AI Virtual Agent Training via Customer Communication Patterns
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Solution Overview
Problem
Conventional virtual agents rely on manually programmed decision trees, limiting their ability to respond to a wide range of user inquiries and requiring human operator intervention for unanticipated questions, which reduces efficiency in customer service.
Innovation Solution
A virtual agent system trained using customer communication data through machine learning techniques, generating input-response pairs and identifying conversation patterns to autonomously respond to user inputs, reducing the need for human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual agents use manually programmed decision trees, then they can provide structured responses to specific inquiries, but they cannot handle a wide range of unanticipated user questions
Solution Approach 1:
The system enables virtual agents to automatically learn and improve their own response capabilities by analyzing customer communication data and generating training sets, eliminating the need for continuous manual reprogramming and expansion of decision trees
Solution Approach 2:
The system pre-processes customer communication data to generate training sets in advance, which are then used to train the virtual agent model before deployment, allowing the agent to learn from historical interactions without real-time human intervention
2Productivity
If virtual agents rely on manual programming, then they can maintain controlled and predictable responses, but they require human operator intervention for unanticipated questions, reducing efficiency
Solution Approach 1:
The system automatically extracts conversation patterns from customer data, generates training sets, and retrains the virtual agent without human intervention, enabling continuous self-improvement and reducing the need for human operators to handle unanticipated questions
Solution Approach 2:
The system uses customer communication data as feedback to continuously improve the virtual agent's performance by identifying conversation patterns and generating targeted training sets that address gaps in the agent's knowledge
3Adaptability or versatility
If virtual agents are highly scripted to address specific inquiries, then they can provide consistent service for known issues, but they cannot respond to a broad variety of user requests
Solution Approach 1:
The system pre-processes and structures customer communication data into standardized training sets with input texts and response texts, allowing the virtual agent to learn consistent response patterns across diverse inquiry types before deployment
Solution Approach 2:
The system extracts and copies effective conversation patterns from historical customer communications to create training examples, allowing the virtual agent to replicate successful human-agent interaction patterns across different scenarios
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for an crowd sourced training of an artificial intelligence system. One of the methods includes generating a training set using the customer communication information. The method includes training an artificial intelligence system using the training set. The method includes extracting at least one conversation pattern using the artificial intelligence system. The method includes the actions of instructing a chat application to process the at least one conversation pattern.