Knowledge Graph Metadata Network for Local Digital Asset Management
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Solution Overview
Problem
Traditional digital asset management systems require substantial computational resources and external data stores, making them resource-intensive and unsuitable for devices with limited storage capacity, such as smartphones, as they rely on databases for organizing and retrieving digital assets.
Innovation Solution
A knowledge graph metadata network is generated based on a collection of digital assets, allowing for digital asset management without traditional databases, thereby reducing the need for external resources and enabling local processing on devices with limited storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional database systems are used for digital asset management, then digital assets can be organized and retrieved, but substantial computational resources and external data stores are required
Solution Approach 1:
The patent extracts the essential metadata information from digital assets and stores it in a knowledge graph structure on the device itself, separating the metadata management function from the heavy database infrastructure. This allows asset retrieval to proceed using lightweight local queries rather than resource-intensive remote database operations.
Solution Approach 2:
The patent creates a knowledge graph that copies and structures essential metadata from digital assets in a format optimized for local processing. Instead of storing and querying entire asset databases remotely, the system creates a compact local representation that enables efficient retrieval without requiring substantial external computational resources.
2Ease of operation
If databases are used to manage digital assets, then organization and retrieval are enabled, but external data stores and remote servers are required
Solution Approach 1:
The patent enables devices to manage their own digital assets locally using knowledge graphs stored on-device. The system performs self-service by maintaining and querying its own metadata structure without requiring external data stores or remote servers, thereby simplifying the overall system architecture while preserving organization and retrieval capabilities.
Solution Approach 2:
The patent segments the digital asset management system into modular components: a knowledge graph structure for metadata organization, local processing capabilities for queries, and on-device storage. This segmentation eliminates the need for complex centralized database infrastructure while maintaining full asset management functionality at the device level.
3Productivity
If substantial computational resources are allocated to digital asset management, then asset processing and retrieval can be performed, but processing power for other tasks is reduced
Solution Approach 1:
The patent uses lightweight knowledge graph structures that require minimal computational resources to maintain and query. Instead of employing heavy-duty database systems, the system uses simplified metadata representations that can be processed efficiently with minimal impact on overall device processing power, enabling other tasks to run concurrently.
Solution Approach 2:
The patent changes the fundamental parameters of asset management by transitioning from full-asset database storage to compact metadata-based knowledge graphs. This parameter change reduces the computational complexity from O(n) full asset processing to O(1) metadata lookups, dramatically lowering resource consumption while maintaining retrieval effectiveness.
Data Source
AI summary
Techniques of generating a knowledge graph metadata network (metadata network) for digital asset management (DAM) are described. A DAM logic/module can obtain one or more first metadata assets describing characteristics associated with digital assets (DAs) in the DA collection. The DAM logic/module can also determine second metadata asset(s) and third metadata asset(s) describing characteristics associated with DAs in the DA collection based on the first metadata asset(s). The DAM logic/module can generate at least some of the metadata assets as nodes in a metadata network associated with the DA collection. The DAM logic/module can also determine, for at least two of the metadata assets, a correlation between the at least two metadata assets. The DAM logic/module can generate an edge in the metadata network between the nodes that represent the at least two metadata assets to represent the determined correlation.