Natural Language Understanding for IVR Query Processing
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Solution Overview
Problem
Interactive voice response (IVR) systems face high opt-out rates, leading to increased costs and prolonged wait times for customers seeking live agent assistance, as they struggle to effectively respond to user queries in an automated manner.
Innovation Solution
Implementing a customer service system that uses natural language understanding to process user queries offline, allowing for more accurate recognition and response, and offering users the option to record queries for later automated response, reducing the need for immediate live agent interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If an IVR system uses traditional automated response methods, then the system can handle customer calls automatically, but the opt-out rate increases significantly (30-65%) and user satisfaction decreases
Solution Approach 1:
The patent replaces traditional keyword-matching and rigid decision-tree IVR systems with a natural language processing system that uses machine learning models to understand and respond to customer queries. This substitution enables the automated system to handle complex, unstructured language inputs effectively, reducing opt-out rates while maintaining high automation levels.
Solution Approach 2:
The system dynamically adjusts response strategies based on analyzed parameters such as query complexity, customer sentiment, and historical interaction patterns. By changing these parameters in real-time, the IVR system adapts to individual customer needs, improving satisfaction while maintaining automation.
2Ease of operation
If an IVR system queues users for live agents, then users can receive human assistance, but wait times become significant (several minutes or longer) and handling costs increase
Solution Approach 1:
The natural language processing system performs preliminary analysis of customer queries before routing to live agents. By pre-processing and understanding the query context in advance, the system prepares relevant information and reduces the time needed for live agent intervention, thereby decreasing overall hold times while maintaining access to human assistance when needed.
3Measurement precision
If an IVR system uses real-time natural language understanding, then the system can respond accurately to user queries, but processing speed and response time may be reduced
Solution Approach 1:
The system performs natural language processing offline or in advance whenever possible, pre-analyzing queries and preparing responses before the actual customer interaction. This preliminary action allows the system to maintain high accuracy while minimizing real-time processing delays, as the heavy computational work is completed beforehand.
Solution Approach 2:
The natural language processing system is divided into multiple processing stages (e.g., intent recognition, entity extraction, sentiment analysis, response generation) that can operate in parallel or be selectively applied. This segmentation allows the system to process only the necessary components of each query in real-time, maintaining accuracy while improving response speed.
Data Source
AI summary
The present invention uses natural language understanding to increase the ability of a customer service system to respond to a user's query in an automated manner. A customer service system receives a query from a user and offers the user the option of having the system contact the user at a later time with an answer. If the user accepts the offer, the customer service system processes the query offline, including providing the query to a natural language understanding interpreter. The system uses the natural language understanding interpretation to determine if the user's query is in a database of frequently-asked queries. For each query in the database of frequently-asked queries, there is a predetermined response protocol. If the user's query substantially matches a query in the database, the IVR system contacts the user with an automated response in accordance with the predetermined response protocol for the query.