Media Item Descriptor Matching for Design Template Selection
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Solution Overview
Problem
Users face challenges in selecting suitable design templates from a vast array of options in digital design software, as they often struggle to describe their desired template using appropriate keywords, leading to a time-consuming search process and difficulty in replacing existing media items within templates.
Innovation Solution
A computer-implemented method that utilizes a machine learning model to analyze media items, generate descriptors, and identify matching design templates by comparing user-input media items with stored templates, allowing for automatic selection and replacement of media items within templates, thereby reducing the number of templates to choose from and simplifying the selection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users are provided with a large number of design templates, then the variety and quality of design options are improved, but the time and effort required for template selection increases
Solution Approach 1:
The system pre-processes and stores media item descriptors along with their associated design templates before user interaction. When a user selects a media item, the system immediately queries pre-computed descriptors to identify matching templates, eliminating the need for manual browsing through large template catalogs and significantly reducing selection time while maintaining diverse options.
Solution Approach 2:
The patent introduces media item descriptors as an intermediary between users and design templates. Instead of users directly searching through templates or manually describing their needs, the system uses automatically generated descriptors as a mediator to bridge the gap, enabling intelligent matching that reduces selection effort while preserving template variety.
2Ease of operation
If users manually search for design templates using keywords, then the search process becomes more controllable, but the difficulty in accurately describing desired templates increases
Solution Approach 1:
The system performs self-service by automatically generating media item descriptors without requiring user input. The descriptor generation process autonomously analyzes selected media items and creates accurate descriptions, eliminating the burden on users to manually craft search queries while maintaining precise template matching through automated semantic understanding.
3Ease of operation
If the system replaces existing media items in templates, then the customization process is simplified, but the complexity of media item replacement increases
Solution Approach 1:
The system implements feedback mechanisms where selected media items automatically trigger descriptor generation and template matching. The feedback loop continues as matched templates are presented with their associated media items, allowing users to directly replace existing media in templates based on automatic matching results, simplifying the overall replacement process while managing system complexity through structured feedback handling.
Data Source
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.