Media Frame Analysis for Automatic Content Completion Detection

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Solution Overview

Problem

Existing media capture devices lack a mechanism to detect the completion of specific types of media content, such as advertisements or public service announcements, leading to user inconvenience when switching channels.

Innovation Solution

A system utilizing a change determination engine and a machine learning model to analyze media frames, determining when a change in media content occurs and identifying the type of content, enabling automatic channel switching or notifications upon content completion.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a mechanism is added to detect completion of specific media content types, then content completion detection capability is improved, but device complexity increases

Engineering Contradiction:

Improvecontent completion detection capabilityVSAvoiddevice complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The media content analysis is divided into multiple independent components: a change detector that identifies frame transitions, a machine learning model that classifies content types, and a completion detector that determines when content ends. Each component handles a specific aspect of the detection task, improving reliability without requiring a monolithic complex system.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary actions by detecting content type classification before determining completion. The machine learning model pre-classifies media content as advertising, informational, or other types, which then guides the completion detection logic. This staged approach improves detection capability while managing complexity through structured processing.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If frame-by-frame analysis is performed to detect content completion, then measurement precision is improved, but processing time increases

Engineering Contradiction:

Improvecontent completion detection precisionVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system extracts only the essential features needed for content type classification and completion detection, rather than processing all frame data equally. The change detector identifies significant frame transitions, and the machine learning model extracts key content characteristics, eliminating unnecessary processing and reducing time loss while maintaining precision.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system applies partial action by performing complete frame analysis only when change detection indicates potential content transitions. During stable content periods, less intensive monitoring is used, reducing processing time while maintaining detection precision through targeted analysis of critical moments.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20260025422A1Content completion detection for media content

Publication Date: 2026.01.22 NAGRAVISION SA

AI summary

Systems and techniques are described herein for processing media content. For example, a process can include obtaining a first media frame and a second media frame. The process can include generating, using a first change detector, a first tag indicating a change above a first change threshold has occurred in the second media frame relative to the first media frame. The process can further include generating, using a machine learning model, a second tag indicating that media content of the second media frame is associated with a particular type of media content. The process can further include determining, based the first tag and the second tag, that the media content of the second media frame is associated with the particular type of media content.