LLM Call Summaries for Accurate Incident Documentation
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Solution Overview
Problem
Existing meeting information capture methods, whether manual or technological, often result in inaccurate and incomplete records, leading to information loss that hampers effective decision-making and incident resolution.
Innovation Solution
A call analysis system utilizing a large language model to generate real-time summaries of meeting transcriptions, incorporating batch processing and verification techniques to ensure accuracy and completeness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual meeting minutes are taken to record important information, then information capture is attempted, but accuracy and completeness deteriorate leading to information loss
Solution Approach 1:
The system enables self-service by automatically generating meeting summaries without requiring manual intervention. The AI model processes transcriptions and generates accurate summaries autonomously, eliminating the need for manual note-taking while maintaining high accuracy and completeness of information capture.
Solution Approach 2:
The patent replaces the mechanical process of manual note-taking with an automated AI-based system. The mechanical action of manually recording information is substituted by an intelligent system that automatically processes transcriptions and generates summaries, significantly reducing information loss while requiring minimal human effort.
2Productivity
If technologies are used to generate transcripts of meetings, then information capture is automated, but accuracy and completeness still deteriorate resulting in limited usefulness
Solution Approach 1:
The patent introduces an AI-based summary generation system as an intermediary between raw transcriptions and final meeting records. This intermediary layer processes the automated transcriptions, corrects errors, extracts key information, and generates accurate summaries, thereby improving measurement precision while maintaining high productivity from automated transcription.
Solution Approach 2:
The system implements feedback mechanisms where the AI model continuously refines summary generation based on the quality of input transcriptions. By analyzing transcription accuracy and adjusting processing accordingly, the system maintains high productivity while improving measurement precision through iterative refinement and verification.
3Reliability
If manual post-mortem documentation is used for incident resolution, then incident information is recorded, but accuracy deteriorates leading to inadequate reference for future problems
Solution Approach 1:
The system performs preliminary action by generating incident documentation summaries during or immediately after incident resolution calls, rather than relying on delayed post-mortem documentation. The AI model processes call transcriptions and creates reliable incident records in real-time or near-real-time, ensuring accuracy while eliminating the time loss associated with manual post-mortem documentation.
4Speed
If large language models process transcription data in real-time, then summary generation speed is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the transcription processing into manageable batches rather than processing all data at once. The system segments transcription data into chunks that can be processed sequentially or in parallel by the large language model, achieving real-time summary generation speed while managing system complexity through modular batch processing architecture.
Data Source
AI summary
Methods, systems, and techniques for call analysis are disclosed, comprising receiving transcription data for a call; providing the transcription data to a large language model with an associated prompt to generate a summary of the call based on the transcription data; and receiving the summary of the call output from the large language model.