Communication Search Analytics for Real-Time Call Feedback
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Solution Overview
Problem
Existing call center monitoring systems fail to provide real-time feedback to agents due to the impracticality of supervisors listening to all calls and post-call analytics being ineffective during live interactions, necessitating improved methods for real-time monitoring and performance evaluation.
Innovation Solution
A real-time automated monitoring system that provides dynamic graphical representations of communication performance, including language and acoustic characteristics, to supervisors and agents, using computer-based analytics to analyze voice and text communications for immediate feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervisors manually monitor calls to provide real-time feedback, then feedback quality improves, but the number of calls that can be monitored is limited
Solution Approach 1:
The patent replaces manual supervisor monitoring with an automated computer-based system that uses speech recognition and natural language processing to analyze calls. This substitution enables the system to monitor all calls simultaneously without human limitations, providing both comprehensive coverage and detailed analytical feedback through automated transcription, sentiment analysis, and performance metric calculation.
Solution Approach 2:
The system enables calls to self-analyze through automated speech-to-text conversion and self-evaluation against predefined performance criteria. The computer-based system automatically transcribes calls, identifies key performance indicators, calculates sentiment scores, and generates feedback reports without requiring supervisor intervention for each call, thus scaling monitoring capacity while maintaining feedback quality.
2Measurement precision
If post-call analytics are used to evaluate performance, then comprehensive analysis is achieved, but real-time feedback during calls is not provided
Solution Approach 1:
The system performs preliminary analysis by continuously transcribing and analyzing call content in real-time as the call progresses. Speech recognition converts spoken words to text during the call, and natural language processing immediately evaluates compliance with scripts, identifies sentiment changes, and detects performance metrics, enabling feedback to be prepared and delivered at the conclusion of the call without requiring post-call processing.
Solution Approach 2:
The system maintains continuous analysis throughout the entire call duration rather than analyzing only after the call ends. The computer-based system continuously transcribes speech, updates performance metrics in real-time, and maintains a running analysis of sentiment and compliance, ensuring that comprehensive evaluation occurs continuously and feedback is ready immediately when the call concludes.
3Loss of information
If all calls are monitored and analyzed, then complete performance visibility is achieved, but system complexity and processing requirements increase
Solution Approach 1:
The system segments call analysis into distinct modular components: speech recognition for transcription, natural language processing for content analysis, sentiment analysis for emotional tone, and compliance checking for script adherence. Each module processes specific aspects of the call independently, allowing the system to handle comprehensive analysis of all calls through distributed, specialized processing units that can be scaled and managed separately.
Solution Approach 2:
The computer-based system performs multiple functions within a single integrated platform: real-time transcription, performance metric calculation, sentiment analysis, compliance verification, and feedback generation. This multi-functional approach consolidates what would otherwise require separate systems for each analysis type, reducing overall system complexity while maintaining comprehensive monitoring capabilities across all calls.
Data Source
AI summary
Systems and methods for searching communications may include receiving a selection from a user of at least one of an acoustic characteristic, a language characteristic, or a category, accessing a database comprising a plurality of communications, the plurality of communications labeled with acoustic characteristics, language characteristics, and categories, restricting the plurality of communications to a subset of the plurality of communications including the user's selection of the at least one acoustic characteristic, language characteristic, or category, generating at least one of a word cloud or a tree for presenting a plurality of topics identified in the subset of the plurality of communications, and receiving a selection of one of the plurality of topics, the receiving causing a display of a further subset of the subset of the plurality of communications, the further subset having the selected topic.