Call-Modeling System for Real-Time Sales Conversation Analysis

The system segments the conversation analysis into distinct modules: automatic speech recognition (ASR) for transcription, natural language processing (NLP) for topic modeling, and separate analytics components for different conversation aspects. This modular architecture manages complexity by dividing the overall system into independent, manageable components that can be developed and maintained separately.

The patent introduces intermediary components such as transcription services that convert speech to text, and NLP layers that bridge raw conversation data with actionable insights. These intermediaries simplify the processing pipeline by transforming complex data formats into standardized structures that downstream analytics components can efficiently process.

The system performs preliminary actions by pre-computing topic models, preparing transcription frameworks, and establishing analysis pipelines before conversations occur. Historical conversation data is processed in advance to create reference models and patterns that can be quickly matched against ongoing conversations, reducing real-time computational requirements.

The system implements partial analysis by focusing on key conversation elements and critical moments rather than processing every aspect of the conversation with equal depth. Analytics are applied selectively to portions of the conversation that most influence outcomes, such as objection handling segments or closing attempts, rather than uniformly analyzing the entire conversation stream.

The feature extraction process is segmented into specialized components: ASR systems handle transcript generation, separate affective computing modules detect emotional signals through voice tone and speech patterns, and personality analysis components process linguistic patterns independently. Each segment focuses on specific features, improving detection accuracy while managing complexity through division of labor.

The patent replaces manual or rule-based feature detection with automated machine learning models. Instead of programming explicit rules for detecting emotions or personality traits, the system uses trained ML models that automatically identify these features from raw conversation data, reducing the difficulty of measurement while improving precision.