Generative AI Ad Matching During Prompt and Response Generation
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Solution Overview
Problem
The development and deployment of artificial neural networks, particularly generative models like diffusion models and large language models, are costly, and there is a need to offset these costs and generate revenue through relevant advertisement integration.
Innovation Solution
Integrate advertisement matching techniques into generative AI/ML models, allowing advertisements to be displayed during the process of generating responses or outputs, such as text or images, by modifying prompts or responses on various devices, including on-device, intermediary, or server-based systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generative AI/ML models are deployed to provide text and image generation services, then user access and model functionality are improved, but development and deployment costs increase
Solution Approach 1:
The system generates revenue autonomously by integrating advertisement delivery into the AI model's output generation process. The AI model itself becomes the vehicle for ad delivery, modifying its own output to include relevant advertisements without requiring separate billing infrastructure or user payments, thus making the system self-sustaining.
Solution Approach 2:
The AI generation model serves dual purposes: providing text/image generation services to users and simultaneously delivering targeted advertisements. This multi-functionality allows the same computational resources and model outputs to fulfill both user service and revenue generation objectives, reducing the need for separate ad delivery infrastructure.
2Loss of energy
If advertisements are integrated into AI model outputs, then revenue generation is improved, but user experience and output quality may deteriorate
Solution Approach 1:
Advertisements are selectively inserted into specific locations within the AI output based on contextual relevance rather than uniformly throughout. The system identifies appropriate positions in the generated text or image where ads naturally fit the context, ensuring local optimization of both user experience and revenue generation without compromising overall output quality.
Solution Approach 2:
The system continuously monitors user interactions with advertised content and adjusts future ad insertion strategies based on this feedback. By tracking which advertisements are engaged with and how users respond to modified outputs, the system refines its ad integration approach to maintain user experience while optimizing revenue generation over time.
Data Source
AI summary
An apparatus has one or more memories and one or more processors coupled to the memory. The processor(s) is configured to receive a text input to a generative artificial intelligence/machine learning (AI/ML) model. The processor(s) is also configured to generate, with the generative AI/ML model, a text output based on the text input. The processor(s) is further configured to determine an advertisement related to the text input and/or the text output. The processor(s) is still further configured to modify the text input and/or the text output with the advertisement. The processor(s) is also configured to display the advertisement while receiving the text input and/or while generating the text output by generating the advertisement for selected text of the text input and/or the text output.