Multimodal Digital Asset Search Using Composite AI Indexing
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Solution Overview
Problem
Existing digital asset management and search technologies rely on manual tagging and labeling, which is time-consuming and prone to errors, and struggle to integrate complex multimodal aspects like geometry, materials, and spatial relationships, leading to inefficient and inaccurate search results.
Innovation Solution
A multimodal AI-based indexing and search pipeline that automates the process by generating composite indices using graph data and multimodal embeddings, allowing simultaneous handling of visual, textual, and spatial data types, eliminating the need for manual tagging and reducing redundant searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual tagging and labeling are used for digital asset management, then the system is simple to implement, but the process is time-consuming and prone to errors
Solution Approach 1:
The system automatically generates tags and labels by analyzing digital asset metadata, visual content, and contextual information without requiring manual human intervention. The AI model autonomously categorizes and organizes assets based on their inherent properties and relationships
Solution Approach 2:
Manual tagging operations are replaced with an automated AI-based system that processes digital assets through machine learning models, substituting human mechanical actions with computational processes that are faster and more accurate
2Device complexity
If traditional search methods are used for digital assets, then the system complexity is low, but the system cannot integrate complex multimodal aspects like geometry, materials, and spatial relationships
Solution Approach 1:
The system creates a composite search index that integrates multiple data types (metadata, visual features, spatial relationships, material properties) into a unified structure, allowing simultaneous querying across different modalities while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The search system is designed to handle multiple types of queries simultaneously - text-based searches, image-based searches, spatial relationship queries, and material property searches - all through a single unified interface that adapts to different search needs
3Measurement precision
If separate indexing is performed for different data types, then each index can be optimized for its specific data type, but multiple I/O operations are required reducing search efficiency
Solution Approach 1:
The system merges multiple separate indexes (metadata index, visual index, spatial index) into a single composite index structure that can be accessed in one I/O operation. This composite index maintains the organizational benefits of separate indexing while eliminating the need for multiple sequential access operations
Data Source
AI summary
Embodiments of the present disclosure relate to multimodal AI-based search for digital assets via an indexing and/or search pipeline. With respect to the indexing pipeline, some embodiments obtain first data and second data associated with a first digital asset. Such data represents different data types or modalities of the same digital asset. After obtaining the first and second data, some embodiments then generate a composite index. After the composite index is built such index can then be used to execute a query via the search pipeline. To execute the query some embodiments compute a relevance score for each digital asset, of multiple digital assets, based at least in part on a measure in which each digital asset satisfies one or more parameters or conditions for two or more data types of the query. Various embodiments then rank each digital asset and present one or more associated indicators.