IVR Optimization via Deep Multimodal Sequence Auto-Encoder Clustering
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Solution Overview
Problem
Current IVR systems lack an efficient and reproducible method to analyze transaction logs for optimization, often relying on human analysis that is inaccurate and subjective, leading to inconsistent performance improvements.
Innovation Solution
A method involving the receipt of IVR transaction logs, extraction of IVR journeys, transformation into vectors using a deep multimodal sequence auto-encoder model, clustering with K-Means, and determination of optimization parameters such as containment rate, which identifies patterns in successful and failed journeys for performance enhancement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human analyzers manually review IVR transaction logs to determine optimization parameters, then subjective expertise can be applied, but the analysis becomes inaccurate, non-reproducible, and unable to handle large volumes of logs
Solution Approach 1:
The patent replaces the mechanical system of manual human analysis with an automated computational system. The system uses processors to execute algorithms that extract IVR journeys from transaction logs, transform them into vectors using deep multimodal sequence auto-encoder models, and perform clustering analysis. This substitution eliminates human subjectivity and bias while enabling analysis of large volumes of logs at machine speed, thereby simultaneously improving accuracy and productivity.
Solution Approach 2:
The system enables self-service analysis by automatically processing IVR transaction logs without human intervention. The automated pipeline extracts journeys, transforms them into vectors, performs clustering, and determines optimization parameters independently. This self-service capability allows the system to handle large volumes of logs consistently and reproducibly, removing the bottleneck of manual human review while maintaining high accuracy through objective algorithmic analysis.
2Reliability
If human analyzers review IVR transaction logs, then their specific experience and knowledge can be utilized, but the analysis becomes biased and non-reproducible
Solution Approach 1:
The patent replaces human judgment with objective computational algorithms. The system uses deep multimodal sequence auto-encoder models to transform IVR journeys into vectors and applies clustering algorithms to identify patterns. These algorithms execute consistently without human bias, ensuring that the same input logs always produce the same analysis results. This eliminates the subjectivity and non-reproducibility inherent in human analysis while maintaining reliability through deterministic computational processes.
3Productivity
If automated systems are used to analyze IVR transaction logs, then productivity and consistency are improved, but the complexity of the analysis system increases
Solution Approach 1:
The patent segments the complex analysis task into distinct modular components: (1) extraction of IVR journeys from transaction logs, (2) transformation of journeys into vectors using deep multimodal sequence auto-encoder models, (3) clustering of vectors to identify patterns, and (4) determination of optimization parameters from clusters. Each module performs a specific function and can be independently developed, tested, and optimized. This segmentation manages system complexity while enabling high-speed automated processing of large volumes of logs.
Data Source
AI summary
Methods and systems for optimization of interactive voice recognition (IVR) system processes are provided. One or more desired optimization parameters can be determined based on an IVR transaction log that includes a plurality of IVR journeys. The IVR journeys can be filtered, transformed into vectors and/or clustered.