Digital Asset Replacement via Placeholder Search Context
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Solution Overview
Problem
Conventional techniques for replacing digital assets in digital content are inefficient and lack context, as they rely solely on the digital asset itself for search queries, leading to reduced accuracy and increased computational load, forcing users to repeatedly search for similar assets.
Innovation Solution
Associating search query data with placeholder data within digital content, allowing the system to leverage the original search context for accurate and efficient replacement of digital assets, maintaining relevance and reducing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the digital asset itself is used as a basis to perform a search for replacement, then the search can be performed using the asset's visual content, but the search results are limited to similar images and may depart from the original search context
Solution Approach 1:
The system performs preliminary action by storing the original search query data associated with the placeholder data before the asset replacement process begins. This preserves the original search context (keywords, filters, and parameters) so that when replacement is needed, the system can reuse this pre-stored information rather than losing it.
Solution Approach 2:
The placeholder data serves as an intermediary element that links the digital asset to the original search query data. Instead of directly using the asset for search (which loses context), the placeholder acts as a mediator that retains references to both the asset location and the original search parameters, enabling context-aware replacement.
2Adaptability or versatility
If conventional techniques force users to repeatedly perform searches to locate a digital asset of interest, then users can find similar assets, but the process becomes computationally inefficient and time-consuming
Solution Approach 1:
The system performs preliminary action by pre-storing the search query data and associated parameters with the placeholder data during the initial asset insertion. This preparation allows subsequent replacement operations to directly reuse the stored search context, eliminating the need for repeated searches and significantly improving productivity.
Solution Approach 2:
The system enables self-service by automatically retrieving and reusing the stored search query data when asset replacement is needed. Instead of requiring users to manually perform repeated searches, the system autonomously leverages the pre-stored search context to efficiently locate replacement assets, reducing user effort and computational overhead.
3Ease of operation
If the digital asset itself is used for search, then the search can proceed without additional metadata, but the computational resources of the digital content sharing system are unnecessarily consumed
Solution Approach 1:
The system performs preliminary action by storing the search query data (including keywords, filters, and parameters) with the placeholder data during initial asset insertion. This pre-computation and storage of search parameters eliminates the need to reprocess visual content for subsequent searches, significantly reducing computational resource consumption while maintaining operational simplicity.
Data Source
AI summary
Digital asset association techniques with search query data are described. In one example, A first digital asset is displayed at a location within digital content in a user interface. The location is specified using placeholder data of the digital content. An input is received via selection of an option as part of the user interface to initiate a search. Search query data associated with the placeholder data is obtained in response to the input. A search is initiated for a second digital asset using the search query data. The second digital asset is displayed at the location within the digital content in the user interface as specified by the placeholder data.