Conversation Agent Intent Routing for Data Interpretation
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Solution Overview
Problem
Conventional conversation systems based on rule templates lack flexibility and adaptability, requiring high development costs and long cycles to handle complex and changing real-world application needs, limiting their ability to address new or unexpected user requests.
Innovation Solution
A conversation agent utilizing artificial intelligence and natural language processing to understand user intentions, generate accurate responses, and provide data interpretation and diagnostic services, leveraging generative language models to improve efficiency and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule templates are used for conversation systems, then development costs and cycles are reduced for simple scenarios, but flexibility and adaptability to new or unexpected user requests deteriorate
Solution Approach 1:
The patent implements a dynamic conversation system that transitions from static rule templates to adaptive AI models. The system dynamically adjusts its processing approach based on input complexity, switching between rule-based responses for simple queries and generative language models for complex or novel requests, thereby achieving both cost efficiency and adaptability
Solution Approach 2:
The patent segments the conversation handling into multiple levels: rule template matching for straightforward scenarios, intent recognition for semi-structured queries, and generative modeling for complex open-ended requests. This segmentation allows the system to apply appropriate processing power to each scenario type, reducing overall development costs while maintaining high adaptability
2Manufacturing precision
If manually written dialogue rules and templates are used, then specific predetermined scenarios are handled accurately, but the system lacks flexibility for complex and changing real-world application needs
Solution Approach 1:
The system dynamically adapts its response generation mechanism based on the complexity and novelty of user inputs. For routine queries, it uses pre-defined templates ensuring accuracy; for novel or complex scenarios, it activates intent recognition and generative models that provide flexibility while maintaining contextual accuracy through learned patterns
Solution Approach 2:
The patent introduces an intermediary intent recognition layer between rule templates and generative models. This intermediary component analyzes user inputs to determine whether to invoke rule-based responses, template filling, or full generative modeling, thereby seamlessly bridging the gap between precision and flexibility
3Adaptability or versatility
If generative language models are used, then adaptability to diverse scenarios and user intentions is improved, but computational resources and processing time increase
Solution Approach 1:
The patent segments language processing into hierarchical levels: exact rule matching for simple cases, template-based generation for semi-structured responses, and full generative modeling only for complex novel queries. This segmentation ensures generative models are invoked minimally, reducing computational resource consumption while maintaining high adaptability for scenarios that truly require it
Solution Approach 2:
The system applies partial generative action by using template filling and parameter binding for routine queries rather than full generative modeling. This partial approach provides sufficient adaptability for common scenarios while significantly reducing computational overhead compared to always using complete generative models
4Productivity
If AI and natural language processing are used, then conversation efficiency and accuracy are enhanced, but system complexity increases
Solution Approach 1:
The patent segments the AI system into modular components: intent recognition module, template selection module, parameter extraction module, and response generation module. Each module performs a specific function with well-defined interfaces, reducing overall system complexity while enabling high conversation efficiency through specialized processing in each segment
Solution Approach 2:
The patent implements a universal template system that serves multiple functions: structured response formatting, parameter binding, and context maintenance. This multi-functional template approach reduces the need for separate specialized components, thereby reducing system complexity while maintaining high productivity across diverse conversation scenarios
Data Source
AI summary
A conversation agent is described. An example method includes receiving a user request from a user interface of a conversation agent; determining a predicted intent of the user request; selecting a prompt template from a plurality of prompt templates corresponding to respective intents based on the predicted intent of the user request; generating a prompt using the prompt template and the user request; processing the prompt using a generative language model to generate an output; and displaying, on the user interface, a response to the user request generated based on the output.