Dialogue Tree Generation from Customer Interaction Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current self-help systems for contact centers require manual and time-consuming configuration, which is inefficient and costly, and do not effectively adapt to the varied ways customers approach problems, leading to suboptimal user experiences.
Innovation Solution
A method for generating a dialogue tree for automated self-help systems by analyzing recorded interactions between customers and agents, computing feature vectors, and grouping similar interactions based on similarity metrics, success rates, and customer satisfaction, to configure and personalize the system dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual configuration is used for self-help systems, then system customization is achieved, but configuration time and cost increase significantly
Solution Approach 1:
The system automatically generates dialogue trees by analyzing recorded customer-agent interactions without requiring manual configuration. The self-help system configures itself by extracting dialogue patterns, flow structures, and resolution paths from historical data, eliminating the need for manual system setup and reducing configuration time to zero.
Solution Approach 2:
The system pre-processes and stores recorded interactions in structured formats with extracted features and metadata. This preliminary organization of interaction data enables automatic dialogue tree generation when needed, transforming unstructured historical data into actionable configuration templates without manual intervention during the actual configuration phase.
2Adaptability or versatility
If standardized dialogue flows are used in self-help systems, then system complexity is reduced, but adaptability to varied customer approaches deteriorates
Solution Approach 1:
The system dynamically generates dialogue trees by analyzing actual customer interaction patterns from recorded data. Instead of using fixed standardized flows, the dialogue structure adapts to reflect real customer behavior, language patterns, and problem-solving approaches. The system updates dialogue trees continuously as new interaction data becomes available, ensuring ongoing adaptability without manual reconfiguration.
Solution Approach 2:
The system extracts and utilizes multiple parameters from recorded interactions including dialogue sequence, language patterns, customer sentiment, resolution outcomes, and interaction duration. By varying dialogue flow parameters based on these extracted features, the system achieves high adaptability to different customer approaches while maintaining manageable complexity through automated parameter extraction and analysis.
3Manufacturing precision
If detailed analysis of recorded interactions is performed, then dialogue tree accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system extracts only the essential features and patterns from recorded interactions that are necessary for dialogue tree generation, such as dialogue flow sequences, key transition points, and resolution paths. By selectively extracting relevant information rather than analyzing every detail of each interaction, the system achieves high dialogue tree accuracy while minimizing processing time and computational resource requirements.
Solution Approach 2:
The system segments the dialogue tree generation process into distinct stages: interaction data collection, feature extraction, pattern identification, and tree construction. Each segment processes specific aspects of the data with optimized algorithms, allowing parallel processing and reducing overall computation time while maintaining comprehensive analysis for accuracy.
Data Source
AI summary
A method for generating a dialogue tree for an automated self-help system of a contact center from a plurality of recorded interactions between customers and agents of the contact center includes: computing, by a processor, a plurality of feature vectors, each feature vector corresponding to one of the recorded interactions; computing, by the processor, similarities between pairs of the feature vectors; grouping, by the processor, similar feature vectors based on the computed similarities into groups of interactions; rating, by the processor, feature vectors within each group of interactions based on one or more criteria, wherein the criteria include at least one of interaction time, success rate, and customer satisfaction; and outputting, by the processor, a dialogue tree in accordance with the rated feature vectors for configuring the automated self-help system.