Pre-training Virtual Chat Interfaces for Topic Accuracy
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Solution Overview
Problem
Users face challenges when interacting with virtual agent interfaces in remote network management platforms, such as time-consuming conversational exchanges and incorrect topic initiation, leading to unsatisfactory user experiences.
Innovation Solution
A software application that utilizes a natural language understanding unit and pre-defined mappings to quickly and correctly determine the sought-after conversation topic based on user inputs, enabling the virtual agent interface to carry out the appropriate conversation flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional virtual agent interfaces are used without pre-training, then the system structure remains simple, but users experience time-consuming back-and-forth conversational exchanges and incorrect topic initiation
Solution Approach 1:
The patent applies preliminary action by pre-training the virtual agent interface with conversation topics and mappings before actual use. The system pre-processes and stores conversation flows, topic mappings, and response patterns in advance, allowing the agent to quickly retrieve and execute appropriate conversations without time-consuming back-and-forth exchanges during actual user interactions.
2Reliability
If traditional virtual agent interfaces are used without pre-training, then the system remains easy to operate, but users encounter incorrect topic initiation and unsatisfactory user experience
Solution Approach 1:
The system performs preliminary action by pre-training the virtual agent with accurate topic mappings and conversation flows before deployment. This pre-processing ensures that when users interact with the agent, the topic initiation accuracy is high because the system has already learned the correct associations between user inputs and appropriate conversation topics during the pre-training phase.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously learns from user interactions and refines its topic mapping accuracy. The virtual agent analyzes user inputs, determines the appropriate conversation topic, and provides responses that feed back into the system for further training and improvement, thereby increasing reliability over time.
3Measurement precision
If pre-defined conversation flows and mappings are implemented, then conversation accuracy improves, but the device complexity increases due to additional components
Solution Approach 1:
The patent applies segmentation by dividing the conversation system into distinct modular components: topic identification modules, conversation flow management modules, mapping storage modules, and response generation modules. Each module handles a specific aspect of conversation processing, which improves topic determination accuracy while making the overall complex system more manageable and maintainable through clear separation of concerns.
Data Source
AI summary
A computing system may include persistent storage and a software application. The persistent storage may contain (i) pre-defined conversation flows respectively corresponding to conversation topics and (ii) pre-defined mappings that respectively associate the conversation topics to conversational expression(s) with matching semantic meanings. The software application may be configured to: (i) receive, from a computing device and by way of a virtual agent interface, a conversational expression; (ii) based on the pre-defined mappings, determine a particular conversation topic associated with a particular conversational expression, the particular conversational expression having a matching sematic meaning that is within a similarity threshold of a semantic meaning of the received conversational expression; and (iii) in response to determining the particular conversation topic, carry out, by way of the virtual agent interface, a particular conversation flow of the pre-defined conversation flows that corresponds to the particular conversation topic.