ML Framework for Tuning Interactive Voice Response Systems
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Solution Overview
Problem
Interactive Voice Response (IVR) systems face challenges in accurately interpreting caller inputs due to complex machine learning models, making it difficult to test and remediate misinterpretations, leading to resource waste and potential misdirection of caller inquiries.
Innovation Solution
A machine learning framework that allows for granular testing of individual models within a group, using interim models to process and track inputs and outputs, and applying testing techniques to identify and replace misinterpreting models, thereby improving the accuracy and relevance of responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple interconnected machine learning models are used to interpret caller inputs, then the system's interpretive capability is improved, but the difficulty of testing and tracking inputs/outputs increases
Solution Approach 1:
The patent segments the interconnected machine learning models into individually testable components by implementing a testing framework that isolates each model's input and output. This allows granular testing of specific models within the larger system, making it possible to track and identify which particular model is causing misinterpretations without having to test the entire complex system as a monolithic unit.
Solution Approach 2:
The patent introduces intermediary testing mechanisms that act as mediators between the machine learning models and the testing framework. These intermediaries capture and record inputs and outputs at each model stage, enabling detailed tracking and analysis of how data flows through the interconnected models without disrupting the models' operational integrity.
2Measurement precision
If the IVR system uses complex machine learning models to understand caller context, then the accuracy of understanding is improved, but the time and resources required for testing and remediation increase
Solution Approach 1:
The patent implements preliminary testing actions by establishing a comprehensive testing framework before the IVR system is deployed or before complex remediation is needed. This framework pre-configures test cases, input/output tracking mechanisms, and model isolation capabilities, so that when testing or remediation is required, the process can proceed efficiently without having to set up these foundational elements from scratch.
Solution Approach 2:
By segmenting the testing process into model-specific test cases and tracking individual model performance, the patent enables focused remediation efforts. When a misinterpretation is detected, the framework quickly identifies which specific model is at fault, allowing remediation to be targeted at that single model rather than requiring comprehensive system-wide testing and adjustment.
3Productivity
If the IVR system uses multiple machine learning models to process caller inputs, then the system's processing capability is improved, but the resource waste from misinterpretations increases
Solution Approach 1:
The patent implements feedback mechanisms that monitor the performance of each machine learning model in real-time during IVR operations. When a model produces a misinterpretation or incorrect output, the feedback system detects this error and triggers remediation actions, such as rerouting the caller input to alternative models or adjusting the problematic model's parameters. This feedback loop prevents resource waste by quickly correcting misinterpretations before they lead to incorrect caller routing or extended handling times.
Data Source
AI summary
An artificial intelligence (“AI”) system for tuning a machine learning interactive voice response system is provided. The AI system may perform analysis of outputs generated by the machine learning models. The AI system may determine an expected model output for a given test input. The AI system may determine accuracy, precision and recall scores for an actual output garneted in response to the test input. The system may determine performance metrics for interim outputs generated by individual machine learning models within the interactive voice response system. The AI system may replace malfunctioning models with replacement models.