CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of India Patent Application No. 202011031721, filed Jul. 24, 2020, which is hereby incorporated by reference in its entirety.

FIELD

The specification generally relates to the automated analysis of digital content.

BACKGROUND

The amount of user generated digital content, including media items like videos, music and movies, is rapidly increasing. For example, users can create and upload video files and/or audio files to media sharing sites. Due to easy access to the internet and the growing popularity of digital content delivery services, more and more digital content is becoming accessible to users.

Some of this content may be subject to content distribution guidelines and/or standards, in order to be shared and/or distributed to users. For example, certain platforms may consider violent or adult content as unsuitable and/or inappropriate based on community standards and prevent distribution of such content. In another example, certain contents may be considered as private information and any distribution of such content is prevented that may lead to breach of privacy. In such circumstances it is necessary to deploy a scalable process for evaluating content and removing content deemed inconsistent with these guidelines and/or standards. For example, digital content can be evaluated and filtered to prevent children from being exposed to content that would be inappropriate to view.

SUMMARY

In general, media sharing sites and/or services can employ rating entities, including human or computer implemented raters, to evaluate digital content before distribution to their users. Content review is an extremely time consuming and expensive process. It is therefore imperative to identify ways to improve evaluation efficiency while maintaining high accuracy.

The methods described in this document provides an efficient means of evaluating digital content by partitioning digital media items such as videos into segments and/or scenes, and classifying the segments into predetermined classes. For example, a content platform can classify the segments into predetermined classes such as “Violence”, “Conversation”, “Street”, “Nudity”, “Animation” and “Private”. After classifications have been assigned, the segments are clustered together. These clusters contain segments from across multiple media items, not just from within one item. The clusters are then presented to a rating entity in a single user interface, for further evaluation. This type of presentation to the rating entity provides an easy method for approval or refusal of all or a subset of segments by the rating entity. After evaluation of the segments, media items are identified such that for each media item, all the segments corresponding to the media item are approved by the rating entity.

In general, one innovative aspect of the subject matter described in this specification can be embodied in methods including the operations of segmenting into scenes across media items and then clustering rather than clustering at the media item level. The specific benefit is that at the media item level (rather than segment level) far fewer items are likely to be highly similar to each other than at the scene level. Therefore there would be more clusters, that are harder to review (because of length), leading to reduced efficiency.

In general, one innovative aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving, a plurality of media items wherein the media item is a digital video; generating, a plurality of segments of each media item from among the plurality of media items; classifying, each segment from among the plurality of segments generated from the plurality of media items into one or more predetermined classes; clustering, to generate a cluster of segments for each of two or more predetermined classes; presenting, a cluster of segments to a rating entity for further evaluation; receiving, a response from the rating entity indicating an approval of whether the cluster of segments does not violate content distribution guidelines; identifying, by the response from the rating entity, the media items from among the plurality of media items for which all the segments were approved by the rating entity; and distributing, the media items that were identified indicating that the media items does not violate the content distribution guidelines.

Other embodiments of this aspect include corresponding methods, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices. These and other embodiments can each optionally include one or more of the following features.

Methods can further include presenting, a cluster of segments to two or more rating entities for further evaluation; receiving, a response from each of the two or more rating entities indicating two or more approval of whether the cluster of segments does not violate content distribution guidelines; generating, a final approval by combining the responses from each of the two or more rating entities.

Methods may include classifying each segment from among the plurality of segments into two or more predetermined classes includes using a machine learning algorithm to classify the segments into two or more pre-determined classes.

Methods may include receiving a response from the rating entity further include an approval of a subset of segments from the multiple segments in a cluster that indicates that the subset of segments does not violate content distribution policies wherein a rating entity can be a live rating entity or a computer implemented technique capable of evaluating segments generated from media items.

Methods may further include a cluster of segments that is presented to a rating entity that includes one or more segments that have already been rated by another rating entity.

Methods may also include one or more segments of a particular media item that are not rated when an initial segment of the particular media item is determined to violate the content distribution guidelines.

Particular embodiments of the subject matter described in this specification can be implemented to realize one or more of the following advantages. For example, the techniques discussed throughout this document can evaluate media items to detect inappropriate content within the media item, which prevents distribution of the media items with inappropriate content to end users.

Existing techniques that are implemented to detect and filter inappropriate content, evaluate media items as a whole (i.e. the entire duration of the media item such as a video). In contrast, the techniques discussed throughout this document evaluate batches of media segments that are generated from the media items such that the entire process of evaluation consumes less time. Specifically, the enhanced techniques described herein can evaluate the most sensitive content in a prioritized manner, so that evaluations can be made more quickly while minimizing the time devoted to evaluating non-sensitive portions.

Other implementations of this and other aspects include corresponding systems, apparatus, and computer programs, configured to perform the actions of the methods, encoded on computer storage devices. A system of one or more computers can be so configured by virtue of software, firmware, hardware, or a combination of them installed on the system that in operation cause the system to perform the actions. One or more computer programs can be so configured by virtue of having instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.

The details of one or more implementations of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other potential features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims

