Generative AI Ad Matching Within Prompt and Response Flow

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

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 through effective advertisement integration.

Innovation Solution

Integrate advertisement matching techniques within generative AI/ML models, allowing advertisements to be displayed during the process of generating responses, either by modifying prompts or responses, to create ad matching opportunities at various stages of user interaction.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of energy

If advertisement integration is implemented in generative AI/ML models, then revenue is generated to offset development and deployment costs, but user experience may be degraded due to ad exposure

Engineering Contradiction:

Improvedevelopment and deployment costsVSAvoiduser experience degradation

Core Design Contradiction:

Loss of energyVSObject-affected harmful factors

Solution Approach 1:

The system performs advertisement matching and selection in advance during the text generation process, before the final output is presented to the user. This allows ads to be integrated seamlessly into the response text, offsetting costs while maintaining a natural user experience without abrupt ad interruptions

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses an intermediary advertisement matching mechanism that connects the generated text with relevant advertisements. This intermediary layer processes both the user input and potential ads, selecting and integrating appropriate advertisements that contextually fit the conversation, thereby reducing user experience degradation

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If advertisements are modified into the text input or output, then ad matching opportunities are increased, but text generation quality and coherence may be compromised

Engineering Contradiction:

Improvead matching opportunitiesVSAvoidtext generation quality

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The system applies advertisement modification locally and selectively within the text, rather than uniformly throughout. It identifies specific positions in the generated text where advertisements can be integrated without disrupting the overall coherence, maintaining high text quality while creating ad matching opportunities

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system applies partial modification by selecting only certain portions of the generated text for advertisement integration, rather than modifying the entire output. This selective approach preserves the quality and coherence of the text while still providing sufficient ad matching opportunities to generate revenue

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250348910A1Advertisement matching for generative artificial intelligence/machine learning (ai/ML) models

Publication Date: 2025.11.13 QUALCOMM INC

AI summary

An apparatus has one or more memories and one or more processors coupled to the one or more memories. The one or more processors is configured to receive an input to a generative artificial intelligence/machine learning (AI/ML) model. The one or more processors is also configured to generate, with the generative AI/ML model, an output based on the input, the output comprising a generated image. The one or more processors is further configured to determine an advertisement related to at least one of the input or the output. The one or more processors is still further configured to display the advertisement and the output of the generative AI/ML model by displaying the advertisement and the output.