AI Communication Event Extraction for Contact Center Data
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Solution Overview
Problem
In large contact centers, identifying and summarizing relevant customer-agent interactions from a vast volume of raw data is challenging, requiring an efficient means to compile and highlight key interactions in a condensed and consumable format to provide valuable insights and save time.
Innovation Solution
A system utilizing artificial intelligence to analyze audio, video, and textual interactions, generating a 'highlight reel' that can be customized by criteria such as contact type, customer mood, emotion, or geographical region, with automated execution for sampling and system improvements, and allowing for interactive playback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system processes all communication events to ensure complete analysis, then measurement precision is improved, but processing time and system resources increase significantly
Solution Approach 1:
The patent extracts only the most relevant communication events from the complete dataset by applying multiple filtering criteria (customer mood, contact type, duration, region) to identify and process only the essential samples needed for meaningful analysis, rather than processing all events
Solution Approach 2:
The system segments the communication events into distinct categories based on multiple criteria (contact type, customer mood, duration limit, geographical region) and processes each segment separately, allowing efficient sampling of specific subsets rather than handling the entire dataset uniformly
2Measurement precision
If the system analyzes all communication events in detail, then measurement precision is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent extracts only the essential features and events needed for analysis by applying specific criteria filters, reducing the complexity of processing while maintaining measurement precision through targeted analysis of relevant samples
Solution Approach 2:
The system applies different processing qualities to different segments of data based on their relevance, focusing computational resources on high-value events that meet specific criteria while using lighter processing for less important events
3Loss of information
If the system provides detailed interaction data, then information completeness is improved, but ease of operation decreases due to data overload
Solution Approach 1:
The patent extracts only the most relevant information from complete interaction data by filtering based on user-defined criteria, presenting condensed samples that maintain essential insights while reducing data overload and improving ease of consumption
Solution Approach 2:
The system segments detailed interaction data into manageable samples based on multiple criteria, organizing information into digestible portions that preserve completeness of key insights while improving operational ease through reduced data volume
Data Source
AI summary
Communication events often produce a significant volume and variety of data. While such data is often useful as a teaching or configuration tool or as a source of troubleshooting information, such information often results in “information overload.” By providing a “highlight reel,” communication data comprising only relevant information, which may be further limited to a specific number of duration of events, allows for key data to be identified for presentation and avoid the need to further process, store, or otherwise maintain irrelevant or less relevant data.