Screen Capture Action Recognition for Call Center Process Analysis
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Solution Overview
Problem
In call center environments, manual process recognition methods are time-consuming and inefficient, requiring significant human intervention to understand interactions and identify process inefficiencies, which hinders process improvement initiatives and can lead to missed details.
Innovation Solution
An automatic computer-implemented process recognition system that captures screen shots of a call center agent's desktop, analyzes interactions, and classifies actions using image analysis and heuristic rules to generate event logs, enabling real-time recognition of processes without human observation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual process recognition methods are used with consultants observing call center agents, then process interactions can be understood, but the time investment is large and process improvement initiatives are delayed
Solution Approach 1:
The patent replaces manual mechanical observation by consultants with an automated computer vision system that captures screenshots and uses machine learning models to recognize processes. This substitution eliminates the time-consuming manual observation while maintaining or improving recognition accuracy through automated image analysis and pattern recognition algorithms.
Solution Approach 2:
The system enables the call center environment to self-analyze its own processes by automatically capturing screenshots of agent screens and using embedded machine learning models to identify interactions with applications and data fields. This self-service approach eliminates the need for external consultants and accelerates process improvement initiatives.
2Loss of information
If manual review of recorded sessions is performed, then process details can be analyzed, but the time required to complete process improvement initiatives increases
Solution Approach 1:
The patent replaces manual review of recorded sessions with an automated system that captures screenshots during live agent interactions and uses machine learning to extract process details in real-time. This automated approach maintains comprehensive process information while dramatically increasing the speed of process improvement initiatives through rapid automated analysis.
Solution Approach 2:
The system performs preliminary automated analysis of agent interactions by capturing screenshots and identifying processes in real-time during call center operations. This preliminary action provides immediate process insights without waiting for manual review, enabling faster process improvement decisions while preserving detailed process information.
3Loss of information
If consultants manually observe and document agent processes, then process sequences can be identified, but process deliveries are hindered
Solution Approach 1:
The patent replaces manual consultant observation and documentation with an automated system that captures screenshots and uses machine learning to identify process sequences automatically. This substitution maintains complete process sequence information while dramatically improving process delivery efficiency by eliminating manual documentation steps and enabling rapid automated analysis.
Data Source
AI summary
A system for recognizing processes performed by a call center agent during a session may collect input data by initiating a screen capture logging thread to capture screen shots of a desktop of the electronic device during a session. The system analyzes the input data to generate one or more events, generates a mid-level event log comprising one or more of the events, and performs action recognition on the mid-level event log to ascertain one or more actions that were performed within the one or more graphical user interfaces by the call center agent during the session.