AI Engine for IVR Intent Detection and Load Balancing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Interactive Voice Response (IVR) systems often lead to processing delays and resource inefficiencies, as they connect customer requests to contact center agents through series of decisions, overloading certain resources while underutilizing others.
Innovation Solution
A computing platform that trains a machine learning model using recorded IVR sessions to identify user intents and provide automated responses, routing client requests to an AI engine rather than contact center resources, thereby dynamically balancing processing loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional IVR systems connect customer requests to contact center agents through a series of decisions, then customer service can be provided, but processing delays occur and resources are inefficiently utilized
Solution Approach 1:
The patent introduces an AI engine as an intermediary between customers and contact center agents. The AI engine processes customer requests using machine learning models trained on historical IVR sessions, acting as a mediator that handles routine inquiries automatically while escalating complex issues to human agents, thereby reducing processing delays and improving overall service efficiency
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models using historical IVR session data before actual customer interactions. This preliminary training enables the AI engine to quickly understand customer intents and provide accurate responses without requiring real-time analysis, thus eliminating processing delays during live interactions
2Productivity
If traditional IVR systems route customer requests through contact center agents, then customer inquiries can be handled, but certain computing resources become overloaded while others remain underutilized
Solution Approach 1:
The AI engine serves as an intelligent intermediary that dynamically routes customer requests based on their complexity and the current state of computing resources. By analyzing request patterns and resource availability, the AI engine balances the load across available resources, preventing overload on specific systems while ensuring efficient utilization of all enterprise computing resources
Solution Approach 2:
The system implements dynamic resource allocation where the AI engine continuously monitors computing resource states and adjusts routing decisions in real-time. This dynamic approach allows the system to adapt to changing resource availability and demand patterns, optimizing resource utilization and preventing load imbalances without requiring static, over-provisioned infrastructure
Data Source
AI summary
Aspects of the disclosure relate to using machine learning to simulate an interactive voice response system. A computing platform may establish a virtual assistant session with a mobile banking application executing on a mobile device, which may include authenticating at least one authentication credential associated with an online banking account. The computing platform may receive an assistance message from the mobile device requesting assistance. Using a machine learning model, the computing platform may identify an intent of the assistance message. The computing platform may generate a response message based on the intent of the assistance message. The computing platform may send the response message and one or more commands directing the mobile device to output an audio response file based on the response message to the mobile device, which may cause the mobile device to convert the response message into the audio response file and output the audio response file.