Diffusion-Based Ad Image Generation with On-Device Personalization
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Solution Overview
Problem
Current advertising technologies face challenges in creating high-quality, personalized advertisements without relying on data tracking, ensuring user privacy, and achieving hyper-personalization with precise product representation and branding.
Innovation Solution
A system that trains an AI model on user data to generate personalized digital image advertisements with fine-grained image control and branding assurance, executed on a client device for trusted compute resources, using conditional diffusion models and low-rank adaptation (LoRA) to reduce data transmission overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tracking-based advertising methods are used to create detailed user profiles for highly targeted advertising, then advertising efficiency is enhanced, but user privacy is compromised
Solution Approach 1:
The system enables on-device generation of personalized advertisements using local user data and pre-trained AI models. The client device performs inference operations locally to create personalized ads without transmitting sensitive user data to external servers, allowing the system to serve itself with minimal external intervention while preserving privacy.
Solution Approach 2:
A trusted compute environment acts as an intermediary between user data and advertisement generation. The system uses encrypted processing and trusted execution environments to enable personalized ad creation while maintaining privacy guarantees, serving as a mediator that reconciles the need for data processing with privacy preservation requirements.
2Loss of substance
If generative AI models are used to create personalized advertisements on demand, then data transmission is reduced, but image quality and branding precision are insufficient
Solution Approach 1:
The AI models are pre-trained offline on extensive datasets containing product images, branding elements, and advertising best practices. This preliminary training phase prepares the models with high-quality knowledge before deployment, enabling them to generate accurate personalized advertisements with proper branding when executed on-device, without requiring real-time data transmission.
Solution Approach 2:
The system applies different processing qualities to different aspects of ad generation. High-precision operations for product representation and branding are performed using the pre-trained model's learned knowledge, while less critical personalization elements can use simpler processing, optimizing both quality and resource usage on the client device.
3Productivity
If comprehensive user data is collected for hyper-personalization, then engagement and conversion increase, but privacy preservation is compromised
Solution Approach 1:
The client device performs self-service by generating personalized advertisements locally using stored user preferences and profile data. This eliminates the need to transmit comprehensive user data to external servers for personalization, allowing hyper-personalized ad creation that maintains user privacy through local processing.
Solution Approach 2:
The system segments data processing into local and remote components. Sensitive user data remains and is processed locally on the client device, while only non-sensitive model updates or aggregated analytics are transmitted remotely. This segmentation enables personalization while preserving privacy by keeping sensitive data localized.
Data Source
AI summary
A system has a server to train an artificial intelligence model on training data characterizing a good or service to form a trained model. A client device is associated with a user. The client device executes instructions on a processor to receive the trained model via a network connection to the server, collect user data and obtain a personalized digital image advertisement from the trained model and user data. The personalized digital image advertisement includes a synthetic digital image formed by a trained machine learning model. The personalized digital image advertisement is transformed with fine-grained image control modifications, quality assurance operations, and branding assurance operations to form a final personalized digital image advertisement. The final personalized digital image advertisement is presented on the client device.