View source for 18374449. QUICK SERVICE RESTAURANT ORDER PREPARATION AND DELIVERY OPTIMIZATION simplified abstract (Insight Direct USA, Inc.)

=QUICK SERVICE RESTAURANT ORDER PREPARATION AND DELIVERY OPTIMIZATION=


==Organization Name==

[[:Category:Insight Direct USA, Inc.|Insight Direct USA, Inc.]]


[[Category:Insight Direct USA, Inc.]]

==Inventor(s)==

[[:Category:Andrew Schwenker of Mahomet IL (US)|Andrew Schwenker of Mahomet IL (US)]][[Category:Andrew Schwenker of Mahomet IL (US)]]

[[:Category:Antoine E. Hall of Spring Hope NC (US)|Antoine E. Hall of Spring Hope NC (US)]][[Category:Antoine E. Hall of Spring Hope NC (US)]]

[[:Category:Ilya Eliashevsky of Milford CT (US)|Ilya Eliashevsky of Milford CT (US)]][[Category:Ilya Eliashevsky of Milford CT (US)]]

[[:Category:Ryan Miller of Columbus OH (US)|Ryan Miller of Columbus OH (US)]][[Category:Ryan Miller of Columbus OH (US)]]

[[:Category:Tony Lunt of Union KY (US)|Tony Lunt of Union KY (US)]][[Category:Tony Lunt of Union KY (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.



[[Category:G06Q10/0637]]
[[Category:G06Q50/12]]