AI Banner Ad Generation Using Graph Models and Genetic Algorithms
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Solution Overview
Problem
Current banner ads are passive and inefficient, as they are not personalized to individual customers, and their generation relies on human methods like A/B testing, which are time-consuming and limited.
Innovation Solution
An AI solution framework that uses MMKG, AI generation, and AI recommendation to create personalized banner ads by analyzing datasets of banner ads, evaluating their components, and generating multiple ads using a genetic algorithm, with the highest-scoring ads presented for human review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human methods like A/B testing are used to generate and select banner ads, then the ads can be targeted to specific customers, but the process is time-consuming and limited in efficiency
Solution Approach 1:
The patent replaces manual human methods (A/B testing) with an automated AI system that uses machine learning models, genetic algorithms, and neural networks to generate, evaluate, and select banner ads. This substitution of mechanical human processes with automated computational systems resolves the contradiction by maintaining targeting precision while dramatically improving generation efficiency and productivity.
Solution Approach 2:
The AI system performs self-evaluation of generated banner ads using automated scoring mechanisms that assess design quality, relevance to target customers, and predicted performance. The system autonomously selects the best ads without requiring extensive human review and A/B testing, thereby resolving the efficiency limitation while maintaining targeting accuracy through intelligent self-assessment.
2Ease of manufacture
If non-personalized content is delivered in banner ads, then the generation process is simple, but the ads fail to appeal to individual customer interests
Solution Approach 1:
The patent applies local quality by customizing specific elements of banner ads (images, text, colors, layout) based on individual customer characteristics, preferences, and behavior data. The AI system generates personalized variations of ad components tailored to each customer segment while maintaining a standardized generation framework, thus achieving both ease of manufacture through systematic processes and adaptability through localized personalization.
Solution Approach 2:
The system dynamically changes multiple parameters of banner ads including visual elements, text content, color schemes, and layout configurations based on customer profiles. The AI model adjusts these parameters automatically to create personalized ads that appeal to individual customer interests while maintaining generation simplicity through automated parameter optimization rather than manual design.
3Reliability
If extensive A/B testing is conducted to select banner ads, then the best performing ads can be identified, but extensive time and money expenditure is required
Solution Approach 1:
The patent performs preliminary evaluation and selection of banner ads using AI-based scoring mechanisms before actual deployment. The system predicts ad performance through machine learning models that analyze design quality, customer relevance, and historical data, pre-selecting the most promising ads. This preliminary action reduces or eliminates the need for extensive A/B testing, maintaining reliability in ad selection while significantly reducing time and cost expenditure.
Solution Approach 2:
The AI system incorporates feedback loops that continuously learn from customer interactions, click-through rates, and conversion data to refine ad selection and generation. This automated feedback mechanism replaces manual A/B testing by providing real-time performance evaluation and automatic optimization, ensuring reliable ad performance selection without the time and cost overhead of traditional extensive testing programs.
Data Source
AI summary
An AI-based system for the generation, evaluation, and prediction of performance of banner ads for presentation to specific customers or types of customers. A data set of existing banner ads, ad materials, and design and marketing parameters is used to generate a graph-based model. The graph-based model is used to generate original banner ads, which are then evaluated by a machine learning model, which assigns them scores. The highest-scoring banner ads are then presented to customers. The invention can also use a genetic algorithm in combination with iterative evaluation and generation to diversify design and choose the highest ranked banner ads.