AI Chat Response Cost Allocation and Energy Monitoring
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Solution Overview
Problem
Current chatbot systems that utilize advanced AI models for generating human-like text responses consume excessive energy, leading to high computational costs and inefficient resource usage, with existing monetization approaches failing to incentivize load distribution effectively.
Innovation Solution
A method and system that calculate and charge clients based on the estimated energy consumption for responding to their requests using a trained AI model, allowing for micropayments or fractional payments, which are only required when a threshold is exceeded, and incentivize feedback to improve the model's performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If advanced AI models are used to generate human-like text responses, then the capability to respond to complex questions is improved, but energy consumption and computational costs increase excessively
Solution Approach 1:
The system dynamically adjusts computational parameters based on request characteristics. The AI model modifies its processing intensity, response length, and computational depth according to the complexity and urgency of each client request, thereby reducing unnecessary energy consumption while maintaining high capability when needed
Solution Approach 2:
The system applies partial computation only when necessary. Instead of always using full AI model capacity, it uses simplified response mechanisms for routine queries and reserves advanced AI processing for complex questions, reducing overall energy consumption while maintaining versatility
2Manufacturing precision
If AI models are used to generate sophisticated answers, then the quality of responses is improved, but computational costs become excessive
Solution Approach 1:
The system changes computational parameters dynamically based on request analysis. It adjusts model precision, processing depth, and computational resources allocated to each request, ensuring high-quality responses are generated only when necessary and using fewer resources for routine queries
Solution Approach 2:
The computational cost structure is made dynamic rather than static. The system continuously adapts its resource allocation based on real-time request characteristics, client history, and current system load, optimizing the balance between response quality and computational cost
3Quantity of substance
If existing monetization approaches are used, then cost recovery is achieved, but load distribution is not incentivized
Solution Approach 1:
The system implements a feedback mechanism where clients receive information about the computational costs associated with their requests and the impact of their usage patterns. This feedback loop incentivizes clients to adjust their behavior, distribute loads more effectively, and make informed decisions about their AI usage, thereby achieving both cost recovery and improved load distribution
Data Source
AI summary
A computer-implemented method for providing a response to a client request includes the steps of: receiving a client request over an interface from a client device; determining a client identity of a user issuing the request and/or the client device; determining costs for responding to the request, as a function of electrical power consumption by a trained artificial model; transmitting a cost indication to the client device based on the determined costs; determining the response to the client request using the trained artificial intelligence model; transmitting the response to the client device; allocating an amount to be paid for the transmission of the response, with or without concurrently requiring payment of the amount, using the client identity, wherein the amount is based at least in part on the cost indication; monitoring a total allocated amount associated with the client identity; and transmitting a payment request, the payment request for at least partially settling the total allocated amount associated with the client identity when the total allocated amount exceeds a predetermined threshold amount.