Call Center Action Automation via NLP and ML Sentiment Analysis
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Solution Overview
Problem
Existing call center systems lack the ability to reliably detect and respond to real-time customer communication content and sentiment, making it difficult to provide immediate and effective customer service.
Innovation Solution
A method that aggregates data streams from multiple sources, including communication data and external data, using Natural Language Processing (NLP) and Machine Learning (ML) models to predict events and automatically take actions by applying sensor data structures with defined rules, generating notification messages, and executing actions based on these messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated action detection is implemented using NLP and ML models, then service quality and response timeliness are improved, but system complexity and computational resources increase
Solution Approach 1:
The system segments the complex automated detection task into multiple independent components: data aggregation from multiple sources, NLP processing for sentiment analysis, ML model inference for event prediction, and action determination. Each component operates independently and can be optimized separately, reducing overall system complexity while maintaining high service quality.
Solution Approach 2:
The patent introduces intermediary components including a data aggregation layer that mediates between multiple data sources and the processing engine, and a sentiment analysis layer that mediates between raw communication data and event prediction. These intermediaries simplify the architecture by creating clear separation of concerns and standardized interfaces.
2Speed
If real-time processing of communication data is performed, then response speed is improved, but computational load and processing time increase
Solution Approach 1:
The system implements periodic batch processing where communications are processed in successive batches rather than continuously one-by-one. This allows the system to accumulate data efficiently and process it at optimized intervals, reducing peak computational load while maintaining real-time responsiveness through appropriately sized batch intervals.
Solution Approach 2:
The patent applies sentiment analysis and event prediction to successive batches of communications rather than every single communication in real-time. This partial processing approach reduces computational load while still providing timely responses for the most critical cases, balancing speed and resource consumption.
3Measurement precision
If multiple data sources are aggregated and processed, then detection accuracy is improved, but data processing complexity and time increase
Solution Approach 1:
The system performs preliminary data aggregation from multiple sources before the main processing stage. By pre-collecting and organizing data from communications, sentiment analysis, and external sources in advance, the main event detection and action determination processes can operate more efficiently on already-prepared data, reducing overall processing time while maintaining high detection accuracy.
Data Source
AI summary
A method and system for automatically executing an action within a call center environment. Data is aggregated from multiple data sources into a combined data stream. One of the data sources is a source of data corresponding to at least one communication processed by the call center one of the data sources can be a source of external data representing activity that is external to the call center. The combined data stream is processed into successive batches of data corresponding to one or more communications between a call center agent and a communicating party received by the call center. A sensor data structure specifying at least one rule is applied to the batches of data. The at least one rule can include a machine learning model and a configuration data structure based on historical data from the multiple data sources. When it is determined that at least one of the batches satisfies the at least one rule, a notification message relating to the one or more communications is generated. The call center executes an action specified by the sensor data structure based on the notification message. The action can address a situation corresponding the at least one communication. The actions can be various actions such as notifying a specified party, generating an API call, or the like.