Virtual Agent Topic Flow Switching via Intent Recognition
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Solution Overview
Problem
Current virtual agents in cloud computing environments lack advanced natural language understanding capabilities, particularly in selecting and switching between topic flows during conversations, leading to inefficiencies and redundant information prompts.
Innovation Solution
A virtual agent designed to select and switch between topic flows based on identified user intents, storing and applying all conveyed intents to avoid redundant information requests, and supporting global topic flows for standardized interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a simple keyword-based virtual agent is used, then the device complexity is reduced, but the natural language understanding capability deteriorates
Solution Approach 1:
The virtual agent is segmented into multiple specialized components: intent识别 module, entity extraction module, topic flow selection module, and dialogue state tracking module. Each component handles a specific aspect of natural language understanding, improving overall capability while maintaining manageable complexity through modular design.
Solution Approach 2:
The virtual agent employs a universal topic flow management system that can handle multiple dialogue scenarios and intent types through a single integrated framework. This multi-functional approach enables the agent to adapt to different conversation contexts without requiring separate specialized systems for each scenario.
2Ease of operation
If a decision tree-based virtual agent is used, then the ease of operation is improved, but the adaptability to different conversation topics deteriorates
Solution Approach 1:
The virtual agent implements dynamic topic flow switching that adapts conversation paths based on real-time intent recognition and dialogue state. The system can dynamically transition between different topic flows, resume interrupted conversations, and adjust the dialogue trajectory based on user responses, providing both ease of operation and high adaptability.
Solution Approach 2:
The agent incorporates feedback mechanisms where user responses are continuously analyzed to update dialogue state and adjust subsequent conversation flow. This feedback loop enables the system to adapt to user needs in real-time while maintaining structured dialogue control through predefined topic flows and intent handling rules.
3Loss of time
If a simple virtual agent is used, then the loss of time is reduced, but the loss of information increases due to redundant prompts
Solution Approach 1:
The virtual agent performs preliminary entity extraction and intent identification from user inputs before initiating the main dialogue flow. By pre-processing and storing extracted information in dialogue state, the system avoids redundant information requests and can proceed directly to actionable topics, reducing both conversation time and information loss.
Solution Approach 2:
The agent maintains continuous dialogue state tracking across conversation turns, preserving extracted entities and identified intents throughout the interaction. This continuity ensures that information is retained and reused rather than repeatedly requested, eliminating redundant prompts while maintaining efficient conversation flow.
Data Source
AI summary
Present embodiments are directed to a virtual agent with improved natural language understanding (NLU) capabilities. The disclosed virtual agent enables topic selection and topic changes during natural language exchanges with a user. The virtual agent is designed to select suitable topic flows to execute based on intents identified in received user utterances, including selection of an initial topic flow in response to a topic identified in a first user utterance, as well as switching between topic flows mid-conversation based on identified topic changes. The virtual agent is also capable of considering all intents and entities conveyed during the conversation, which enables the virtual agent to avoid prompting the user to provide redundant information. Furthermore, the virtual agent is capable of executing topic flows as part of a global topic flow, which enables the virtual agent to perform a number of predefined activities as part of each interaction with the user.