Event Stability Determination for Loose Coupling
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Solution Overview
Problem
The variability in configurations of electronic event invitations across different event organizers in Internet-based conferencing platforms leads to inconsistencies in how URLs are presented, making it challenging to automatically log attendees into events, and existing solutions require synchronous maintenance of multiple event coordination systems.
Innovation Solution
A computer-implemented method that uses a machine learning model to determine the stability of events based on various attributes, compares this stability to a threshold, and propagates stable events from one event coordination system to another, enabling loose coupling and selective synchronization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple event coordination systems are used to handle different invitation configurations, then the system can accommodate various URL presentation formats, but the system complexity and maintenance burden increase
Solution Approach 1:
The patent applies universality by creating a single event coordination system that can handle multiple URL presentation formats (formally composed URLs, HTML-composed URLs, and textual representations) through a unified machine learning-based stability determination mechanism, eliminating the need for multiple specialized systems
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between the diverse invitation configurations and the event coordination system. This model determines event stability based on multiple attributes and enables the system to selectively propagate events, thereby managing complexity while maintaining adaptability
2Reliability
If all events are propagated between event coordination systems, then complete event synchronization is achieved, but the reliability and consistency of event data deteriorate due to instability
Solution Approach 1:
The patent applies preliminary action by determining event stability before propagation occurs. The machine learning model evaluates multiple event attributes and predicts stability in advance, allowing the system to selectively propagate only stable events, thereby ensuring data consistency while maintaining synchronization productivity
Solution Approach 2:
The patent implements feedback by using the machine learning model to continuously evaluate event stability based on multiple attributes. This feedback mechanism allows the system to dynamically determine which events should be propagated, ensuring that only reliable events are synchronized between systems
3Adaptability or versatility
If synchronous maintenance of multiple event coordination systems is implemented, then all invitation formats are supported, but the operational complexity and resource requirements increase
Solution Approach 1:
The patent applies universality by designing a single event coordination system with multi-functional capability to handle all invitation formats through one unified interface, eliminating the need for synchronous maintenance of multiple specialized systems and thereby reducing operational complexity
Data Source
AI summary
Machine-learning-based determination of event stability is provided to facilitate loose coupling of event coordination systems. The method includes training a machine learning model to determine stability of events of an event coordination system based on a plurality of attributes of the events, and using the machine learning model to determine an event stability for an event of the event coordination system. Further, the method includes comparing the determined event stability for the event to a stability threshold, and determining that the event is a stable event based on the event stability extending the stability threshold. Based on determining that the event is the stable event, the stable event is propagated from the event coordination system to another event coordination system.