AND HISTORICAL DATA

Background

[0001] Many quick-service restaurants include vehicular drive-thru service that allows drive-thru customers to place, pay for and receive delivery of a food order from a vehicular drive-thru lane, without the drive-thru customers needing to leave their vehicles. The drive-thru lane includes an order station along the drive-thru lane that may be located remote of the restaurant building. At the order station a customer places an order by communicating with an attendant, such as by microphone and speaker. The attendant is positioned within the restaurant at a payment window that is located along the drive-thru lane and remote from the order station. As the order is received by the attendant, the attendant enters the order in point-of-sale equipment, such as an electronic cash register. After placing an order, the customer drives downstream to the payment window and pays the attendant for the order. The customer then is directed to a downstream pick-up window in the building to receive the customer's order from another attendant within the building. Brief Description of the Drawings

[0002] FIG. 1 illustrates a block diagram of an example computing device including instructions for customer-specific operation of a drive-thru using sensors and historical data, in accordance with the present disclosure.

[0003] FIG. 2 illustrates a block diagram of an example environment for customer- specific operation of a drive-thru using sensors and historical data, in accordance with the present disclosure.

[0004] FIG. 3 illustrates a block diagram of an example environment for customer- specific operation of a drive-thru using sensors and historical data, in accordance with the present disclosure.

[0005] FIG. 4 illustrates a block diagram of an example method for customer- specific operation of a drive-thru using sensors and historical data, in accordance with the present disclosure.

[0006] FIG. 5 illustrates a block diagram of an example system for customer- specific operation of a drive-thru using sensors and historical data, using multiple sensors and external storage, in accordance with the present disclosure.

Detailed Description

[0007] In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific examples in which the disclosure may be practiced. It is to be understood that other examples may be utilized and structural or logical changes may be made without departing from the scope of the present disclosure. The following detailed description, therefore, is not to be taken in a limiting sense, and the scope of the present disclosure is defined by the appended claims. It is to be understood that features of the various examples described herein may be combined, in part or whole, with each other, unless specifically noted otherwise. [0008] Much business globally is done via drive-thru order entry and service windows on the exterior of business establishments. Some of the more common businesses having such drive-thru windows facilitating business are fast-food restaurants, pharmacies or drug-stores, and coffee shops. Many situations involving the provision of goods in today's marketplace are predicated on the ability of a customer to efficiently place an order and receive the meal (or other goods) with the correct items in a quick and accurate manner. Human interaction between the customer and the employee leaves room for time-costly mistakes due to either user error or misinterpretation through language barriers, speech impediments or the hard of hearing, inaudible conversation due to faulty drive-thru speakers, etc. These mistakes can lead to fewer return customers due to lower satisfaction ratings stemming from either poor customer service, processing incorrect orders, lengthy wait times, interruptions from implementing new technologies, and so on. [0009] In addition, a point-of-sale or drive-thru transaction is limited in its ability to receive orders and deliver goods by the human factors involved, i.e. the process may move as fast as the employee can physically work. For example, timing for processing an order is limited by how quickly the employee is able to take the customer's order, listen to the customer's order, record the order, confirm the order, prepare the order, process the payment manually, and deliver the ordered items to the customer.

[0010] Customer-specific operation of a drive-thru using sensors and historical data, in accordance with the present disclosure provides actionable data for the drive-thru business about the customers in the drive-thru line prior to the customer reaching the order entry call box. In this manner historical data on the customers as well as artificial intelligence (Al) inferenced information can be used to improve efficiency and quality for a drive-thru commercial exchange window.

[0011] An example of the present disclosure includes a non-transitory computer- readable medium storing instructions that when executed cause a processor to receive from a plurality of sensors in a drive-thru, data indicative of a vehicle in a drive-thru. The computer-readable medium includes instructions to identify a customer in the drive-thru using the received data, and retrieve from a data storage, historical customer data based on the identified customer. The computer- readable medium also includes instructions to generate customer-specific instructions for operation of the drive-thru using the historical customer data.

[0012] Another example of the present disclosure includes a method, comprising receiving from a plurality of sensors in a drive-thru, data indicative of a vehicle in a drive-thru. The method includes identifying a customer in the drive-thru using the received data, and retrieving from a data storage, historical customer data based on the identified customer. The method includes using the data received from the plurality of sensors, identifying partner offerings for the customer. The method also includes generating customer-specific instructions for operation of the drive-thru, using the historical customer data and the partner offerings.

[0013] Another example of the present disclosure includes a system, comprising a network-accessible computing device, a plurality of sensors associated with a drive-thru and communicatively coupled to the network-accessible computing device, and a non-transitory computer-readable medium storing instructions. The instructions, when executed, cause the computing device to identify a customer in a vehicle in the drive-thru using data received from the plurality of sensors. The instructions when executed, also cause the computing device to retrieve from a data storage, historical customer data based on the identified customer. The instructions cause the computing device to generate customer-specific instructions for operation of the drive-thru using the historical customer data, and print customer-specific drive-thru packaging based on the historical customer data.

[0014] Turning now to the figures, FIG. 1 illustrates a block diagram of an example computing device 100 including instructions for customer-specific operation of a drive-thru using sensors and historical data, in accordance with the present disclosure. As illustrated in FIG. 1 , the computing device 100 may include a processor 101 , and a non-transitory computer-readable storage medium 103.

[0015] The processor 101 may be a central processing unit (CPU), a semiconductor-based microprocessor, and/or other hardware device suitable to control operations of the computing device 100. Computer-readable storage medium 103 may be an electronic, magnetic, optical, or other physical storage device that contains or stores executable instructions. Thus, computer-readable storage medium 103 may be, for example, Random Access Memory (RAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a storage device, an optical disc, etc. As used herein, the term ‘non-transitory’ does not encompass transitory propagating signals. As described in detail below, the non- transitory computer-readable storage medium 103 may be encoded with a series of executable instructions 105-111.

[0016] As illustrated in FIG. 1 , the computing device 100 includes a non-transitory computer-readable medium 103 storing instructions 105 that when executed cause the processor 101 to receive from a plurality of sensors in a drive-thru, data indicative of a vehicle in a drive-thru. As described more thoroughly herein, the computing device 100 may use a plurality of sensors linked to the computing device 100 and accompanying instructions to observe vehicles entering a drive- thru and approaching a business establishment with a drive-thru. The plurality of sensors may be disposed in the drive-thru and/or near the drive-thru. A non-limiting example of a sensor that may be communicatively coupled to the computing device 100 includes a global frame capture imager. The global frame capture imager may include a 2 megapixel color imager with global shutter, such as the OmniVision ^® OG02B1 B image sensor. The global frame capture imager may include a high resolution, color and global shutter imager. Global shutter allows for instantaneous capture of the image and minimization of image blur for a moving vehicle. [0017]Another non-limiting example of a sensor that may be communicatively coupled to the computing device 100 includes a laser imaging, detection and ranging (LiDAR) image capture trigger element. As described further herein, the LiDAR image capture trigger element may perform license plate image capture in and/or near the drive-thru. A non-limiting example of a LiDAR image capture trigger element includes the Benewake ^® TFMini Plus 12mLiDAR Range Sensor. [0018] Another non-limiting example of a sensor that may be communicatively coupled to the computing device 100 includes a fixed focus camera with near- infrared (NIR) flash image irradiator. Such image capture device may allow for dual or serially color and NIR images to be captured. A non-limiting example of a NIR flash image irradiator includes the Digikey ^® OD85030030 high powered LED array. [0019] Another non-limiting example of a sensor that may be communicatively coupled to the computing device 100 includes a kiosk in elevated position, such as at approximately 8 feet from the ground. The elevated order call box may provide a look-down perspective for un-encumbered viewing of a next vehicle in the drive- thru.

[0020] Another non-limiting example of a sensor that may be communicatively coupled to the computing device 100 includes a single board computer (SBC) with graphics processing unit (GPU) compute capabilities. The SBC may provide image capture and artificial intelligence (Al) augmented optical character associator (OCA) functionality for identifying license plate characters. The SBC may also include Wi-Fi or Ethernet communications capabilities, allowing the SBC to communicate with a business server, such as a restaurant server, and/or other network data storage location. A non-limiting example of a SBC includes the Nvidia ^® Jetson Nano. Additional and/or different sensors may be communicatively coupled to the computing device 100, and the examples provided herein are for illustrative purposes.

[0021] The sensors described herein may capture and process a variety of different types of data and communicate the same to the computing device 100. For instance, the sensors may capture the vehicle’s license plate number, images of the faces of the inhabitants inside the vehicle, and/or identifying information of the vehicle. Non-limiting examples of identifying information of the vehicle that may be captured include toll tag information, the vehicle color, the vehicle make, the vehicle model, and/or the year of the vehicle, among others. The sensors may also capture digital signatures associated with the vehicle, such as digital signatures from a toll tag or other digital pass, and/or any digital signatures associated with inhabitants of the vehicle, such as cellular signals, Bluetooth ^® signals, and/or digital signals from smart watches.

[0022]The instructions 107, when executed, cause the processor 101 to identify a customer in the drive-thru, using the received data. For instance, in some examples, the received data includes license plate image capture data. In such examples, the instructions 107 to identify the customer include instructions to generate optical character recognition (OCR) data from the license plate image capture data, and identify the customer based on the OCR data.

[0023]The instructions 109, when executed, cause the processor 101 to retrieve from a data storage, historical customer data based on the identified customer. The historical data may include previous business transactions involving the customer, previous orders the customer placed, taste profiles of the customer, preferences that the customer has specified, and/or other information regarding purchase history of the customer. The data storage may be a local data storage and/or a remote data storage location, such as a cloud storage location.

[0024] By obtaining sensor data and retrieving historical customer data, historical associations and Al based inferencing may be used to improve service by implementing new drive-thru order mechanisms. Put another way, the instructions 111 , when executed, cause the processor 101 to, using the historical customer data, generate customer-specific instructions for operation of the drive-thru. For instance, based on historical customer data for that vehicle and/or customer, the computing device 100 may begin preparing the most likely order prior to the customer reaching the order console. As another illustration, based on various factors such as weather, time of day, customer order history, local and/or nationwide correlations, Al interference, etc. the computing device 100 may generate customer-specific instructions for operation of the drive-thru.

[0025] FIG. 2 illustrates a block diagram of an example environment 200 for customer-specific operation of a drive-thru using sensors and historical data, in accordance with the present disclosure. Vehicle 227 may be detected by a plurality of sensors 221 , 223, and 219. As discussed with regards to FIG. 1 , more and/or fewer sensors may be included. Sensor 221 may include a LiDAR image capture trigger element that may perform license plate image capture in and/or near the drive thru. Sensor 223 may include a fixed focus camera with a near-infrared (NIR) flash image irradiator. Such image capture device may allow for dual or serially color and NIR images to be captured. Sensor 219 may include a global frame capture imager. The global frame capture imager may include a high resolution, color and global shutter imager. Global shutter allows for instantaneous capture of the image and minimization of image blur for a moving vehicle.

[0026]The information captured by sensors 221 , 223, and 219 may be communicated to a single board computer (SBC) 217 for processing of the image(s). Data from the SBC 217 may be transferred to a host computing device 215 that may provide for the historical data associations for the customer and Al based inference associations. In some examples, the host computing device 215 may perform the functions of the computing device 100 described with regards to FIG. 1. The SBC 217 may transfer data to the computing device 215 via a gigibit ethernet connection 231 , a Wi-Fi connection 229 or other communications channel. The host computing device 215 may communicate with a cloud storage location 213 to retrieve customer preferences associated with the identified license plate, facial recognition features, vehicle identification numbers, vehicle identification features, etc. The host computing device 215 may communicate with a cloud storage location 213 to extrapolate Al generated taste associations for the customer based on the universe of customer data. For instance, based on a global pool of taste profiles for various customers, a taste association for the customer may be generated.

[0027]The taste associations generated by the cloud storage location 213 may be sent by Ethernet or equivalent circuitry 233 to a kiosk 225. Kiosk 225 may display menus, including customized menus, and receive orders from the customer in vehicle 227. The kiosk 225 may include additional sensors, such as a microphone (not illustrated). Additionally, the kiosk 225 may include a graphical user interface for displaying menus. [0028] In various examples, the kiosk 225 may display a customer-specific menu, based on the historical customer data received from the host computing device 215, the Al based inference associations from the cloud storage location 213, and/or the information received from the sensors 221 , 223, and 219. For instance, if the customer frequently orders item X, then the menu can suggest the customer purchase item X or items similar to it. Also, because artificial intelligence determined that customers who generally purchased item X also purchased item Y, the customer-specific menu may suggest that the customer may try item Y.

[0029] Using artificial intelligence, a taste profile for the customer may be generated which identifies the types of flavors that the customer likes. Based on this taste profile, a customer-specific menu may be provided to the customer which includes items that fit the taste profile of the customer. Artificial intelligence may also be used to generate customized menus based on the weather, or national and/or local events. For instance, if it is cold out, the cloud storage location 213 may suggest soup and/or hot chocolate as menu items whereas if it is warm out, the cloud storage location 213 may suggest cold cut sandwiches and iced tea as menu items.

[0030] As such, referring back to FIG. 1 , the instructions 111 to generate customer- specific instructions for operation of the drive-thru may include instructions to identify from the historical customer data, taste association data for the customer, and generate a customized menu for the customer including recommended items based on the taste association data. Similarly, the computer-readable medium 103 may store instructions that when executed cause the processor to receive from a network location, a condition at the drive-thru, wherein the condition is selected from the group including: a current weather condition, a projected weather condition, a past weather condition, a time of day, a day of week, and a particular event, and generate a customized menu selection based on the historical customer data and the condition.

[0031] In some examples, the received data (e.g., from sensors 221 , 223, 219) includes image capture data from inside the vehicle. As such, customer-specific menu options may be provided based on image capture data from inside the vehicle. For instance, the sensors 221, 223, 219 may detect that a child or a plurality of children are in the vehicle 227. Based on the presence of children in the vehicle 227, the kiosk 225 may present a customer-specific menu that includes a kids menu, and/or include items that children frequently purchase.

[0032] Although examples of customer-specific menus are provided with regards to kiosk 225, examples are not so limited. For instance, a customer-specific menu may be provided by cellular phone direct to the customer while they wait in the drive-thru. The customer-specific menu may be provided as a visual menu on a mobile phone or other mobile computing device, and/or verbally on a mobile phone or other mobile computing device. As such referring to FIG. 1 , the computer- readable medium 103 may store instructions that when executed cause the processor 101 to, based on the identity of the customer, retrieve a cellular number for the customer from the data storage, and provide a cellular order option to the customer while in the drive-thru, wherein the cellular order option provides an audio and/or visual menu via a cellular device of the customer.

[0033] FIG. 3 illustrates a block diagram of an example environment 302 for customer-specific operation of a drive-thru using sensors and historical data, in accordance with the present disclosure. FIG. 3 includes similar components to the components described with regards to FIG. 2. For instance, the environment 302 may include a cloud storage location 313 and host computing device 351 communicatively coupled to the cloud storage location 313. The environment 302 may also include sensors 321 , 323, and 319. Sensors 321 , 323, and 319 may be similar to sensors 221 , 223, and 219. Cloud storage location 313 may be similar to cloud storage location 213. The environment 302 may also include a kiosk 325, which may be similar to kiosk 225. In the example illustrated in FIG. 3, a weather sensor 355 may also be present in the drive-thru. The environment 302 may include a host computing device 351 that facilitates interoperability between devices. The host computing device 351 may be communicatively coupled to a plurality of items, such as in an Internet of Things (loT) arrangement. For instance, the host computing device 351 may be coupled to the global frame capture imager 319 by a universal serial bus (USB) connection. The host computing device 351 may be coupled to the LiDAR image capture trigger element 321 by a universal asynchronous receiver-transmitter (UART) connection. The host computing device 351 may be coupled to the weather sensor 355 by a USB or MODbus connection. The host computing device 351 may be coupled to the kiosk 325 by an Ethernet or equivalent connection.

[0034] FIG. 4 illustrates a block diagram of an example method 457 for customer- specific operation of a drive-thru using sensors and historical data, in accordance with the present disclosure. As illustrated in FIG. 4, the method 457, includes at 459, receiving from a plurality of sensors in a drive-thru, data indicative of a vehicle in a drive-thru. As described with regards to FIG. 2 and FIG. 3, the vehicle in the drive-thru may be captured using various image capture devices.

[0035] At 461 , the method 457 includes identifying a customer in the drive-thru using the received data. Referring to FIG. 2 or FIG. 3, the customer may be identified using their license plate number, by facial recognition, by toll pass number, by digital signature, or by other means. At 463 the method 457 includes retrieving from a data storage, historical customer data based on the identified customer. As discussed with regards to FIG. 2, historical data may be maintained for each customer, pertaining to past purchases for the customer. Historical customer data may be stored and retrieved from a cloud storage location (e.g., 213 illustrated in FIG. 2). The cloud storage location may also store extrapolated Al information that may be learned from historical customer data, such as buying patterns, taste profiles, and others.

[0036] At 465, the method 457 includes using the data received from the plurality of sensors, identify partner offerings for the customer. For instance, the drive-thru may partner with other businesses, also known as partners. Based on demographics of the customer and/or information obtained from the plurality of sensors, partner offers may be identified for the customer. For instance, a gas sensor in the drive-thru may capture the hydrocarbon footprint of the vehicle and a partner offering for a fuel efficient vehicle manufacturer may be identified. As another illustration, a microphone in the drive-thru may capture the sound of the vehicle and identify a defective muffler or catalytic converter of the vehicle. A partner offering may be identified for an auto mechanic to repair the vehicle. As yet another example, the sensors may identify children in the vehicle in the drive-thru, and partner offerings may include coupons for retail establishments and/or toy stores near the drive-thru.

[0037] At 467 the method 457 includes generating customer-specific instructions for operation of the drive-thru, and using the historical customer data and the partner offerings. In some examples, the method includes providing the partner offerings to the customer on a display of a mobile device of the customer. In some examples, the method includes providing the partner offerings to the customer on a display of an ordering kiosk in the drive-thru. In some examples, the method includes printing the partner offerings for the customer in the drive-thru, such as on a bag provided to the customer in the drive-thru. Customized coupons may be provided on the outside of the food order bag for the customer.

[0038] In some examples, the method 457 includes receiving from a global positioning system (GPS), location data for the customer relative to the drive-thru, and based on the location data and responsive to a determination that the customer is within a threshold distance of the drive-thru, providing digital advertising data to a plurality of devices located within a proximity of the customer. For instance, if a customer is at a traffic light down the street from a restaurant, a personalized advertisement for the burger that the customer orders could be displayed on the graphics signage of restaurant as the customer drives by the restaurant.

[0039] FIG. 5 illustrates a block diagram of an example system 567 for customer- specific operation of a drive-thru using sensors and historical data, using multiple sensors and external storage, in accordance with the present disclosure. In the example of FIG. 5, a network-accessible computing device 509 is communicatively coupled to a plurality of sensors 569-1 , 569-2, ... 569-N associated with a drive- thru. As described herein, multiple sensors such as a camera, audio, radio frequency sniffers, toll tag readers, etc., may provide customer identification information. Similarly, the sensor information may provide customer biological information and customer electronic device information or signature to enable more accurate customization of menu selections.

[0040] The system also includes a non-transitory computer-readable medium 513 storing instructions 571 that when executed cause the computing device 509 to identify a customer in a vehicle in the drive-thru using data received from the plurality of sensors 569. The computer-readable medium may further include instructions 573 that when executed cause the computing device 509 to retrieve from a data storage, historical customer data based on the identified customer. The computer-readable medium may further include instructions 575 that when executed cause the computing device 509 to generate customer-specific instructions for operation of the drive-thru using the historical customer data.

[0041] The computer-readable medium may further include instructions 577 that when executed cause the computing device 509 to print customer-specific drive- thru packaging based on the historical customer data. For instance, a custom bag may be printed for the customer with their name on it, and/or with coupons relevant for the customer on the packaging. In some examples, the instructions to print customer-specific drive-thru packaging include instructions that when executed cause the computing device to print images on the drive-thru packaging that are customized for the identified customer and include content selected from the group including the customer name, partner coupons, and coupons selected for the customer based on the historical customer data.

[0042] In some examples, the plurality of sensors includes a toll tag reader, and the non-transitory computer-readable medium includes instructions to identify the customer by reading a toll tag on the vehicle using the toll tag reader and retrieving an identity of the customer from a network location storing toll tag information. [0043] In some examples, the plurality of sensors includes a radio frequency transmitter/receiver, and the non-transitory computer-readable medium includes instructions to identify the customer by identifying a plurality of digital signatures in the vehicle, and using the digital signatures, identifying the customer in the drive- thru.

[0044] Although specific examples have been illustrated and described herein, a variety of alternate and/or equivalent implementations may be substituted for the specific examples shown and described without departing from the scope of the present disclosure. This application is intended to cover any adaptations or variations of the specific examples discussed herein. Therefore, it is intended that this disclosure be limited only by the claims and the equivalents thereof.