Patent Publication Number: US-2023140304-A1

Title: Method for pizza preparation

Description:
INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS 
     Any and all applications for which a foreign or domestic priority claim is identified in the Application Data Sheet as filed with the present application are hereby incorporated by reference under 37 CFR 1.57. 
     BACKGROUND 
     Restaurants use food preparation stations in their kitchens. A typical food preparation station has food pans containing food ingredients. Restaurant workers prepare a dish using ingredients from the food pans. A change of ingredient location may confuse restaurant workers. 
     SUMMARY 
     One aspect of the present disclosure provides a method for use in food preparation. The method comprises: providing a food preparation station comprising a preparation table, indicating lights, at least one camera, at least one display, a pan array comprising a plurality of food pans which comprises a first food pan containing a first ingredient; providing at least one recipe database and at least one ingredient database; capturing at least one image of the pan array using the at least one camera; processing the at least one captured image to identify ingredients appearing on the at least one image and determine a location of each of the identified ingredients; updating the at least one ingredient database such that each identified ingredient is linked to the determined location thereof on the at least one ingredient database; and providing a step-by-step consecutive set of guidance for a worker to follow while monitoring the worker&#39;s food preparation. 
     A guidance for a step using the first ingredient may comprise displaying a first instruction for processing the first ingredient on the at least one display using data from the at least one recipe database, and turning on at least one of the indicating lights for indicating the first ingredient at a first location linked to the first ingredient on the at least one ingredient database. When the first food pan containing the first ingredient is moved to a second location on the pan array or the first ingredient is transferred from the first food pan to a second food pan located at the second location, the first ingredient is linked to the second location on the at least one ingredient database with the processes of capturing at least one image of the pan array, processing the at least one captured image and updating the at least one ingredient database may be performed. 
     A subsequent guidance using the first ingredient may comprise turning on at least one of the indicating lights for indicating the first ingredient at a second location linked to the first ingredient on the at least one ingredient database, rather than the first location. A subsequent guidance using the first ingredient may be for a step in another recipe, for a later step in the same recipe, or for the same step of the same recipe that is run at a later time. 
     In the foregoing method, the at least one ingredient database may store each identified ingredient, the determined location linked to each identified ingredient, and at least one of the indicating lights that is associated with each determined location. In the method, the step-by-step consecutive set of guidance may comprise guidance for a first step of a recipe followed by guidance for a second step of the recipe after completion of the first step. The at least one camera may further capture images of the preparation table and food being prepared thereon, wherein the completion of the first step is confirmed based on the captured images of the food being prepared on the preparation table and further based on a completion criterion for the first step from the at least one recipe database. The at least one camera may comprise a first camera configured to capture images of the preparation table and a second camera configured to capture images of the pan array. 
     An aspect of the present disclosure provides a method or use in food preparation. The method comprises: capturing, using at least one camera, images of pizza preparation on a table performed by a person, wherein the pizza preparation comprises a sauce step for spreading sauce on a pizza dough placed on the table, a cheese step for adding cheese over the pizza dough, and a pepperoni step for placing pepperoni slices over the pizza dough. The method further comprises determining whether each of the sauce step, the cheese step and the pepperoni step is completed based on at least part of the captured images real time while the pizza preparation is being performed; and upon determining completion of each of the steps, providing in-situ guidance to the person for the next step or action. 
     Completion of the sauce step may be determined when the sauce is spread more than a predetermined percentage of a 2-dimensional area of the pizza dough. Determining completion of the sauce step may not use at least one captured image in which the person&#39;s hand overlays at least part of the pizza dough. 
     Completion of the cheese step may be determined when the cheese is placed more than a predetermined percentage of the 2-dimensional area of the pizza dough or a sauced area within the 2-dimensional area. Determining completion of the cheese step may not use at least one captured image in which the person&#39;s hand overlays at least part of the cheese. 
     Completion of the pepperoni step may by determined when the count of pepperoni slices placed over the pizza dough is greater than a predetermined number. Determining completion of the pepperoni step does not use at least one captured image in which the person&#39;s hand overlays at least one pepperoni placed over the pizza dough. 
     Determining the completion of the sauce step may comprise one or more of the following steps: processing a first image among the captured images captured during the sauce step to identify a first group of pixels, each of which is located within an outer boundary of the pizza dough, obtaining the 2-dimensional area of the pizza dough based on the count of pixels of the first group, processing the first image or its modified version to identify a second group of pixels, each of which belongs to a sauce area where the sauce is applied over the pizza dough, obtaining a 2-dimensional size of the sauce area based on the count of pixels of the second group, and computing a percentage of the 2-dimensional size of the sauce area with reference to the 2-dimensional area of the pizza dough. 
     Determining the completion of the sauce step may comprise one or more of the following steps: processing a second image from the at least one camera or a modified version thereof to locate a first group of pixels each representing the sauced area; obtain the 2-dimensional area of the sauced area based on the numbers of pixels in the first group; processing the second image or the modified version thereof to locate a second group of pixels each representing the cheese; and obtain a 2-dimensional area of the cheese based on the numbers of pixels in the first group; determine the sauce is spread over the predetermined percentage of the 2-dimensional area of the sauced area based on the 2-dimensional area of the cheese. 
     Determining completion of the pepperoni step may comprise identifying each pepperoni slice placed over the pizza dough, determining if each identified pepperoni is in a size larger or smaller than a predetermined size, and counting the identified pepperoni slices each of which is larger than the predetermined size. 
     Determining completion of the cheese step may comprise one or more of the following steps: overlaying a grid pattern on the 2-dimensional area of the pizza dough or the sauce area of a second image of the captured images captured during the cheese step, for each grid unit of the grid pattern, determining if the cheese occupies the grid unit based on a color of the grid unit, and counting the number of grid units occupied by the cheese. In determining completion of the cheese step, a representative color may be computed for each grid unit, and the representative color may be compared against a predetermined color value to determine if the cheese occupies the grid unit. 
     The representative color may be an average of pixel color values of pixels within each grid unit. When the cheese has a first color, and the sauce has a second color, determining that the cheese occupies a grid unit may be based on either or both of the first and second colors. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee. 
         FIG.  1    is a flow chart for preparing a pizza according to an implementation. 
         FIG.  2 A  illustrates a kitchen system according to an implementation. 
         FIG.  2 B  is a side view of the station of  FIG.  2 A . 
         FIG.  3    illustrates a food pan array viewed from the top according to an implementation. 
         FIG.  4 A  is a photograph of an example food preparation station according to an implementation. 
         FIG.  4 B  is a photograph showing a food pan array of the example station of  FIG.  4 A . 
         FIG.  4 C  shows a camera system of the example station of  FIG.  4 A . 
         FIG.  4 D  shows a light indicator of the example station of  FIG.  4 A . 
         FIG.  5    is a flow chart of overall process of providing food preparation guide to a person according to an implementation. 
         FIG.  6 A  illustrates data of a recipe according to an implementation. 
         FIG.  6 B  illustrates data of food preparation history according to an implementation. 
         FIG.  6 C  illustrates data of a person according to an implementation. 
         FIG.  7    is a flowchart of determining and storing locations of food ingredients according to an implementation. 
         FIG.  8    illustrates data of food ingredients and their locations according to an implementation. 
         FIG.  9    is a flowchart of providing a step-by-step food preparation guidance according to an implementation. 
         FIG.  10    is a flowchart of providing guidance for an individual step of a recipe according to an implementation. 
         FIG.  11    is a flowchart of determining progress of a recipe step according to an implementation. 
         FIG.  12 A  is an example screen for a dough preparation step according to an implementation. 
         FIG.  12 B  is a photograph of a pizza dough being prepared according to an implementation. 
         FIG.  13 A  illustrates a screen for a sauce adding step according to an implementation. 
         FIG.  13 B  is a photograph of a sauce adding step according to an implementation. 
         FIG.  14 A  is an example screen for a cheese adding step according to an implementation. 
         FIG.  14 B  is a photograph of a cheese adding step according to an implementation. 
         FIG.  14 C  is another photograph of a cheese adding step according to an implementation. 
         FIG.  15 A  is an example screen for a topping adding step according to an implementation. 
         FIG.  15 B  is a photograph of a topping adding step according to an implementation. 
         FIG.  16    illustrates a screen for a topping adding step according to an implementation. 
         FIG.  17    illustrates a screen notifying a completed food preparation according to an implementation. 
         FIG.  18    is an example screen to provide performance feedback according to an implementation. 
         FIG.  19    illustrates one or more computing systems for use with one or more implementations. 
     
    
    
     DETAILED DESCRIPTION 
     Hereinafter, implementations of the present invention will be described with reference to the drawings. These implementations are provided for better understanding of the present invention, and the present invention is not limited only to the implementations. Changes and modifications apparent from the implementations still fall in the scope of the present invention. Meanwhile, the original claims constitute part of the detailed description of this application. 
     Food Preparation Station 
     Restaurants use food preparation stations in their kitchens. A typical food preparation station has a food preparation table and food pans containing food ingredients. Restaurant workers (workers) prepare a food on the food preparation table using ingredients from the food pans. 
     Recipe Guidance and Food Pan Indicating Light 
     To help workers prepare food, guidance for preparing food may be provided on the food preparation station. Workers may follow such instructions to prepare food. The station may be provided with indication lights for indicating food pans. To help workers locate ingredients quickly, the station may turn on an indicating light to indicate a food pan containing a particular ingredient to be used at a particular step of the instructions. Sometimes, however, the food pan indicated with the indicating light may contain another ingredient, which may confuse workers. 
     Tracking Changes of Ingredient Location 
     An enhanced food preparation station may be associated with a system that tracks location changes of the food pans or ingredients contained in the food pans. The system may have the very current location of each ingredient contained in each food pan. Then, the system can use the accurate location of each ingredient from the system and turn on the indicating light(s) for indicating the correct ingredient to be used at each step of the instructions. The configuration and operation of an enhanced food preparation station will be described with reference to an example recipe. 
     Pepperoni Pizza 
       FIG.  1    illustrates a flow chart for preparing a pepperoni pizza on a food preparation station before the pizza is baked in a pizza oven or furnace. Step 1 is preparing a dough, which is followed by Step 2 for adding sauce on the dough. Then, at Step 3, cheese is added over the sauce, which is followed by Step 4 for adding pepperoni over cheese. As exemplified, a flow of preparing a pizza includes steps of sequentially stacking a food ingredient over a pizza dough. While a pepperoni pizza recipe is discussed herein, the station can guide a person to prepare different pizzas and various dishes other than pizzas. 
     FOOD PREPARATION SYSTEM 
     Food Preparation Station 
       FIG.  2 A  illustrates a kitchen system according to an implementation.  FIG.  2 B  illustrates a side view of the station of  FIG.  2 A .  FIG.  3    illustrates a food pan array viewed from the top. The food preparation station  100  of  FIG.  2 A  includes a food preparation table  110  and a food pan array  120 . The station  100  further includes a display  130 , light indicators  140 , at least one camera  150 , a computing system  160 , a database  170 , and an ID card reader  180 .  FIG.  4 A  to  FIG.  4 D  are photographs of an example food preparation station  4100 . 
     Food Preparation Table 
     The food preparation table  110  provides a working surface on which food is prepared.  FIG.  2 B  shows a person  210  preparing a pizza  220  on the table  110 . The table  110  is adjacent to the food pan array  120  such that the person  210  can pick up food ingredients from the array  120  without having to step toward the array  120 . The station of  FIG.  4 A  has a food preparation table  4120  with two pizzas  4121 ,  4122  being prepared. The table  4120  is sized such that two persons can work at the same time. 
     Food Pan Array 
     A food pan array is for temporarily storing food ingredients. The food pan array  120  of  FIG.  3    includes a frame  310  and a plurality of food pans  320  placed on the frame  310 .  FIG.  4 B  shows another food pan array  4110 . In the example of  FIG.  3   , the food pans  320  are arranged in 6 columns and 2 rows. A food pan array may have a different arrangement from the examples. 
     Food Pans 
     In an implementation, each one of the food pans  320  is a container for storing one or more food ingredients. The pans may be in the same size or different sizes. The pans may be in the same shape or different shapes. A food pan may be used with or without a lid or cover.  FIG.  4 B  example food pans  4420  containing ingredients to prepare pizzas. 
     Food Pan Frame—Rail Structure 
     In an implementation, the frame may have a rail structure on which one or more food pans are placed. Referring to  FIG.  4 B , the food pan array  4110  have two elongated bars (rails)  4410  on which food pans  4420  are placed in a row. Each food pan has a flange to be slidably placed on the two elongated rails such that each food pan can slide along the rails  4410  and change its location in the array  4110 . 
     Food Pan Frame—Recesses 
     In an implementation, the frame may include a plurality of recesses (or holes), each of which is to receive one or more food pans. One or more food pans can be placed into each recess. In embodiments, a frame may have a structure different from the examples for holding one or more food pans. 
     Light Indicators 
     In an implementation, light indicators are used to visually indicate locations of food ingredients. Referring to  FIG.  3   , a light indicator  141  is provided above a pepperoni pan  321 . When pepperoni is needed for the pizza  220 , the indicator  141  may be selectively turned on to draw the person&#39;s attention to the pan  321  and to indicate location of pepperoni while the other light indicators are not turned on. Alternatively, to indicate the pepperoni pan  321 , the indicator  141  may be turned off while all the other light indicators are turned on. 
     Location of Light Indicators 
     In  FIG.  2 A , for example, the light indicators  140  are installed on the frame  310 . In implementations, one or more lights may be attached to a pan of the array  120  such that the lights are visible to the person  210 . In implementations, a lighting device such as a spotlight installed over the station may highlight a particular food pan to indicate ingredient contained therein. 
     Positional Association Between Indicator and Pan 
     Light indicators may be arranged according to a predetermined layout from which the person  210  can recognize which pan is associated which light and will pay attention to a particular pan when an indicator is on. For example, in  FIG.  3   , a series of light indicators  142  are installed along an upper edge of the frame  310  and above Row 2 of food pans. The light indicators  142  are sized and arranged such that each indicator is positioned right above its corresponding food pan of Row 2. From the arrangement, the person  210  recognizes that the indicator  141  is associated with the pepperoni pan  321  as it is the closest to the pan  321 , and will pay attention to the pepperoni pan  321  when the indicator  141  is on. In  FIG.  3   , for another example, a light strip  144  is installed along a lower edge of the frame  310  and under Row 1 of food pans, and a group of six lights  146  is right under the sauce pan  323 . Turning on the six lights  146  would suggest the person  210  to pay attention to the sauce pan  323  rather than other pans because the sauce pan  323  is the closest pan right above the lights  146 . 
     Indicator Not Suggesting a Particular Pan 
     In  FIG.  3   , among the lights  145  of the light strip  144 , two lights  148  are not distinctively close to a particular pan, and do not overlap any food pan along a column direction. While the system may turn on a group of lights  147  to indicate the cheese pan  324  and turn on another group  146  to indicate the sauce pan  324 , the system may not turn on the two lights  148  interposed between the two groups  146 ,  147 . In implementations, the system may not operate an indicator in association with a particular food pan when the person would not recognize that the pan is associated with the indicator from the indicator&#39;s location on the frame  310 . 
     Two or More Indicators for a Single Pan 
     In implementations, two or more indicators are assigned to a single food pan. Referring to  FIG.  4 D , a light indicator  4140  includes two LED light strips  4141 ,  4142  installed above a food pan  4421 . The two strips  4141 ,  4142  may operate together or independently to draw a person&#39;s attention to the pan  4421 . When two pizzas  4121 ,  4122  are being prepared on the table  4120  as shown in  FIG.  4 B , the lower strip  4141  may be turned when the pan&#39;s ingredient is needed for the left pizza  4121 , and the upper strip  4142  may be turned on when the pan&#39;s ingredient is needed for the right pizza  4122  although not limited thereto. 
     Controlling Indicators Referencing to Database 
     To indicate locations of food ingredients using light indicators, the system may have location information for each indicator and also have information of which indicator is associated which ingredient. In implementations, for each food ingredient, the system stores the location of the ingredient in connection with one or more light indicators that has positional association with the ingredient as exemplified in  FIG.  8   . When an ingredient is needed to prepare the pizza  220 , the system may locate one or more light indicators to turn on based on link between the ingredient and the one or more light indicators on the database. 
     Operation Modes of Light Indicators 
     A light indicator may stay turned-on, flashes, or change its color and brightness to indicate location of its corresponding food ingredient or to indicate a status of the food ingredient. The light indicator may operate in a way different from the example to draw the person&#39;s attention. 
     Display 
     The display  130  is for displaying food preparation information for the person  210  working at the station  100 . For example, the display  130  may display one or more of a received order, instructions to prepare an ordered pizza, the current progress of pizza preparation, and a performance feedback after the pizza is prepared. 
     Location of Display 
     The display  130  may be placed over the food pan array  100  although not limited thereto. In an implementation, the display  130  may be installed next to the table such that the person can see the pizza  200  and the display  130  at the same time. In implementations, the display  130  is facing the person  210  such that the person can read information on the display while preparing the pizza  220  on the table  110 . 
     Two or More Displays 
     In an implementation, a food preparation may use two or more displays. In  FIG.  4 A , the station  4100  has two independent displays  4131 ,  4132 . The left display  4131  may provide guidance for a first person to prepare the left pizza  4121 , and the right display  4132  may provide guidance for a second person to prepare the right pizza  4122  although not limited thereto. 
     Camera 
     The system includes one or more cameras  150  for capturing images of the table  110  and the array  120 . Referring to  FIG.  2 B , a camera  152  is installed for monitoring food ingredients in the pans  320 , and another camera  151  is installed for monitoring the pizza  220  being prepared on the table  110 . In an implementation, a single camera may monitor both of the table  110  and the food pan  320 . In the station of  FIGS.  4 A to  4 C , a camera  4151  is provided for monitoring food preparation on the table  4120  and another camera  4152  is provided for monitoring food ingredients in the array  4110 . 
     Camera Location 
     The camera of  FIG.  2 B  is installed over the food pan array  120  and the display  130  to not interfere the person&#39;s sight or action. In  FIG.  4   , the two cameras  4150  are installed over the displays  4131 ,  4132  and the food pan array  4110 . In implementations, a camera system may be at a location different from the examples. 
     Additional Monitoring Devices 
     In an implementation, the station  100  includes a device other than a camera to monitor food ingredients or the pizza  220  being prepared. For example, one or more thermometers may monitor temperature of each food ingredient or the pizza. A weight measurement system can be used to measure the weight of the pizza  220  or a food ingredient contained in a food pan. A laser scanner or a light detection and ranging (LIDAR) device may be used for measuring a thickness of a food ingredient (e.g., pizza dough, cheese over the pizza dough) or for measuring location and distribution of an ingredient on the pizza  220 . In an implementation, a device other than the examples may be used. 
     Computing System 
     The computing system  160  is for process information relating to operation of the station  100 . The computing system  160  is connected to the display  130 , the light indicators  140 , the camera  150 , the database  170  and the ID card reader  180 . The computing system  160  may communicate with a device outside the station  100 . In an implementation, the computing system  160  can be outside a kitchen where the food preparation table  110  is located, and communicates with other devices of the station  100  via a communication network. In an implementation, the computing system  160  communicates with another computing system to obtain information of an order for a pizza. In an implementation, the computing system  160  can use computing power of another system (e.g., cloud computing). An example architecture of one or more computers systems for use with one or more implementations will be described in detail with reference to  FIG.  19   . 
     Database 
     The database  170  is for storing data for providing food preparation guidance. The database  170  may be one or more of a local data store of the computing system  160  and a remote data store connected to the computing system  160  via a communication network. The database  170  may store a plurality of recipes that may be prepared at the station, profiles of worker or person, and history of food preparation works done at the station  100 . For each recipe, the database  170  may store information of necessary ingredients, and locations of the ingredients. For each worker or person, the database  170  may store a skill level for each pizza and history of food preparation works. The database  170  may store additional data other than the example, and may not store one or more of the examples. Data stored on the database  170  will be described in detail with reference to other drawings. 
     ID card Reader 
     The ID card reader  180  is for check-in and check-out of the person  210  at the station  100 . The station may include 100 includes one or more of an ID card reader, a keypad, and a face recognition system. The station  100  may include a device other than the example devices.  FIG.  4 A  shows two ID card readers  4181 ,  4182  installed on a frame of the array  4110 . 
     PROVIDING FOOD PREPARATION GUIDANCE 
       FIG.  5    is a flow chart for providing guidance to prepare food, here a pizza. In response to an assignment to prepare a pizza at the station  100 , the system may retrieve data of a worker or person, retrieve recipe data of the ordered pizza, and provide guidance according to the retrieved recipe data. 
     Retrieving Worker Data (S 510 ) 
     In response to a check-in of the person or worker  210  or upon initiation of ***, the computing system  160  may locate the person&#39;s profile on the database  170 . The computing system may load data of the located profile on its local memory, or may use data already stored on its local memory without newly retrieving data from the database  170 . An example profile of a worker will be discussed with reference to  FIG.  6 C . This step is optional and may be omitted. 
     Retrieving Recipe (S 520 ) 
     In response to an order for the pizza  220  or upon initiation, the computing system  160  locates the pizza&#39;s recipe on the database  170  and loads data of the recipe on a local memory. This step S 520  may precede the step of retrieving worker data S 510 . The two steps S 510 , S 520  may be performed in parallel. In an implementation, the computing system  160  uses data stored on its local memory without newly retrieving recipe data from the database  170 . An example recipe (pepperoni pizza) will be discussed with reference to  FIG.  6 A . 
     Providing Guidance (S 530 ) 
     Based on the recipe data and the person&#39;s profile, the system may provide a food preparation guidance to the person  210 . For example, the system may display a text instruction on the display  130 , play an audio or video guide, and turn on a light indicator to notify location of a pizza ingredient. The system may provide different instructions based on the person&#39;s experience level or work history related to the current recipe. Example data for use in providing food preparation guidance will be described in detail with reference to  FIG.  6 A  to  FIG.  6 C . 
     RECIPE DATA 
     Recipe Data 
       FIG.  6 A  shows data of an example recipe stored on the database  170 .  FIG.  6 B  show an example food preparation history.  FIG.  6 C  shows example data of a worker (a station user). According to  FIG.  6 A , the database stores, for each recipe, recipe name  610 , step number  620 , instruction  630 , ingredient  640  and step completion requirement  650 . According to  FIG.  6 B , the database stores a log of completed orders. For each order, the database stores an order number  681 , a recipe name  610 , a Worker ID  670 , Time of Order Received  682 , Time of Order Completed  683 , and Preparation Speed Rating  684 . According to  FIG.  6 C , the database stores profiles of workers. For each worker, the database stores a worker ID  670 , one or more recipes  610 , a preparation time rating  681 , and a preparation quality rating  682 , and an experience level  680 . In implementations, the database stores data in a way different from the example of  FIG.  6 A  to  FIG.  6 C . The database  170  may store additional data different from the example, and may not store one or more of the example data. 
     Recipe Name ( 610 ) 
     The recipe name  610  is for uniquely identifying each recipe on the database  170 . When an order for ‘pepperoni pizza’ is received, a corresponding recipe  600  can be located using the recipe&#39;s name  610 . In an implementation, information other than the name of pizza may be used. For example, a predetermined code of a pizza may be used for delivering order information to the computing system  160 , and the computing system  160  locates a corresponding recipe using the predetermined code. 
     Sequence Number ( 620 ) 
     The example recipe  600  of ‘pepperoni pizza’ has four steps in total. Each step is numbered according to its order in the recipe, from Step 1 to Step 4. A recipe may have steps fewer or more than four. The database  170  may store the step order in a way different from the example of  FIG.  6 A . 
     Instruction ( 630 ) 
     For each step of the example recipe  600 , the database may store one or more instructions to help the person  210  during each of the recipe steps. The instructions may include one or more of a text message, an audio message and a video guide predetermined for the recipe step. For example, when the person  210  needs to perform Step 1 (preparing a dough), the system may locate a first message  631  linked to Step 1 and deliver the first message to the restaurant worker. 
     Text Instructions 
     In an implementation, the first message  631  includes a text instruction “Prepare a 10-inch dough”, the second message  632  includes a text instruction “Place sauce on ¾ of dough”, the third message  633  includes a text instruction “Place cheese to cover 90% of sauce”, the message  534  includes a text instruction “Place 12 slices of pepperoni”. These text messages may be presented on the display  130  to guide a restaurant worker. 
     Audio and Video instructions 
     In an implementation, the database stores an audio or video instruction for a recipe step, and the system plays the audio/video instruction at the beginning or during the recipe step. For example, when Step 1 is completed, the system delivers a voice instruction saying “Place sauce on ¾ of dough” for Step 2. For another example, during Step 2, the system may play a video guide showing how to apply sauce repeatedly on the display  130 . 
     Selective Instructions Based on Monitoring of Food Preparation 
     In implementations, among instructions stored on the database  170 , the system may provide one or more instructions selectively based on monitoring of the pizza  220 . The system may select one or more instructions among a set of predetermined instructions based on one or more features identified from monitoring of the pizza being prepared. In implementations, the system may generate a new instruction that is suitable for the current status of the pizza  220 . For example, during Step 2 (adding sauce), the system may request to add more sauce when it is determined the amount of added sauce is not sufficient to complete Step 2. 
     Ingredient ( 640 ) 
     For each step of the recipe  600 , one or more ingredients are linked on the database  170 . For example, Step 1 for preparing a dough is linked to ‘dough’, and Step 2 for adding sauce is linked to ‘sauce’. In an implementation, no ingredient may be linked to a recipe step when the step does not involve addition or removal of an ingredient. 
     Completion of Recipe Step ( 650 ) 
     For each step of the recipe  600 , the database  170  stores one or more requirements to determine whether the step is completed. The requirements may include one or more of (1) a desirable amount or count of an ingredient to be added (or removed) during the current step, (2) a size of an ingredient on the pizza  220 , (3) a shape of the ingredient, (4) a desirable position of the ingredient, (5) distribution of the ingredient, (6) distance between individual pieces of the ingredient, (7) a temperature of the pizza  220 , (8) a predetermined time limit of the current step, and (9) a quality or status of the ingredient (e.g., freshness, frozen, melt, chopped, deformation). For example, the system may determine that Step 4 (adding pepperoni) is completed when at least 12 slices of pepperoni (each sized greater than a predetermined minimum size) are added on the pizza  220 . In an implementation, a requirement different from the examples may be used to determine a completed step. 
     Evaluating Preparation Quality of Recipe Step 
     In an implementation, the system may evaluate the quality pizza preparation for each of the recipe step. To evaluate the preparation quality, the system may consider one or more features discussed above for determining step completion. In an implementation, the system may evaluate a recipe step using one or more criteria different the step completion requirements. For example, the system may compute a rating for Step 4 (adding pepperoni) based distribution of pepperoni slices on the pizza  220  when completion of Step 4 is be determined based on the count of the pepperoni slices. In an implementation, the database  170  may store one or more criteria to evaluate a preparation quality of the pizza  220  for each recipe step. 
     Work History 
     The database  170  may stores records of orders prepared (or bring prepared) at the station  100 . As shown in  FIG.  6 B , the database  170  may store, for each order, one or more of an order number  681  uniquely identifying the order, the name of ordered pizza  610 , an identification  670  of a person who prepared the ordered pizza, a time when the order is received  682 , a time when the ordered pizza is prepared  683 , and a speed rating of pizza preparation work  684 . In an implementation, the database  170  may store a data different from the examples of  FIG.  4   . In an implementation, the database  170  may store pizza orders prepared at a station other the station  100   
     Worker ID ( 670 ) 
     The database  170  may stores a worker ID that is uniquely identifying a worker on the database. When a person taps his ID card to the card reader  180 , the computing system may obtain the person&#39;s ID (HKL) and locate data of the person on the database. In an implementation, as shown in  FIG.  6 B , a worker ID is linked with orders  681  the worker prepared such that the worker&#39;s performance or experience level may be determined based on the person&#39;s order history. 
     Preparation Speed Rating ( 684 ) 
     The system may compute, for each completed order, a rating that represents how fast the ordered pizza had been prepared. The system may compute a preparation time of the ordered pizza using the order received time  682  and the pizza completion time  683 , and compares it with a predetermined desirable preparation time for the ordered pizza to determine the speed rating  684 . The system may measure the preparation time of the pizza from the start of the first recipe step on the table. In an implementation, the system may measure a completion time and evaluate preparation speed for each recipe step. 
     Worker Profile 
     In  FIG.  6 C , the database  170  stores a profile for each worker of the station  100 . For each worker, the database  170  may store one or more of a Worker ID  670 , recipe names  610  of pizzas the worker prepared, a preparation speed rating  684  representing the worker&#39;s pizza preparation speed, and a preparation quality rating  685  representing the worker&#39;s work quality, and an experience level  690  of the worker. In an implementation, the database  170  may store data different from the examples. 
     Preparation Quality Rating ( 685 ) 
     The system may compute a preparation quality rating representing how properly the worker prepared pizzas in accordance with their predetermined recipes and quality standards. For example, for each recipe of pizzas a worker prepared, the system may evaluate preparation quality for each individual step of the recipe, and compute a percentage of steps satisfying a predetermined quality standard. The preparation quality rating  685  can be determined in a way different from the example. 
     Experience Level ( 690 ) 
     The database  170  may store an experience level for each recipe linked to the worker ID  670 . The experience level for a recipe may be determined based on one or more of the number of pizzas the worker prepared using the recipe, the worker&#39;s preparation time rating  684 , and the worker&#39;s preparation quality rating  685 . The experience level may be determined considering another factor different from the examples. 
     Different Instructions for Different Experience Levels 
     In an implementation, in providing guidance to prepare the pizza  220 , the system may consider the profile of the person  210  preparing the pizza  220  at the station  100 . The system may provide different instructions based on one or more of the person&#39;s experience level  690  and the ratings  684 ,  685  about the ordered pizza (its recipe). For example, the system may provide no or limited guidance when the worker is well experienced about the ordered pizza, and may provide a more detailed guidance when the worker has a lower level of experience about the ordered pizza. 
     UPDATING FOOD INGREDIENT LOCATION 
     The kitchen system indicates the location of an ingredient within the pan array while food is being prepared. To inform the location, the system needs to have the current location of the necessary ingredient, and the specific light indicator associated with the current location of the ingredient. The system performs a process to keep data current for notifying the locations of food ingredients within the pan array. 
     Process of Updating Ingredient Locations 
       FIG.  7    is an example process to update locations of food ingredients. The process includes capturing images of the food pan array (S 710 ), processing captured images to determine the location of each food ingredient (S 720 ), determining one or more indicators associated with the location of each food ingredient (S 730 ), storing association between food ingredients and light indicators on the database  170  (S 740 ). 
     Capturing Images of Food Pan Array (S 710 ) 
     At least one camera captures images of the array  120 . The images of the array  120  may be captured continuously, periodically or intermittently. The captured images are then sent to the computing system  160  (or another computing device) for further processing. In implementations, the camera  150  may acquire a video of the array  120  continuously, and send at least part of the video frames to the computing system of another computing device. 
     Identifying Ingredients in Pans 
     The computing system  160  may process one or more images of the array  120  to identify food pans and food ingredients. In implementations, the computing system  160  with appropriate software processes one or more images to locate each food pan in the images. In implementations, the computing system  160  may perform image segmentation of camera image(s) using a machine-trained model, and identify one or more food pans (or food ingredients) corresponding to segment(s) in the camera images(s). In implementations, for each identified food pan, the computing system may compute one or more features (e.g., color, shape, and size, volume) of its contained material, and determine that a particular ingredient is contained in the pan when the computed feature(s) match the ingredient&#39;s feature(s) stored on the database. The system may identify food pans or food ingredients using an approach different from the examples. 
     Determining Location of Ingredient (S 720 ) 
     The computing system  160  determines location of each food pan (or food ingredient) identified from processing of the images of the array  120 . In implementations, the computing system  160  may process the images of the array  120  to determine a reference (e.g., a corner point, a center point) for each pan and to compute a coordinate of the pan&#39;s reference point from a reference point of the frame  310  (e.g., a corner point, a center point). The computing system  160  may store the computed coordinate on the database  170  as the location of the pan&#39;s food ingredient. In implementations, when food pans are arranged columns and rows as in  FIG.  3   , the system may store the location of the pepperoni pan  321  as Row 2, Column 2 as shown in  FIG.  8   . 
     Determining Indicator Corresponding to Ingredient (S 730 ) 
     The system may determine one or more indicators that will draw attention to a particular food pan based on positional relationship between the indicator and the ingredient. Referring to  FIG.  3   , the light indicators  142 ,  144  are installed on the frame according to a predetermined layout. The location of the pepperoni pan  321  (Row 2, Column 2) is determined from processing of camera images. The system may assign the indicator  141  to the pan  321  as no other indicator is closer to the pan  321  and no other pan is closer to the indicator  141 . In implementations, the system may associate an indicator with a pan when they are within a predetermined distance from each other although not limited thereto. In implementations, the system may use a map of food pan array that defines one or more indicator assignment zones. For each zone of the food pan array, the system assigns at least one light indicator based on positional association between the zone and the indicator such that turning on the indicator would draw the person&#39;s attention to the zone. When it is determined that an ingredient (or a pan) is located at an indicator assignment zone, the system associates or links, on the database, the ingredient (or the pan) to the indicator assigned to the zone such that the indicator may be turned on to indicated location of the ingredient. 
     Updating Database to Store Indicator Associated with Ingredient (S 740 ) 
     The system may store on the database  170  information of which light indicator is associated with which food ingredient. Each food ingredient may be linked to at least one light indicator on the database. In  FIG.  8   , for example, cheese is linked to the location of the cheese pan  324  (Row 1, Column 3) which is linked to the light group  147 , and accordingly cheese is linked to the light group  147 . Based on this association between cheese and the light group  147 , the system may operate the light group  147  to indicate the location of cheese in the array  120 . 
     Updating Pan Location Changes Real Time 
     In implementations, the system may perform the process of  FIG.  7    continuously, periodically or intermittently to maintain the database  170  current and to reflect a pan location without delay. The system may perform the process independent of providing step-by-step instructions for the pizza  220 . The system may perform the process while it is providing instructions to prepare the pizza  220  such that the system can update the database real-time in response to a pan location change during the preparation of the pizza. The system may perform the process during a waiting time after completing a pizza such that a pan location change is reflected on the database before preparing another pizza. 
     Responding to Location Change Due to Food Pan Refill 
     Sometimes, location of a food pan may be moved in the food pan array  120  after refilling the food pan. For example, when the person  210  refills the sauce pan  323  and the cheese pan  324  after preparing a first pizza, the person  210  by mistake may switch locations of the two pans. In response to such pan location change, based on processing of camera images(s), the system updates the database such that the sauce pan  323  is linked to the light  147  and the cheese pan is linked to the light  146 . Subsequently when the person  210  prepare a second pizza, the system may turn on the light  147  when sauce is need for the second pizza while it turned on the light  146  when sauce was need for the first pizza. 
     Monitoring of Additional Feature—Ingredient Amount 
     Besides monitoring locations of food ingredients, the computing system  160  may processes one or more images from the camera  150  to monitor amount (for example, volume) of each food ingredient. The system may determine whether there are enough ingredients in the food pans considering one or more of a received order, an expected order, and a predetermined amount. When it is determined that a food pan does not store enough food ingredient, the system may provide an instruction to refill the food pan. In an implementation, the system may use a weight sensor, a LIDAR system, or another sensor other than the camera system for monitor amount of a food ingredient. 
     STEP-BY-STEP FOOD PREPARATION GUIDANCE 
       FIG.  9    is a flowchart of providing a step-by-step food preparation guidance based on the example recipe  600 . The system may provide guidance for each step sequentially from the first step (Step 1) to the fourth step (Step 4). Operation of the system for each step will be described in detail referencing to other drawings. 
     Providing Guidance of Individual Recipe Step 
       FIG.  10    is a flowchart of providing guidance for an individual step of a recipe according to an implementation. The process may include providing one or more instructions of the current step (S 1010 ), indicating location of an ingredient necessary for the current step (S 1020 ), and determining if the current step is completed based on monitoring of the pizza  220  being prepared (S 1030 ). The process of  FIG.  10    will be explained below using the example recipe  600 . 
     Providing Instruction of Current Step (S 1010 ) 
     The system may locate one or more instructions  630  linked to the current step on the database  170 , and provide the instructions to the person  210  working at the station  100 . For example, for Step 1 (preparing dough), the system may retrieve the message  631  linked to Step 1 from the database  170 , and control the display  130  to present the retrieved message. In  FIG.  12   , the text instruction “Prepare a 10-inch dough” is presented on the display  130  for Step 1. 
     Activating Indicator Associated with Ingredient of Current Step (S 1020 ) 
     The system may locate, on the database  170 , one or more light indicators linked to an ingredient necessary for the current step. To indicate the location of the necessary ingredient, the system may turn on the one or more light indicators, and turn off other indicators that are not linked to the necessary ingredient. For example, for Step 4 (adding cheese), the system refers to the database  170  shown in  FIG.  8    to locate the light group  146  that is linked to ‘cheese’. Then, the system may turn on the segment  146  of the light strip to indicate location of cheese in the food pan array  100 . 
     Determining Step Completion (S 1030 ) 
     For each recipe step, the system may determine whether the current step is completed to move on to the next step. The system may locate one or more completion requirements  650  of the current step from the database of  FIG.  6 A , and may determine the current step is completed when the requirements are satisfied. For example, the completion requirement for Step 4 is to add at least ‘twelve’ slices of pepperoni. The system may process one or more images of the pizza being prepared, count pepperoni placed, and determine that Step 4 is completed when the count reaches twelve. An example process for determining step completion will be described in more detail referencing to  FIG.  11   . 
     Completion of Recipe 
     In an implementation, when it is determined that the current step is completed, the system turns off indicator lights activated for the current step, and proceeds to provide guidance for the next step of the recipe. The system may provide a notification that the current step is completed. In an implementation, when it is determined that the last step is completed, the system provides a notification that the pizza is ready for serving to a customer or ready for a further processing. An example screen of  FIG.  17    shows a notification that all steps at the station  100  are completed and the pizza  220  is ready to bake. 
     DETERMINING COMPLETION OF INDIVIDUAL RECIPE STEP 
     Determining Based on Monitoring of Pizza 
       FIG.  11    shows a flowchart of determining completion of a recipe step based on monitoring of a pizza being prepared. The process may include capturing images of the pizza  220  being prepared (S 1110 ), processing the images to identify one or more ingredients on the pizza  220  (S 1120 ), computing a progress index of the current step (S 1130 ), determining whether the current step is completed (S 1140 ), and repeating the steps (from S 1110  to S 1140 ) when the current step is not completed. 
     Capturing Images of Pizza Being Prepared (S 1110 ) 
     One or more cameras may be used to monitor a dish being prepared. Referring to  FIG.  2 B , the camera  151  may, periodically or intermittently, capture images of the pizza  220  and send the images to the computing system  160  or another computer for further processing. The camera  151  may acquire a video of the table  110  continuously, and send one or more frames of the video to a computing device for further processing. 
     Image Processing to Identify Food Ingredient (S 1120 ) 
     The system may process one or more images from the camera  150  to identify one or more food ingredients on the pizza  220  being prepared. In an implementation, the computing system  160  detects an object in an image, determines feature(s) (e.g., color, shape, and size) of the object, and determines a food ingredient when the object&#39;s feature(s) matches the food ingredient&#39;s data stored on the database. The computing system  160  may use various algorithms other than the examples for identifying food ingredients. In an implementation, the computing system  160  uses a machine-trained model for identifying food ingredient(s) from the camera image(s). For example, the computing system may perform image segmentation of a camera image to find one or more segments each corresponding to an object in the image, to find boundaries separating the segments, and to classify pixels of the images into the segments. 
     Determining Visible Features of Food Ingredients 
     In an implementation, the system may process the camera image(s) to determine one or more features for each food ingredient appearing in the camera image(s). For each ingredient, the system may determine one or more of size, count, location and color although not limited thereto. For example, for Step 1 (preparing dough) of the example recipe, the system may compute a size, an area and a color of the dough for use in determining completion of Step 1. For Step 4 (placing 12 slices pepperoni), the system may determine one or more of the number of pepperoni slices added on the pizza  220 , the size of each pepperoni slice, and the location of color each pepperoni slice. 
     Determining Non-visible Feature 
     In an implementation, the system may determine one or more non-visible features not relying on visual of food ingredients in the camera images. For example, the system may obtain one or more of the temperature of the pizza, the weight of the pizza, and time elapsed for the current step although not limited thereto. 
     Determining Progress Index (S 1130 ) 
     In an implementation, the system may compute an index (measure) representing progress of the current step using one or more features obtained from monitoring of the pizza  220  being prepared. The progress index may be based one or more of the visible features, one or more of the non-visible features, and combination of thereof. Example progress indices will be discussed in detail with reference to  FIG.  12 A  to  FIG.  16   . 
     Determining Step Completion (S 1140 ) 
     The system may determine the current step&#39;s completion when the current step&#39;s progress index reaches a predetermined threshold (e.g., 100%). The system may determine the current step&#39;s completion when the completion requirement  650  of the current step is satisfied. Once it is determined that the current step is completed, the system starts to provide guidance for the next step. 
     STEP-BY-STEP GUIDANCE FOR EXAMPLE RECIPE 
     Screen for Dough Preparation Step 
       FIG.  12 A  is an example screen  1200  for Step 1 (dough preparation) of the example recipe  600 .  FIG.  12 B  is a photograph of an example pizza dough. In  FIG.  12 A , the screen  1200  presents the pizza&#39;s name  1210 , the current step&#39;s number  1220 , a text instruction for the current step  631 , an image (or a video stream)  1230  of the pizza being prepared, a progress indicator  1240 , and time elapsed for the order  1260 . 
     Progress Based on Size of Dough 
     Step 1 is to prepare a ‘ 10-inch’ dough. The system may process one or more images of the dough  1250  to compute the dough&#39;s size (e.g., length, diameter, 2-dimensional area). The system may compute progress of Step 1 using the computed dough size. In  FIG.  12 A , the current progress of 90% is computed as a ratio of the computed dough&#39;s size (9 inches) with the required size (10 inch) for completing Step 1 although not limited thereto. In an implementation, the system may consider one or more of the dough&#39;s shape, 2-dimensional area, thickness, freshness and color to determine progress of Step 1 although not limited thereto. 
     Completion of Dough Preparation Step 
     The system may determine completion of Step 1 when the dough&#39;s size satisfies Step 1&#39;s predetermined requirement. In an implementation, when a pre-baked dough is used for the pizza  220 , the system may determine completion of the dough preparation step when the pre-baked dough is placed on the table  110 . After determining completion of Step 1, the system starts to provide guidance for the next step in the recipe, Step 2. 
     Screen for Sauce Adding Step 
       FIG.  13 A  is an example screen  1300  for Step 2 (applying sauce) of the example recipe  600 . Referring to  FIG.  13 A , the screen presents an image  1330  featuring the dough  1250  prepared at Step 1 and sauce  1350  applied over the dough. The screen may also present an instruction  632  for Step 2 and a progress indicator  1340 .  FIG.  13 B  is a photograph of an example pizza dough with sauce added. 
     Progress Based on Area of Sauce 
     Step 2 is to apply sauce over ¾ of the dough prepared at Step 1. The system may process one or more images of the pizza being prepared to compute a 2-dimensional area of the dough  1250  and a 2-dimensional area of the sauce  1350  placed on the dough. Using the computed areas, the system may compute a ratio of the sauce area to the required area (¾ of the dough area) as the progress measure of Step 2. In an implementation, the system may compute the dough&#39;s area assuming the dough is in a circular shape and using the diameter of the dough. In an implementation, as shown in  FIG.  13 B , the system may draw a box  1371  surrounding a dough  1372 , and may use the box&#39;s area for computing the progress measure. The system may use a processing different from the examples. 
     Image Segmentation to Identify Sauced Area 
     In implementations, the system may process the image  1330  using a machine-trained model to identify a first group (segment) of pixels as the sauced area  1350  and to identify a second group (segment) of pixels as the dough  1250  that is not cover with the dough. The system may compute an area of the sauced area  1350  using the number of pixels in the first group, compute an area of the dough using on the number of pixels in the second group, and compute a ratio between the two areas for evaluating progress of Step 2. If the first group (sauce) is of 600 pixels in the image  1330  and the second group (dough not covered with the sauce) is of 400 pixels, the system may determine that 60% of the dough is covered with the sauce. 
     Completion of Sauce Placing Step 
     The system may determine completion of Step 2 when the sauced area  1350  is larger than a predetermined percentage of the 2-dimensional area of the dough. In an implementation, the system may determine completion of Step 2 using a criterion other than the area ratio. 
     Example Screen for Cheese Adding Step 
       FIG.  14 A  is an example screen  1400  for Step 3 (adding cheese) of the example recipe  600 . Referring to  FIG.  14 A , the screen presents an image  1430  featuring the dough  1250  prepared at Step 1, the sauce  1350  applied at Step 2, and cheese  1450  added over the dough. The screen also presents the instruction  633  for Step 3 and a progress indicator  1440 .  FIG.  14 B  is a photograph of a pizza when cheese is being added.  FIG.  14 C  is another photograph showing a cheese adding process. 
     Computing Progress of Cheese Adding Step 
     Step 3 is to place cheese to cover 90% of sauce. The system may process one or more images of the pizza being prepared to compute a 2-dimensional area of the sauce  1350  and a 2-dimensional area of cheese added the sauce  1350 . The system may compute a ratio of the area of cheese to the area of the sauce as the progress measure  1440  of Step 3. A different process may be used to compute the progress measure. 
     Virtual Grid to Compute Progress of Cheese Adding Step 
     In an implementation, the system may use a grid of virtual segments to determine how much cheese is placed on the sauce  1350 . In  FIG.  14 A , the system overlays the grid  1470  over the sauced area  1350  to virtually partitioning the sauced area into a plurality of sauced segments  1471 . For each unit segment, the system determines whether it is covered with cheese or not, counts the number of cheese-covered segments, and computes a ratio of the cheese-covered segments to the entire sauced segments as the current progress  1440  of Step 3. In determining a cheese-covered segment, the system identifies a cheese-covered portion inside a segment based on the color of cheese and the color of sauce, and determines the segment is a cheese-covered segment when the cheese-covered portion is greater than a predetermined percentage of the segment area. In an implementation, the system identifies compute a representative color (e.g., average) of the segment, and determine the segment is a cheese-covered segment when the average color is closer to that of the cheese although not limited thereto. In  FIG.  14 B , each of the green boxes  1472  represents a cheese-covered segment. In an implementation, the system may compute a progress index of Step 3 using a process different from the example. 
     Image Segmentation to Identify Cheese 
     In implementations, the system may process the image  1430  using a machine-trained model to classify a first group (segment) of pixels as cheese, a second group (segment) of pixels as sauce. The system may count the number of pixels for each group in the image  1430  (or its modified version), compute a 2-dimensional area for each group, and determine progress of Step 3 using the pixel counts and the computed areas. For example, if the first group (cheese) is of 300 pixels in the image  1430  and the second group (sauce on the dough) is of 700 pixels, the system may determine that 30% of the sauce is covered with the cheese. 
     Completion of Cheese Adding Step 
     In an implementation, the system may determine completion of Step 3 when cheese is placed more than a predetermined percentage of the 2-dimensional area of the pizza dough or a sauced area within the 2-dimensional area (when the computed progress reaches 100%) although not limited thereto. Subsequent to completion of Step 3, the system may provide an instruction to start Step 4. 
     Example Screen for Pepperoni Adding Step 
       FIG.  15 A  is an example screen for a pepperoni adding step. The screen  1500  presents a current image  1530  featuring the dough  1250 , the sauce  1350 , and cheese  1450  prepared at Step 3. The screen also presents an instruction  634  for Step 4 and a progress indicator  1540 .  FIG.  15 B  is a photograph of a pepperoni pizza being prepared. 
     Progress Based on Counting of Pepperoni 
     Step 4 is to add 12 slices of pepperoni over the cheese place at Step 3. The system may process a current image of the pizza to identify pepperoni slices and to count pepperoni slices added over the cheese. In  FIG.  15 A , the current progress of Step 4 (50%) is computed as the ratio of the current number of pepperoni slices (six) to the predetermined number (twelve) although not limited thereto. In an implementation, the system may count a pepperoni slice when it is greater than a predetermined size. The system may not count a pepperoni slice when it does not meet a predetermined requirement for pepperoni. 
     Determining Completion of Pepperoni Adding Step 
     The system may determine completion of Step 4 when the count of pepperoni slices reaches the predetermined number of twelve although not limited thereto. Subsequent to completion of Step 3, the system may provide an instruction to bake the pizza ( FIG.  17   ). 
     Progress Index When Food is Not Fully Visible 
       FIG.  16    shows another example screen  1600  of Step 4 that is subsequent to the screen  1500 . In  FIG.  16   , a hand  1610  is adding the seventh pepperoni slice  1670  to the pizza of the image  1530  (having 6 pepperoni slices), but only five pepperoni slices are visible in the image  1630 . If a progress index of Step 4 is computed based on the number of currently visible pepperoni slices, the progress should lower than the 50% shown in  FIG.  15 A . It may confuse the person  210  if the system lowers the progress index real-time when a hand is obstructing the camera&#39;s view. To avoid such confusion, the system may not update a progress index when the pizza being prepared is not fully visible. In an implementation, the computing system  160  processes a camera image to determine the food being prepared is fully visible in the image, and does not consider the image for computing a progress index or evaluating a food preparation quality when the pizza is not fully visible. 
     Computing Progress Using Machine-Trained Model 
     For example, the system uses a machine-trained model to compute a progress for a recipe step and to determine completion of the recipe step. In an implementation, the system may train a model such that the model outputs a progress index of a recipe step in response to an input of an image of a pizza being prepared. For example, the system uses a machine-trained model configured to determine completion of Step 3 in response to an image featuring cheese covering a sauced dough. 
     Recipe Completion Message 
     When the last step of a current recipe is completed, the system may present a screen that the food is ready for serving or for a further processing.  FIG.  17    is an example screen  1700  notifying that a pizza prepared at the system is ready to bake. 
     Performance Feedback 
       FIG.  18    is an example screen  1800  provided after completing all four steps of the example recipe. The feedback screen  1800  includes, for each step, (1) a first performance indices  1810  based on preparation time and (2) a second performance indices  1820  based on preparation quality. In an implementation, the system may provide an additional performance index, and may not provide one or more of the example performance indices. 
     Performance Rating Based on Preparation Time 
     In implementations, when a person performs each step of the recipe, the system collects data to evaluate the person&#39;s performance for each step. For example, the system measures a completion time for each step, compares the measured completion time with a predetermined desirable completion, and computes a performance index representing how fast the worker completed the step. In an implementation, the system updates the person&#39;s preparation time rating  693  using the first performance indices  1810 . 
     Performance Based on Preparation Quality 
     In implementations, at the end of each recipe step, the system evaluates the step using one or more criteria for determining a properly-performed step. Examples of the criteria were explained in connection with example recipe data. In an implementation, for Step 2, the system computes a performance index representing how evenly the sauce spreads on the dough. In an implementation, the system updates the person&#39;s preparation quality rating  693  using the second performance indices  1820 . 
     MACHINE-TRAINED MODEL (ARTIFICIAL INTELLIGENCE) 
     In implementations, the computing system  160  uses a machine-trained model for determining location of a food ingredient, and monitoring progress of a recipe step. 
     Machine-Trained Model for Identifying Food Ingredients 
     A machine-trained model of an implementation is configured to, in response to an input of data of a photographic image, output information of one or more food ingredients featured in the photographic image. In an implementation, the system may use a machine-trained model configured to perform image segmentation of a camera image for identifying objects (pans, food ingredients) in the image. 
     Data Set for Training Machine-Trainable Model 
     A data set for training of a model includes a number of data pairs. Each pair includes input data for the training machine-trainable model and desirable output data (label) from the model in response to the input data. For example, for a machine-trainable model to identify food ingredients, the input data includes an image of a predetermined size that features one or more food ingredients, and the desirable output data includes one or more identifiers (names) of the featured food ingredients. For another example, for a machine-trainable model to evaluating progress of a recipe step, the input data includes images of food being prepared, and the desirable output data includes a percentage indicating progress of a food preparation step. 
     Training of Machine-Trainable Model 
     In an implementation, a supervised learning technique can be used to prepare the machine-trained model. Any known learning technique can be applied to the training of the model as long as the technique can configure the model to output, in response to training input images, a name (identifier) of food ingredient within a predetermined allowable error rate. 
     Various Structure of Machine-Trained Model 
     In an implementation, a convolutional neural network (CNN) is used to construct the machined trained model. In general, a convolutional neural network requires a smaller number of model parameters when compared to a fully connected neural network. In an implementation, a neural network other than CNN can be used. 
     COMPUTING SYSTEM 
     General Architecture 
       FIG.  19    depicts an example architecture of a computing system  160  that can be used to perform one or more of the techniques described herein or illustrated in other drawings. The general architecture of the computing system  160  includes an arrangement of computer hardware and software modules that may be used to implement one or more aspects of the present disclosure. The computing system  160  may include many more (or fewer) elements than those shown in  FIG.  19   . It is not necessary, however, that all of these elements be shown in order to provide an enabling disclosure. 
     Hardware 
     As illustrated, the computing system  160  includes a processor  1610 , a network interface  1620 , a computer readable medium  1630 , and an input/output device interface  1640 , all of which may communicate with one another by way of a communication bus. The network interface  1620  may provide connectivity to one or more networks or computing systems. The processor  1610  may also communicate with memory  1650  and further provide output information for one or more output devices, such as a display (e.g., display  1641 ), speaker, etc., via the input/output device interface  1640 . The input/output device interface  1640  may also accept input from one or more input devices, such as a camera  1642  (e.g., 3D depth camera), a keyboard, a mouse, a digital pen, a microphone, a touch screen, a gesture recognition system, a voice recognition system, an accelerometer, a gyroscope, a thermometer, an optical temperature measurement system, a sonar, a LIDAR device, a laser device, etc. 
     Software—Computer Program Instructions 
     The memory  1650  may store computer program instructions (grouped as modules in some implementations) that the processor  1610  executes in order to implement one or more aspects of the present disclosure. The memory  1650  may include RAM, ROM, and/or other persistent, auxiliary, or non-transitory computer-readable media. The memory  1650  may store an operating system  1651  that provides computer program instructions for use by the processor  1610  in the general administration and operation of the computing system  160 . The memory  1650  may further include computer program instructions and other information for implementing one or more aspects of the present disclosure. In one implementation, for example, the memory  1650  includes a user interface module  1652  that generates user interfaces (and/or instructions therefor) for display, for example, via a browser or application installed on the computing system  160 . In addition to and/or in combination with the user interface module  1652 , the memory  1650  may include an image processing module  1653 , a machine-trained model  1654  that may be executed by the processor  1610 . The operations and algorithms of the modules are described in greater detail above with reference to other drawings. 
     Multiple Components 
     Although a single processor, a single network interface, a single computer readable medium, a singer input/output device interface, a single memory, a single camera, and a single display are illustrated in the example of  FIG.  19   , in other implementations, the computing system  160  can have a multiple of one or more of these components (e.g., two or more processors and/or two or more memories). 
     Other Considerations 
     Logical blocks, modules or units described in connection with implementations disclosed herein can be implemented or performed by a computing device having at least one processor, at least one memory and at least one communication interface. The elements of a method, process, or algorithm described in connection with implementations disclosed herein can be embodied directly in hardware, in a software module executed by at least one processor, or in a combination of the two. Computer-executable instructions for implementing a method, process, or algorithm described in connection with implementations disclosed herein can be stored in a non-transitory computer readable storage medium. 
     OTHER CONSIDERATIONS 
     Although the implementations of the inventions have been disclosed in the context of certain implementations and examples, it will be understood by those skilled in the art that the present inventions extend beyond the specifically disclosed implementations to other alternative implementations and/or uses of the inventions and obvious modifications and equivalents thereof. In addition, while a number of variations of the inventions have been shown and described in detail, other modifications, which are within the scope of the inventions, will be readily apparent to those of skill in the art based upon this disclosure. It is also contemplated that various combinations or sub-combinations of the specific features and aspects of the implementations may be made and still fall within one or more of the inventions. Accordingly, it should be understood that various features and aspects of the disclosed implementations can be combined with or substituted for one another in order to form varying modes of the disclosed inventions. Thus, it is intended that the scope of the present inventions herein disclosed should not be limited by the particular disclosed implementations described above, and that various changes in form and details may be made without departing from the spirit and scope of the present disclosure as set forth in the following claims.