Deep Learning Speech Matching for Telecommunication Script Adherence

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

In telecommunication interactions, customer service agents often deviate from pre-defined scripts due to the flexible nature of communication, making it difficult to determine resource usage efficiency and effectiveness, leading to potential misuse or misallocation of resources.

Innovation Solution

Implementing deep learning-based semantic matching and clustering to analyze telecommunication interactions by comparing digitally-encoded speech representations of agent scripts and voice recordings, allowing for the division of interactions into sections based on semantic similarity, thereby identifying deviations and optimizing resource utilization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If pre-defined scripts are provided to guide telecommunication interactions, then service quality and consistency are improved, but agents cannot adapt flexibly to the wide range of possible customer problems

Engineering Contradiction:

Improveservice quality consistencyVSAvoidagent flexibility

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system creates digital copies of speech representations from both the agent script and actual customer interactions. These copied representations are then processed through deep learning models to enable automated analysis without restricting agent flexibility during actual communications.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent introduces an intermediary automated analysis system that processes recorded interactions separately from the actual communication flow. This intermediary system uses clustering algorithms and speech representation comparison to evaluate script adherence without interfering with agent-customer interactions.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If agents are allowed to deviate from scripts for flexible communication, then adaptability to customer problems is improved, but resource usage efficiency and effectiveness deteriorate

Engineering Contradiction:

Improvecommunication flexibilityVSAvoidresource usage efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system implements automated feedback by comparing actual interaction speech representations against script speech representations. The clustering analysis provides quantitative feedback on script adherence, enabling managers to assess resource efficiency while agents maintain communication flexibility.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces manual monitoring and analysis of telecommunication interactions with an automated deep learning-based system. This substitution eliminates the need for human reviewers to manually assess each interaction, significantly improving analysis efficiency while maintaining comprehensive evaluation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Loss of information

If manual monitoring and analysis of telecommunication interactions is performed, then resource usage can be assessed, but the complexity and time required for analysis increases

Engineering Contradiction:

Improveresource usage assessmentVSAvoidanalysis complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The system transforms complex speech data into standardized parameter representations using deep learning models. By converting speech into comparable numerical representations and applying clustering algorithms, the system simplifies the analysis process while capturing comprehensive interaction characteristics.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent creates digital copies of speech representations that can be analyzed without handling the original complex audio data. These copied representations enable efficient comparison and clustering operations, reducing analysis complexity while preserving all necessary information.

Inventive Principle:

Principle #26Copying

4Measurement precision

If comprehensive analysis of all telecommunication interactions is conducted, then service quality metrics are improved, but the time and computational resources required increase

Engineering Contradiction:

Improveservice metric accuracyVSAvoidanalysis time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs clustering analysis on speech representations to identify representative interaction patterns. By analyzing grouped patterns rather than every individual interaction in detail, the system achieves comprehensive service quality assessment while reducing total analysis time through efficient pattern recognition.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12118986B2System and method for automated processing of natural language using deep learning model encoding

Publication Date: 2024.10.15 CONDUENT BUSINESS SERVICES LLC

AI summary

Automated systems and methods are provided for processing natural language, comprising obtaining first and second digitally-encoded speech representations, respectively corresponding to an agent script for and a voice recording of a telecommunication interaction; generating a similarity structure based on the speech representations, the similarity structure representing a degree of semantic similarity between the speech representations; matching markers in the first speech representation to markers in the second speech representation based on the similarity structure; and dividing the telecommunication interaction into a plurality of sections based on the matching.