Machine Learning Form Filling for Customer Service Analytics
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Solution Overview
Problem
Existing systems for analyzing interaction data from calls, chats, and meetings rely on cumbersome word, phrase, and pattern recognition methods that require pre-set lists, and lack automated coaching and analytics for customer service agent training.
Innovation Solution
The use of machine learning approaches to analyze interactions, extract data, and provide automated coaching and analytics, including robotic customer simulation for training and feedback, and automated interaction summarization and form filling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If word, phrase, and pattern recognition methods are used to analyze interaction data, then data analysis can be performed, but the system becomes cumbersome and requires pre-set lists of words, phrases, and patterns
Solution Approach 1:
The patent replaces traditional mechanical pattern matching systems with machine learning-based natural language processing. Instead of using rigid pre-set lists of words and phrases, the system employs ML models that can automatically learn and adapt to new interaction patterns from data, eliminating the need for manual configuration of recognition rules while reducing system complexity.
Solution Approach 2:
The machine learning system performs self-learning and self-adjustment to handle new interaction patterns without human intervention. The system automatically processes interaction data, identifies patterns, and updates its models autonomously, replacing the manual process of creating and maintaining pre-set lists with automated self-service functionality.
2Productivity
If machine learning approaches are used to analyze interactions and provide automated coaching, then productivity and automation increase, but the system complexity and computational requirements increase
Solution Approach 1:
The machine learning system is designed to perform multiple functions including interaction analysis, pattern recognition, coaching generation, and performance evaluation within a single integrated platform. This multi-functionality consolidates what would otherwise require multiple separate systems, reducing overall complexity while maintaining high automation capabilities.
Solution Approach 2:
The system incorporates feedback loops where coaching interactions and their outcomes are fed back into the machine learning models to continuously improve performance. This feedback mechanism allows the system to learn from actual usage patterns and refine its coaching strategies automatically, increasing productivity while managing complexity through adaptive improvement rather than static complex design.
Data Source
AI summary
Disclosed herein is a method for generating insights from words and phrases mapped in high-dimensional space. The method includes obtaining a plurality of communications. The plurality of communications comprise a plurality of words and phrases. Further, the method includes obtaining a model configured through training to cluster word representations of the plurality of communications. The method also includes applying a constraint to at least a group of the plurality of communications to obtain at least one modified group. The constraint is at least one of a keyword, a phrase, or groupings of words in phrases. Once the modified group is determined, the method proceeds to representing the at least one modified group into word representations then determining a category for the at least one modified group.