Dynamic Event Clustering and Room Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large events often consume significant network overhead, processing resources, and power due to the registration process and subsequent follow-up questions, as well as the creation and maintenance of unused chat rooms and overfilled event rooms.
Innovation Solution
A system utilizing machine learning models to generate dynamic questions and clusters for events, conserving resources by integrating question responses into the registration process, identifying user clusters for targeted networking, and recommending location changes to avoid overfilled rooms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If follow-up questions are sent to users after registration, then user engagement and event matching improve, but network overhead and power consumption increase
Solution Approach 1:
The system incorporates question responses directly into the registration process, collecting user preferences and interests beforehand rather than sending follow-up questions after registration. This preliminary collection of data allows for immediate event matching without requiring additional network transmissions or power consumption post-registration.
2Adaptability or versatility
If chat rooms are created for all potential user groups, then networking opportunities improve, but network overhead and processing resources increase
Solution Approach 1:
The system creates chat rooms selectively based on user responses and matching algorithms, rather than creating rooms for all potential user groups. Each chat room is tailored to specific user interests and event preferences, ensuring that networking opportunities are provided where they are most needed while avoiding unnecessary processing overhead.
3Ease of operation
If event rooms are allocated without capacity optimization, then event accessibility improves, but network congestion and outages occur
Solution Approach 1:
The system monitors event room capacities and user registration patterns in real-time, using this feedback to dynamically allocate resources and prevent overfilling. When approaching capacity thresholds, the system can redirect users to alternative events or time slots, maintaining accessibility while preventing network congestion and outages.
Data Source
AI summary
In some implementations, a conferencing system may receive a set of responses, corresponding to a set of questions, associated with a set of users. The conferencing system may identify, for a first user in the set of users, a subset of matching users in the set of users, based on the set of responses. The conferencing system may output an indication of the subset of matching users. The conferencing system may generate, by applying a machine learning model to the set of responses, at least one suggested cluster for a breakout subset of the set of users. The conferencing system may determine, based on the at least one suggested cluster, at least one possible location. The conferencing system may output an indication of the at least one suggested cluster and the at least one possible location.