Call Center Data Mining with LLM Summarization
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Solution Overview
Problem
Large organizations face challenges in processing high volumes of customer support calls and emails in real time due to computational limitations, leading to a backlog and inability to generate meaningful analytics quickly enough to inform business decisions.
Innovation Solution
Utilizing a large language model to summarize call transcripts, followed by vectorization and clustering to identify client support topics, and refining models based on feedback, enabling real-time processing and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional analytics processing is used on large volumes of call center traffic, then comprehensive analysis can be performed, but the processing time becomes too slow to provide real-time feedback
Solution Approach 1:
The patent segments the call center traffic into smaller manageable batches that can be processed in parallel. Each batch is independently analyzed and then aggregated to form comprehensive analytics, enabling both high-quality analysis and faster processing by distributing the computational load across multiple processing units
Solution Approach 2:
The system performs preliminary actions by pre-processing and preparing data structures in advance. Call metadata is extracted and organized before the main analytics processing occurs, and results are pre-formatted for quick delivery, reducing the time required for complete analytics generation while maintaining comprehensive analysis quality
2Productivity
If processing speed is increased to achieve real-time analytics, then feedback timeliness improves, but computational resources and costs increase
Solution Approach 1:
The patent applies local quality by differentiating processing intensity based on data characteristics. High-priority calls or calls with specific attributes receive more intensive analysis, while routine calls receive streamlined processing. This selective approach maintains real-time processing capability while reducing overall computational resource consumption across the entire call center traffic volume
3Loss of information
If all call center traffic is analyzed in detail, then comprehensive insights are obtained, but the system becomes overwhelmed and cannot keep pace with incoming traffic
Solution Approach 1:
The system extracts only the most critical and actionable insights from call center traffic rather than analyzing every detail of each call. By identifying and extracting key metrics, sentiment indicators, and action items, the system maintains comprehensive analytics coverage while processing throughput keeps pace with incoming traffic volume
Data Source
AI summary
A disclosed method may include (i) transforming an original corpus of support call transcriptions for support calls received at a telecommunication provider at least in part by prompting a large language model to summarize each support call transcript in the original corpus of support call transcripts for the support calls received at the telecommunication provider to output a summary corpus of large language model generated summaries of support call transcriptions, (ii) extracting from the summary corpus of large language model generated summaries of support call transcriptions a ranked ordering of client support topics for clusters within the summary corpus of large language model generated summaries of support call transcriptions, and (iii) resolving, by the telecommunication provider, the client support topics in an actual order that is determined at least in part based on the ranked ordering of client support topics for clusters within the summary corpus.