Context-Aware Image Generation for Real-Time Personalized Ads
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Solution Overview
Problem
Current digital marketing campaigns often utilize static ads that lack personalization and engagement, leading to low click-through rates and ineffectiveness due to non-targeted advertisements.
Innovation Solution
A context-aware image generation system that utilizes machine learning models to generate personalized and dynamic advertisements based on user data, including demographic and engagement metrics, to create context-aware images tailored to individual user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static ads are used in digital marketing campaigns, then device complexity is reduced and ease of manufacture is improved, but user engagement and click-through rates deteriorate due to lack of personalization
Solution Approach 1:
The patent implements dynamic ad generation where previously static advertisements are transformed into dynamic, context-aware images that adapt in real-time based on user data, device context, and engagement metrics. The system dynamically assembles ad components and generates personalized content rather than displaying fixed static images.
Solution Approach 2:
The system changes multiple parameters simultaneously including user demographics, device type, location, time of day, and engagement history to generate personalized advertisements. These parameter changes enable the system to create highly tailored ad content that adapts to each user's specific context and preferences.
2Productivity
If personalized advertisements are generated using machine learning models, then user engagement and click-through rates are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing user data, pre-generating ad variations, and pre-computing recommendation scores before actual ad delivery. Machine learning models are trained in advance on historical data, and candidate advertisements are prepared beforehand to reduce real-time processing requirements.
Solution Approach 2:
The system implements self-service mechanisms where the ad generation system automatically retrieves user data, selects appropriate ad components, and generates personalized advertisements without requiring manual intervention. The machine learning models autonomously make decisions about ad content selection and customization based on input features.
3Adaptability or versatility
If context-aware image generation is implemented in real-time, then advertisement relevance and user appeal are enhanced, but system complexity and computational requirements worsen
Solution Approach 1:
The patent segments the ad generation system into distinct modular components including data retrieval modules, machine learning inference modules, ad assembly modules, and rendering modules. Each component handles a specific aspect of the generation process, allowing for independent optimization, maintenance, and scaling of individual functions.
Solution Approach 2:
The system implements universal, multi-functional components that can handle various types of user data, device configurations, and ad formats through a single unified architecture. The machine learning models and generation algorithms are designed to work across multiple contexts and ad types rather than requiring separate specialized systems.
Data Source
AI summary
In some embodiments, a computer-implemented method, includes capturing, for a payment network associated with a payment card of a user of a digital platform, context-aware image generation data; generating, at the payment network associated with the payment card, a context-based personalized prompt based upon the context-aware image generation data, the context-based personalized prompt being associated with the user of the digital platform; utilizing the context-based personalized prompt to generate a context-aware image, the context-aware image being a transformer-based context aware image; and providing the context-aware image to the digital platform for dynamic view by the user of the digital platform. In some embodiments, the computer-implemented method further includes transforming the context-based personalized prompt into a context-based text embedding in order for the context-aware image to serve as the transformer-based context aware image.