Automated Image-Text Association System Using Weighted Tagging
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Solution Overview
Problem
Existing methods for associating relevant word passages with images are time-consuming and limited, requiring significant human effort or restricting choices to predesigned products, thus failing to efficiently produce emotionally impactful products with a wide range of image and text combinations.
Innovation Solution
A method and system that automatically associate images with text passages by obtaining and weighting tags, associating them with concepts, and selecting relevant passages based on emotional and biblical concepts, using a computer system with rules and filters to produce emotionally impactful products efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to associate relevant word passages with images, then the quality and emotional impact of the product can be maintained, but the process becomes time-consuming and requires significant human effort
Solution Approach 1:
The system performs self-service by automatically analyzing image content, generating relevant tags, and selecting appropriate text passages without human intervention. The computer system independently completes the entire workflow from image analysis to product generation, eliminating the need for manual curation while maintaining high-quality associations
Solution Approach 2:
The patent replaces the mechanical human effort of manually selecting and associating text passages with images by implementing an automated computer-based system. This system uses algorithms to analyze images, generate tags, and select passages, substituting human cognitive and manual work with computational processes
2Productivity
If predesigned products with limited word passages are used, then the production time is reduced, but the selection range and adaptability are limited
Solution Approach 1:
The system transitions from static predesigned products to dynamic, customizable products. By automatically generating tags and selecting passages based on specific image content, the system adapts to different images and produces unique text associations, providing unlimited versatility while maintaining fast production through automation
Solution Approach 2:
The patent changes the parameter of product customization from fixed (predesigned) to variable (dynamic generation). By adjusting the image input, the system generates different tag sets and selects different passages, creating infinitely varied product combinations without sacrificing production speed
3Measurement precision
If a comprehensive analysis of all image components and concepts is performed, then the accuracy of passage selection is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system segments the complex task of passage selection into distinct stages: image analysis, tag generation, tag weighting, concept association, and passage selection. This segmentation allows each component to be processed independently with appropriate algorithms, improving accuracy while managing computational complexity through modular processing
Solution Approach 2:
The patent introduces tags and concepts as intermediary elements between image analysis and passage selection. These intermediaries bridge the gap between visual content and text passages, enabling accurate matching without requiring direct complex analysis between all image components and all possible passages
Data Source
AI summary
A system and method automatically associates an image and passage from a text. In the system and method, a user can choose or supply an image and the system and/or method will choose a limited selection of relevant word passages for the image from a relatively large volume of potential passages. The system and method utilize a computer system wherein a concept generator and a passage generator processes the content of the image so as to assign words to describe the content, then weight the descriptive words (tags) and assign passages based on the tags and weighting. The passages can be filtered so as to remove inappropriate passages.