Large Language Model Analysis of Customer Service Resolution Status

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Solution Overview

Problem

Existing customer support systems lack efficient and accurate methods to determine the resolution status of customer issues during interactions, requiring significant human intervention and leading to inconsistent and time-consuming post-interaction summaries.

Innovation Solution

Implementing large language models to analyze interaction transcripts and generate automated issue resolution indications, including binary status and narrative summaries of actions taken or reasons for unresolved issues, reducing reliance on human summarization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual summarization by representatives is used, then issue resolution status can be recorded, but the process is time-consuming and inconsistent

Engineering Contradiction:

Improveaccuracy of issue resolution determinationVSAvoidtime required for post-interaction summarization

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical process of human representatives summarizing interactions with an automated system using large language models. The LLM analyzes interaction transcripts to automatically determine issue resolution status and generate summaries, eliminating the time-consuming manual process while maintaining or improving accuracy through consistent application of resolution criteria.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by allowing the interaction transcript itself to provide the resolution status information through automated analysis. The LLM extracts and determines resolution status directly from the transcript content without requiring external human intervention, making the system self-sufficient in generating resolution determinations.

Inventive Principle:

Principle #25Self-service

2Productivity

If manual summarization is used, then issue resolution can be tracked, but human resources are significantly required

Engineering Contradiction:

Improveefficiency of issue resolution analysisVSAvoidhuman resource requirements in contact centers

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent substitutes human representatives with an automated LLM-based system to perform issue resolution analysis. This replacement dramatically improves productivity by processing transcripts automatically without human intervention, while reducing the complexity of human resource requirements as representatives are no longer needed for manual summarization tasks.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The large language model serves as an intermediary between the interaction transcript and the resolution status determination. It mediates the analysis process by automatically interpreting transcript content and generating resolution determinations, eliminating the need for direct human involvement in this analytical task.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If automated systems are implemented, then efficiency improves, but accuracy of resolution determination may be compromised

Engineering Contradiction:

Improvespeed of issue resolution analysisVSAvoidaccuracy of resolution status determination

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent implements automated LLM-based analysis that maintains high accuracy while improving speed. The system achieves this by using sophisticated language models trained to accurately interpret interaction transcripts and determine resolution status, providing both the efficiency of automation and the precision of human-level understanding.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback mechanisms where the LLM's resolution determinations can be reviewed and refined. This feedback loop ensures accuracy by allowing validation and correction of automated determinations, maintaining high measurement precision while preserving the productivity benefits of automation.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250292024A1Automatic determination of customer service resolution status and explanation

Publication Date: 2025.09.18 VERINT AMERICAS INC

AI summary

Certain aspects of the present disclosure provide techniques for providing an issue resolution indication for an interaction. A method includes obtaining an interaction transcript; obtaining a resolution prompt; determining one or more issues presented to the first entity by the second entity, generating, with a first large language model based on the interaction transcript, the one or more issues, and the resolution prompt, one or more issue resolution indications; generating, for each of the one or more issue resolution indications indicating resolved status, a first narrative summarizing one or more actions implemented to resolve the one or more issues; generating, for each of the one or more issue resolution indications indicating unresolved status, a second narrative summarizing a reason the one or more issues are unresolved; and outputting the first narrative or the second narrative with each respective one of the one or more issue resolution indications.