Automated Appointment Matching via Preference Correlation
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Solution Overview
Problem
Current scheduling systems fail to efficiently match open appointments with user preferences and availability, leading to issues such as cancellations and no-shows, which can be costly for businesses, and lack effective notification and incentive systems to keep or make appointments.
Innovation Solution
A network-based system that provides automated calendar availability notification and interactive acceptance by users associated with service providers, businesses, and individuals, using a matching engine that calculates a Preference Correlation Coefficient to match users with appointment openings based on preferences, availability, and lead time, and offers calculated rewards to ensure appointments are kept.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional scheduling systems are used, then simplicity is maintained, but appointment filling efficiency is poor and no-shows/cancellations occur frequently
Solution Approach 1:
The system enables automated self-matching between users and appointments through the Preference Correlation Coefficient calculation, where the matching engine automatically processes user preferences, availability, and lead time requirements without manual intervention, thereby improving filling efficiency while the automated nature conceals the underlying complexity
Solution Approach 2:
The matching engine acts as an intermediary component that mediates between users and appointments by calculating compatibility scores based on preferences and availability, then facilitating the connection through automated notifications and acceptance mechanisms, thus resolving the contradiction between simple operation and complex processing
2Reliability
If automated notification systems are implemented, then appointment keeping improves, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating Preference Correlation Coefficients and pre-identifying compatible users before appointments are needed, then sends automated notifications in advance, ensuring high appointment keeping rates while the pre-computed matching logic simplifies the actual execution phase
Solution Approach 2:
The system implements feedback mechanisms where users receive automated notifications based on their profile data, and the system adjusts its matching based on user responses and behaviors, improving reliability while the feedback loop automates what would otherwise be manual coordination
3Loss of time
If manual scheduling is used, then system simplicity is maintained, but time consumption and labor costs increase
Solution Approach 1:
The system replaces manual mechanical scheduling processes with automated computer-based calculation and communication systems, where the Preference Correlation Coefficient algorithm automatically determines matches and electronic notifications automatically convey appointment details, dramatically reducing time consumption while the software implementation manages the complexity
Data Source
AI summary
Automated matching, notification, and acceptance/rejection of appointment or opening in a calendar/schedule via network-based systems and methods, including application over social networks and website based connection between users and service providers. Notifications include offers of rewards and/or incentives, which are in the form of discounts, promotions, and/or a currency such as a cryptocurrency.