Meeting Priority Scoring via Machine Learning
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Solution Overview
Problem
In large organizations, users often face challenges with managing overlapping meetings, leading to forgotten or less important meetings, and inefficient attendance, as traditional scheduling systems lack the ability to prioritize meetings and provide timely notifications.
Innovation Solution
A group-based communication platform utilizes a machine learning model to determine priority scores for meetings, sends notifications to attendees, and suggests alternative times or cancels conflicting meetings, while also analyzing user behavior to optimize future meeting scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional scheduling systems are used to manage meetings, then users can schedule and attend meetings, but users may forget important meetings or attend less important ones due to overlapping schedules
Solution Approach 1:
The system provides feedback to users by sending notifications about upcoming meetings and their priority levels. The notification system delivers meeting information to users in real-time, allowing them to adjust their attendance based on the importance scores provided by the machine learning model.
Solution Approach 2:
The machine learning model acts as an intermediary between the calendar system and the user. It processes meeting data, determines priority scores, and provides recommendations to users, serving as a mediator that translates raw meeting information into actionable guidance for attendance decisions.
2Adaptability or versatility
If users are invited to multiple meetings, then collaboration opportunities increase, but users may be invited to meetings they are not required to attend
Solution Approach 1:
The system performs preliminary analysis of meeting invitations before the user commits to attending. The machine learning model evaluates meeting importance and provides priority scores in advance, allowing users to make informed decisions about which meetings to attend before they start, rather than wasting time in unnecessary meetings.
Solution Approach 2:
The system changes the parameter of meeting evaluation by introducing priority scores based on multiple factors such as attendee hierarchy, meeting topic, and historical data. This transforms the traditional binary attend/decline decision into a nuanced selection process where users can prioritize meetings based on quantified importance metrics.
3Ease of operation
If manual meeting management is used, then users have full control over their schedules, but scheduling efficiency decreases and overlapping meetings are not optimized
Solution Approach 1:
The system provides self-service capabilities by automatically analyzing meeting schedules, calculating priority scores, and generating attendance recommendations without requiring manual user intervention. The machine learning model autonomously processes meeting data and provides actionable insights, freeing users from manual scheduling optimization while maintaining their control over final decisions.
Solution Approach 2:
The system replaces manual mechanical scheduling processes with an automated machine learning-based system. Instead of users manually reviewing and prioritizing meetings, the ML model automatically processes scheduling data, eliminating the time-consuming manual effort while preserving user autonomy in making final attendance decisions.
4Measurement precision
If priority scoring is implemented for meetings, then users can identify important meetings, but the system complexity increases
Solution Approach 1:
The machine learning model serves multiple functions: it analyzes meeting data, calculates priority scores, generates notifications, and provides recommendations. By consolidating these diverse functions into a single ML system, the patent reduces overall system complexity while achieving precise meeting importance measurement through a unified multi-functional component.
Data Source
AI summary
Techniques for generating priority scores associated with meetings via group-based communication platforms are discussed herein. For example, a group-based communication platform may receive, from a user of the group-based communication platform, a request to generate a meeting between additional users of the group-based communication platform. The group-based communication platform may then input the request into a machine-learning model and receive a priority score associated with an importance of the meeting. Based at least in part on one or more calendars associated with the additional users, the group-based communication platform may determine an availability of the additional users. The group-based communication platform may then send, to the additional users, an invitation to the meeting including the priority score, and receive at least one response.