Abstract:
A food preparation predicting system and method in one embodiment includes a detector for obtaining customer information, a memory including program instructions for obtaining customer information from the detector, associating the customer information with stored customer specific preference information, generating a pseudo-order based upon the associated preference information, generating food preparation information based upon the pseudo-order, and displaying the food preparation information, and a processor operably connected to the camera and the memory for executing the program instructions.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     The present application claims the benefit of the filing date of Provisional Application Ser. No. 61/124,361, filed Apr. 16, 2008. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates generally to the restaurant industry and, more specifically, to restaurants with drive-thru services. 
     BACKGROUND OF THE INVENTION 
     Restaurants which feature drive-thru windows strive to provide food products to customers in a quick and convenient manner. In many such restaurants, sales through the drive-thru windows account for fifty to eighty percent of the total sales of the restaurant. Accordingly, the success of such businesses depends on the rapid provision of the desired food product. One of the main delays in providing service to customers is the delay in preparing the food order. The delay may be ameliorated by prior preparation of the food products on the restaurant&#39;s menu. Food products may be prepared and kept in a warming area so that restaurant employees can quickly assemble a food order from the products in the warming area, bag it, and hand it to a customer. This approach decreases the time needed to serve customers thereby making the drive-thru service more efficient. 
     The trade-off with the foregoing approach is that sufficient numbers of products must be prepared without knowledge of the tendencies of any particular customer. Thus, if a business inaccurately predicts the number of a particular product that will be ordered, the product served may not be fresh, thereby disenfranchising a customer. Alternatively, products may be scrapped after a predetermined time in the warming area. In either event, the business may suffer loss either directly due to spoilage or indirectly through loss of customers. Therefore, accurately determining the number of different products to be kept in the warming area or other staging area is critical to the success of a restaurant. 
     Generally, prediction as to the amount of product that will be sold is fairly accurate over longer periods of time. For example, a prediction of the expected sales for a particular month based upon the sales in the previous month is fairly reliable. Such predictions can further be modified to account for seasonal, promotional and event based changes in product demand with relative accuracy. Monthly predictions, however, do not provide the information necessary to maintain an optimum number of products in a staging area for a particular time of day. Even hourly predictions are of limited value when a product has a shelf life on the order of minutes. 
     In another approach to food product preparation management, a computer program may be used to predict the number of products to be maintained in a staging area based upon the sales data for the product over some immediately previous time frame. Thus, the number of products to be prepared is a function of the number of products just sold. This approach, while using data which is relatively fresh, is still based upon historical data which is not necessarily indicative of actual future activity. 
     Therefore, a need exists for a system which increases the accuracy of a restaurant&#39;s prediction of the demand for particular products. What is further needed is a system and method which is responsive to the historical tendencies of specific customers. 
     SUMMARY OF THE INVENTION 
     A food preparation predicting system and method in one embodiment includes a detector for obtaining customer information, a memory including program instructions for obtaining customer information from the detector, associating the customer information with stored customer specific preference information, generating a pseudo-order based upon the associated preference information, generating food preparation information based upon the pseudo-order, and displaying the food preparation information, and a processor operably connected to the camera and the memory for executing the program instructions. 
     In another embodiment, a method of predicting food preparation requirements includes obtaining customer information, associating the customer information with stored customer specific preference information, generating a pseudo-order based upon the associated preference information, generating food preparation information based upon the pseudo-order and displaying the food preparation information. 
     In a further embodiment, a system for providing food preparation information includes a detector positioned to detect a vehicle in a drive-thru lane, a memory including program instructions for obtaining customer information from the detector, identifying a specific customer using the customer information, obtaining historical ordering data for the identified customer, predicting a food product to be ordered by the customer, generating food preparation information based upon the prediction, and displaying the food preparation information, and a processor operably connected to the detector and the memory for executing the program instructions. 
     The above described features and advantages, as well as others, will become more readily apparent to those of ordinary skill in the art by reference to the following detailed description and accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  depicts an elevational perspective view of a restaurant with a drive-thru window; 
         FIG. 2  shows a block representation of the restaurant of  FIG. 1  with an exemplary embodiment of a control system in which the subject invention may be used; and 
         FIG. 3  shows a flowchart of an exemplary process for identifying a customer and associating the identified customer with a stored preference so as to predict the food order of the customer in accordance with principles of the invention. 
     
    
    
     DETAILED DESCRIPTION 
     Referring to  FIG. 1 , there is depicted a representation of a restaurant generally designated  100 . The restaurant  100  includes a seating area  102 , a service counter  104  and a drive-thru window  106 . Four terminals  108 ,  110 ,  112  and  114  are located at the service counter  104  while a fifth terminal  116  is located at the drive-thru window  106 . A camera  118  and an ordering station  120  are located alongside a drive-thru lane  122  which extends alongside the drive-thru window  106  outside of the restaurant  100 . 
     A restaurant control system  130  is shown in  FIG. 2 . The control system  130  includes I/O devices  132 , a processing circuit  134  and a memory  136 . The I/O devices  132  may include a user interface, graphical user interface, keyboards, pointing devices, remote and/or local communication links, displays, and other devices that allow externally generated information to be provided to the processing circuit  134 , and that allow internal information of the control system  130  to be communicated externally. 
     The processing circuit  134  may suitably be a general purpose computer processing circuit such as a microprocessor and its associated circuitry. The processing circuit  134  is operable to carry out the operations attributed to it herein. Program instructions  138  are stored within the memory  136  along with databases  140 . The program instructions  138 , which are described more fully below, are executable by the processing circuit  106  and/or any other components as appropriate. 
     Databases  140  include a price database  142 , an inventory database  144 , and a customer preference database  146 . In one embodiment, the databases  140  are populated using object oriented modeling. The use of object oriented modeling allows for a rich description of the relationship between various objects. While shown as located within the restaurant  100 , portions of the control system  130  including any one or more of the databases  140  may be remote from the restaurant  100 . 
     A communications network  150  provides communications between the control system  130  and headsets  152 , a staging area  154 , the camera  118 , the ordering station  120  and the terminals  108 ,  110 ,  112 ,  114  and  116 . In the embodiment described herein, the communications network  150  is a wireless communication scheme implemented as a wireless area network. A wireless communication scheme identifies the specific protocols and RF frequency plan employed in wireless communications between sets of wireless devices. To this end, the processing circuit  134  employs a packet-hopping wireless protocol to effect communication by and among the processing circuit  134 , the headset  152 , the camera  118 , the ordering station  120  and the terminals  108 ,  110 ,  112 ,  114  and  116 . 
     The headsets  152  in this embodiment are connected to the processing circuit  134  through a terminal, although other communication schemes may be used. The staging area  154  is a location for staging of food that is to be served. The staging area  154 , which may include heat lamps, a refrigeration source, or other environmental control devices may be provided with sensors which generate data indicative of the number and types of food products that are present in the staging area. Such sensors may include weight sensors, optical sensors and the like. 
     Referring to  FIG. 3 , there is depicted a flowchart, generally designated  160 , setting forth an exemplary manner of operation of the system shown in  FIGS. 1 and 2  by execution of the program instructions  138  by the processing circuit  134  and/or other components described above. At step  162  a customer vehicle approaches the camera  118  within the drive-thru lane  122  and the camera  118  obtains an image of the vehicle. In this embodiment, the camera  118  obtains an image of the license plate of the vehicle. The camera  108  may be positioned to obtain either the front license plate of a vehicle, the rear license plate of the vehicle or both. To this end, more than one camera  118  may be provided. Customer identification may further include identifying the number of passengers in a vehicle. 
     The image data is transmitted to the processing circuit  134  which performs an image analysis to characterize the obtained image. A record is created at the step  164  using the results of the analysis. The processing circuit  134  then performs a search of the customer preference database  146  at the step  166  to determine if any stored records can be associated with the characterized image. If such stored records are found, the processing circuit  134  determines if the stored records show an ordering tendency. For example, the stored data may indicate that the customer associated with the obtained image always orders a cheeseburger, large fries and a large chocolate milkshake. Alternatively, the stored data may indicate that on weekdays the customer generally orders one group of food products but on weekends the customer orders a different group of products. 
     In the event that a tendency or preference is identified, the process  160  continues to the step  170  and creates a pseudo-order. As used in this example, a pseudo-order is an order generated based upon the historical ordering habits of the customer associated with the vehicle imaged by the camera  118 . Thus, customer specific preferences are the actual historical ordering habits of a particular customer. At the step  172 , the processing circuit  134  generates food preparation data. The food preparation data is information indicative of the amount of product that should be placed in-process in consideration of the pseudo-orders and, if desired, other information. In generating food preparation data, pseudo-orders may be assigned a confidence factor or “weight.” Thus, if a customer almost always requests a certain food product or combination of products, the pseudo-order may be given a high weight. If a customer generally orders either of two alternative products, each of the alternative products may be given a medium weight. 
     Once the predicted or pseudo-order is generated and, if desired, assigned a weight, the processing circuit  134  generates food preparation data by combining the pseudo-order with other pseudo-orders and customer orders entered through one of the terminals  108 ,  110 ,  112 ,  114  and  116  which have not been filled. This is the number of the particular product “on-order.” Next, the number of product “in-process” is determined. Products “in-process” may include the products that are located in the staging area  154  as indicated by the data from the staging area  154 . Additionally, and/or alternatively, the inventory database  144  may be queried to determine the number of products that have been removed from inventory but not yet served. 
     Using the “on-order” data and the “in-process” data, the processing circuit  134  determines whether or not additional product should be prepared. This determination may include a consideration of the amount of product desired to be maintained in the staging area  154 . For example, ten units of a particular product may be desired to be available in the staging area  154  during a certain period of time such as lunch hour while only three units of the particular product are desired at other times. Once the food preparation data has been generated at the step  172 , the food preparation data is displayed at the step  174  and restaurant employees can begin any necessary activities to provide sufficient product in the staging area. If desired, an aural and/or visual alarm may be activated upon display of new preparation data. 
     In the embodiment of  FIG. 3 , the pseudo-order generated at the step  170  is also displayed at the step  176 . The display may be provided at the ordering station  120 . For example, when the customer vehicle approaches the ordering station  120 , a display may be rendered which asks if the customer would like the products shown on the pseudo-order. Alternatively, the order may be displayed at the terminal  116  located at the drive-thru window  106  for use by restaurant personnel. At the step  178 , the processing circuit  134  determines if the order has been verified. Order verification from the customer or an employee may be obtained by any desired input method such as the use of voice recognition software, a touch screen or a physical button. 
     If the pseudo-order is verified at the step  178 , the system changes the status of the pseudo-order at the step  180 . This may be accomplished, for example, by changing the pseudo-order to an actual order or by modifying the weight of the pseudo-order. At the step  182 , the processing circuit  134  updates the food preparation data that was generated at the step  172  to account for the food products actually ordered by the specific customer and displays the updated preparation data at the step  184 . 
     Thereafter, the food is served at the step  186  and the “on-order” data and “in-process” data is modified to account for the fulfillment of the order. Additionally, at the step  188  the customer preference database  146  is updated to reflect the most recent activity associated with the customer identified at the steps  162 ,  164  and  166 . The data stored may include the time of day, the day of the week, the time of year, whether or not any special promotions were in effect, or any other data useful in predicting an ordering tendency. The process  160  then ends at the step  190 . 
     Returning to the step  166 , in the event there are no stored records associated with the customer data obtained at the step  162 , then there is no tendency data available and the process continues at the step  192  at which point the customer&#39;s food order is taken and entered into the system at the step  194 . The process  160  then proceeds in the manner set forth above and ends at the step  190 . 
     Similarly, if at the step  178  the product or products ordered by the customer does not match the product or products identified in the pseudo-order generated at the step  170 , then the product or products are deleted from the pseudo-order at the step  196  and the process continues at the step  192  at which point the customer&#39;s order is taken and entered into the system at the step  194 . The process  160  then proceeds in the manner set forth above and ends at the step  190 . 
     While this invention has been described as having a preferred design, the subject invention can be further modified within the spirit and scope of this disclosure. This application is therefore intended to cover any variations, uses, or adaptations of the subject invention using its general principles.