ML Predictive Caching for Contact Center Media Playback
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Solution Overview
Problem
The processing of media files for playback in contact centers is time-consuming and resource-intensive, leading to significant delays in media file playback, as existing systems require real-time processing, decoding, and transcoding, which can tax computing resources and impact user experience.
Innovation Solution
An ML model is trained to predict media file playback requests, allowing for precaching of media files before playback, reducing processing time and resource usage by selectively preprocessing and caching only predicted media files based on historical playback patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If media files are processed in real-time for playback, then playback quality is maintained, but playback response time increases significantly
Solution Approach 1:
The system performs media file processing in advance before playback requests are made. Media files are pre-processed, transcoded, and cached in a ready-to-play state ahead of time, so when a playback request occurs, the file is already prepared and can be delivered immediately without real-time processing delays.
Solution Approach 2:
The system dynamically determines which media files to pre-process based on predicted playback likelihood. An ML model analyzes historical data to identify files with high probability of being played back, and only those files are pre-processed and cached, optimizing the balance between preparation effort and playback performance.
2Loss of time
If all media files are pre-processed and cached, then playback response time is reduced, but computing resource consumption increases
Solution Approach 1:
Instead of pre-processing all media files, the system selectively pre-processes only a subset of files that are predicted to have high playback probability. This partial action approach reduces computing resource consumption while still achieving fast playback response for the most likely candidates.
Solution Approach 2:
The system performs preliminary processing only on selected media files based on ML predictions of playback likelihood, rather than all files. This selective pre-processing reduces the overall computing burden while maintaining fast playback performance for predicted high-probability files.
3Use of energy by moving object
If media files are processed on-demand, then resource consumption is minimized, but user experience deteriorates due to waiting time
Solution Approach 1:
The system pre-processes media files in advance based on predicted playback needs, so when users request playback, the files are already ready. This eliminates waiting time and improves user experience without requiring all files to be pre-processed, thus avoiding excessive resource consumption.
Solution Approach 2:
The system uses historical playback data and ML models to predict which files users are likely to request, creating a feedback loop that optimizes pre-processing decisions. This intelligent feedback mechanism ensures resources are spent on files that will actually improve user experience.
4Adaptability or versatility
If real-time transcoding is performed, then media compatibility is ensured, but processing time increases
Solution Approach 1:
The system performs transcoding and format conversion in advance during the pre-processing stage, so when playback is requested, the media file is already in the correct format. This eliminates real-time transcoding delays while ensuring compatibility across different playback devices and platforms.
Data Source
AI summary
A system and method are provided to predict media playback requests of media files to decrease response times to the media playback requests. The system includes a processor and a computer readable medium operably coupled thereto, to perform predictive caching operations which include receiving metadata from an interaction stream after recording a media file of an interaction, determining contacts corresponding to users identified in the metadata that are recorded in the media file from the metadata, accessing an ML model for predictive caching of media files, determining, using the ML model and a plurality of model features for the ML model, a first prediction for a first playback of the media file, predicting the first playback of the media file by at least one of the contacts based on the first prediction, caching the media file in the data cache for a time period based on the predicting.