Speech Recognition Query Formation for Call Center Automation
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Solution Overview
Problem
Existing spoken language understanding (SLU) systems in call centers require customer service representatives to manually search for information, leading to longer waiting times for callers and decreased customer satisfaction.
Innovation Solution
Implementing a system that automatically recognizes speech, determines the meaning of the utterance, and forms a query to search for relevant web pages or data resources, allowing for immediate retrieval and display of relevant information to the representative.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual information search by customer service representatives is used, then system complexity is low, but waiting time increases and productivity decreases
Solution Approach 1:
The system enables self-service by automatically performing information retrieval without human intervention. The automated information retrieval system processes customer inquiries, searches knowledge bases, and retrieves relevant information independently, eliminating the need for manual searching by customer service representatives and thereby reducing waiting time.
Solution Approach 2:
The patent replaces the mechanical manual searching process with an automated electronic information retrieval system. Instead of representatives manually navigating knowledge bases, the system uses automated query processing, natural language understanding, and electronic search mechanisms to retrieve information, significantly reducing waiting time while accepting increased system complexity.
2Productivity
If automated information retrieval is implemented, then productivity increases and waiting time decreases, but device complexity increases
Solution Approach 1:
The automated information retrieval system is designed with multi-functionality to handle various types of customer inquiries across different domains. The system can process diverse query types, search multiple knowledge bases, and adapt to different information retrieval needs, thereby increasing productivity while managing complexity through a unified versatile platform.
Solution Approach 2:
The system introduces an automated information retrieval intermediary that acts as a mediator between customer inquiries and knowledge bases. This intermediary component handles the complex tasks of query interpretation, information searching, and result formulation, thereby increasing productivity while isolating the complexity within a dedicated intermediary layer rather than requiring complex modifications throughout the entire system.
3Ease of operation
If manual searching is used, then system cost is low, but customer satisfaction decreases due to longer waiting times
Solution Approach 1:
The system enables self-service by automatically providing information to customers without requiring manual intervention from representatives. The automated retrieval system processes inquiries and delivers relevant information quickly, improving ease of operation from the customer's perspective and enhancing satisfaction while accepting the necessary system complexity to achieve this automation.
Data Source
AI summary
A combination and a method are provided. Automatic speech recognition is performed on a received utterance. A meaning of the utterance is determined based, at least in part, on the recognized speech. At least one query is formed based, at least in part, on the determined meaning of the utterance. The at least one query is sent to at least one searching mechanism to search for an address of at least one web page that satisfies the at least one query.