Multimedia Metadata Generation Through Logical Entity Merging

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Solution Overview

Problem

Existing methods for generating metadata for multimedia content, such as video and audio files, are inaccurate and inefficient, leading to excessive resource usage and poor search results due to manual tagging and automated processes that fail to accurately identify logical entities within the content.

Innovation Solution

A content processing device that uses multimodal data analysis to divide multimedia content into logical entities, generate embeddings, compare similarities, and merge entities into sets to create more accurate metadata tags, leveraging machine learning and computer vision techniques.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual tagging methods are used to generate metadata, then human judgment can identify content characteristics, but the process is time-consuming and resource-intensive

Engineering Contradiction:

Improvemetadata accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical tagging with automated machine learning models that analyze video and audio content. The system uses computer vision to identify visual elements and speech recognition to process audio, substituting human mechanical judgment with automated computational analysis to generate metadata tags efficiently.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables content to be automatically tagged through self-service processing. The machine learning models independently analyze the multimedia content and generate metadata without requiring human intervention, allowing the system to serve itself in the metadata generation task.

Inventive Principle:

Principle #25Self-service

2Productivity

If simple automated processes are used for metadata generation, then processing speed increases, but accuracy deteriorates due to inability to accurately identify logical entities

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidentity identification accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent segments the multimedia content into distinct logical entities such as scenes, objects, actions, and audio elements. By dividing the complex content analysis into manageable segments, the system can process each entity type with specialized machine learning models, improving both efficiency and accuracy of entity identification.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system combines multiple machine learning models and analysis techniques to create a composite metadata generation system. It integrates computer vision models, speech recognition models, and entity relationship analysis to achieve high accuracy in identifying logical entities while maintaining processing efficiency.

Inventive Principle:

Principle #40Composite materials

3Measurement precision

If detailed analysis of all content is performed, then metadata accuracy improves, but resource consumption increases excessively

Engineering Contradiction:

Improvemetadata accuracyVSAvoidcomputational resource usage

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent applies partial analysis by focusing computational resources on identifying the most significant logical entities and characteristics in the content. Rather than analyzing every detail equally, the system prioritizes key entities that contribute most to metadata accuracy, reducing unnecessary computational expenditure while maintaining high accuracy for critical information.

Inventive Principle:

Principle #16Partial or excessive action

4Reliability

If manual tagging is used to ensure accurate content identification, then search result quality improves, but scalability deteriorates

Engineering Contradiction:

Improvesearch result qualityVSAvoidsystem scalability

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent creates a universal machine learning-based metadata generation system that can handle multiple types of multimedia content (video, audio, different formats) through a single platform. The system uses general-purpose models that can be applied across diverse content types, ensuring consistent reliability and search quality while enabling scalable processing of large content volumes.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250247588A1Systems and methods for automated metadata generation for multimedia content using multimodal data

Publication Date: 2025.07.31 VERIZON PATENT & LICENSING INC

AI summary

In some implementations, a device may receive a multimedia content file. The device may divide the multimedia content file into a set of logical entities. The device may generate a set of embeddings for each logical entity of the set of logical entities. The device may compare groups of logical entities, of the set of logical entities, to generate a similarity metric. The device may selectively merge, based on the similarity metric satisfying a threshold, a pair of logical entities, in a group of logical entities of the groups of logical entities, to generate one or more logical entity sets. The device may process the one or more logical entity sets to generate one or more metadata tags for the one or more logical entity sets. The device may store a metadata file including the one or more metadata tags.