Meeting-Team Matching Using Invitee and Topic Similarity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing collaboration systems face challenges in efficiently associating teams with scheduled meetings, particularly when multiple teams can be selected or a new team needs to be created due to a lack of matching existing teams.
Innovation Solution
A method and browser plugin that analyze communication data to determine a match metric for existing teams and create a new team if necessary, using a match threshold value to associate the appropriate team with meeting attendees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple existing teams are available for selection, then the system has flexibility in team assignment, but it becomes difficult to determine which team to associate with the meeting
Solution Approach 1:
The system uses communication data (emails, chat messages, file shares) as feedback to automatically determine team associations. By analyzing existing communication patterns between meeting attendees, the system objectively identifies which team members are most actively collaborating, eliminating the difficulty of manual team selection while preserving flexibility through data-driven matching.
Solution Approach 2:
The system performs self-service by automatically analyzing communication data and determining team associations without requiring manual intervention. The algorithm autonomously processes email exchanges, chat messages, and file sharing patterns to identify the most appropriate team for each meeting, resolving the matching difficulty while maintaining adaptability.
2Adaptability or versatility
If no existing team matches the meeting attendees, then a new team can be created, but this increases system complexity
Solution Approach 1:
The system performs preliminary analysis of communication data before meeting assignment to determine whether existing teams match the attendees. By pre-processing communication patterns and identifying potential team associations in advance, the system avoids the complexity of creating new teams when unnecessary, only initiating team creation when the analysis confirms no suitable existing team exists.
Solution Approach 2:
The system introduces an intermediary analysis layer that evaluates communication data to determine team matching suitability. This intermediary process acts as a mediator between meeting requests and team assignments, automatically deciding whether to associate with existing teams or trigger new team creation, thereby managing system complexity through structured decision logic.
3Device complexity
If manual team association is used, then system complexity is reduced, but collaboration efficiency decreases
Solution Approach 1:
The system enables self-service by automatically analyzing communication data and performing team associations without manual intervention. The algorithm processes email exchanges, chat messages, and file sharing patterns to autonomously determine optimal team assignments, significantly improving collaboration efficiency while managing complexity through automated decision-making.
Solution Approach 2:
The system uses real-time feedback from communication data to dynamically determine team associations. By continuously monitoring email, chat, and file sharing patterns, the system adapts team assignments based on actual collaboration behavior, enhancing productivity while maintaining manageable complexity through data-driven automation.
Data Source
AI summary
A method for associating a meeting invitation with a team includes receiving a meeting invitation from an email system comprising names of invitees and receiving team information from a team system comprising names of team members and team topics. Using a trained machine learning model, a match metric representing similarity between teams and the meeting invitation is calculated. Calculating the match metric includes determining an attendee score based on invitees who are team members and a topic score based on comparison between a meeting topic and team topics. The match metric is based on the attendee score and topic score. The trained machine learning model uses names of invitees, names of team members, and team topics. The method further includes adding to the meeting invitation a link to the team with the highest match metric.