Simulated Live Agent Engine for Multilingual IVR Translation
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Solution Overview
Problem
Conventional automated systems are inadequate for users with limited English proficiency, as they are typically developed for a single language, limiting their ability to navigate and access services effectively, especially when requests require human interaction or customized responses.
Innovation Solution
A customer care system that transitions requests from an automated system to a simulated live agent engine, using a machine interpreter to translate between languages, allowing a first language-speaking human agent to process and deliver services in the user's language, thereby enabling communication and service provision in the user's native tongue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If an automated system is developed for a single language, then the system complexity is reduced and operational efficiency is improved, but the adaptability to users speaking different languages deteriorates
Solution Approach 1:
The automated system is enhanced with language identification and translation capabilities, allowing it to serve multiple language groups through a single system. The system automatically detects the user's language and provides translated responses, making the system universally accessible without requiring separate automated systems for each language.
Solution Approach 2:
A translation module acts as an intermediary between the automated system and users speaking different languages. The system identifies the user's language and uses the translation module to communicate in the user's native language, bridging the language gap without requiring human agents.
2Adaptability or versatility
If the automated system is enhanced to support multiple languages, then the adaptability to different language users is improved, but the system complexity increases
Solution Approach 1:
A translation module serves as an intermediary component that handles all language translation operations. This modular approach allows the core automated system to remain relatively simple while the translation module manages the complexity of supporting multiple languages through automatic language identification and translation.
Solution Approach 2:
The system performs automatic language identification and translation without requiring manual intervention or configuration for each language interaction. The system autonomously detects the user's language and translates responses, eliminating the need for complex manual language management.
3Extent of automation
If LEP users are forced to navigate English-based IVR, then the automation level is maintained, but the ease of operation deteriorates
Solution Approach 1:
The translation module acts as an intermediary that translates IVR prompts and responses into the user's native language, making the automated system operable for LEP users without reducing the automation level. Users can navigate the IVR in their native language while the system remains fully automated.
4Ease of operation
If human agents are used for all language interactions, then the ease of operation for LEP users is improved, but the productivity and operational efficiency deteriorates
Solution Approach 1:
The translation module serves as an automated intermediary that handles routine language translation tasks, eliminating the need for human agents to handle simple translation requests. This preserves operational efficiency while still providing language support to LEP users through automated translation rather than manual human intervention.
Data Source
AI summary
A configuration is implemented to establish, with a processor, a customer care system based on a first human-spoken language. Further, the configuration receives, at an automated system, a request from a user through a dedicated communication channel for a service, the request being in a second human-spoken language. Moreover, the configuration determines, with the processor, an identity of the second human-spoken language based on the dedicated communication channel. The configuration also determines, with the processor, that the automated system is unable to fulfill the request. Additionally, the configuration transitions, with the processor, the request from the automated system to a simulated live agent engine that generates a simulated live agent session. The simulated live agent engine sends the request to a machine interpreter that translates the request into the first human-spoken language.