Automated Semantic Clustering for Contact Center Interaction Analysis
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Solution Overview
Problem
Conventional systems for analyzing communications in contact centers rely on manual processes, which are time-consuming and inefficient, failing to categorize conversations accurately unless predefined keywords or phrases are identified, and require significant human input for Bayesian network development.
Innovation Solution
An automated system that extracts fragments from interactions, filters, clusters, and generates a hierarchical taxonomy of topics and categories, allowing for the identification of new concepts and trends without human assistance, using semantic distances and co-occurrence analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of communication data is performed, then concepts and patterns can be identified, but the process is time-consuming and delays result determination
Solution Approach 1:
The patent replaces manual mechanical analysis with automated computational text mining and natural language processing systems. The system automatically extracts concepts, entities, and relationships from communication data using algorithms such as co-occurrence analysis, semantic networks, and machine learning models, eliminating the need for human analysts to manually review and categorize communications while maintaining or improving identification accuracy.
2Productivity
If conventional keyword-based categorization is used, then predefined concepts can be identified, but new concepts and trends cannot be detected
Solution Approach 1:
The patent implements dynamic concept detection that adapts to new topics and trends automatically. The system uses evolving semantic networks, machine learning models that continuously learn from new data, and flexible entity recognition that can identify emerging concepts without requiring predefined categories. This allows the system to maintain high processing speed while simultaneously detecting new concepts and adapting to changing communication patterns.
Solution Approach 2:
The patent creates a universal analysis framework that can handle both predefined keyword matching and discovery of new concepts through multiple analytical methods including text mining, semantic analysis, and pattern recognition. This multi-functional system can process communications using various techniques simultaneously, making it adaptable to different types of analyses while maintaining efficient processing.
3Measurement precision
If Bayesian networks are used for correlation identification, then event relationships can be analyzed, but significant human input is required for network development
Solution Approach 1:
The patent implements self-service Bayesian network construction where the system automatically builds and updates correlation models from communication data without requiring manual configuration. The system uses algorithms that automatically identify relevant variables, determine relationship structures, and update network parameters based on observed data patterns, eliminating the need for experts to manually design and maintain complex Bayesian networks while preserving analytical accuracy.
Solution Approach 2:
The patent performs preliminary automated preparation of data and model structures before correlation analysis. The system pre-processes communication data, identifies potential relationships, and prepares initial network configurations automatically, reducing the complexity of subsequent analysis steps and minimizing the human input required for Bayesian network development and maintenance.
Data Source
AI summary
A method for detecting and categorizing topics in a plurality of interactions includes: extracting, by a processor, a plurality of fragments from the plurality of interactions; filtering, by the processor, the plurality of fragments to generate a filtered plurality of fragments; clustering, by the processor, the filtered fragments into a plurality of base clusters; and clustering, by the processor, the plurality of base clusters into a plurality of hyper clusters.