Automated Media Asset Suggestions Using Knowledge Graph Metadata
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Solution Overview
Problem
Users face challenges in managing large collections of user media items, such as finding relevant items for multimedia presentations and determining suitable audio media assets, due to the resource-intensive nature of manual searching and the time required to curate meaningful sequences, themes, and transitions.
Innovation Solution
A method and system that utilize a knowledge graph metadata network to request, rank, and output candidate media assets for user media items, suggesting media assets based on contextual analysis to automate the process of selecting relevant media for multimedia presentations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual searching and curation methods are used to select user media items for presentations, then users can find relevant items, but the process becomes resource-intensive and time-consuming
Solution Approach 1:
The system enables self-service by automatically analyzing user media items using AI/ML algorithms to identify relevant items, sequences, themes, and transitions without requiring manual user intervention. The system serves itself by autonomously curating presentation content from the user's media collection.
Solution Approach 2:
The patent replaces the mechanical manual searching and curation process with an automated computational system using AI/ML algorithms. This substitution eliminates the need for manual sifting through media items while maintaining or improving selection quality through intelligent analysis.
2Reliability
If users manually determine suitable audio media assets for presentations, then they can select appropriate soundtracks, but the process requires significant time and effort
Solution Approach 1:
The system automatically analyzes the visual content, mood, and characteristics of user media items to self-determine suitable audio media assets. The system serves itself by autonomously matching soundtracks to presentations without requiring users to manually search or evaluate audio options.
Solution Approach 2:
The patent introduces an intermediary AI/ML system that bridges user media items and suitable audio assets. This intermediary automatically analyzes media characteristics and recommends appropriate soundtracks, simplifying the selection process while ensuring reliability of matches.
3Ease of operation
If automated systems are used to suggest media assets, then user effort is reduced, but the system complexity increases
Solution Approach 1:
The patent extracts the complex AI/ML analysis functionality into a separate automated system component, isolating the complexity from the user interface. Users interact with a simple interface while the extracted complex processing occurs automatically in the background.
Solution Approach 2:
The system segments the complex curation task into distinct automated components: media analysis, sequence determination, theme identification, transition selection, and audio asset matching. Each segment is handled by specialized algorithms working together to reduce overall user effort.
Data Source
AI summary
Techniques for suggesting media assets, the technique including: requesting a set of candidate media assets for a set of user media items based on a knowledge graph metadata network describing the set of user media items; receiving metadata for the set of candidate media assets; determining one or more sets of ranked media assets based on the received metadata; and outputting the determined one or more sets of ranked media assets.