Digital Asset Tagging With AI-Generated Natural Language Labels
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Solution Overview
Problem
Existing systems lack efficient methods for automatically generating relevant tags for digital assets, which hinders effective search and distribution of digital content.
Innovation Solution
A digital asset manager utilizes AI models to interpret online reviews and generate natural language tags based on asset attributes, enabling intelligent search and distribution through a product page.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging is used for digital assets, then tag accuracy can be maintained, but time consumption and labor requirements increase significantly
Solution Approach 1:
The system enables digital assets to self-tag by automatically extracting attributes and generating tags from the asset content itself. The processing system analyzes the digital asset's attributes and generates appropriate tags without requiring manual intervention, allowing the system to serve itself rather than requiring human labor for tagging operations.
Solution Approach 2:
The patent replaces manual mechanical tagging processes with automated computational systems. Instead of humans manually assigning tags, the system uses processing systems that automatically extract attributes, generate tags, and associate them with digital assets, substituting human cognitive labor with automated information processing mechanisms.
2Loss of time
If automated tagging systems are implemented, then time consumption decreases, but system complexity increases
Solution Approach 1:
The tagging system is divided into distinct functional modules: attribute extraction components, tag generation components, and tag association components. Each module handles a specific aspect of the tagging process independently, making the overall complex system more manageable and easier to implement through modular architecture.
Solution Approach 2:
The system introduces an intermediary processing layer that sits between the digital asset and the tag. This processing system acts as a mediator that automatically extracts attributes from the asset and transforms them into appropriate tags, simplifying the interaction between assets and tags while managing system complexity through a dedicated intermediary layer.
3Device complexity
If existing search methods are used, then system simplicity is maintained, but search effectiveness and discoverability of digital assets deteriorate
Solution Approach 1:
The system changes the parameters of search by introducing semantic attribute-based searching instead of traditional keyword matching. By extracting and utilizing multiple attributes from digital assets (such as content type, format, characteristics), the search system can effectively retrieve assets based on their semantic properties, dramatically improving search effectiveness while adding only minimal complexity to the search interface.
Data Source
AI summary
A server-implemented method for publishing digital assets for distribution is disclosed. The method may include receiving a digital asset from a digital asset manager, identifying a plurality of attributes associated with the digital asset, and generating, based on the plurality of attributes, a plurality of natural language tags that correspond to the digital asset. The method may also include associating the plurality of natural language tags with the digital asset, generating a product page for the digital asset that includes at least one natural language tag, and publishing the digital asset for distribution to a client computing device by way of the digital asset manager. The plurality of natural language tags may be exposed to query functions implemented by the digital asset manager, and the client computing device may display the product page on a display device that is communicatively coupled to the client computing device.