Generative AI Ad Integration With Prompt-Based Personalization
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Solution Overview
Problem
Current AI models lack effective integration of advertising capabilities, limiting their profitability and versatility in generating content with integrated advertisements.
Innovation Solution
AI models are configured to generate content with integrated advertisements, display ads before or alongside results, offer credits for viewing ads, analyze user prompts for tailored ads, and incorporate user and advertiser inputs to create personalized and effective advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI models integrate advertising capabilities, then profitability and versatility are improved, but device complexity and operational complexity increase
Solution Approach 1:
The patent combines multiple functions (content generation, advertising display, credit management, user engagement tracking) into a single integrated AI model system. The model simultaneously generates AI content, selects and displays advertisements, manages user credits, and tracks engagement metrics, thereby achieving versatility without proportionally increasing complexity through modular integration.
Solution Approach 2:
The AI model is designed to perform multiple functions: generating creative content, selecting relevant advertisements, displaying ads to users, awarding credits for ad engagement, and tracking user interactions. This multi-functional design allows a single system to handle both content creation and advertising operations, improving versatility while maintaining manageable complexity through unified architecture.
2Productivity
If advertisements are displayed to users, then revenue is improved, but user experience and satisfaction may deteriorate
Solution Approach 1:
The patent applies local quality by making advertisements personalized and context-relevant to each user. The AI model analyzes user preferences, behavior patterns, and content interests to select ads that match individual user profiles. This targeted approach ensures ads are displayed in contexts where they are more likely to be engaging and relevant, thereby reducing user annoyance while maintaining revenue generation.
Solution Approach 2:
The system implements feedback mechanisms by tracking user interactions with advertisements (clicks, views, engagement duration) and using this data to refine future ad selections. The AI model continuously learns from user responses, adjusting ad placement and content to improve user experience while optimizing revenue. User feedback loops allow the system to adapt to individual preferences, reducing negative impacts on user satisfaction.
3Ease of operation
If credits are awarded for viewing ads, then user engagement is improved, but system complexity and operational overhead increase
Solution Approach 1:
The patent implements a self-service credit system where users automatically receive credits for viewing advertisements without requiring manual intervention. The AI model autonomously tracks ad views, calculates credit awards based on engagement metrics, and updates user balances in real-time. This automated approach improves user engagement through effortless credit accumulation while minimizing operational overhead by eliminating manual credit management.
Solution Approach 2:
The system manages complexity by dynamically adjusting credit parameters based on ad performance and user behavior. The AI model modifies credit award amounts, viewing thresholds, and engagement weights according to real-time data, allowing flexible optimization of user engagement without requiring complex manual configuration. Parameter-based control enables adaptive credit management that responds to changing conditions without increasing structural complexity.
Data Source
AI summary
Systems and methods of advertising in connection with AI systems and models, such as generative AI, in which user content is received from a user, advertiser content is received from an advertiser, and the AI system generates output influenced by both the user content and the advertiser content, and the resulting output is made available to the user.