ML Event Notification Engine for Redundant Communication Elimination
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Solution Overview
Problem
Conventional event notification systems are stateless, leading to redundant communications and a lack of context-based determinations for communication channels and formats, negatively impacting user experience.
Innovation Solution
The implementation of machine learning techniques to automatically manage event-related communication data by predicting communication channels and formats based on historical data, using a machine learning-based event communication channel and format prediction engine, and eliminating redundant notifications through a hashing algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional event notification systems are used, then communication can be sent to inform users of process progress, but redundant communications occur that adversely impact user experience
Solution Approach 1:
The system performs preliminary actions by comparing event notification data against historical notifications before sending communications. This prevents redundant communications by checking if the same event has already been notified, thereby eliminating duplicate messages while maintaining comprehensive coverage of important events.
Solution Approach 2:
The system implements feedback mechanisms by continuously learning from historical notification data and user interactions. Machine learning models analyze patterns in past communications to determine which events require notification and which are redundant, improving communication efficiency over time while reducing harmful redundant messages.
2Adaptability or versatility
If conventional event notification systems are used, then notifications can be sent through various channels, but the system lacks capabilities for determining optimal channels and formats based on event context
Solution Approach 1:
The system dynamically changes communication parameters (channel selection, message format, timing) based on event context and historical data analysis. Machine learning models determine optimal parameters for each notification by analyzing event characteristics, user preferences, and contextual factors, thereby improving adaptability and user experience.
Solution Approach 2:
The system performs self-service by automatically selecting appropriate communication channels and formats without requiring manual configuration. The machine learning models autonomously analyze event context and determine the most suitable notification method, reducing operational complexity while enhancing adaptability to different situations.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for automatically managing event-related communication data using machine learning techniques are provided herein. An example computer-implemented method includes obtaining event-related communication data generated in connection with one or more systems associated with at least one enterprise; comparing identifying information pertaining to one or more event notifications within the event-related communication data to identifying information pertaining to multiple historical event notifications; predicting, for the one or more event notifications upon determining that the identifying information pertaining to the one or more event notifications differs from the identifying information pertaining to the multiple historical event notifications, at least one communication channel and at least one communication format by processing at least a portion of the event-related communication data using machine learning techniques; and performing one or more automated actions based on the at least one predicted communication channel and the at least one predicted communication format.