Adaptive Contact Center Log Generation via Speech Recognition
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Solution Overview
Problem
Contact center interaction logs are often inaccurately and inefficiently created by agents, leading to increased labor costs and potential errors, as existing systems fail to adapt to industry-specific terminology, scripted processes, and changing business practices.
Innovation Solution
A system and method for automatically and adaptively generating contact center logs using an analysis pipeline that includes audio capture, automatic speech recognition, transcript normalization, and log generation with both real-time and global models, facilitating agent review and feedback-driven model updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If agents manually create interaction logs, then the logs can be created with industry-specific terminology and scripted process details, but the documentation time and labor costs increase significantly
Solution Approach 1:
The system enables self-service by automatically generating interaction logs without requiring manual agent input. The automated log generation system processes customer interactions and creates logs independently, freeing agents from the time-consuming manual documentation task while maintaining high accuracy through adaptive learning from industry-specific terminology and scripted processes.
Solution Approach 2:
The patent replaces the mechanical manual writing process with an automated computational system. Instead of agents physically typing or handwriting logs, an automated system using speech recognition, text processing, and machine learning algorithms generates logs automatically, dramatically reducing documentation time while maintaining or improving accuracy.
2Productivity
If automated systems are used to generate logs, then documentation time is reduced, but the systems fail to adapt to industry-specific terminology and changing business practices
Solution Approach 1:
The system incorporates feedback mechanisms where the automated log generation process learns from industry-specific terminology and scripted processes encountered during operation. By analyzing patterns in customer interactions and agent corrections, the system continuously adapts its models to improve accuracy in generating logs with correct industry terminology and business practice details.
Solution Approach 2:
The patent implements dynamic adaptability by allowing the automated system to evolve its understanding of industry terminology and business practices over time. The system's models are not static but dynamically adjust based on learned patterns from processed interactions, enabling it to stay current with changing business practices while maintaining high generation speed.
3Reliability
If agents create logs during interactions or immediately after, then the logs capture accurate interaction details, but the process increases labor costs and reduces efficiency
Solution Approach 1:
The automated system performs the log creation function that previously required agent service. By taking over this task completely, the system eliminates the time agents spend on documentation while ensuring logs are generated immediately after interactions based on recorded data, maintaining completeness without impacting agent productivity.
Solution Approach 2:
The system performs preliminary action by capturing and processing interaction data during the conversation itself through speech recognition and text processing. This preliminary processing ensures that when the log is generated, all necessary information is already structured and ready, ensuring completeness while requiring minimal additional agent effort.
Data Source
AI summary
A system and method for providing an adaptive Interaction Logging functionality to help agents reduce the time spent documenting contact center interactions. In a preferred embodiment the system uses a pipeline comprising audio capture of a telephone conversation, automatic speech transcription, text normalization, transcript generation and candidate call log generation based on Real-time and Global Models. The contact center agent edits the candidate call log to create the final call log. The models are updated based on analysis of user feedback in the form of the editing of the candidate call log done by the contact center agents or supervisors. The pipeline yields a candidate call log which the agents can edit in less time than it would take them to generate a call log manually.