Call-Modeling System for Real-Time Sales Conversation Analysis
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Solution Overview
Problem
Sales phone conversations are largely unanalyzed, preventing optimization for desired outcomes due to lack of accessible content for modeling, despite advances in automatic speech recognition and natural language processing.
Innovation Solution
A call-modeling system that analyzes voice conversations in real-time using features like transcripts, emotional signals, and personality traits to generate probabilities for possible outcomes and provide on-call guidance to improve conversation outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sales conversations are left unanalyzed, then simplicity is maintained, but the ability to optimize for desired outcomes is lost
Solution Approach 1:
The patent introduces an automatic speech recognition system as an intermediary that transcribes conversations into text, enabling subsequent natural language processing without requiring direct complex analysis of speech signals. This mediator layer simplifies the overall system architecture while enabling advanced optimization capabilities.
Solution Approach 2:
The patent replaces manual analysis of sales conversations with automated natural language processing and machine learning algorithms. This substitution transforms the mechanical process of human review into an automated computational system that can analyze conversations at scale without proportional increases in complexity.
2Loss of information
If automatic speech recognition is applied to transcribe conversations, then accessibility of conversation content is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs speech-to-text transcription and basic natural language processing during or immediately after the conversation occurs, rather than analyzing raw speech later. This preliminary action ensures content is accessible when needed while distributing processing load over time.
Solution Approach 2:
The patent applies different levels of processing intensity to different portions of conversations based on their importance. Critical segments receive more thorough analysis while less important portions receive lighter processing, optimizing the balance between content accessibility and processing time.
3Measurement precision
If detailed analysis of conversation features is performed, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent divides conversation analysis into multiple independent feature extraction modules, each handling specific aspects such as emotional signals, personality traits, and topic modeling. This segmentation allows for precise measurement of individual features while managing overall computational complexity through modular processing.
Solution Approach 2:
The patent develops a multi-functional natural language processing system that simultaneously extracts multiple types of features (emotional, personality, topical) from the same conversation data. This universal approach improves prediction accuracy without proportionally increasing complexity by reusing the same processing infrastructure for multiple analysis goals.
Data Source
AI summary
An action item identification system automatically determines action items by analyzing conversations of representatives with customers. The action item identification system retrieves recordings of various conversations, extracts features of each of the conversations, and analyzes the features to determine a set of features that is indicative of an action item associated with the corresponding conversation. The set of features is further analyzed to generate the action item in an action item manifest (a) as a summary of what is discussed in the conversations or (b) verbatim from the conversations.