AI Models With Modular Ad Injection for Personalized Content
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Solution Overview
Problem
Current AI models lack effective integration of advertising capabilities, limiting their profitability and versatility in generating content.
Innovation Solution
AI models are adapted to generate content with integrated advertising, including displaying ads before or alongside results, incorporating ads into results, modifying ads based on user prompts, and providing credits for viewing ads, while allowing users to interact with and influence ad content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI models integrate advertising capabilities, then profitability is improved, but device complexity increases
Solution Approach 1:
The patent segments the advertising system into separate functional modules: ad selection module, ad generation module, ad injection module, and ad management module. Each module handles specific tasks independently, allowing the complex advertising functionality to be added without overwhelming the core AI model architecture.
Solution Approach 2:
The AI model is designed to serve multiple functions: generating user-requested content, selecting appropriate advertisements, generating ad content, and injecting ads into output. This multi-functionality allows a single system to handle both content generation and advertising tasks, improving profitability without requiring entirely separate systems.
2Productivity
If ads are displayed to users, then profitability is improved, but user engagement quality deteriorates
Solution Approach 1:
The patent applies local quality by making advertisements personalized and context-relevant to each user and query. Instead of generic ads, the system selects and generates ads that match user interests and the specific content being accessed, improving engagement quality while maintaining profitability.
Solution Approach 2:
The system implements feedback mechanisms where user interactions with ads (clicks, views, conversions) are tracked and used to refine future ad selection and generation. This continuous feedback loop ensures ads remain relevant and engaging while maximizing profitability.
3Productivity
If AI-generated content is modified to include ads, then advertising effectiveness is improved, but content purity deteriorates
Solution Approach 1:
The patent extracts advertising elements as separate, identifiable components from the AI-generated content. Ads are injected as distinct modules or segments that can be clearly distinguished from the primary content, maintaining content purity while enabling effective advertising.
Solution Approach 2:
The system uses intermediary markers and metadata to separate ad content from user-requested content. These intermediaries allow the system to manage ad injection transparently, ensuring users can distinguish between generated content and advertisements while maintaining both content integrity and advertising effectiveness.
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.