A method, a set-top box, and a non-transitory computer readable medium for log based issue prediction. The method includes receiving, on a processing server, system log files from a customer-premises equipment, the system log files containing events that are logged by an operating system of the customer-premises equipment; parsing, by the processing server, the events of the system log files to processes and mapping the processes to one or more components of the customer-premises equipment; extracting, by the processing server, features from the mapped processes of the one or more components of the customer-premises equipment; classifying, by the processing server, the extracted features with a first machine learning algorithm; and predicting, by the processing server, anomalies in one or more components of the customer-premises equipment with a second machine learning algorithm using the classified features from the first machine learning algorithm.

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.

This document describes, among other things, systems, methods, devices, and other techniques for using information about how long various videos were presented at client devices to determine subsequent video recommendations and search results. In some implementations, a computing can include a modeling apparatus, a front-end server, a request manager, one or more video file storage devices, a video selector, or a combination of some or all of these. The video selector can select video content for a particular digitized video among a plurality of digitized videos to serve to a computing device responsive to a request. The selection can be based at least in part on how long the particular digitized video has been presented at client devices associated with users having characteristics that match one or more characteristics of the user that submitted the request for video content, as indicated by the modeling apparatus.

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.

Proactively identify cable network impairments based on telemetry data from customer-premises equipment (CPE) devices is disclosed. In some embodiments, a maintenance service retrieves telemetry parameters from CPE devices at a customer site communicatively coupled to a cable network infrastructure. The maintenance service analyzes the telemetry parameters to detect cable network impairments experienced by the CPE devices at the customer site (based on, e.g., whether a telemetry parameter from any CPE devices fails a corresponding telemetry threshold, whether the same telemetry failure is experienced by all CPE devices at the customer site, whether other neighboring customer sites also experience the same failure on all CPE devices, and/or whether a high post main tap (HPMT) parameter and an HPMT magnitude (HPMTM) parameter for the customer site fail corresponding thresholds, according to some embodiments). The maintenance service then assigns a maintenance classification that indicates a recommended service technician type for the customer site.

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.

Provided are an automatic video editing method and a portable terminal, the method comprising: obtaining a video to be edited; extracting key frames of the video to be edited; inputting the key frames to a pre-trained scene classification method and a pre-trained target detection method, and respectively obtaining scene type markers and target object markers of the key frames; selecting multiple video segments from the video to be edited which satisfy preset editing criteria; respectively calculating average scores of the multiple video segments by means of a pre-trained image quality scoring method; respectively obtaining the video segment having the highest average score of each shot type for splicing.

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.

Methods and apparatus to correct for deterioration of a demographic model to associate demographic information with media impression information are disclosed. An example method includes estimating first and second ages of audience members based on demographic information; estimating a third age of an audience member who is not included in the audience members; applying a window function to the second ages to determine a distribution of ages based on the third age; multiplying window values by the first ages to determine corrected first age components; dividing a total of the corrected first age components by a sum of the window values to determine an estimated age of the audience member at a first time; and determining the corrected age of the audience member at a second time based on the estimated age of the audience member at the first time and a time difference between the first and second times.

Systems and methods for time-shifted prefetching of predicted content for wireless users. Prefetching video data can include retrieving video data and feature data and generating a video candidate set including selected videos of the related video data. The method can further include determining predicted videos using a machine learning algorithm. 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 further include prefetching the predicted videos by downloading the predicted videos.