Real-Time Ad Content Generation Using LLM and GAN Personalization
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Solution Overview
Problem
Existing DCO systems lack the ability to create intricate and engaging narratives that adapt to multiple user parameters, fail to leverage real-time data for dynamic ad content generation, and do not integrate data sources such as real-time product inventory, user location, and temporal context.
Innovation Solution
A process that predicts a user's most preferred product using a trained neural network and real-time data, generates a script with a large language model, and customizes content with a generative adversarial network (GAN) to create personalized real-time advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If dynamic ads use real-time data and advanced algorithms to personalize content, then user engagement and relevance are improved, but system complexity and computational resources required increase
Solution Approach 1:
The system divides the ad personalization process into separate functional modules: user preference prediction module, creative generation module, and optimization module. Each module handles specific tasks independently, making the complex system more manageable and maintainable while enabling specialized processing for each function.
Solution Approach 2:
The patent introduces an intermediary processing layer that receives raw real-time data, pre-processes it, and feeds it to the creative generation models. This intermediary layer filters and prepares data, reducing the computational burden on the main generation models and improving overall system efficiency.
2Productivity
If real-time content generation is performed within one second or less, then user experience and engagement are improved, but computational speed requirements and processing complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing user preference models, product information, and creative templates before actual ad generation is needed. When real-time generation is required, the system only needs to retrieve pre-processed data and apply it to current context, dramatically reducing the computation time needed for real-time generation.
Solution Approach 2:
The system dynamically adjusts the level of personalization and complexity based on real-time constraints and user context. For time-sensitive scenarios, the system prioritizes speed by using simplified generation models or pre-fetched content, while allowing more complex processing when time permits, thus optimizing the balance between performance and computational resources.
Data Source
AI summary
An embodiment predicts a most preferred product in a plurality of products, the predicting using a trained neural network, data of a user, and real-time availability data of the plurality of products, wherein the most preferred product is specific to the user. An embodiment generates, in real time, using a large language model, a script, the script comprising natural language text comprising a customized offer of the most preferred product to the user An embodiment generates, in real time, using the script and a generative adversarial network (GAN), a content customized to the most preferred product and the user, wherein the content comprises at least one of an audio portion and a video portion. An embodiment presents, on a device corresponding to the user, the content.