Generative AI Answer Generation with Hybrid Ad Monetization
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Solution Overview
Problem
Conventional techniques lack an orthodox scheme for monetization using generative AI, failing to enable well-suited monetization strategies for conversions generated via the Internet.
Innovation Solution
An information processing apparatus that utilizes generative AI to generate answer contents including an advertisement selected based on user and answer criteria, and determines the cost for advertisers based on user interactions with the advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional conversion-based billing techniques are used, then billing for Internet conversions is enabled, but no orthodox monetization scheme for generative AI is established
Solution Approach 1:
The patent changes the billing parameter from conventional conversion-based billing to a hybrid model that incorporates both conversion metrics and generative AI-specific parameters such as answer quality, user engagement duration, and advertisement interaction. This allows the system to adapt to generative AI's unique value proposition while maintaining reliable monetization through multiple measurable factors.
Solution Approach 2:
The patent creates a universal monetization framework that can handle both traditional Internet conversions and generative AI-specific interactions. The system simultaneously tracks advertisement clicks, user engagement with generated answers, and conversion events, making the monetization scheme applicable to diverse AI applications and business models.
2Ease of operation
If generative AI is used to generate answer contents with advertisements, then user engagement is improved, but cost determination for advertisers becomes complex
Solution Approach 1:
The patent implements a feedback mechanism where user engagement metrics (clicks, dwell time, interactions with generated answers) are continuously collected and fed back into the cost determination algorithm. This allows advertiser costs to be dynamically adjusted based on actual performance data, simplifying the complexity by using real-world feedback rather than complex predictive modeling.
Solution Approach 2:
The patent introduces an intermediary layer that separates the complex generative AI processes from the advertiser cost determination. This intermediary component aggregates and processes user engagement data into standardized metrics that can be directly used for billing, shielding advertisers from the underlying complexity of AI operations while maintaining accurate cost calculation.
Data Source
AI summary
An information processing apparatus according to the present application includes a generation unit, a provision unit, and a determination unit. Upon receipt of a question from a user, the generation unit causes candidates for advertisement and the question to be input to generative AI and thereby causes the generative AI to generate answer contents including an advertisement selected from the candidates according to at least one of the user and an answer generated by the generative AI as an answer to the question. The provision unit provides the answer contents that the generative AI was caused to generate by the generation unit, to the user. The determination unit determines cost an advertiser of the advertisement is to be charged, according to operation on the advertisement by the user.