Granular Media Content Rating via Playback Segmentation
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Solution Overview
Problem
Current media content rating systems are inadequate as they rely on user input and do not provide granular enough data, leading to inaccurate assessments of user preferences, as they often log entire media file plays regardless of completion or only account for overall file ratings, failing to differentiate between full and partial plays.
Innovation Solution
A highly granular media content rating system that automatically logs and rates portions, segments, or clips of media content based on playback frequency and duration, providing detailed metrics that can be communicated to network entities for improved marketing and advertising strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current rating systems log entire media file plays regardless of completion, then the system is simple to implement, but the measurement precision deteriorates because it cannot differentiate between full and partial plays
Solution Approach 1:
The patent segments the media content into distinct portions (e.g., chapters, scenes, tracks) and tracks playback of each segment separately. This allows the system to measure whether a user played a complete portion or only part of it, thereby improving measurement precision without requiring complex user input mechanisms.
Solution Approach 2:
The system pre-defines portions of media content with identifiable boundaries and metadata before playback occurs. This preliminary structuring enables automatic tracking of playback completion status without requiring real-time user input or complex processing during playback.
2Productivity
If rating systems rely on user input for ratings, then the system is simple to implement, but the productivity deteriorates because users must take additional actions beyond playing media
Solution Approach 1:
The system automatically generates ratings by monitoring playback behavior itself. The act of playing media content self-generates the rating data through automatic tracking of playback completion, eliminating the need for separate user rating actions while maintaining ease of operation.
Solution Approach 2:
The system uses playback completion status as feedback to automatically determine ratings. When a user completes a portion of media content, the system automatically records this completion event and uses it to generate rating metrics, creating a closed-loop system that improves productivity without increasing user burden.
3Measurement precision
If rating systems only log complete media file plays, then the measurement precision improves for full plays, but the loss of information increases because partial plays are not captured
Solution Approach 1:
By dividing media content into smaller portions and tracking each segment's playback status independently, the system captures both complete and partial play information. This segmentation allows precise measurement of what was actually viewed or listened to without losing data about incomplete plays.
Solution Approach 2:
The system tracks playback at a granular level, recording even partial playback of portions as meaningful data points. This approach treats partial plays not as incomplete data but as valuable information about user engagement, thereby reducing information loss while maintaining measurement precision.
4Loss of information
If rating systems provide only overall file ratings, then the device complexity is low, but the loss of information increases because granular portion ratings are not provided
Solution Approach 1:
The system structures ratings hierarchically, maintaining both overall media file ratings and individual portion ratings. This segmentation of rating data allows granular information about specific portions to be preserved and analyzed separately while still providing aggregate overall ratings, thereby reducing information loss without overwhelming complexity.
Solution Approach 2:
The rating system serves multiple functions simultaneously: it provides overall file ratings for broad trends, individual portion ratings for detailed analysis, and playback completion data for accuracy verification. This multi-functionality maximizes the value of collected data while using a unified tracking mechanism that manages complexity.
Data Source
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AI summary
Methods, apparatus and computer program products are provided for managing media content ratings metrics at a network entity, comprising receiving media content ratings metrics from a wireless communication device, wherein the ratings metrics rate media content based on the amount of playing of one or more portions of the media content on the wireless communication device, and performing a media content ratings-related task based on the received media content rating metrics. The rating method herein disclosed is highly-granular in nature, in that, the rating method allows for ratings to be based on the number of times that portions, segments, clips of the media file are played or otherwise executed, as opposed to basing the media content rating solely on the number of times that the media file is played.