Generative AI Transcript Labeling for Objective Customer Service Analysis
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Solution Overview
Problem
Existing customer service communication systems rely heavily on subjective human interpretation, which is inefficient and resource-intensive, lacking an objective and automated method to analyze and summarize interactions between agents and customers.
Innovation Solution
A generative AI system segments and labels text transcripts of customer interactions using computerized large language models (LLMs) to identify contextual categories, generating detailed summaries and graphical interfaces for intuitive analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human agents manually interpret and analyze customer service communications, then the analysis can be performed with current technology, but the process is inefficient and resource-intensive
Solution Approach 1:
The patent replaces manual human interpretation with an automated AI-based system that uses natural language processing and machine learning algorithms to analyze customer service communications. The system automatically processes transcripts, identifies patterns, generates summaries, and extracts key information without human intervention, thereby eliminating the time-consuming manual analysis process while maintaining or improving analysis quality through computational efficiency
Solution Approach 2:
The system enables self-service analysis by automatically processing and interpreting customer service communications independently. The AI model continuously learns from data patterns and performs analysis autonomously, generating insights, summaries, and actionable information without requiring human operators to manually review each communication, thus significantly reducing time loss and improving productivity
2Measurement precision
If subjective human interpretation is used to analyze communications, then flexibility and context understanding are maintained, but objectivity and consistency are compromised
Solution Approach 1:
The patent replaces subjective human judgment with objective AI-based analysis that uses predefined criteria and algorithms to consistently evaluate customer service communications. The system applies uniform standards for categorization, sentiment analysis, and pattern recognition across all data, eliminating variability introduced by different human interpreters while maintaining ease of operation through automated processing
Solution Approach 2:
The system incorporates feedback mechanisms where AI-generated analysis can be reviewed and refined, and where the model continuously learns from new data to improve its objectivity. The feedback loop ensures that the system maintains high measurement precision by adjusting its analysis criteria based on performance metrics and emerging patterns, while the automated nature preserves operational simplicity
3Productivity
If automated AI systems are implemented to interpret communications, then productivity and resource efficiency are improved, but system complexity increases
Solution Approach 1:
The patent segments the complex AI system into distinct functional modules including text processing units, pattern recognition algorithms, summary generation components, and data visualization interfaces. Each module handles a specific aspect of communication analysis independently, which simplifies the overall system architecture and makes it more manageable despite the high-level automation and processing speed improvements
Solution Approach 2:
The system employs a universal AI platform that can handle multiple types of communications (phone calls, chats, emails) and perform various analysis functions through a single integrated framework. This multi-functionality reduces the need for separate specialized systems for different communication types, thereby managing complexity while maintaining high productivity across diverse communication channels
4Loss of information
If detailed analysis of each communication is performed, then actionable insights are obtained, but resource consumption increases
Solution Approach 1:
The patent extracts only the most critical information from each communication using AI-based pattern recognition and prioritization algorithms. The system identifies and pulls out key patterns, sentiments, and actionable insights while filtering out redundant or less important data, thereby maintaining information completeness for decision-making while significantly reducing the computational resources required to process the entire communication dataset
Solution Approach 2:
The system applies partial analysis by focusing computational resources on the most impactful aspects of communications rather than uniformly processing every detail. The AI model identifies high-value information patterns and directs analysis efforts toward those areas, obtaining sufficient actionable insights without the excessive resource consumption that would result from exhaustive analysis of all communication elements
Data Source
AI summary
In an example method, a system accesses first data including text transcripts of a plurality of voice calls, and generates, based on the first data, labeled representations of the voice calls using the one or more computerized LLMs. The system generates the labeled representations by determining a plurality of contextual categories associated with the voice calls, segmenting the text transcript into a plurality of transcript segments, and associating each of the transcript segments with a respective one of the contextual categories. Further, the system generates second data representing the labeled representations of the voice calls and stores the second data using the one or more hardware storage devices.