Restaurant Wait Time Prediction via Table Turn Analysis
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Solution Overview
Problem
Current restaurant management systems provide only rough estimates of wait times for customers, lacking accuracy and periodic updates, which can lead to uncertainty for guests waiting for tables.
Innovation Solution
Integrating the ConnectSmart Hostess and Kitchen systems to analyze customer preferences, table status, meal stages, and kitchen events to predict and update wait times based on factors like party size, meal preparation times, and table turn times, ensuring accurate and timely information is provided to waiting customers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the restaurant uses a simple manual wait time estimation system, then the system complexity is low and ease of operation is high, but the measurement precision of wait time quotes is poor
Solution Approach 1:
The patent combines multiple data sources including table status information, meal stage tracking, kitchen event data, and historical table turn metrics into a unified wait time prediction system. This integration of disparate information streams enables accurate wait time estimation without requiring complex manual processes.
Solution Approach 2:
The system continuously monitors actual table turn times and uses this feedback to refine and update predicted wait times in real-time. By comparing expected versus actual dining durations and adjusting future predictions accordingly, the system maintains high accuracy while operating through automated algorithms rather than complex manual calculations.
2Measurement precision
If the restaurant implements real-time monitoring of all tables and kitchen operations, then the measurement precision of wait time quotes improves, but the use of energy and computational resources increases
Solution Approach 1:
The system extracts only the critical data elements needed for wait time prediction from the broader restaurant operations data stream. Rather than processing all possible information, it focuses on key indicators such as current meal stage, table status, and relevant kitchen events, thereby reducing computational overhead while maintaining prediction accuracy.
Solution Approach 2:
The system implements monitoring at strategic intervals and focuses on the most influential factors affecting wait times. By partially monitoring the system state rather than continuously analyzing every detail, it achieves sufficient accuracy for customer information while conserving computational resources.
3Loss of information
If the system provides frequent updates of wait time information to waiting customers, then the loss of information is reduced and customer satisfaction improves, but the device complexity and operational burden increase
Solution Approach 1:
The system automatically generates and communicates wait time updates to customers without requiring manual intervention from restaurant staff. The automated notification system pulls data from existing monitoring systems and delivers updates through electronic channels, eliminating the operational burden of manual information dissemination while keeping customers well-informed.
Solution Approach 2:
The patent introduces an automated communication intermediary that bridges the gap between the wait time calculation system and customers. This intermediary handles all customer interactions regarding wait time information, freeing staff from direct involvement in providing updates while ensuring consistent and accurate information delivery.
4Measurement precision
If the restaurant uses detailed tracking of meal stages and kitchen events, then the measurement precision of table turn prediction improves, but the device complexity and data processing requirements worsen
Solution Approach 1:
The system segments the dining process into distinct measurable stages (appetizer, main course, dessert, etc.) and tracks transitions between these stages. By breaking down the complex dining experience into discrete, trackable events, the system achieves precise table turn prediction through manageable data collection and processing of individual stage transitions rather than attempting to monitor the entire process as a single complex variable.
Data Source
AI summary
The inventive method uses data received concerning the status of various tables in the establishment, the criteria provided by the customer as well as the status of the meals being prepared in a variety of courses for customers already seated to best determine how much time a new customer will have to wait before a suitable table is available where the customer may be seated. When a customer enters the restaurant, they provide information to the hostess or host including the party size and various table preferences. The host or hostess enters this information into a computer using a keyboard touch screen input or a wireless remote device. Based upon the preferences chosen by the party, the system assigns the most predictable table and periodically scans each existing table to determine that table's status. The method gains additional accuracy by obtaining detailed kitchen status information concerning meals being prepared for each table. Through determining table status and kitchen status, the system may predict “table turns,” that is, when a particular table is most likely to be vacated. Periodically, once the data from all of the tables and the kitchen has been updated, the projected table turn times for each table are updated. With this updating having been accomplished, projected wait time for each party may be updated. Then, revised quoted wait times are given to customers while waiting.