AI Metadata Generation for Context-Rich Digital Asset Search
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Solution Overview
Problem
Current digital asset management systems rely heavily on manual or crowdsourced metadata generation, leading to inconsistent, incomplete, and erroneous metadata, limiting search accuracy and flexibility, and existing machine learning models fail to capture context-specific details.
Innovation Solution
An AI-based metadata generation system that uses multi-modal models to automatically extract metadata attributes from digital assets by processing visual and textual cues, allowing users to define custom metadata fields through YAML configuration, and storing the metadata in an index for efficient retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual or crowdsourced metadata generation is used, then implementation simplicity is maintained, but metadata consistency and completeness deteriorate
Solution Approach 1:
The system enables digital assets to generate their own metadata automatically through machine learning models, eliminating the need for manual tagging by users. The multi-modal model processes the digital asset directly and generates relevant metadata attributes, allowing the asset to 'serve itself' in terms of metadata creation.
Solution Approach 2:
The patent replaces the mechanical process of manual metadata entry with an automated machine learning system. The multi-modal model uses visual and textual processing to automatically extract and generate metadata, substituting human manual operations with an intelligent automated system.
2Measurement precision
If multi-modal machine learning models are used for metadata generation, then metadata completeness and search accuracy improve, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing by generating visual representations of digital assets before metadata extraction. The multi-modal model pre-processes the asset data and generates relevant metadata attributes in advance, which can then be stored and reused for multiple search operations, reducing repeated processing time.
Solution Approach 2:
The metadata generation process is divided into distinct segments: visual representation generation, multi-modal model processing, and metadata extraction. This segmentation allows each component to be optimized independently and enables parallel processing of multiple digital assets simultaneously.
3Adaptability or versatility
If custom metadata fields are allowed through YAML configuration, then system flexibility improves, but device complexity increases
Solution Approach 1:
The system provides a universal metadata generation framework that can handle multiple digital asset types and custom metadata fields through a single configurable interface. The YAML configuration file serves as a universal mechanism for defining custom fields, making the system adaptable to different domains without requiring separate systems.
Solution Approach 2:
The YAML configuration file acts as an intermediary between user requirements and the machine learning model. Users define their custom metadata needs in the YAML file, which then translates these requirements into model-specific parameters, shielding users from the underlying system complexity while maintaining flexibility.
4Productivity
If automated metadata generation is implemented, then productivity improves by reducing manual tagging, but measurement precision of metadata quality may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the multi-modal model continuously learns from generated metadata and search results. The model can be fine-tuned based on performance metrics and user feedback, improving metadata accuracy over time while maintaining high productivity through automation.
Data Source
AI summary
Embodiments of the present disclosure relate to AI-based metadata generation for digital asset search. In operation, some embodiments first receive a prompt requesting one or metadata attribute values to be associated with a digital asset. Some embodiments then generate a response to the prompt based at least on a model processing a representation of the prompt and a representation of the digital asset. The response includes one or more metadata attribute values of the digital asset. After the response is generated, some embodiments then store the response using an index. The index is configured to facilitate retrieval of the digital asset as a search result candidate. Various embodiments of the present disclosure have various technical effects and benefits relative to existing technologies, such as improved computer search accuracy, significant reduction in the need for manual tagging, flexibility, scalability, and variability, among others.