Ontology Programming for Automated Call Flow Analysis
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Solution Overview
Problem
Current data analysis systems in call centers require manual analysis to determine context and identify patterns, which is time-consuming and labor-intensive, lacking automation in understanding call flows and success rates.
Innovation Solution
The implementation of machine learning-based ontology programming that automatically trains to understand business environments, identifies themes, and conducts funnel analysis to extract relevant information from communication data, enabling automated analysis of call flows and success rates without human oversight.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data analysis is used to determine context and identify patterns, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent replaces manual mechanical analysis with automated discourse analysis systems that use natural language processing and machine learning algorithms to identify call flow patterns, replacing human analysts while maintaining analytical precision
Solution Approach 2:
The system performs self-training through automated learning from communication data, enabling the discourse analysis system to automatically improve its pattern recognition capabilities without requiring continuous manual intervention or oversight
2Productivity
If automated analysis systems are implemented, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system performs preliminary self-training by processing and learning from communication data before actual analysis tasks, preparing the discourse analysis model in advance to ensure accurate pattern identification when deployed
Solution Approach 2:
The system uses feedback mechanisms where analysis results are continuously evaluated and used to refine the discourse analysis model, improving measurement precision through iterative learning from actual call flow data
3Loss of information
If comprehensive data analysis is performed, then loss of information is reduced, but loss of time increases
Solution Approach 1:
The system extracts only the most relevant call flow patterns and themes from communication data using discourse analysis, isolating key information from the entire dataset to provide comprehensive insights without requiring complete analysis of all data points
Solution Approach 2:
The system focuses on analyzing specific portions of calls that contain critical call flow information rather than transcribing and analyzing every word, achieving sufficient understanding through partial analysis of key segments
Data Source
AI summary
The disclosed solution uses machine learning-based methods to improve the knowledge extraction process in a specific domain or business environment. By formulizing a specific company's internal knowledge and terminology, the ontology programming accounts for linguistic meaning to surface relevant and important content for analysis. Based on the self-training mechanism developed by the inventors, the ontology programming automatically trains itself to understand the business environment by processing and analyzing a defined corpus of communication data. For example, the disclosed ontology programming adapts to the language used in a specific domain, including linguistic patterns and properties, such as word order, relationships between terms, and syntactical variations. The disclosed system and method further relates to leveraging the ontology to assess a dataset and conduct a funnel analysis to identify patterns, or sequences of events, in the dataset.