SYSTEM AND METHOD FOR AI BASED TAILORED ADVERTISING CONTENT GENERATION

Smart overview of the Invention

The proposed system and method utilize artificial intelligence to generate personalized advertising content that is both contextually relevant and engaging. By integrating connectionist and symbolic AI techniques, the invention creates dynamic user experiences through the use of specialized agent networks, knowledge graphs, and retrieval-augmented generation. The platform focuses on delivering ads that enhance user engagement while maintaining security, traceability, and respect for user preferences.

Background

Advancements in user experience (UX) design have aimed to create more engaging digital interactions. However, existing UX methodologies often rely on static templates and predefined rules, limiting their adaptability to individual user needs. This limitation is particularly evident in fields like gaming and e-commerce, where the demand for personalized content is high. Current approaches struggle with creating immersive experiences across multiple domains such as sound, visuals, and tactile feedback.

Innovative Approach

This invention addresses these limitations by employing generative AI techniques like large language models and reinforcement learning to automatically create adaptive content. The system leverages statistical analysis, machine learning, and user feedback to refine user models and advertising strategies. The goal is to develop a more agile, context-aware platform that enhances digital interactions across various devices and scenarios.

System Description

The system comprises a computing device with memory and processing capabilities that execute programming instructions. These instructions enable the device to process user queries through specialized agents interacting with knowledge graphs and databases. The agents generate query responses that include curated advertising content optimized for layout and appearance. User feedback is continuously collected to refine the content further.

Preferred Embodiments

• System Embodiment: Involves a computing device that processes user queries using specialized agents, optimizing ad content layout, broadcasting responses through interfaces, and updating based on feedback.
• Method Embodiment: Steps include receiving queries, processing through agent interactions, accessing knowledge systems for response generation, optimizing content presentation, and iterating based on user feedback.
• Another System Embodiment: Similar to previous embodiments but emphasizes distributed computational graphs for accessing knowledge systems to enhance query responses.