Machine-Learning Meeting Location Selection for Multi-Attendee Convergence
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Solution Overview
Problem
Organizing in-person meetings with multiple attendees is challenging due to the difficulty in identifying a location that is convenient and accessible to all participants, considering their varying locations and preferences, which requires significant human input and often results in inefficient use of resources and time.
Innovation Solution
A system and method utilizing machine-learning models to predict attendees' locations and preferences, automatically identify a meeting location that meets their requirements, and facilitate reservations, optimizing geographical convenience and preference satisfaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual methods are used to determine meeting locations by contacting each attendee, then individual preferences and restrictions can be considered, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system enables automated self-service by using machine learning models to predict attendee locations and automatically identify suitable meeting venues without requiring manual coordination. The ML model processes attendee data, predictions, and venue information autonomously to generate meeting location recommendations, eliminating the need for organizers to manually contact each attendee.
Solution Approach 2:
The patent replaces the mechanical manual process of contacting and coordinating with each attendee with an automated computational system. Machine learning models and algorithms substitute human effort in predicting locations, evaluating preferences, and identifying venues, transforming a labor-intensive manual task into an automated digital process.
2Adaptability or versatility
If organizer manually coordinates with each attendee to find convenient location, then location accessibility can be optimized, but the complexity of the process increases
Solution Approach 1:
The machine learning model acts as an intermediary between attendee preferences and meeting location selection. The ML model processes and interprets attendee data, location predictions, and venue information, mediating the complex coordination task by translating multiple constraints into automated recommendations that satisfy attendee preferences without requiring direct manual negotiation.
Solution Approach 2:
The system changes parameters from manual qualitative assessments to automated quantitative analysis. The ML model processes numerical data including predicted locations, distance metrics, and preference weights to objectively evaluate venue suitability, replacing subjective manual judgment with data-driven parameter optimization that adapts to attendee preferences.
3Productivity
If automated systems are used to identify meeting locations, then time efficiency improves, but accuracy in considering individual preferences may deteriorate
Solution Approach 1:
The system performs preliminary actions by collecting and processing attendee preference data, location history, and venue information before the meeting scheduling decision is needed. Machine learning models pre-process this data to create predictive models of attendee locations and preferences, enabling rapid automated identification of suitable venues while maintaining precision through pre-analyzed preference matching.
Solution Approach 2:
The system incorporates feedback mechanisms where machine learning models continuously learn from attendee responses, location predictions, and preference data. The automated system refines its accuracy by processing feedback from previous meeting arrangements and attendee preferences, improving the precision of preference matching while maintaining high-speed automated operation through learned patterns.
Data Source
AI summary
A system and method for facilitating convergence includes receiving a request to schedule a meeting at a meeting time, retrieve at least one of user data, contextual data, facility data and map data, the user data including a list of a plurality of meeting participants for the meeting, providing at least one of the list of the plurality of meeting participants, the meeting time, the user data, and the facility data to a trained machine-learning (ML) model for predicting a location at which two or more of the plurality of meeting participants will be located within a given time period prior to the meeting time, receiving the predicted location as an output from the trained ML model, and identifying a meeting location from among one or more meeting venues based on the predicted location of the two or more of the plurality of meeting participants, and providing the meeting location for display in a first selectable user interface element.