AI Breakout Session Assignment and Moderator Scheduling

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Solution Overview

Problem

Current conferencing methods suffer from inefficient breakout sessions due to incorrect participant assignments, lack of performance monitoring, inadequate moderator utilization, and difficulty in maintaining topic focus, leading to unproductive meetings.

Innovation Solution

A system utilizing natural language processing, machine learning, and artificial intelligence to determine breakout session topics, assign participants based on skill scores, monitor discussion context, and generate moderator schedules to ensure productive sessions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual assignment of participants to breakout rooms is used, then flexibility in assignment is improved, but assignment accuracy and productivity deteriorate due to incorrect or ad hoc assignments

Engineering Contradiction:

Improveflexibility in assignmentVSAvoidassignment accuracy

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system performs self-service by automatically analyzing participant profiles, skills, and meeting context to generate optimal breakout room assignments without manual intervention. The AI engine autonomously processes participant data, determines skill gaps, and creates balanced groupings that maximize productivity while maintaining operational simplicity.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual assignment process with an AI-based automated system that uses natural language processing and machine learning algorithms. This substitution eliminates human error in assignments while maintaining flexibility through programmable parameters for customization.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If moderators are present in all breakout rooms continuously, then monitoring capability is improved, but time efficiency and productivity deteriorate due to improper use of moderator time

Engineering Contradiction:

Improvemonitoring capabilityVSAvoidmoderator time efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system implements feedback mechanisms by continuously monitoring breakout room discussions through natural language processing and generating real-time analytics on engagement levels, topic adherence, and participant contributions. This feedback enables intelligent scheduling of moderator interventions only when and where needed, optimizing moderator time while maintaining effective oversight.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The moderator deployment model transitions from static continuous presence to dynamic selective intervention. The AI system dynamically adjusts moderator scheduling based on real-time analysis of breakout room performance metrics, sending targeted notifications to moderators when specific intervention conditions are met, thereby maximizing their time efficiency.

Inventive Principle:

Principle #15Dynamics

3Reliability

If multiple breakout rooms are monitored simultaneously, then comprehensive oversight is improved, but system complexity and difficulty of detection increase

Engineering Contradiction:

Improvecomprehensive oversightVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDifficulty of detecting and measuring

Solution Approach 1:

The monitoring system segments the complex task of overseeing multiple breakout rooms into modular AI agents, each responsible for analyzing specific metrics (engagement, topic adherence, participant contributions) in individual rooms. These segmented analysis functions aggregate their findings to provide comprehensive oversight, reducing overall system complexity while maintaining thorough monitoring capability.

Inventive Principle:

Principle #1Segmentation

4Productivity

If AI-based participant assignment is implemented, then assignment accuracy is improved, but computational resource requirements and system complexity increase

Engineering Contradiction:

Improveassignment accuracyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by pre-processing and storing participant profile data, skills, and meeting context information before the actual assignment process. This pre-computation of participant attributes and meeting parameters reduces the computational burden during real-time assignment, maintaining high accuracy while managing system complexity through staged processing.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12526366B2Systems and methods for creating and managing breakout sessions for a conference session

Publication Date: 2026.01.13 ADEIA GUIDES INC

AI summary

Systems and methods for creating, monitoring, and managing a breakout conference for a conference call are disclosed. The methods determine topics for breakout rooms and their complexity scores. A breakout room is created for the topics, including separate breakout rooms for complex topics. An expertise score based on a plurality of factors for each device associated with a participant is also calculated. Devices are assigned to separate breakout rooms based on either just the expertise score or if the expertise score meets the threshold of the complexity score. Performance within the breakout rooms is displayed in real-time, such as in a graph. A moderator schedule is generated based the performance within the breakout rooms, where priority is given to a breakout room that has a negative performance over a breakout room with a positive performance.