Attention Weighted Recurrent Neural Network for Conversation Analysis
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Solution Overview
Problem
Current methods for training agents in customer interactions are subjective, time-consuming, and inefficient, relying on scripted dialogues and manual analysis of recorded calls to evaluate agent performance.
Innovation Solution
The implementation of an Attention Weighted Recurrent Neural Network Encoder-Decoder (AWRNNED) model to automatically analyze conversations between agents and customers, identifying effective agent sentences that elicit specific customer responses and providing real-time recommendations to steer conversations effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of recorded calls is used to evaluate agent performance, then subjective evaluation can be performed, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces the mechanical manual analysis system with an automated neural network system. The AWRNNED model automatically processes conversation transcripts, identifying relationships between agent sentences and customer responses without human intervention, thereby eliminating the time-consuming manual review process while maintaining or improving evaluation accuracy through consistent algorithmic assessment.
Solution Approach 2:
The system enables self-service evaluation where the conversation analysis performs automatic performance assessment without requiring external manual analysis. The neural network independently evaluates agent effectiveness by analyzing conversation patterns, sentence relationships, and customer responses, making the evaluation process autonomous and efficient.
2Ease of manufacture
If scripted dialogues are used for agent training, then structured learning can be provided, but the training becomes inefficient and less adaptable to real conversations
Solution Approach 1:
The patent transforms static scripted dialogues into dynamic, adaptive training content. The system analyzes real conversation transcripts to identify effective agent sentences and customer response patterns, then uses this dynamic information to generate personalized training recommendations. This allows training materials to adapt continuously based on actual performance data, improving both efficiency and relevance.
Solution Approach 2:
The system implements feedback loops where agent performance is automatically evaluated through neural network analysis, and training recommendations are generated based on identified strengths and weaknesses. This continuous feedback mechanism enables agents to improve efficiently by learning from actual conversation outcomes rather than static scripts, enhancing training productivity.
3Ease of operation
If real-time conversation analysis is implemented, then immediate feedback can be provided to agents, but the system complexity increases
Solution Approach 1:
The patent creates a multi-functional system where the AWRNNED model serves multiple purposes: it analyzes conversation transcripts, identifies sentence relationships, evaluates agent performance, generates training recommendations, and provides real-time feedback. This universal approach consolidates multiple functions into a single neural network system, managing complexity through integration rather than multiplication of separate components.
Solution Approach 2:
The neural network acts as an intermediary between raw conversation data and actionable insights. The AWRNNED model processes complex transcript information and translates it into simplified performance evaluations and training recommendations, mediating between the complexity of real conversation analysis and the simplicity of actionable feedback for agents.
Data Source
AI summary
Techniques are provided for training, by a system operatively coupled to a processor, an attention weighted recurrent neural network encoder-decoder (AWRNNED) using an iterative process based on one or more paragraphs of agent sentences from respective transcripts of one or more conversations between one or more agents and one or more customers, and based on one or more customer response sentences from the respective transcripts, and generating, by the system, one or more groups respectively comprising one or more agent sentences and one or more customer response sentences selected based on attention weights of the AWRNNED.