Online Session Scheduling via Behavior Pattern Tracking
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Solution Overview
Problem
Coordinating online sessions among multiple users is challenging due to varying schedules and preferences, leading to frustration in finding a mutually convenient time for activities like online gaming.
Innovation Solution
A method and system that track users' behavior patterns and preferences to suggest optimal time slots for online sessions, sending invitations, and managing schedules to ensure maximum participation, using a controller with network interface, processor, and memory to monitor user data and profile information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual scheduling coordination is used, then users can control their own schedules, but the scheduling process becomes complex and time-consuming
Solution Approach 1:
The patent introduces a third-party scheduling service as an intermediary between users. This service automatically handles the complex coordination tasks by monitoring user availability, suggesting optimal time slots, and managing invitations, thereby simplifying the scheduling process without requiring users to directly coordinate with each other.
Solution Approach 2:
The scheduling system performs self-service by automatically monitoring user behavior patterns, determining availability, and generating scheduling recommendations without requiring manual input from users. The system autonomously processes the scheduling logic, reducing the burden on users while maintaining schedule accuracy.
2Adaptability or versatility
If multiple communication methods are used for scheduling, then more scheduling options are available, but the time required to coordinate increases
Solution Approach 1:
The system performs preliminary actions by pre-monitoring user behavior patterns and pre-determining availability windows before actual scheduling is needed. This allows the system to prepare and present optimal time slot recommendations in advance, reducing the time required during the actual coordination process.
Solution Approach 2:
The scheduling system implements feedback mechanisms by continuously monitoring user responses to invitations and availability changes. This feedback loop allows the system to dynamically adjust recommendations and automatically resolve conflicts, reducing the iterative communication back-and-forth between users.
3Measurement precision
If user behavior is monitored to predict time slots, then scheduling accuracy improves, but data processing complexity increases
Solution Approach 1:
The system creates a simplified model or copy of user behavior patterns rather than processing raw behavioral data directly. By abstracting user activities into standardized availability profiles and preference templates, the system reduces data processing complexity while maintaining prediction accuracy for optimal time slot recommendations.
Data Source
AI summary
Scheduling an online session including: determining desired time slots for an online session; determining potential users to participate in the online session; sending invitations to the potential users; receiving responses to the invitations thereby identifying participants for the online session; and entering the online session into the calendars of the participants of the online session.