Booking Engine Predicts Unattendance Using Segmentation
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Solution Overview
Problem
Conventional systems lack effective mechanisms to ensure that individuals represented by user accounts actually attend events or job shifts they have booked, leading to risks associated with unqualified or unreliable workers booking shifts without showing up to complete tasks.
Innovation Solution
The Booking Engine employs an online platform to monitor and manage risk by segmenting user accounts based on activity features, using objective function algorithms to determine cut-off scores and predict unattendance likelihoods, triggering appropriate booking actions and notifications across different time ranges to ensure qualified and reliable workers are booked for job shifts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional booking systems allow any user to book events without verification, then booking accessibility is improved, but the reliability of event attendance deteriorates
Solution Approach 1:
The patent segments workers into different groups based on their reliability scores, which are calculated from historical booking and attendance data. This segmentation allows the system to apply different booking rules and risk management strategies to different worker segments, thereby maintaining high accessibility for reliable workers while protecting against unreliable bookings.
Solution Approach 2:
The system performs preliminary risk assessment and reliability evaluation before allowing bookings to occur. By calculating reliability scores in advance based on historical data and using predictive analytics to assess future attendance likelihood, the system prevents problematic bookings before they happen rather than dealing with issues after they occur.
2Reliability
If the system implements strict verification to ensure worker reliability, then attendance reliability is improved, but booking efficiency and speed deteriorate
Solution Approach 1:
The system performs reliability verification in advance by calculating scores based on historical data before the booking process. This preliminary assessment creates a trust foundation that enables faster, more automated booking decisions without requiring time-consuming manual verification at the point of booking.
Solution Approach 2:
The system uses automated algorithms and machine learning models to perform reliability assessments without human intervention. The automated calculation of reliability scores and prediction of attendance likelihood eliminates the need for manual background checks or verification processes, maintaining both high reliability and booking speed.
3Measurement precision
If the system monitors and segments workers based on detailed activity features, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the worker population into segments based on multiple activity features such as booking history, cancellation patterns, and attendance records. This segmentation approach allows the system to analyze complex multidimensional data in a structured way, improving prediction accuracy by identifying patterns within specific segments while managing complexity through organized categorization.
Solution Approach 2:
The system uses a unified framework that processes multiple different activity features through common analytical algorithms. The same segmentation and prediction infrastructure handles various types of worker behavior data, eliminating the need for separate complex systems for each type of analysis and reducing overall system complexity.
4Reliability
If the system triggers multiple booking actions across different time ranges, then risk management is improved, but operational complexity increases
Solution Approach 1:
The patent implements dynamic booking actions that automatically adjust based on the current time range relative to the event and the predicted attendance likelihood. The system transitions between different actions (such as sending reminders, allowing cancellations, or blocking bookings) based on dynamic conditions, enabling effective risk management across different time periods while using automated rules to manage operational complexity.
Solution Approach 2:
The system uses feedback from prediction models about attendance likelihood to automatically trigger appropriate booking actions. The prediction outcomes feed back into the booking management process, enabling the system to adjust its behavior based on real-time assessments without requiring manual intervention for each decision, thus managing complexity through automated feedback loops.
Data Source
AI summary
Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to a Booking Engine. The Booking Engine generates a prediction, for one of a plurality of distinct sequential time ranges defined as occurring prior to a start of an event, regarding a likelihood of an occurrence of an unattendance by one or more individuals at the event. Each individual is represented by a respective user account. Based on the prediction, the Booking Engine triggers one or more booking actions specific to a particular time range of the prediction. The Booking Engine triggers one or more notifications in accordance with the one or more booking actions for the event.