example, video, image, and/or audio data can be analyzed to identify and classify content included in the data using various classification models and object and text recognition approaches. Thereafter, the identification and classification can be used to control presentation and/or access to the content and/or portions of the content. For example, based on the classification, portions of the content can be modified (e.g., replaced, removed, degraded, etc.) using one or more techniques (e.g., media replacement, media removal, media degradation, etc.) and then presented.

Various embodiments described herein support or provide for predicting a rating for a media asset for one geographic region based on a reference rating of the media asset for another geographic region.

Example methods, apparatus, systems, and articles of manufacture are disclosed to estimate population reach for different unions based on marginal ratings. An example apparatus includes a reach determiner to determine a population reach estimate of a union of time intervals for which media ratings data is available, the population reach estimate based on a pseudo universe estimate of a population audience corresponding to the union of the time intervals; a pseudo universe determiner to determine a pseudo universe estimate of a recorded audience corresponding to the union of the time intervals; determine the pseudo universe estimate of the population audience based on the pseudo universe estimate of the recorded audience; and iteratively update the pseudo universe estimate of the population audience to reduce an error; and a consistency checker to adjust the population reach estimate, the reach determiner to output the population reach estimate of the union.

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.

A big data acquisition and analysis system based on intelligent image recognition, and an application method. The system comprises an intelligent cloud server. The intelligent cloud server comprises a computing server and a storage server. An image recognition system composed of a data reading module, a video stream data processing module, an AI image recognition module, a data storage module and a model tuning module is carried in the computing server, and a video stream storage database, a video stream management module and a data center database which are interactively connected to each other are arranged in the storage server. According to the present invention, non-private real digital behaviors of consumers are restored and higher commercial value is generated, the problems of delay, omission, slow speed, large error and high cost are not generated in the process, a consumer real-time digital behavior analysis bottleneck is solved, business analysis is closer to the reality, more valuable analysis results are brought to a brand party, and a brand global optimization consumption path is guided.

Methods, systems, and apparatuses, including computer programs encoded on a computer storage medium, for facilitating analyzing media items and to filter inappropriate media items before distribution to the users. In one aspect, a method includes partitioning digital media items such as videos into segments and/or scenes, and classifying the segments into predetermined classes such as “Violence”, “Conversation”, “Street”, “Nudity”, “Animation”. After classifications have been assigned, the segments are clustered and/or grouped together before presenting the segments belonging to a particular cluster to a rating entity in a single user interface, for further evaluation. After evaluation, the segments of the media items that were approved by the rating entity are used to identify media items for which all the segments were approved by the rating entity before distributing the media items to the users.