Interactive Voice Router Blending Human Agents and Speech Recognition
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Solution Overview
Problem
Current interactive response systems, such as IVR and speech recognition technologies, often provide a less than satisfactory customer experience due to their limited ability to understand everyday language and require customers to respond in a narrow range, leading to high agent turnover and training costs, and offshore outsourcing can result in misunderstandings and poor service quality.
Innovation Solution
An interactive response system that uses a software-based router to seamlessly blend human customer service agents and speech recognition, allowing multiple agents to evaluate customer input for accurate interpretation, providing agent portability, simplified training, and dynamic workload balancing, while ensuring secure handling of sensitive data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional IVR systems with menu structures are used, then system automation is improved, but customer experience and ease of operation deteriorate
Solution Approach 1:
The patent introduces human agents as intermediaries between the automated IVR system and customers. The human agent listens to the customer's natural language input, interprets the intent, and translates it into commands for the automated system, thereby bridging the gap between automated efficiency and natural interaction
Solution Approach 2:
The interaction process is divided into distinct segments: the customer speaks naturally to the human agent, the agent processes the input separately, and then the automated system executes the interpreted commands. This segmentation allows each component to operate in its optimal mode
2Extent of automation
If speech recognition technology with predetermined response ranges is used, then automation is improved, but understanding of everyday language and adaptability deteriorate
Solution Approach 1:
The human agent serves as an intermediary who bridges the limitation of predetermined speech recognition responses. The agent understands everyday language nuances and translates them into the structured format required by the automated system, enhancing adaptability without reducing automation
Solution Approach 2:
The human agent performs multiple functions: listening to natural speech, interpreting intent, translating to system commands, and handling edge cases. This multi-functionality allows the system to handle diverse language patterns while maintaining automated processing
3Reliability
If one human agent assists a customer for the full duration of the interaction, then customer service quality is improved, but agent availability and productivity deteriorate
Solution Approach 1:
The customer interaction is segmented into portions that require human interpretation versus automated processing. The human agent only handles the portions requiring interpretation, while automated systems handle routine tasks, allowing the agent to serve multiple customers simultaneously
Solution Approach 2:
The human agent acts as an intermediary for specific interpretation tasks rather than managing the entire interaction. This allows the agent to be more efficient and available for multiple customers while maintaining service quality for complex cases
4Measurement precision
If multiple human agents are used to interpret customer input, then accuracy is improved, but system complexity and cost deteriorate
Solution Approach 1:
Multiple agents provide interpretations that are compared and validated against each other. The system uses feedback from multiple sources to determine the most accurate interpretation, improving precision through cross-validation
Solution Approach 2:
The patent combines multiple human agent interpretations with automated speech recognition results. By merging these different interpretation sources, the system achieves higher accuracy while distributing the complexity across multiple components
Data Source
AI summary
An interactive voice and data response system that directs input to a voice, text, and web-capable software-based router, which is able to intelligently respond to the input by drawing on a combination of human agents, advanced speech recognition and expert systems, connected to the router via a TCP/IP network. The digitized input is broken down into components so that the customer interaction is managed as a series of small tasks rather than one ongoing conversation. The router manages the interactions and keeps pace with a real-time conversation. The system utilizes both speech recognition and human intelligence for purposes of interpreting customer utterance or customer text. The system may use more than one human agent, or both human agents and speech recognition software, to interpret simultaneously the same component for error-checking and interpretation accuracy.