ML Framework for Communication Data Identification and Alert Generation
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Solution Overview
Problem
Current systems lack an integrated solution to preemptively identify and address potential failures across various communication channels, leading to unresolved queries, excessive redirections, spam, Denial of Service incidents, and poor user experience due to inefficiencies in handling disasters and high-traffic conditions.
Innovation Solution
A machine learning framework that determines the status of underlying services, classifies user interactions, and generates customized notifications based on interaction patterns to proactively manage communication flows, reducing manual intervention and resource waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a robust system is implemented to manage communication interactions, then user experience and query resolution improve, but system complexity and resource usage increase
Solution Approach 1:
The system segments communication interactions into distinct channels (voice, chat, email, social media) and processes each through specialized machine learning models. This segmentation allows the complex system to be divided into manageable modular components, each handling specific interaction types, thereby improving reliability without overwhelming system complexity.
Solution Approach 2:
Machine learning models serve as intermediaries between users and the communication management system. These models automatically classify interactions, identify patterns, and generate notifications, acting as a mediator that reduces the need for direct human intervention and simplifies the overall system architecture while maintaining high reliability.
2Reliability
If manual intervention is increased to handle unresolved queries, then user experience improves, but resource usage and operational costs increase
Solution Approach 1:
The system enables self-service through automated machine learning models that independently classify interactions, identify user patterns, and generate appropriate notifications without human intervention. This self-service capability resolves the contradiction by maintaining high user experience reliability while minimizing resource consumption, as the system handles itself autonomously.
Solution Approach 2:
The system implements feedback loops where machine learning models continuously analyze interaction outcomes and refine their classification and notification generation. This feedback mechanism ensures high user experience reliability by automatically adapting to improve service quality while reducing the need for additional manual resources.
3Loss of time
If real-time classification and notification generation is implemented, then response time improves, but processing speed and computational resources decrease
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models with extensive interaction data before deployment. This preliminary training enables the models to quickly classify new interactions and generate notifications in real-time without requiring extensive computational resources during actual operation, thus improving response time while maintaining processing speed.
Data Source
AI summary
Systems, computer program products, and methods are described herein for communication data identification and alert generation via a machine learning framework. The present disclosure includes determining a first status of an underlying service, receiving a stream of interaction data from a first user via at least one channel, classifying an interaction via a first machine learning model, generating a first notification signal via a second machine learning model, wherein the second machine learning model may be provided, as inputs, the first status of the underlying service and an interaction pattern associated with the identity, and transmitting the first notification signal via the at least one channel to a first endpoint device associated with the first user.