Affinity-Based Gathering Invitation Automation
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Solution Overview
Problem
In today's social media society, people engage less in-person and face challenges in planning gatherings due to time, money, and logistics, while traditional methods lack systematic approaches to promote meaningful relationships and efficient invitation management.
Innovation Solution
A system and method for facilitating in-person interactions by matching users based on likability indices, using hashtag descriptors for personality types, and providing a platform for tracking affinity and aversion, which automates gathering invitations and feedback processes to streamline planning and promote meaningful connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional invitation methods are used where hosts manually select and send invitations to individuals, then the host has control over invitees, but the planning effort and time required increases significantly
Solution Approach 1:
The system enables self-service by allowing gatherings to automatically invite themselves to potential participants based on affinity calculations. The host defines parameters (min/max participants, skill requirements, date range) and the system autonomously identifies and invites compatible users, eliminating manual invitation efforts while maintaining quality control through algorithmic matching.
Solution Approach 2:
The system performs preliminary actions by pre-calculating affinity scores between users before gatherings occur. The affinity database is built in advance through user feedback, and the invitation system uses these pre-computed metrics to automatically select and invite compatible participants, reducing both planning time and effort for future gatherings.
2Reliability
If hosts manually track and confirm participant availability, then participation accuracy improves, but logistics effort increases
Solution Approach 1:
The system implements continuous feedback loops where participants indicate their availability and attendance status. This feedback is automatically processed to update the affinity database and improve future matching. The system reliably tracks participation by comparing expected vs. actual attendance and uses this information to refine invitation strategies, reducing logistics complexity through automated adjustment.
Solution Approach 2:
The invitation system is dynamic and adaptive, automatically adjusting invitation strategies based on real-time feedback from participants. The system can modify min/max participant thresholds, skill requirements, and date ranges based on actual gathering outcomes and participant responses, maintaining reliability while reducing fixed logistical complexity.
3Adaptability or versatility
If no systematic approach is used to award good behavior and penalize bad behavior, then the system remains simple, but meaningful relationship development is hindered
Solution Approach 1:
The system implements a comprehensive feedback mechanism where participants rate their experiences with other users after gatherings. This feedback is systematically processed to update affinity scores, creating a self-learning system that adapts to user preferences and behaviors. The feedback loop enables meaningful relationship development by continuously improving matching accuracy while managing complexity through automated processing.
Solution Approach 2:
The affinity database is self-updating through automated processing of user feedback. The system autonomously calculates affinity scores, updates user profiles, and refines matching algorithms without requiring manual intervention. This self-service approach enables sophisticated relationship building capabilities while keeping the system relatively simple through automation.
4Quantity of substance
If gathering invitations are made public to all users, then participant pool increases, but the quality of matches decreases
Solution Approach 1:
The system applies local quality by making gathering invitations publicly visible but using localized affinity filtering to display them only to users with high compatibility scores. Each user sees a customized view of gatherings based on their specific affinity profile, ensuring that while the participant pool is large, each user receives targeted invitations from highly compatible potential participants.
Solution Approach 2:
The system changes the parameter of invitation visibility from binary (public/private) to a continuous spectrum based on affinity scores. Invitations are made visible to users above a certain affinity threshold, dynamically adjusting the effective participant pool size based on match quality. This allows the system to maintain both large overall participation and high individual match quality simultaneously.
Data Source
AI summary
Various embodiments relate generally to dating/friendship finder application systems. An online and in-person gathering system which includes a method for tracking affinity and aversion between users by requesting individual user's feedback on other users based on post-gathering interactions amongst them. System tracks and discloses affinity and aversion feedback towards another user to facilitate decision making with regards to attending or not attending a gathering. Gathering invites are visible or invisible to users based on the affinity and aversion responses from hosts (users planning a gathering) and prospective participant-users. Through empirical affinity and aversion feedback, system identifies proclivity towards personality types defined by the hashtag descriptors provided by users, as well as provide relevant ranking for the presentation of other users, gatherings and 3rd party content objects.