Contact Center Evaluation Form Generation from Interaction Transcripts
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Solution Overview
Problem
The generation of evaluation forms for contact center agents often requires in-depth knowledge and relies on domain experts or external agencies, leading to inaccuracies and the need for manual correction due to poorly drafted questions and infrequent updates.
Innovation Solution
Automatically generate evaluation forms using machine learning to identify interaction intents, create evaluation categories, and generate questions based on interaction transcripts, allowing for quick updates and domain-specific question adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If domain experts or external agencies are used to generate evaluation forms, then the evaluation forms can be professionally drafted, but the process is time-consuming and costly
Solution Approach 1:
The system enables automatic self-generation of evaluation forms by processing interaction recordings through machine learning models. The system extracts intents, generates categories, and creates evaluation questions autonomously without requiring manual intervention from domain experts, thereby reducing time and cost while maintaining quality
Solution Approach 2:
The patent replaces the manual mechanical process of expert review and form creation with an automated machine learning-based system. The system uses NLP models to process audio recordings, extract meaningful intents, and generate structured evaluation forms automatically, substituting human expertise with computational intelligence
2Reliability
If evaluation forms are manually created and updated, then domain-specific accuracy can be maintained, but updates are slow and productivity is reduced
Solution Approach 1:
The system dynamically generates evaluation forms based on real-time analysis of interaction recordings. When new service features or customer needs are identified in the recordings, the system automatically updates evaluation categories and questions to reflect current domain requirements, enabling rapid adaptation without manual intervention
Solution Approach 2:
The system continuously processes interaction recordings and uses the extracted intents to feedback into evaluation form generation. This closed-loop approach ensures that evaluation forms are automatically updated based on actual customer interactions and service delivery patterns, maintaining domain accuracy while enabling rapid updates
3Ease of operation
If evaluation questions are not tailored to specific interactions, then the evaluation process is simpler, but measurement precision and evaluation accuracy decrease
Solution Approach 1:
The system segments the evaluation process into distinct stages: intent extraction, category generation, and question formulation. By breaking down the complex task of creating tailored evaluation questions into manageable segments processed by specialized machine learning models, the system achieves both simplicity in operation and high measurement precision through automated domain-specific analysis
Data Source
AI summary
A system and method for generating evaluation forms from interaction recordings may include a computing device; a memory; and a processor, the processor configured to: identify one or more interaction intents from an interaction transcript; generate one or more evaluation categories for the one or more interaction intents using machine learning; generate evaluation questions for the one or more evaluation categories using machine learning; and provide an evaluation form based on the evaluation questions.