Waypoint Detection for Contact Center Analysis
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Solution Overview
Problem
Conventional call center management systems lack the ability to provide a comprehensive evaluation of customer interactions across multiple communication channels and fail to offer detailed analysis, leading to inefficiencies in assessing CSR performance due to their limited scope and depth.
Innovation Solution
A contact center analysis system that segments and annotates communication data using various features such as temporal, lexical, and audio features, employs clustering algorithms, and trains machine learning classifiers to identify and label relevant segments, enabling detailed analysis and evaluation of interactions across multiple channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated tools are used for call center analysis, then productivity is improved through automated processing, but measurement precision deteriorates because tools are limited to rudimentary analysis of easily quantifiable metrics only
Solution Approach 1:
The patent segments communication data into multiple levels: raw communication records, extracted features (temporal, lexical, audio), clustered segments, and annotated waypoints. This segmentation allows automated processing at each level while preserving the ability to perform detailed analysis on specific segments, thus resolving the contradiction between automation and analysis depth.
Solution Approach 2:
The patent introduces machine learning classifiers and clustering algorithms as intermediaries between raw communication data and final analysis results. These intermediaries automatically process large volumes of data while maintaining the capability to identify nuanced patterns and criteria, thereby achieving both high productivity and measurement precision.
2Measurement precision
If conventional systems analyze the entirety of communications for detailed scrutiny, then measurement precision is improved, but loss of time increases when multiple communications need to be reviewed
Solution Approach 1:
The patent extracts key features and segments from communications (such as temporal patterns, lexical content, and audio characteristics) and analyzes these extracted elements rather than reviewing entire communications. This extraction approach enables detailed analysis of critical portions while significantly reducing the time required to review multiple communications.
Solution Approach 2:
The patent performs preliminary processing of communication data by extracting features, clustering segments, and identifying potential waypoints before detailed review. This preliminary action prepares the data in advance, allowing administrators to quickly access and analyze only the most relevant segments rather than reviewing entire communications from scratch.
3Adaptability or versatility
If conventional systems evaluate communications across multiple channels, then adaptability is improved, but device complexity increases due to the need to handle various communication types
Solution Approach 1:
The patent implements a universal analysis framework that handles multiple communication channels (voice, text, email, chat) through a single system architecture. The system extracts features and applies clustering algorithms uniformly across different communication types, enabling multi-channel support without proportionally increasing system complexity.
Data Source
AI summary
A contact center analysis system can receive various types of communications from customers, such as audio from telephone calls, voicemails, or video conferences; text from speech-to-text translations, emails, live chat transcripts, text messages, and the like; and other media or multimedia. The system can segment the communication data using temporal, lexical, semantic, syntactic, prosodic, user, and/or other features of the segments. The system can cluster the segments according to one or more similarity measures of the segments. The system can use the clusters to train a machine learning classifier to identify one or more of the clusters as waypoints (e.g., portions of the communications of particular relevance to a user training the classifier). The system can automatically classify new communications using the classifier and facilitate various analyses of the communications using the waypoints.