Machine Learning Media Annotation for Precise Content Selection

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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 audio content and capturing relevant media segments.

Innovation Solution

Utilizing machine learning models to generate metadata for media content, which is then used to create efficient automated annotations such as audio descriptions and summaries, tailored to individual user preferences.

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 audio content and capturing relevant media segments

Engineering Contradiction:

Improvegeneration speed of media annotationsVSAvoidquality of content selection

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The patent introduces machine learning models as intermediary components between the raw media content and the final annotation output. These models analyze video frames, audio signals, and metadata to automatically identify and select relevant segments, objects, and actions, thereby maintaining high precision in content selection while enabling automated high-speed processing of large volumes of media content

Inventive Principle:

Principle #24Intermediary (Mediator)

2Manufacturing precision

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

Engineering Contradiction:

Improvequality of media annotationsVSAvoidgeneration speed of media annotations

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The patent replaces manual mechanical processes with automated machine learning-based systems. The ML models perform analysis, selection, and annotation generation tasks that were previously done manually, thereby dramatically increasing productivity while maintaining quality through algorithmic precision and consistency

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Quantity of substance

If large volumes of media content are processed, then quantity of output increases, but loss of time increases due to the extensive processing required

Engineering Contradiction:

Improvevolume of media content processedVSAvoidprocessing time

Core Design Contradiction:

Quantity of substanceVSLoss of time

Solution Approach 1:

The patent implements preliminary processing steps including pre-extraction of metadata, pre-segmentation of video content, and pre-training of machine learning models. These preliminary actions prepare the data in advance, enabling faster processing when large volumes of media content need to be annotated, thereby reducing the time loss associated with processing extensive content libraries

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12382115B2Machine learning based media content annotation

Publication Date: 2025.08.05 NAGRAVISION MEDIA UK LTD

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.