Topic Jump Map for Human-Machine Dialogue Guidance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current human-machine dialogue systems are passive, waiting for user input and lacking clear intentions, which limits their ability to provide a seamless and interactive experience, especially when users forget topics or cannot articulate their queries effectively.
Innovation Solution
A human-machine dialogue method that generates a topic jump map based on correlation intentions among jump topics, allowing the system to proactively recommend related topics and guide the conversation step-by-step towards a target topic, ensuring efficient operation and improved user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the system waits for user input to initiate dialogue, then the system operates passively with clear user intent, but the system cannot proactively guide conversation or assist users who forget topics
Solution Approach 1:
The system pre-generates a topic jump map with multiple potential conversation paths and jump topics before user input occurs. This preliminary preparation enables the system to quickly provide relevant topic recommendations when users need guidance or forget the original conversation topic, without waiting for explicit user requests.
Solution Approach 2:
The dialogue system dynamically switches between passive response mode (when user intent is clear) and active guidance mode (when users need topic recommendations). The system adjusts its behavior based on real-time dialogue context and user needs, making the conversation flow more naturally and adaptively.
2Reliability
If the system provides multiple rounds of dialogue to collect parameters, then the system ensures complete information gathering, but the dialogue becomes lengthy and user burden increases
Solution Approach 1:
Instead of waiting to collect all parameters before providing assistance, the system provides partial topic recommendations based on the information already available. This allows the dialogue to progress with incomplete information, reducing user burden while still maintaining reliability through iterative refinement of recommendations as more parameters are collected.
Solution Approach 2:
The topic jump map serves as an intermediary structure that connects user queries with relevant information even when parameters are incomplete. It provides intermediate topic recommendations that can guide users toward their goals without requiring all parameters to be explicitly collected first.
3Measurement precision
If the system asks clarifying questions to understand user needs, then the system improves understanding accuracy, but the system loses initiative and becomes more passive
Solution Approach 1:
The system pre-generates topic jump maps with multiple potential interpretation paths and jump topics before asking clarifying questions. This preliminary preparation allows the system to present informed topic recommendations based on initial understanding, then refine these recommendations as users provide clarifications, rather than starting from scratch after each clarification.
Solution Approach 2:
The system uses topic recommendations as feedback to test its understanding of user intent. By presenting jump topics based on current understanding and observing user responses, the system iteratively refines its interpretation without needing to ask explicit clarifying questions for every ambiguity, maintaining initiative while improving understanding accuracy.
Data Source
AI summary
Disclosed is a human-computer dialogue method including determining a set number of jump topics about a target topic, and generating a topic jump map converging to the target topic based on the correlation intensions among the set number of jump topics; after an initial response to a user's dialogue request, selecting from the topic jump map a jump topic to which the user's dialogue request relates as an initial topic for a first round of recommendation; after completing a human-machine dialogue of the initial topic, determining a jump topic to jump according to the jump probability of jumping out of the initial topic to the k jump topics at the downstream level for a next round of recommendation; and gradually guiding from the initial topic to the target topic by step-by-step recommendation. A more fluent and efficient human-machine dialogue based on a clear communication goal can be realized.