Audio Stream Health-Trait Detection for Support Escalation

The patent segments the contact center operations into multiple digital agents, each with distinct voice characteristics. Instead of using a single voice for all interactions, the system divides the service function across multiple specialized agents that can be selectively deployed based on customer preferences and interaction contexts, thereby achieving personalization while maintaining operational efficiency through automated routing.

The system changes the voice parameter (acoustic characteristics) of digital agents to create diverse interaction experiences. By varying voice attributes such as tone, pitch, and style across different agents, the contact center can offer personalized customer experiences without complicating the underlying operational framework, as the voice parameter becomes a configurable attribute rather than a fixed constraint.

The patent implements a unified audio processing pipeline that serves multiple functions: transcribing customer speech to text, analyzing audio characteristics for sentiment detection, and extracting voice features for agent matching. This multi-functional approach reduces overall system complexity by consolidating what could be separate independent systems into a single integrated processing framework that handles all audio-related tasks through standardized interfaces.

The system introduces an intermediary layer of pre-trained machine learning models that handle complex audio analysis tasks. Rather than building complex analysis algorithms from scratch, the patent uses intermediary models for speech-to-text conversion, sentiment analysis, and voice feature extraction, which simplifies the core system architecture while still achieving sophisticated personalization capabilities through these specialized intermediate components.