Participant Device Grouping via Interest-Based Rating
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Solution Overview
Problem
In electronically facilitated conferences, facilitators lack information on participant devices' interests and expertise, leading to unproductive discussions due to random assignment of devices to groups, often constrained by limited breakout groups and participant capacity.
Innovation Solution
A system and method for grouping participant devices based on rated subjects, where highly rated thought objects are distributed to devices, and each device is assigned to a group discussing a subject it rated highly, within constraints set by the facilitator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If participant devices are randomly assigned to groups, then the facilitator can quickly organize discussions, but the productivity and quality of discussions decrease due to lack of alignment with participant interests
Solution Approach 1:
The system performs preliminary actions by collecting participant feedback and ratings about their interests and expertise before the actual group assignment. This advance preparation enables informed assignment decisions that align participants with relevant discussion topics, thereby improving discussion quality without adding operational complexity during the event
Solution Approach 2:
Participants actively provide their own information about interests and expertise through feedback mechanisms and ratings. This self-service approach allows the system to automatically generate meaningful group assignments based on participant-provided data, eliminating the need for complex manual facilitation while improving assignment quality
2Adaptability or versatility
If the number of breakout groups is limited, then meeting room availability constraints are satisfied, but the adaptability of the system to participant preferences decreases
Solution Approach 1:
The system applies local quality by making group assignments highly specific and tailored to each participant's individual interests and expertise rather than using uniform grouping. Each participant receives a customized assignment based on their local preferences, maximizing adaptability within the constrained number of groups
Solution Approach 2:
The system changes parameters by using multiple dimensions of participant characteristics (interests, expertise, feedback ratings) rather than relying solely on the number of groups. This allows the system to achieve high adaptability through parameter optimization rather than increasing group quantity
3Productivity
If more thought objects are distributed for rating, then participant engagement increases, but the loss of time for processing and distributing information increases
Solution Approach 1:
The system implements partial action by selectively distributing only the most relevant thought objects to each participant based on their expressed interests and past behavior patterns. Rather than distributing all thought objects to all participants, the system provides a curated subset, maintaining engagement while reducing processing time
Solution Approach 2:
The system performs preliminary filtering and prioritization of thought objects based on participant profiles before distribution. This advance preparation ensures that participants receive only highly relevant content, maximizing engagement while minimizing the time required for distribution and processing
Data Source
AI summary
Systems and methods for computing a distribution pattern of information whereby, as the rating of thought objects in a roundtable exchange proceeds, thought objects that are more highly rated and are therefore more likely to be chosen as subjects for discussion in groups, are more widely distributed to participant devices for rating, and for computing a pattern of assignment whereby each participant device is assigned to a group discussing a subject which that participant device rated highly, within constraints specified by the facilitator.