Personalized IVR Voice Generation for Low-Latency Call Routing
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Solution Overview
Problem
Existing CTI systems rely on static and prerecorded voice menus that are overly generic and unresponsive to user requests, leading to inefficiencies in call handling and increased network traffic.
Innovation Solution
Implement a generative AI system with machine-learning models to identify user speech and voice characteristics, enabling personalized and real-time interactive voice responses that reduce call queuing and rerouting, thereby optimizing processor resources and memory capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static and prerecorded voice menus are used, then system complexity is reduced, but adaptability to user requests deteriorates
Solution Approach 1:
The patent implements dynamic voice response generation using machine learning models that adapt to individual user characteristics in real-time. The system transitions from static prerecorded menus to dynamic generated responses that adjust speech rate, pitch, and content based on user preferences identified during interactions.
Solution Approach 2:
The system changes multiple parameters including speech rate, pitch, volume, and response content based on user characteristics. The machine learning models analyze and adjust these parameters dynamically to personalize the voice interaction experience for each user.
2Adaptability or versatility
If personalized voice responses with machine learning models are implemented, then adaptability to user requests is improved, but processor computing resources are consumed
Solution Approach 1:
The system performs preliminary analysis of user characteristics during initial interactions and maintains trained models for reuse. By pre-processing user preference data and caching model results, the system reduces the computational burden during subsequent personalized interactions.
Solution Approach 2:
The system creates simplified copies or representations of user profiles and preferences that can be quickly referenced without re-running complex analysis. Once user characteristics are identified, the system uses these stored representations to generate personalized responses efficiently.
3Ease of operation
If real-time voice interaction analysis is performed, then user experience is improved, but execution time and latency increase
Solution Approach 1:
The voice interaction analysis is divided into separate processing stages including speech recognition, characteristic extraction, model inference, and response generation. This segmentation allows parallel processing and optimization of each stage, reducing overall execution time while maintaining real-time performance.
4Reliability
If calls are routed to service representatives, then user service quality is improved, but network traffic and call queue accumulation increase
Solution Approach 1:
The system enables automated voice interactions to handle user requests independently without requiring human representative intervention. The machine learning models understand and process user intents, allowing the system to serve itself by resolving common queries through personalized automated responses.
Solution Approach 2:
The patent introduces an intelligent automated voice system as an intermediary between users and service representatives. This intermediary handles routine interactions and only escalates complex issues to human agents, reducing overall network traffic and call queue accumulation.
Data Source
AI summary
A system includes a memory configured to store user profiles associated with a plurality of users and an interactive voice response (IVR) system configured to service calls. The system includes processors configured to receive a call from a first user, generate a first voice interaction configured to prompt the first user to perform an utterance of a second voice interaction, and detect the utterance of the second voice interaction. The processors are configured to execute a first machine-learning model trained to identify speech and voice characteristics of the first user and to generate a third voice interaction based on the identified speech and voice characteristics. In response to identifying an intent and one or more named entities of the request, the processors are configured to initiate the execution of one or more interactions with the first user profile in accordance with the identified intent and one or more named entities.