AI Tag Generation for Digital Asset Search and Retrieval
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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 retrieval of digital assets based on user queries.
Innovation Solution
A digital asset manager utilizes AI models to interpret online reviews and generate natural language tags for digital assets, associating them with the assets and making them searchable for client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging methods are used for digital assets, then tag accuracy can be maintained, but time consumption and labor costs increase significantly
Solution Approach 1:
The system enables automatic self-tagging of digital assets through AI models that autonomously analyze asset attributes, metadata, and content without requiring manual human intervention. The digital asset manager automatically generates relevant tags by processing asset information through machine learning algorithms, allowing the system to serve itself rather than relying on external manual tagging operations.
Solution Approach 2:
The patent replaces the mechanical manual tagging process with an automated AI-based system. Instead of human operators manually analyzing and assigning tags to digital assets, the system uses machine learning models to automatically generate tags based on asset attributes, metadata, and content analysis, substituting human mechanical operations with automated computational processes.
2Productivity
If no automated tagging system is implemented, then system complexity remains low, but search and retrieval efficiency of digital assets deteriorates
Solution Approach 1:
The system performs preliminary tagging actions automatically during the digital asset upload and management process. By pre-generating tags through AI analysis before search operations occur, the system prepares the asset metadata in advance, enabling faster and more efficient search and retrieval operations without requiring complex real-time processing during user queries.
Solution Approach 2:
The patent introduces an AI-based tagging system as an intermediary layer between digital assets and search functions. This intermediary automatically generates and manages tags that bridge the gap between raw asset data and user search queries, improving search efficiency without directly complicating the core asset storage and retrieval infrastructure.
3Adaptability or versatility
If generic tagging approaches are used, then implementation simplicity is maintained, but tag relevance to user queries decreases
Solution Approach 1:
The system applies local quality by generating tags that are specifically tailored to each digital asset's unique attributes, metadata, and content characteristics. Rather than applying uniform generic tags across all assets, the AI model analyzes individual asset properties to create customized, context-relevant tags that accurately reflect the specific content and improve user query matching.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting tag generation based on varying asset attributes, metadata types, and content characteristics. The AI model modifies tagging parameters and approaches according to the specific properties of each digital asset, enabling adaptable and relevant tag creation while maintaining implementation simplicity through automated parameter adjustment rather than manual configuration.
Data Source
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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.