Visual Element Sequencing via AI Scoring and Depth-First Search
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Solution Overview
Problem
The current methods for sequencing visual elements, such as in fashion shows, rely heavily on intuition and are time-consuming, lacking an efficient way to optimize the composition and presentation sequence of compound visual elements to maximize impact.
Innovation Solution
The use of artificial intelligence models to generate and score possible visual element combinations and sequences, training AI models on historical data to rank and optimize pairings, and employing depth-first search to determine optimal presentation sequences based on visual attributes and impact scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional intuition-based methods are used to sequence visual elements, then the process is simple to implement, but it is time-consuming and inefficient
Solution Approach 1:
The patent replaces the mechanical/intuitive sequencing process with an automated computer-based system that uses AI models and algorithms to generate, score, and rank visual element sequences automatically, eliminating manual trial-and-error methods and significantly improving productivity while reducing time loss
Solution Approach 2:
The system enables self-service by allowing the computer to autonomously perform the entire sequencing process without human intervention - generating compound visual elements, creating presentation sequences, scoring them based on attributes, and ranking results automatically, making the system independent of manual operation
2Manufacturing precision
If AI models and comprehensive scoring systems are implemented to optimize visual element sequences, then sequencing precision and impact are improved, but system complexity increases
Solution Approach 1:
The patent segments the complex sequencing task into distinct functional modules: a generation module that creates compound visual elements and presentation sequences, a scoring module that evaluates sequences based on multiple attributes, and a ranking module that orders sequences by score. This modular segmentation manages complexity while maintaining high precision through specialized processing at each stage
Solution Approach 2:
The patent introduces an intermediary scoring system that acts as a bridge between the generated visual sequences and the final ranking. The scoring system evaluates sequences based on multiple attributes (visual appeal, coherence, novelty) and converts them into quantifiable scores, mediating the complex evaluation process and enabling accurate ranking without requiring direct complex comparisons between all sequence elements
Data Source
AI summary
A method for managing the composition and presentation sequencing of compound visual elements, the method including generating a graph of all possible compound visual element combinations, generating a set of possible visual element presentation sequences according to a depth-first search (DFS) of the graph, generating a score for each member of the set of possible visual element presentation sequences according to visual element attributes, and enumerating a visual element presentation sequence according to a visual element presentation sequence score.