18374449. QUICK SERVICE RESTAURANT ORDER PREPARATION AND DELIVERY OPTIMIZATION simplified abstract (Insight Direct USA, Inc.)
Contents
- 1 QUICK SERVICE RESTAURANT ORDER PREPARATION AND DELIVERY OPTIMIZATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 QUICK SERVICE RESTAURANT ORDER PREPARATION AND DELIVERY OPTIMIZATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does the machine-learning model adapt to changing variables in real-time?
- 1.11 What is the accuracy rate of the prediction model in real-world scenarios?
- 1.12 Original Abstract Submitted
QUICK SERVICE RESTAURANT ORDER PREPARATION AND DELIVERY OPTIMIZATION
Organization Name
Inventor(s)
Andrew Schwenker of Mahomet IL (US)
Antoine E. Hall of Spring Hope NC (US)
Ilya Eliashevsky of Milford CT (US)
Ryan Miller of Columbus OH (US)
QUICK SERVICE RESTAURANT ORDER PREPARATION AND DELIVERY OPTIMIZATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18374449 titled 'QUICK SERVICE RESTAURANT ORDER PREPARATION AND DELIVERY OPTIMIZATION
Simplified Explanation
The present disclosure describes a method for predicting the time needed to prepare an order at a quick service restaurant, based on the food items ordered and various factors such as time of day, number of employees, pending orders, and ingredient inventory.
- Machine-learning model used to predict preparation time
- Factors considered: time of day, number of employees, pending orders, ingredient inventory
- Order includes at least one food item
- Prediction helps optimize workflow and customer service
Potential Applications
This technology could be applied in various quick service restaurants to improve efficiency and customer satisfaction by accurately predicting order preparation times.
Problems Solved
1. Uncertainty in order preparation times 2. Inefficient workflow management
Benefits
1. Improved customer experience 2. Enhanced operational efficiency 3. Better resource allocation
Potential Commercial Applications
Optimizing order preparation times in fast food chains Improving service speed in busy restaurants
Possible Prior Art
There may be existing systems or methods for predicting order preparation times in restaurants, but the specific combination of factors and machine-learning model described in this disclosure may be novel.
Unanswered Questions
How does the machine-learning model adapt to changing variables in real-time?
The article does not provide details on how the model updates its predictions as variables such as pending orders or ingredient inventory change.
What is the accuracy rate of the prediction model in real-world scenarios?
The article does not mention the accuracy rate of the predictions made by the machine-learning model when applied in actual quick service restaurant settings.
Original Abstract Submitted
The present disclosure presents a method of predicting an amount of time needed to complete preparation of an order at a quick service restaurant. The order includes at least one food item in need of preparation. The method can include receiving the order from a customer, and predicting the amount of time needed to complete preparation of the order using a machine-learning model, based on the food item(s) ordered and on at least one of the following: a time-of-day that the order was placed, a number of employees on duty at the quick service restaurant, a number of other orders currently pending at the quick service restaurant, and inventory of each ingredient needed to complete preparation of the at least one food item.
