AI-Annotated Interaction Playback for Faster Evaluation
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Solution Overview
Problem
Current media-player applications in contact centers require repetitive playback and toggling to annotate interactions, leading to increased time consumption and computer resource usage during evaluation, as supervisors struggle to efficiently locate issues in call-recordings and digital text interactions.
Innovation Solution
A computerized method and system that uses AI models to generate point-in-time annotations and abbreviated media-files based on evaluation-measurements, allowing for direct playback and navigation to specific interaction points via a timeline-bar, reducing the need for full playback and minimizing CPU and memory consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If supervisors listen to entire call-recordings or review entire screen recordings to find issues, then evaluation completeness is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary analysis of the media-file before playback to automatically generate point-in-time annotations identifying potential issues. This pre-processing step creates a structured index of annotated segments that guides subsequent evaluation, allowing supervisors to focus only on relevant portions rather than reviewing entire recordings from scratch.
Solution Approach 2:
The media-file is divided into multiple annotated segments based on automatically identified issues. Each segment is marked with temporal boundaries and descriptive annotations, transforming a continuous long-duration recording into discrete, manageable units that can be selectively reviewed based on evaluation needs.
2Measurement precision
If supervisors repeatedly toggle play and pause to enter annotations, then annotation precision is improved, but computer resource consumption increases
Solution Approach 1:
The system pre-identifies and marks problematic segments during an initial analysis pass, creating a structured annotation framework before the supervisor begins detailed evaluation. This preliminary structuring reduces the need for repeated playback toggling while maintaining annotation accuracy, as supervisors can navigate directly to pre-identified issue locations.
Solution Approach 2:
The system introduces an automated analysis component as an intermediary between the raw media-file and the supervisor's annotation process. This intermediary pre-processes the content to generate structural annotations and issue identifiers, reducing the computational burden on the supervisor's device during interactive evaluation while preserving annotation precision.
3Productivity
If the media-player enables user interaction to search audio files or recorded screens based on questions, then search efficiency is improved, but system complexity increases
Solution Approach 1:
The system pre-generates a structured index of the media-file content during initial processing, organizing information by temporal segments, identified issues, and key events. This pre-computed index enables efficient query responses without requiring complex real-time analysis, allowing users to search and navigate based on natural language questions while keeping the runtime system relatively simple.
Solution Approach 2:
The system creates a simplified copy or representation of the media-file content in the form of structured annotations and metadata. This copy contains essential information about issues, events, and temporal structure, enabling efficient searching and navigation without requiring the full complexity of the original media processing pipeline during user interaction.
Data Source
AI summary
A computerized-method for reducing time of evaluation of an interaction by annotating a media-file of the interaction based on an evaluation-measurement. The computerized-method includes: (i) receiving a request from a user to playback the media-file of the interaction by operating a media-playback service of a recording-player web-application; (ii) configuring the media-playback service to: a. operate an interaction-insights module to generate point-in-time annotations of the media-file, based on parameters of the evaluation-measurement; and b. send the point-in-time annotations and a location of the media-file to the recording-player web-application; and (iii) configuring the recording-player web-application to playback the media-file and upon user-selection to present each point-in-time annotation of the one or more point-in-time annotations, via a UI that is associated to the recording-player web-application, on a timeline-bar as an annotation-marker. Each point-in-time annotation comprising a playhead position in the media-file and a text-annotation related to a parameter of the parameters of the evaluation-measurement.