Integrated Control System for Personalized LLM Pricing
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Solution Overview
Problem
Existing large language models (LLMs) face challenges in providing personalized responses due to lack of access to personal user information, leading to inefficient resource utilization, environmental impact, and inequitable pricing structures, which can frustrate users and discourage usage.
Innovation Solution
An integrated control system that receives user queries, generates a pricing request, transmits it to an LLM for a price indication, determines a response price, requests user confirmation, and allocates the price through a payment system, using personalized data and feedback mechanisms to optimize resource distribution and pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If LLMs operate without access to personal user information, then system simplicity and ease of operation are maintained, but response personalization and quality are worsened
Solution Approach 1:
The patent introduces an intermediary layer (the system architecture) that enables personalization without direct user data processing. The system uses user profiles and context information as intermediaries to customize responses while maintaining operational simplicity. This resolves the contradiction by mediating between user information access and response personalization.
Solution Approach 2:
The system dynamically adjusts response parameters based on user profiles, preferences, and context without requiring explicit user input changes. By changing system parameters (personalization level, response tone, information depth) based on stored user data, the system achieves personalized responses while maintaining ease of operation.
2Manufacturing precision
If users explicitly include all pertinent information in queries, then response quality improves, but user frustration increases and operation complexity worsens
Solution Approach 1:
The system performs preliminary actions by pre-loading user profiles, preferences, and relevant context information before the user makes a query. This preliminary preparation allows the system to automatically incorporate pertinent information into responses without requiring users to explicitly provide it, thus improving response quality while reducing user frustration.
Solution Approach 2:
The system serves itself by automatically retrieving and incorporating relevant user information and context into responses without requiring user intervention. The system self-adjusts based on user profiles and preferences, eliminating the need for users to manually specify all pertinent information while maintaining high response quality.
3Ease of operation
If LLMs use publicly available data sources, then accessibility and ease of use are improved, but resource utilization efficiency and environmental impact worsen
Solution Approach 1:
The system applies local quality by selectively accessing and processing only the specific subset of data most relevant to each user's query and profile, rather than uniformly processing all available data. This localized data processing approach improves accessibility for individual users while reducing overall computational resource consumption and environmental impact.
4Manufacturing precision
If subscription payments are used for LLM access, then service quality and functionality are improved, but user flexibility and ease of operation worsen
Solution Approach 1:
The system implements dynamic pricing models that adapt to user needs, usage patterns, and preferences rather than using fixed subscription plans. Pricing can dynamically adjust based on query complexity, data access requirements, and user profiles, providing service quality improvement while maintaining user flexibility and ease of operation.
Data Source
AI summary
Computer-implemented methods for integrated control systems for providing responses to a user queries comprises the steps of: receiving a user query from a user by an integrated control system; generating a pricing request based on the user query and/or parameters attached to the user; transmitting the pricing request to a large language model, wherein the integrated control system requests the large language model to generate a price indication; receiving by the integrated control system the price indication generated by the large language model; determining based on the price indication a response price; requesting confirmation from the user to allocate the response price; receiving by the integrated control system a confirmation to allocate the response price; allocating the response price using a payment system; and transmitting a response associated with the user query to the user.