IVR Voice Prompt Classification for Computational Load Reduction
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Solution Overview
Problem
Current IVR systems face inefficiencies in automated interactions due to high computational requirements and resource misallocation, leading to increased operational costs and user wait times.
Innovation Solution
The implementation of voice prompt classification machine learning frameworks and IVR navigation tree data objects to reduce predictive inferences and optimize resource allocation by determining the number of computing entities needed for post-prediction processing, facilitating efficient IVR session management and navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional IVR systems perform automated interactions without voice prompt classification, then all possible predictive inferences must be performed for each state node, but this results in high computational requirements and increased operational costs
Solution Approach 1:
The patent applies preliminary action by pre-classifying voice prompts into categories using machine learning frameworks before actual IVR navigation occurs. The system pre-processes and categorizes potential voice prompts, so that during runtime, only relevant predictive inferences need to be performed based on the pre-established classification, thereby reducing real-time computational operations while maintaining navigation accuracy
Solution Approach 2:
The patent segments the computational workload by dividing the IVR state nodes into different groups based on voice prompt classification categories. Instead of performing all predictive inferences for every state node, the system segments the navigation path according to classified prompt types, allowing selective execution of inference operations only for relevant segments, thus reducing overall computational requirements
2Reliability
If IVR systems allocate resources without optimization, then sufficient computing entities are available for all scenarios, but this leads to resource misallocation and increased operational costs
Solution Approach 1:
The patent applies dynamics by implementing dynamic resource allocation that adjusts computing entity distribution based on real-time IVR session characteristics and voice prompt classification results. The system dynamically determines the number of computing entities needed for post-prediction processing, allocating resources flexibly according to actual demand rather than maintaining static over-provisioning, thereby reducing operational costs while ensuring adequate availability
Solution Approach 2:
The patent changes the parameter of resource allocation from fixed to variable based on voice prompt classification outcomes. By modifying resource allocation parameters dynamically according to classified prompt categories and predicted navigation paths, the system optimizes the balance between computing entity availability and operational cost efficiency
3Measurement precision
If IVR systems perform comprehensive predictive inferences for all state nodes, then navigation accuracy is maintained, but this increases user wait times
Solution Approach 1:
The patent reduces user wait times by performing preliminary voice prompt classification before full predictive inference execution. The machine learning framework quickly categorizes the voice prompt, allowing the system to pre-determine which state nodes are relevant and which predictive inferences are necessary, thereby maintaining navigation accuracy while significantly reducing the time users wait for system response
4Productivity
If traditional IVR navigation processes are used without machine learning frameworks, then system complexity remains low, but this results in inefficient resource utilization and higher operational costs
Solution Approach 1:
The patent introduces an intermediary machine learning framework that sits between the voice prompt input and the IVR navigation logic. This intermediary layer performs automated classification and prediction, improving resource usage efficiency by directing navigation only to relevant state nodes. While this adds some system complexity, the automated nature of the framework reduces manual configuration requirements and optimizes overall system performance
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective automated interactions with IVR systems. For example, various embodiments of the present invention provide methods, apparatuses, systems, computing devices, computing entities, and/or the like for facilitating efficient and effective automated interactions with IVR systems using voice prompt classification machine learning models, IVR navigation tree data objects, resource allocation shares for resource utilization categories, and automated IVR session queues for resource utilization categories.