AI Model Bidding for Cost-Aware Response Routing
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Solution Overview
Problem
Existing AI model applications for generating responses, such as ChatGPT, require significant computational resources and energy, leading to high costs without efficient load distribution or fee comparison mechanisms, necessitating an improved method for resource allocation and user selection.
Innovation Solution
A method and system for providing responses using a bidding process that involves receiving client requests, determining client identity, transmitting request information to multiple systems, receiving cost information, transmitting cost indications, generating responses based on selected costs, and allocating payments based on client identity, with optional micropayments and feedback incentives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If AI model applications use massive calculation power for sophisticated answers, then response quality improves, but energy consumption increases
Solution Approach 1:
The patent segments the AI service market into multiple competing systems, each handling a portion of the workload. By dividing the total demand across several systems rather than concentrating all requests on a single massive computational system, the patent reduces overall energy consumption while maintaining response quality through competitive selection.
Solution Approach 2:
The patent changes the parameter of resource allocation from centralized to distributed. Instead of allocating all computational resources to one system, the patent dynamically allocates requests across multiple systems based on their bid prices, effectively changing how computational parameters are distributed to optimize the quality-energy ratio.
2Adaptability or versatility
If multiple AI systems provide services, then user selection and cost comparison improve, but system complexity increases
Solution Approach 1:
The patent introduces a mediator system (the auction/platform system) that manages the complexity of coordinating multiple AI systems. This intermediary handles the bid collection, evaluation, and request routing, allowing users to benefit from multiple system options without directly managing the complexity of integrating and coordinating them.
Solution Approach 2:
The patent implements feedback mechanisms where systems bid based on their cost parameters, and users receive feedback through cost indications and selected system notifications. This feedback loop simplifies the user experience by automatically handling selection based on predefined criteria while maintaining the benefits of multiple system options.
3Productivity
If AI systems are monetarized with fees, then resource allocation efficiency improves, but user accessibility decreases
Solution Approach 1:
The patent implements self-service through automated bid evaluation and selection. The system automatically processes multiple bids, evaluates them according to predefined criteria, and selects the optimal system without requiring manual user intervention. This maintains resource allocation efficiency while preserving ease of operation for users.
Solution Approach 2:
The patent provides feedback to users through cost indications that show pricing information from different systems. This feedback mechanism allows users to understand the economic implications of their requests while the automated selection process maintains accessibility. Users receive transparent pricing information without being burdened by complex selection processes.
Data Source
AI summary
A computer-implemented method provides a response to a client request by receiving the request from a client device; determining a client identity issuing the request; transmitting information indicative of the request to a plurality of AI systems executing respective trained AI model applications; receiving from the AI systems, respective cost information for generating a response; transmitting a cost indication to the client device; processing an authorisation signal from the client device selecting a cost indication for generating a response; transmitting to the system associated with the selected cost indication, instructions to generate the response; receiving a generated response; transmitting the generated response to the client device; allocating the cost indication for the transmission of the response to client account based on the client identity; monitoring a total allocated amount to the client account; and request payment when the total allocated amount exceeds a predetermined threshold amount or period of time.