Image-based kitchen tracking system with order accuracy management

1. A method, comprising:

receiving, by a processing device, image data comprising one or more image frames indicative of a state of a meal preparation area;

determining, by the processing device, one of a meal preparation item or a meal preparation action associated with the state of the meal preparation area based on the image data;

receiving, by the processing device, order data comprising a plurality of pending meal orders;

determining a first association between the one of the meal preparation item or the meal preparation action and a meal preparation entity based on the image data;

determining a second association between (i) a first of the plurality of pending meal orders and (ii) the one of the meal preparation item or the meal preparation action, wherein determining the second association is based on the image data and the first association;

determining, by the processing device, an order preparation error based on the order data, the first association, the second association, and the one of the meal preparation item or the meal preparation action; and

causing, by the processing device, the order preparation error to be displayed on a graphical user interface (GUI).

2. The method of claim 1 , wherein determining one of the meal preparation item or the meal preparation action comprises:

using the image data as input to a machine learning model; and

obtaining one or more outputs of the machine learning model, the one or more outputs indicative of the meal preparation item or the meal preparation action.

3. The method of claim 2 , further comprising:

receiving a user input indicative of an accuracy of the order preparation error; and

using the user input to train the machine learning model.

4. The method of claim 1 , further comprising:

determining an error severity level associated with the order preparation error; and

determining the error severity level meets a threshold condition.

5. The method of claim 4 , further comprising:

receiving second image data comprising one or more image frames indicative of a state of customer queue area; and

determining the threshold condition based on the second image data.

6. The method of claim 1 , further comprising:

determining a first quantity of the meal preparation item based on the image data; and

determining that the first quantity does not meet a target quantity associated with the plurality of pending meal orders, wherein the order preparation error is determined based at least in part on determining that the first quantity does not meet the target quantity.

7. The method of claim 1 , wherein the meal preparation item comprises a meal preparation container, the method further comprising:

determining, based on the image data, contents of the meal preparation container, wherein the contents of the meal preparation container comprises a set of prepared meal items;

determining, based on the order data, a target set of prepared meal items associated with the first of the plurality of pending meal orders; and

determining the order preparation error further based on a comparison between the contents of the meal preparation container and the set of prepared meal items.

8. The method of claim 1 , further comprising:

determining a set of target meal components based on the order data; and

determining an absence of one of the set of target meal components based on the image data, wherein the order preparation error is determined responsive to determining the absence of one of the set of target meal components.

9. The method of claim 1 , further comprising:

determining a location of the meal preparation action based on the image data; and

identifying the meal preparation item based on the location of the meal preparation action and the image data, wherein the order preparation error is determined further based on identifying the meal preparation item.

10. The method of claim 1 , wherein determining the meal preparation action comprises:

determining a second meal preparation action based on a first image frame of the image data; and

determining the meal preparation action based on the second meal preparation action, wherein the meal preparation action occurs outside a line of sight (LOS) of an image capture device associated with the image data.

11. The method of claim 1 , further comprising:

determining a meal preparation tool based on the image data;

determining a third association between one of the plurality of pending meal orders and a preparation entity based on the meal preparation tool;

determining the order preparation error further based on the third association.

12. A system comprising:

a first camera to capture image data comprised of one or more image frames of a first field of view of a meal preparation area;

a memory; and

a processing device, coupled to the memory; to:

receive, from the first camera, the image data;

determine one of a meal preparation item or a meal preparation action associated with a current state of the meal preparation area based on the image data;

receive order data comprising a plurality of pending meal orders;

determine a first association between the one of the meal preparation item or the meal action and a meal preparation entity;

determine a second association between (i) a first of the plurality of pending meal orders and (ii) the one of the meal preparation item or the meal preparation action, wherein determining the second association is based on the image data and the first association;

determine an order preparation error based on the order data, the first association, the second association, and the one of the meal preparation item or the meal preparation action; and

cause the order preparation error to be displayed on a graphical user interface (GUI).

13. The system of claim 12 , further comprising a second camera to capture second image data of a second field of view of the meal preparation area, the second field of view having an overlapping region with the first field of view.

14. The system of claim 12 , wherein determining the order preparation error comprises:

use the image data as input to a machine learning model; and

obtain one or more outputs of the machine learning model, the one or more outputs indicative of the meal preparation item or the meal preparation action.

15. The system of claim 14 , wherein the processing device is further to:

receive an user input indicative of an accuracy of the order preparation error; and

use the user input to train the machine learning model.

16. The system of claim 12 , further comprising an auditory or visual feedback device, wherein the auditory or visual feedback device is to provide an indication of the order preparation error while the plurality of pending meal orders are being prepared.

17. The system of claim 12 , wherein the processing device is further to:

determine a set of target meal components based on the order data; and

determine an absence of one of the set of target meal components based on the image data, wherein the order preparation error is determined responsive to determining the absence of one of the set of target meal components.

18. A method for training a machine learning model to track a meal order within a meal preparation area, comprising: generating, by a computing device, training data for the machine learning model, wherein generating the training data comprises:

identifying, by the computing device, a first training input having at least one of a first meal preparation item or a first meal preparation action identified from first image data indicative of a first state of the meal preparation area and first order data indicating a first plurality of meal orders; and

identifying a first target output for the first training input, wherein the first target output comprises a first association between the at least one of the first meal preparation item or the first meal preparation action and a first meal order of the first plurality of meal orders; and

providing, by the computing device, the training data to train the machine learning model on (i) a set of training inputs comprising the first training input; and (ii) a set of target outputs comprising the first target output, wherein the trained machine learning model is to receive a new input comprising at least one of a new meal preparation item or a new meal preparation action identified from new image data indicative of a new state of the meal preparation area and new order data indicating a new plurality of meal orders and to produce a new output based on the new input, the new output indicating a new association between the at least one of the new meal preparation item or the new meal preparation action and a new meal order of the new plurality of meal orders.

19. The method of claim 18 , wherein the new output further indicates a completion status the new meal order.

20. The method of claim 18 , wherein each training input in the set of training inputs is mapped to a target output in the set of target outputs.

21. A method, comprising:

receiving, by a processing device, image data comprising one or more image frames indicative of a state of a meal preparation area and order data comprising one or more pending meal orders;

determining, by the processing device, one of a meal preparation item or a meal preparation action associated with the state of the meal preparation area based on the image data;

receiving, by the processing device, order data comprising a plurality of pending meal orders;

determining a first association between the one of the meal preparation item or the meal preparation action and a meal preparation entity;

determining a second association between (i) a first of the plurality of pending meal orders and (ii) one of the meal preparation item or the meal preparation action, wherein determining the second association is based on the image data and the first association;

determining, by the processing device, an order preparation error based on the image data and the order data; and

causing, by the processing device, an auditory or visual feedback device to provide an indication of the order preparation error while the one or more pending meal orders are being prepared.

22. The method of claim 21 , further comprising:

causing, by the processing device, a graphical user interface (GUI) to display a notification indicating the order preparation error.

23. The method of claim 22 , wherein the notification comprises a digital model of the meal preparation area, wherein the digital model indicates a location within the meal preparation area associated with the order preparation error.

24. The method of claim 21 , further comprising:

transmitting, by the processing device, a notification to a point-of-sale (POS) system, the notification preventing a first order status of a meal order associated with the meal preparation error from changing to a second order status indicative of meal order completion.

25. The method of claim 21 , wherein the auditory or visual feedback device is disposed within a meal packaging area of the meal preparation area, wherein the indication of the order preparation error is provided during packaging of one of the pending meal orders associated with the order preparation error.