Natural Language Map Modeling for Conversation Intent Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods and systems are unable to accurately predict the content of electronic conversations between users and agents, limiting their ability to proactively address user issues and enhance user satisfaction.
Innovation Solution
The use of Natural Language Map (NLM) modeling and machine learning techniques to generate simulated electronic conversations between users and agents, predicting user intents and assigning appropriate service agents to address these intents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to handle electronic conversations, then the system operates with simple processes, but the ability to accurately predict conversation content is insufficient
Solution Approach 1:
The system performs preliminary analysis of user input to predict conversation content and intent before the actual conversation occurs. By pre-generating predicted conversation paths and assigning appropriate agents in advance, the system improves prediction accuracy while managing complexity through structured preprocessing steps
Solution Approach 2:
The patent introduces intermediary components including intent recognition modules, conversation prediction algorithms, and agent assignment mechanisms that bridge the gap between simple input and accurate conversation prediction. These intermediaries enable complex prediction capabilities while maintaining a manageable system architecture through modular design
2Reliability
If the system generates simulated conversations proactively, then user satisfaction improves, but computing time and resources are consumed
Solution Approach 1:
The system applies partial action by generating simulated conversations only for specific intents that benefit from proactive resolution, rather than all conversations. By selectively applying conversation simulation to high-value scenarios identified through intent analysis, the system improves user satisfaction for critical issues while limiting computing resource consumption to only necessary cases
Solution Approach 2:
The system enables self-service by using automated intent recognition and conversation prediction to handle routine issues without human intervention. The simulated conversations provide self-contained resolution paths that address common user needs automatically, reducing the need for reactive human agent involvement and optimizing the balance between service quality and resource usage
Data Source
AI summary
A determination is made that a customer event has been initiated by a customer. The customer event is associated with a plurality of words. The plurality of words are parsed. Based on a result of the parsing of the plurality of words, an intent of the customer corresponding to the customer event is predicted. Based on the predicted intent, a first simulated service agent of a plurality of service agents is associated with the customer event. Based on the predicted intent, a first set of Natural Language Map (NLM) models for the customer and a second set of NLM models for the first simulated service agent are accessed. Based on the first set of NLM models and the second set of NLM models, a simulated conversation between the customer and the first simulated service agent is generated. The simulated conversation involves the predicted intent.