An estimation device includes: an obtainer that obtains first content associated with a first time and second content associated with a second time, the second time preceding the first time by a predetermined amount of time; a determiner that, by applying first processing for determining a type of content to each of the first content and the second content, obtains first type information indicating a type of the first content and second type information indicating a type of the second content; a calculator that, using the first type information and the second type information, calculates confidence level information indicating a confidence level of the first type information; and an outputter that, using the confidence level information calculated by the calculator, outputs specifying information specifying the type of the first content derived from the first type information.

Systems and methods are provided for detecting a content type of content within a media stream. A computing device may receive a media stream and define a set of media segments that each represent a portion of the media stream. The computing device may identify a first media segment that includes a first boundary and a second media segment that includes a second boundary. The computing device may predict whether the subset of media segments that are positioned between the first media segment and the second media segment include content of a particular content type. The computing device may then transmit an indication of the prediction.

Methods, apparatus, systems and articles of manufacture are disclosed to project ratings for future broadcasts of media. Disclosed example methods include normalizing, with a processor, audience measurement data corresponding to media exposure data, social media exposure data and programming information associated with a future quarter to determine normalized audience measurement data. Disclosed example methods also include classifying a media asset based on the programming information to determine a media asset classification. Disclosed example methods also include building, with the processor, a projection model based on a first subset of the normalized audience measurement data, the first subset of the normalized audience measurement data associated with a first time frame relative to the future quarter, the first subset of the normalized audience measurement data based on the media asset classification, and applying, with the processor, the programming information to the projection model to project ratings for the media asset.

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.

Systems, methods, and devices relating to determining an introduction portion in a video program are described herein. A method may determine first and second hard-matching pairs of video segments in first and second video content such that video fingerprints of the first hard-matching pair match and video fingerprints of the second hard-matching pair also match. The method may classify a third pair of video segments in the first and second video content, sequentially between the first and second hard-matching pairs, as a soft-matching pair of video segments of an introduction portion. The method may use the classification of the third pair of video segments as a soft-matching pair to determine a model configured to determine that a pair of video segments in two video content items are a soft-matching pair of video segments of an introduction portion.

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 memory and processor circuitry 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; 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 in response to a decision to update the pseudo universe estimate of the population audience to reduce an error of the population reach estimate output the population reach estimate of the union when the error of the population reach estimate satisfies a threshold.

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.

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.

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.

Systems and methods are provided herein for processing digital content. A registry includes a set of common registry identifiers used to classify metadata. Further, an interchange layer and registry classification service: receives metadata derived from digital content in a content file; and classifies the metadata, by associating the metadata with one of the common registry identifiers, based upon a classification from a set of classification rules that is associated with a metadata type of the metadata.