Machine Learning Media Annotation for Accessible Descriptions

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Generating media summaries and audio descriptions for large volumes of media content is time-consuming and expensive, and automated processes face challenges in selecting appropriate content and capturing relevant segments.

Innovation Solution

Utilizing machine learning models to generate metadata for media content, which includes character recognition, object detection, emotion analysis, and sentiment analysis, to automate the creation of descriptions such as audio descriptions and media summaries.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated processes are used to generate media summaries and audio descriptions, then productivity increases, but manufacturing precision deteriorates due to difficulties in selecting appropriate content and capturing relevant segments

Engineering Contradiction:

Improvegeneration speed of media content descriptionsVSAvoidaccuracy of content selection and segment capture

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The patent introduces machine learning models as intermediary components between the media content input and the final description output. These models analyze visual frames, audio signals, and text data to generate accurate metadata descriptions that preserve content quality while enabling automated processing at scale.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual human review and selection processes with automated machine learning-based systems. This substitution maintains or improves precision by using trained models to objectively identify and describe relevant content segments without human fatigue or subjectivity, while dramatically increasing productivity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Manufacturing precision

If manual processes are used to generate media summaries and audio descriptions, then manufacturing precision is maintained, but productivity decreases due to time-consuming and expensive processes

Engineering Contradiction:

Improvequality of content selection and description accuracyVSAvoidvolume of media content processed per unit time

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The patent enables the media content processing system to serve itself by using machine learning models to automatically analyze, interpret, and describe content without requiring manual human intervention for each piece of media. This self-service capability maintains quality through consistent application of trained algorithms while scaling productivity to handle large volumes of content.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent changes the operational parameters from manual human processing to automated machine learning processing. This parameter change allows the system to maintain precision through consistent algorithmic application while dramatically increasing the volume of content that can be processed per unit time, thereby resolving the productivity-precision tradeoff.

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If machine learning models are used to generate metadata, then ease of operation improves through automation, but device complexity increases due to the need for multiple processing components

Engineering Contradiction:

Improveautomation level of description generationVSAvoidnumber of processing components and models

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The patent segments the media content processing task into distinct components handled by specialized machine learning models: visual analysis models for video frames, audio analysis models for sound signals, and text analysis models for transcripts. This segmentation improves ease of operation by automating each specific function while organizing complexity into manageable, specialized modules.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates a universal metadata generation system where machine learning models perform multiple functions: analyzing visual content, processing audio signals, interpreting text, and generating coordinated descriptions. This multi-functionality improves ease of operation by providing a single automated system that handles diverse media types, while the shared architecture reduces overall system complexity compared to separate specialized systems.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250337970A1Machine learning based media content annotation

Publication Date: 2025.10.30 NAGRAVISION SA

AI summary

Systems and techniques are described herein for annotating media content. For example, a process can include obtaining media content and generate, use one or more machine learning models, a metadata file for at least a portion of the media content. The metadata file includes one or more metadata descriptions. The process can include generating a text description of the media content based on the one or more metadata descriptions of the metadata file. The process can include annotating the media content use the text description.