Media Item Descriptor Matching for Design Template Selection
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Solution Overview
Problem
Existing digital design software applications are ineffective in identifying the most suitable design templates from a plethora of options, making it challenging for users to select the appropriate template for their designs, especially when hundreds of thousands of templates are available.
Innovation Solution
A computer-implemented method that utilizes a machine learning model to analyze input media items and generate descriptors, which are then compared to descriptors of stored design templates to identify and display suitable candidate templates, allowing users to preview and replace existing media items with their own input media items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a large number of design templates are provided to users, then the variety and quality of design options are improved, but the difficulty of selecting the appropriate template increases
Solution Approach 1:
The system automatically analyzes the input media item and retrieves matching design templates without requiring user manual search. The machine learning model processes the media item characteristics and autonomously identifies suitable templates, eliminating the need for users to manually browse or search through hundreds of thousands of templates.
Solution Approach 2:
A machine learning model acts as an intermediary between the user's input media item and the design templates. The model analyzes the media item characteristics and mediates the matching process by comparing features such as color, style, and composition to retrieve the most relevant templates, simplifying the selection process.
2Ease of operation
If manual search methods are used to find design templates, then user control over the search process is maintained, but time consumption and efficiency are reduced
Solution Approach 1:
The manual mechanical search process is replaced with an automated machine learning-based retrieval system. Instead of users manually browsing or using search queries, the system uses AI algorithms to automatically analyze media items and retrieve matching templates, significantly reducing search time while maintaining relevance.
Solution Approach 2:
The system performs preliminary analysis of the input media item before template retrieval. The machine learning model pre-processes the media item to extract characteristics and features, which then guides the template matching process, ensuring efficient and accurate results without requiring user intervention.
3Measurement precision
If design templates are manually browsed and searched, then users can filter by specific criteria, but the complexity of the search interface increases
Solution Approach 1:
The system automatically determines and applies relevant filtering criteria based on the input media item characteristics. The machine learning model analyzes the media item and autonomously identifies appropriate template categories, styles, and features, eliminating the need for users to manually configure complex search filters or interfaces.
Data Source
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AI summary
A method for automatically generating one or more digital designs is disclosed. The method includes identifying an input media item; processing the input media item to generate an input media item descriptor; and identifying a first target media item from a set of target media items. Each target media item in the set of target media items is associated with a target media item descriptor and a candidate design template, and the first target media item is identified based on a similarity between the input media item descriptor and the target media item descriptor of the first target media item. The method further includes generating a new digital design. The new digital design being based on the candidate design template associated with the first target media item, and generated to replace the first target media item with the input media item.