Communication Analysis Platform for Real-Time Topic and Sentiment Processing
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Solution Overview
Problem
Customer service centers face inefficiencies in managing and analyzing large volumes of customer communications, leading to wastage of computing and financial resources due to batch processing and inefficient routing of communications.
Innovation Solution
Implementing a service platform that uses machine learning models for real-time analysis of user inputs to identify topics, determine sentiment, and update communication and transaction protocols, allowing for efficient routing and resource conservation by identifying the most qualified service representatives and suspending or discontinuing unbeneficial communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If batch processing is used to handle customer communications, then resource consumption is reduced, but processing speed and responsiveness deteriorate
Solution Approach 1:
The system dynamically adjusts processing mode between batch and real-time based on communication characteristics. Machine learning models analyze incoming communications to determine whether they require immediate processing or can be handled in batch, enabling flexible resource allocation that optimizes both speed and energy efficiency.
Solution Approach 2:
The patent changes the processing parameter from static batch processing to dynamic real-time processing based on analyzed communication parameters. By using machine learning to evaluate communication content, urgency, and type, the system adapts processing timing and resource allocation to achieve optimal performance across different scenarios.
2Reliability
If all communications are routed to service representatives, then customer service quality is maintained, but resource efficiency deteriorates
Solution Approach 1:
The system implements self-service by using machine learning models to automatically analyze and process communications that can be handled without human intervention. Routine inquiries, sentiment analysis, and preliminary triage are performed autonomously, freeing service representatives to focus only on complex cases requiring human judgment.
Solution Approach 2:
The patent segments communications into different categories based on machine learning analysis: those requiring immediate human attention, those suitable for automated processing, and those that can be handled in batch. This segmentation enables differentiated handling that optimizes both service quality and resource efficiency.
3Measurement precision
If real-time analysis is implemented for all communications, then processing accuracy is improved, but computing resource consumption increases
Solution Approach 1:
The system applies partial real-time analysis by using machine learning models to analyze only the most critical aspects of communications in real-time (such as sentiment and urgency), while other aspects are processed in batch. This selective approach maintains accuracy for key parameters while reducing overall computational burden.
Data Source
AI summary
A device monitors a communication between a user associated with a user device and a service representative associated with a service representative device, and causes a natural language processing model to perform a natural language processing analysis of a user input of the communication to identify a topic associated with the communication. The device determines a first score associated with the topic, and determines a second score associated with enabling the communication, where the first score and second score indicate a service performance score of an entity. The device causes a sentiment analysis model to perform a sentiment analysis of the communication to determine a sentiment score indicating a level of satisfaction the user has relative to the topic. The device updates a transaction protocol associated with the topic based on the service performance score, and/or updates a communication processing protocol associated with the communication based on the sentiment score.