AI Conversation System for Dynamic Transaction Processing
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Solution Overview
Problem
Existing service platforms are inflexible and require users to navigate through structured interfaces to access desired data and services, making it difficult for users to find information or perform transactions without manual navigation or human assistance.
Innovation Solution
A computer framework that utilizes artificial intelligence (AI) models to provide natural conversation services, allowing users to interact with the system through chat interfaces and enabling the AI to dynamically generate responses and perform transactions by communicating with backend modules via predefined specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predefined information pages with fixed questions and answers are used, then the platform structure remains simple and easy to maintain, but users cannot naturally access desired data and services without manual navigation
Solution Approach 1:
The patent replaces the mechanical navigation system (fixed menus and structured interfaces) with an AI-based natural language processing system. Users can query information through conversational input instead of navigating through predefined pages, allowing the system to dynamically retrieve and present relevant data based on user intent rather than following a rigid structural path.
Solution Approach 2:
The patent introduces an AI model as an intermediary between the user and the platform's data/services. This intermediary processes natural language queries, understands user intent, and translates requests into appropriate data retrieval operations, effectively mediating the interaction without requiring users to navigate the underlying platform structure directly.
2Adaptability or versatility
If pre-generated questions and answers are provided on information pages, then the platform requires less processing power for basic queries, but the system cannot adapt to new or unanticipated user needs
Solution Approach 1:
The patent implements a dynamic query processing system where the AI model adapts its behavior based on the complexity and novelty of each user query. For simple, common queries, the system uses efficient retrieval mechanisms. For complex or novel queries, the system dynamically adjusts its processing strategy, invoking more sophisticated analysis only when necessary, thus balancing adaptability with processing efficiency.
Solution Approach 2:
The patent employs parameter changes in the AI model's processing depth and complexity based on query characteristics. The system adjusts parameters such as retrieval breadth, analysis depth, and response detail level dynamically, allocating processing resources according to the specific requirements of each query rather than using a fixed high-processing mode for all queries.
3Reliability
If information pages are updated whenever platform data changes, then the information remains accurate and current, but the service provider bears additional maintenance burden and time consumption
Solution Approach 1:
The patent implements a self-service information update mechanism where the AI model automatically retrieves and presents updated platform information in response to user queries. When platform data changes, the system automatically reflects these updates without requiring manual intervention to update information pages, as the AI dynamically queries the latest data from the platform's data sources.
Solution Approach 2:
The patent employs preliminary action by continuously monitoring and indexing platform data changes in the background. The system prepares updated information in advance so that when users make queries, the most current data is already ready for immediate retrieval and presentation, eliminating the need for manual updates and reducing information latency.
Data Source
AI summary
Methods and systems are presented for providing an artificial intelligence (AI)-based conversation system for facilitating a conversation with users and processing transactions for the users. The AI-based conversation system includes an AI model coupled with different backend modules. Based on an utterance submitted by a user during a chat session, the AI model is configured to generate instructions for a backend module to perform a transaction for the user based on a prompt template. The AI model also communicates the instructions to the backend module using a protocol specified in the prompt template. Upon receiving an output from the backend module, the AI model is configured to generate content for the chat session based on the output, and provide the content to the user.