System and method for locating customers in facility for order delivery

Abstract

A delivery system may include one or more processors and a memory, the memory stores instructions executable by the one or more processors to cause the one or more processors to: identify one or more attributes of a user in the one or more images and associate the one or more attributes of the user with an order issued by the user. The delivery system may also track one or more attributes of the user in the one or more images over time to identify movement of the user within the environment and, in response to the one or more attributes of the user in the one or more images staying at the location for greater than a threshold time, identify movement of the user within the environment. An association is created between the one or more attributes of the user and the location. The delivery system may then provide instructions to deliver items in the order to the location.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No. 63 / 347,404, filed on May 31, 2022, entitled “SYSTEMS AND METHODS FOR LOCATING A GUEST IN A FACILITY FOR ORDER DELIVERY,” the entire contents of which are hereby incorporated by reference in its entirety for all purposes. Background Art

[0002] This section is intended to introduce the reader to various technical aspects that may be related to various aspects of the present technology, which are described and / or claimed below. This discussion is considered to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read from this perspective, rather than as an admission of the prior art.

[0003] In a restaurant or restaurant facility, a customer may place an order at an ordering station (e.g., a kiosk, a register) and be given a card with a printed number. Then, at a later time, a server may look for a card with a printed number (e.g., visually observe a card with a printed number on a table) to deliver the order to the customer. In some cases, the customer may be given an output device (e.g., a buzzer) or use some other output device (e.g., a mobile phone, a wall-mounted electronic display), and the output device may be instructed to provide a notification indicating that the order is ready at a pickup station. The customer may then proceed to the pickup station to collect the order. In some cases, the customer may wait for his or her name to be called, and this is a notification indicating that the order is ready at the pickup station. Summary of the invention

[0004] The following summarizes certain embodiments that are commensurate in scope with the original claimed subject matter. These embodiments are not intended to limit the scope of the claimed subject matter, but rather these embodiments are intended only to provide a brief overview of possible forms of the subject matter. In fact, the subject matter can encompass a wide variety of forms that may be similar or different from the embodiments set forth below.

[0005] In an embodiment, a delivery system may include one or more processors and a memory storing instructions executable by the one or more processors to cause the one or more processors to: identify one or more attributes of a user in one or more images captured by one or more cameras; and associate the one or more attributes of the user with an order placed by the user. The delivery system may also track the one or more attributes of the user in the one or more images over time to identify the movement of the user within an environment, and in response to the one or more attributes of the user in the one or more images staying at the location for greater than a threshold time, create an association between the one or more attributes of the user and the location. The delivery system may then provide instructions to deliver items in the order to the location.

[0006] In an embodiment, a method of operating a delivery system may include: using one or more processors, identifying one or more attributes of a user in one or more images captured by one or more cameras and associating the one or more attributes of the user with an order placed by the user. The method may also include: using one or more processors, tracking the one or more attributes of the user in the one or more images over time to identify the movement of the user within an environment and creating an association between the one or more attributes of the user and a location in response to the one or more attributes of the user in the one or more images staying at the location for greater than a threshold time. The method may then provide instructions for delivering items in the order to the location.

[0007] In an embodiment, a delivery system may include: one or more processors and a memory storing instructions executable by the one or more processors to cause the one or more processors to: identify one or more attributes of a user in one or more images captured by one or more cameras; and associate the one or more attributes of the user with an order placed by the user. The processor may also track the one or more attributes of the user in the one or more images over time to identify the movement of the user within an environment; and create an association between the one or more attributes of the user and a location in response to the one or more attributes of the user in the one or more images staying at the location for greater than a threshold time. The processor may then provide an output indicating the location to facilitate delivery of items in the order to the location without displaying the one or more images to a person associated with the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings, in which like characters represent like parts throughout the drawings, and in which:

[0009] Figure 1 is a schematic diagram of an embodiment of a delivery system that can be used in a dining environment according to aspects of the present disclosure;

[0010] Figure 2 According to aspects of the present disclosure, a customer Figure 1 A schematic illustration of a delivery system creating an order at a point of sale terminal;

[0011] Figure 3 According to aspects of the present disclosure, Figure 1 a schematic representation of one or more attributes of a customer identified by a delivery system;

[0012] Figure 4 According to aspects of the present disclosure Figure 1 a schematic representation of a delivery system and a dining environment showing an association of a location of the dining environment with one or more attributes of a patron;

[0013] Figure 5 According to aspects of the present disclosure Figure 1 a schematic illustration of a delivery system and dining environment showing delivery of an order to a customer;

[0014] Figure 6 According to aspects of the present disclosure, Figure 1 A flow chart of an embodiment of a process for a delivery system to deliver an order to a location in a dining environment;

[0015] Figure 7 is a diagram showing a group of customers according to aspects of the present disclosure Figure 1 A schematic representation of the delivery system and dining environment; and

[0016] Figure 8 According to aspects of the present disclosure, Figure 1 A flow chart of an embodiment of a process for a delivery system to associate one or more attributes of a customer with a location of a dining environment. DETAILED DESCRIPTION

[0017] When introducing elements of various embodiments of the present disclosure, the articles "a," "an," and "the" are intended to mean that there are one or more of the elements described. The terms "comprising," "including," and "having" are intended to be inclusive, and mean that there may be additional elements in addition to the listed elements. Additionally, it should be understood that references to "one embodiment" or "an embodiment" of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features.

[0018] One or more specific embodiments of the present disclosure will be described below. In order to provide a concise description of these embodiments, not all features of the actual implementation may be described in the specification. It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, many implementation-specific decisions must be made to achieve the developer's specific goals, which may vary from implementation to implementation, such as compliance with system-related constraints and business-related constraints. In addition, it should be appreciated that such development efforts can be complex and time-consuming, but for the ordinary technician who benefits from the present disclosure, it will be nothing more than a routine task of design, fabrication, and manufacturing.

[0019] The present disclosure generally relates to systems and methods for receiving and delivering orders in a dining environment, such as a dining environment. A dining environment may include a variety of features, such as vendors (e.g., restaurants, bakeries, ice cream stands), merchants (e.g., retailers of clothing, accessories, and / or souvenirs), stations (e.g., beverages, condiments, tableware, hand sanitizer, garbage), rest rooms, and / or tables to provide customers (e.g., clients) with a seamless and efficient dining experience. A delivery system may be used to supplement or complement the features of a dining environment to receive orders from customers and facilitate the delivery of orders to customers. In an embodiment, a delivery system may facilitate the delivery of orders to customers in a passive manner (e.g., passive to customers; customers only need to place orders and then walk to the table). For example, a delivery system may facilitate the delivery of orders to customers without providing a physical object (e.g., a card with a printed number, a buzzer, a radio frequency tag, or a reader) to a customer upon receiving an order and / or without providing a notification to a customer (e.g., calling a name, a sound output, a light output, a tactile output, a text message). Furthermore, in embodiments, the customer need not have or use their mobile phone for location tracking and / or linking to an order.

[0020] Advantageously, customers can place orders using suppliers at point-of-sale terminals. Orders can include one or more items (e.g., food, toys, beverages). After placing an order, customers can move within a dining environment to select a location (e.g., table, seat) for a dining experience. For example, a customer can sit down at a table to eat, and the customer can wait for one or more items in the order to be delivered to the location. A delivery system can (e.g., when a customer places an order) identify one or more attributes of the customer and associate one or more attributes with an order placed by the customer. A delivery system can receive image data of a dining environment and track one or more attributes within the dining environment. After a customer selects a location (such as sitting down at a table), a delivery system can associate one or more attributes of the customer with the location, thereby associating the order with the location. After the order is completed (e.g., one or more items in the order are ready to be delivered to the customer), the delivery system can provide instructions to a server (e.g., personnel, employee) to deliver one or more items in the order to the location.

[0021] In an embodiment, a customer may select a location (e.g., a table, a seat), and then leave to visit a temporary location (e.g., a lounge, a station, a merchant, a hand sanitizer station). For example, a customer may sit down at a table, but then walk to a vending machine to purchase a beverage. The delivery system may associate one or more attributes of the customer with the table, and may determine whether leaving the table interrupts the association. The delivery system may consider any of a variety of factors to determine whether an association is created at the table and / or whether leaving the table interrupts the association. Factors may include a corresponding time at the table, a corresponding time away from the table, a type of temporary location, a movement or gesture made by a customer at the table, any items placed on the table, any other customers at the table, and the like. For example, a customer may leave an object (e.g., a water bottle) at a table to claim that the table is theirs. As such, the delivery system may determine that the customer may return to the table and maintain the association of one or more attributes of the customer with the table.

[0022] However, the customer may move from a first location (e.g., a first table) to a second location (e.g., a second table). For example, the customer may determine that the first table is too small. As such, the customer may choose to move from the first table to the second table. The delivery system may determine that this movement of the customer from the first table to the second table is an interruption event. That is, the delivery system may interrupt the association between one or more attributes of the customer and the first table. In embodiments, the delivery system may then associate one or more attributes of the customer with the second table.

[0023] In an embodiment, the delivery system may associate one or more attributes of a customer with a location in response to a time period that a customer spends at a location meeting or exceeding a threshold time period (e.g., dwell time). For example, a customer may sit down at a table for a time period that meets or exceeds a threshold time period, and then the delivery system may associate one or more attributes of the customer with the table. The threshold time period may vary based on any of a variety of factors, such as movements or gestures made by the customer at the table, any items placed on the table, any other customers at the table, corresponding times spent at other locations visited between the point of sale and the location, etc. For example, a customer may sit down at a table, place their bag on the table, and begin playing with their mobile phone, which may indicate an intent to stay at the table during the dining experience, and may cause the delivery system to reduce the threshold time period (e.g., compared to another customer who is standing at the table, not placing their bag on the table, and / or continuing to look around the dining environment instead of playing with their mobile phone). In another example, a customer may stand at a table while the customer is waiting for the previous occupant of the table to clean up the table and leave. Because the customer is in a standing position, the delivery system may increase the threshold time (e.g., compared to another customer who is sitting at the table). As described herein, when an order is completed, the delivery system may provide instructions to the server to deliver the order to the location.

[0024] In an embodiment, a group of customers may access a dining environment. For example, a family unit may access a dining environment for a family meal. The family unit may access a point-of-sale terminal of a supplier to place an order. Members of the family unit may place orders for all members of the family unit. The delivery system may identify one or more attributes of at least one member of the family unit (e.g., the member placing the order). In another example, the group of customers may access a dining environment and place an order individually. Each member of the group may place an order individually at a point-of-sale terminal of a supplier. That is, members of the group may visit different suppliers and / or place different orders at different terminals of the same supplier. Although the group may access separate point-of-sale terminals, the group may be regrouped or reassembled at the same location (e.g., a table) within the dining environment. The delivery system may track one or more attributes of each member as they travel within the dining environment. For example, a member (e.g., the same or different from the member placing the order) may request a table for the group, while other members of the group may access a condiment station, a cutlery station, a lounge, another supplier, etc. The delivery system may determine that a member at a table may be requesting a table for the group, and the delivery system may associate an order with a table as such. In addition, the delivery system can associate all orders in the group's order with the members at the table. As such, the delivery system can provide instructions for delivering all orders of the group to the members at the table.

[0025] Embodiments of the present disclosure are directed to a delivery system that utilizes computer vision technology to associate one or more attributes of a customer with an order made by the customer. The delivery system then utilizes computer vision technology to identify the location of one or more attributes within a dining environment, which then enables the delivery system to associate the order with the location. The delivery system may track the movement of the customer within the dining environment based on one or more attributes. The attributes of the customer may be anonymous attributes, such as hair color, clothing color, clothing items, gait, personal items, accessories, etc. That is, the attribute may not include personally identifiable information (PII). The term PII may include information that directly identifies an individual (e.g., name, address, social security number, phone number) or data elements about an individual (e.g., a combination of gender, race, date of birth, geographic indicators). As described herein, the delivery system may identify one or more attributes of the customer after the completed order, associate one or more attributes with the order, track one or more attributes within the dining environment to associate the location with the one or more attributes, and provide instructions for delivering the order to the location. In other words, the delivery system may associate the order with the location of the customer within the dining environment. Accordingly, the delivery system can facilitate delivery of orders without requiring some type of visual indicator to be provided to patrons within a dining environment for tracking purposes (e.g., a card with a printed number that can be reused by multiple patrons over time) and / or without requiring notification to the patron.

[0026] Considering the above, Figure 1is a schematic diagram of an embodiment of a delivery system 10 that can be used in a dining environment 50 (e.g., a food hall, a food court, a restaurant, a food truck park, an amusement park, etc.). The dining environment 50 may include an open space, such as a walkable area (e.g., a queue or line), where customers can visit (one or more) vendors 52, create orders at (one or more) point of sale terminals 54 of (one or more) vendors 52, select (one or more) tables 58, visit (one or more) stations 60, or otherwise walk through the dining environment 50. The dining environment 50 may include entrances and exits for customers to enter and exit the premises. (One or more) vendors 52 may include restaurants, food trucks, dessert shops (e.g., bakeries, ice cream shops), beverage shops (e.g., juice shops), etc. (One or more) vendors 52 may include (one or more) point of sale terminals 54 that can receive orders from customers. Each point of sale terminal 54 may be a self-service terminal and / or a mobile device, such as a tablet computer. It should be appreciated that at least one of the point-of-sale terminals 54 may include a mobile device (such as a mobile phone) that is owned / carried by one of the customers and that uses an application to interact with the control system 64 or other vendor system to place an order. Each point-of-sale terminal 54 may display a menu of the vendor 52 and allow the customer to complete a transaction with the vendor 52 (e.g., place an order). The point-of-sale terminal(s) 54 may also be operated by and / or include a service provider (e.g., a human service provider) that can take orders from customers and create the order using a mobile device.

[0027] The dining environment 50 may also include various customer areas 56 where customers may be located. The customer areas 56 may include table(s) 58 where customers may sit or stand at the table(s) 58 during their dining experience. For example, customers may sit at table(s) 58 to eat. Table(s) 58 may include or be associated with one or more chairs, which may be movable chairs (e.g., not fixed to the ground or table(s) 58) and / or fixed chairs (e.g., bolted or fastened to the ground or table(s) 58; picnic benches, metal chairs, wooden chairs). Station(s) 60 may be temporary locations(s) that customers typically visit before or after selecting table(s) 58. Station(s) 60 may include restrooms, merchants or retailers, hand sanitizer stations, condiment stations, cutlery stations, trash stations, beverage fountains, and the like. For example, customers may visit a condiment station to obtain ketchup, barbecue sauce, salt, pepper, and the like. Customers may also visit a restroom before sitting at table(s) 58. After the dining experience, customers can clean up their tables and take any trash to a trash station before leaving the dining environment 50.

[0028] In certain embodiments, the dining environment 50 may include one or more cameras 62 that may generate image data (e.g., moving image data, such as video data) of the dining environment 50. The one or more cameras 62 may transmit the image data to a control system 64 (e.g., an electronic control system) for processing (e.g., image analysis, machine learning, artificial intelligence, computer vision). The one or more cameras 62 and the control system 64 may form a delivery system 10. In operation, the delivery system 10 generates and processes image data of the dining environment 50 to identify one or more attributes of a customer. In addition, the delivery system 10 may be trained using machine learning algorithms or artificial intelligence to understand and / or make predictions related to customer locations in the dining environment 50 (e.g., whether to establish an association between an order and a location and / or whether to interrupt an association between an order and a location). For example, the delivery system 10 may be trained using historical data and / or modeling data representing the dining environment 50 to understand patterns of human behavior for selecting a location and / or for leaving a location.

[0029] The control system 64 may include a memory 66 and one or more processors 68 (e.g., a processing circuit system). The memory 66 may include volatile memory (such as random access memory (RAM)) and / or non-volatile memory (such as read-only memory (ROM), an optical drive, a hard drive, a solid-state drive) or any other non-transitory computer-readable medium including instructions for operating the delivery system. The memory 66 may also include: a database of attributes (e.g., characteristics (such as identifiable objects, movements, gestures, clothing colors, gaits, head shapes); threshold time periods), maps (e.g., facility maps of the dining environment 50), patterns of human behavior, historical data, machine learning algorithms, and / or other types of information for controlling the system 64. The processing circuit system 68 may be configured to execute instructions. For example, the processing circuit system 68 may include one or more application-specific integrated circuits (ASICs), one or more field programmable gate arrays (FPGAs), one or more general-purpose processors, or any combination thereof.

[0030] The delivery system 10 may generate image data (via the camera 62) and identify an order placed by a customer at the point of sale terminal 54. The delivery system 10 may receive an indication of an order based on the image data and / or based on information provided to the delivery system 10 by the point of sale terminal 54 (e.g., the control system 64 is communicatively coupled to one or more cameras 62 and / or the point of sale terminal 54 via a wireless or wired network). For example, the delivery system 10 may process the image data to determine that the customer is interacting with the point of sale terminal 54 (e.g., the customer is scrolling a menu at the point of sale terminal 54) and / or that the customer has placed an order (e.g., the order number / identifier is displayed on the screen of the point of sale terminal 54 and is captured in the image data). That is, after entering the payment information, the point of sale terminal 54 may display text on the screen, notifying the customer of the completed transaction. For example, the point of sale terminal may display "Thank you for placing your order" or "Order number 123 confirmation", etc. The delivery system 10 may identify the screen in the image data and identify the order. Additionally or alternatively, the delivery system 10 may receive information from the point-of-sale terminal 54, such as information that the customer has selected one or more items from a menu, and / or information that the customer has entered payment information to place an order (e.g., an order number / identifier is passed to the control system 64). The delivery system 10 may associate a number or other identifier with the order. For example, the order may be "order number 123". In an embodiment, the delivery system 10 may also associate the time when the order was created and / or the location of the point-of-sale terminal 54 where the order was created.

[0031] When a customer is placing an order and / or at the time of placing an order, delivery system 10 may identify one or more attributes of the customer and associate the one or more attributes of the customer with the order. Figure 3As further described in , the one or more attributes may be appearance indicators and may not include personally identifiable information (PII). For example, delivery system 10 may identify the customer's hair color, the customer's relative height or size, the customer's head shape, the customer's gait, the customer's clothing color, and / or the customer's objects (e.g., personal belongings). The one or more attributes may be sufficient to enable differentiation between multiple different customers within dining environment 50 for tracking purposes, but may not indicate or include PII.

[0032] like Figure 4 and Figure 7 As further described in , the delivery system 10 can implement machine learning or computer vision techniques to understand patterns of human behavior and associate one or more attributes of a customer with a location within the dining environment 50. In this way, the delivery system 10 can associate an order with a customer's location. For example, a customer can complete an order at a point of sale terminal 54, select a table 58, and wait for their order. The delivery system 10 can associate one or more attributes of the customer with the table 58 and provide instructions for delivering the order to the table 58. In another example, the customer can select a table 58, leave the object, and then go to the station 60. The delivery system 10 can associate the customer with the table 58 even if the customer leaves. The delivery system 10 can identify the object of the customer 80 on the table 58 and determine that the customer can return.

[0033] In an embodiment, the delivery system 10 may be configured to provide instructions (e.g., audible instructions via a speaker and / or visible instructions via a display) to (one or more) suppliers 52 to facilitate the delivery of the ordered items to the customer. For example, after placing an order, the customer may proceed to the customer area 56 and sit at the table 58. The delivery system (via the control system 64) may store or access a map of the dining environment 50. The map may associate objects within the dining environment 50 with corresponding identifiers (such as letters, numbers, or shapes). For example, the tables 58 may be marked AF, respectively. In another example, the tables 58 may be marked with letters and numbers (such as A1, A2, A3, B1, B2, and B3), respectively. In addition, the seats of the tables 58 may be assigned letters, numbers, or both. For example, the instructions may be to provide order 123 to table A1, seat 3, or to provide order 98 to table A1, seat J. Accordingly, the delivery system 10 may use image data and / or maps to facilitate the delivery of the order.

[0034] To ensure accurate instructions, the delivery system 10 can passively update the map of the dining environment 50. For example, one or more cameras 62 can continuously generate image data of the dining environment 50 while tracking one or more attributes of the customer. The delivery system 10 can also identify the configuration or orientation of the table 58 and / or the state of the table 58 to update the map of the dining environment 50. For example, the customer can push one or more tables 58 together. The delivery system 10 can identify the combined table in the image data and update the map, including the corresponding identifiers of the objects in the dining environment 50. In another example, the delivery system 10 can identify one or more unavailable tables (e.g., waiting for the dishes to be cleared) from the image data, and understand that future customers may not want to sit at one or more unavailable tables. In an instance, the delivery system 10 can output a notification of the unavailable table to the supplier 52. In this way, the delivery system 10 can have a real-time or near real-time understanding of the dining environment 50 and provide accurate instructions for order delivery.

[0035] It should be understood that Figure 1 The layout and arrangement of the dining environment in the embodiment of the present invention are merely exemplary, and the delivery system 10 can be used with any of a wide variety of dining environments arranged in any suitable manner. In addition, certain components of the delivery system 10 can be shared between the vendors 52, and / or corresponding components of the delivery system 10 can be provided for each vendor (e.g., one or more cameras 62 for one vendor and one or more cameras 62 for another vendor). In fact, the delivery system 10 can be shared between / communicated with multiple dining environments 50, or can be dedicated to a dining environment 50.

[0036] Considering the above, Figure 282 is an example illustration of a customer 80 creating and placing an order 82 at a point-of-sale terminal 54 of a supplier 52. For example, the customer 80 may view a menu or a list of items (e.g., merchandise (such as food, toys)) sold by the supplier 52 at the point-of-sale terminal 54. The customer 80 may also select one or more items for purchase to create the order 82. The point-of-sale terminal 54 may be a self-service terminal, a self-service checkout station, a mobile device of the customer 80, etc. For example, the self-service terminal may include a display screen that displays a menu provided by the supplier 52. The customer 80 may scroll through the menu and select one or more items for the order 82. To complete the order 82, the customer 80 may enter payment (e.g., credit card information, cash, electronic transfer). For example, the customer 80 may swipe a credit card or debit card through a card reader on the self-service terminal, or the customer 80 may insert cash into the self-service terminal. The self-service terminal may then display a confirmation message, such as 'Order Completed', 'Thank You for Your Order', 'Order Number 123 Confirmed', etc. As described herein, the delivery system 10 may analyze the image data (from the one or more cameras 62 ) and identify a confirmation message.

[0037] In view of the foregoing, the dining environment 50 may include two customers (e.g., a first customer 80a, a second customer 80b). Distinguishing between customers 80a, 80b to accurately deliver orders may be beneficial to the control system 64. For example, customers 80a, 80b may access the supplier 52 and / or the point-of-sale terminal 54. For example, the first customer 80a may access the first point-of-sale terminal 54a to create a first order 82a. When confirming the first order 82a, the delivery system 10 may identify one or more attributes of the first customer 80a and associate the attribute with the first point-of-sale terminal 54a and the first order 82a. Similarly, the delivery system 10 may associate one or more attributes of the second customer 80b with the second point-of-sale terminal 54b and / or the second order 82b. The delivery system 10 may track one or more attributes of the first customer 80a and the second customer 80b within the dining environment 50 for order delivery.

[0038] In an embodiment, the delivery system 10 may not associate the first customer 80a and the second customer 80b as members of a group. For example, the delivery system 10 may identify that the first customer 80a arrived before the second customer 80b. In another example, the delivery system 10 may identify the first customer 80a as already a member of a group (e.g., excluding the second customer 80b). In an embodiment, the delivery system 10 may associate the first customer 80a and the second customer 80b as members of the same group. For example, the first customer 80a and the second customer 80b may arrive at the dining environment 50 together and / or interact with each other in the dining environment 50 (e.g., before placing orders 82a, 82b). The first customer 80a and the second customer 80b may choose to create their own orders at the point of sale terminal 54. The delivery system 10 may also associate the first order 82a placed by the first customer 80a with the second order 82b placed by the second customer 80b. In this way, the delivery system 10 may have additional data points for determining the location of the group (e.g., to deliver orders 82a, 82b). As referenced Figure 4 As further described, the first customer 80a and the second customer 80b may take different routes (eg, paths) to reach the location.

[0039] Figure 3 is an example illustration of one or more attributes of a customer 80, which may be represented by Figure 1 The delivery system 10 may identify and / or use the delivery system 10 for tracking the customer 80. One or more attributes may be anonymous attributes or appearance indicators rather than PII. For example, the delivery system 10 may identify hair color or hairstyle 90a, head shape 90b, clothing item shape and / or color 90c, accessories 90d, objects (e.g., personal belongings) 90e, gait 90f, etc.

[0040] For example, the delivery system 10 may identify the hair color 90a of the customer 80. The hair color 90a may include black, gray, white, brown, blonde, red, or a combination thereof. The hairstyle 90a may include braids, ponytails, bangs, bald / missing hair, etc. The head shape 90b may be the shape of the customer's face, such as a heart, square, oval, diamond, triangle, etc. The head shape 90b may also include head or facial accessories, such as glasses, hats, piercings, etc. In an embodiment, the delivery system 10 may use a combination of hair color and / or hairstyle 90a and head shape 90b as one or more attributes of the customer 80. Additionally or alternatively, the delivery system 10 may identify the clothing color 90c of the customer 80. For example, the delivery system 10 may identify the color of the clothing, the pattern of the clothing, the design on the clothing, etc. For example, the customer 80 may wear a shirt with a slogan, a cartoon image, or a graphic design.

[0041] In an embodiment, the delivery system 10 may identify one or more accessories 90d of the customer 80. For example, one or more accessories 90d may include earrings, necklaces, bracelets, rings, scarves, hairpins, tie clips, belts, sunglasses, and / or other accessories worn by the customer 80. In the illustrated embodiment, the delivery system 10 may identify a necklace with a charm. Additionally or alternatively, the delivery system 10 may identify one or more personal belongings 90e of the customer 80. One or more personal belongings 90e may include a small bag, a phone, a wallet, a jacket, a backpack, a water bottle, and / or other objects carried by the customer 80. One or more personal belongings 90e may also include a stroller, a pair of crutches, a wheelchair, and / or other objects transported by the customer 80. For example, the delivery system 10 may identify a water bottle as a personal belonging 90e. As described herein, the delivery system 10 may track personal belongings 90e within the dining environment 50 to determine the location of the customer 80.

[0042] In an embodiment, the delivery system 10 can track the gait 90f of the customer 80. For example, the delivery system 10 can identify, distinguish and associate the walking gait 90f with the customer 80. For example, the customer 80 can walk faster or slower relative to the average walking speed. In another example, the customer 80 can take larger or smaller steps relative to the average step length. In another example, some customers 80 may have a unique walking style, such as jumping, bouncing, running, limping, etc. The delivery system 10 can be trained (e.g., by artificial intelligence or machine learning) to identify the walking gait 90f using the customer 80. One or more attributes 90 can also include the size of the customer 80 (e.g., estimated absolute size and / or relative size). For example, the customer can be a child or teenager who can be smaller than other customers. In another example, the customer can be a basketball player and is taller than other customers.

[0043] The delivery system 10 can track multiple attributes of the patrons 80 within the dining environment 50. For example, the delivery system 10 can track the size and walking gait 90f of the patrons 80. In an example, a taller patron may have a longer stride than a shorter patron. In another example, the delivery system 10 can track hair color 90a, accessories 90d, and personal belongings 90e. The delivery system 10 can track any number of attributes 90 and / or combinations of attributes 90 of the patrons 80. The delivery system 10 can also track different attributes 90 for different patrons to thereby efficiently track the attributes 90 that best differentiate the patrons.

[0044] Considering the above, Figure 4is an example illustration of a delivery system 10 tracking a first customer 80a and a second customer 80b within a dining environment 50. For example, the first customer 80a may walk to the customer area 56, select a table 58a, visit the station 60, and then return to the table 58a to wait for his order. As described herein, the delivery system 10 may identify and track one or more attributes 90 of the customers 80a, 80b within the dining environment 50 to provide instructions for order delivery.

[0045] For example, a first customer 80a may travel along route 100 within dining environment 50. As represented by point 102, first customer 80a may enter customer area 56 and visit first table 58a. First customer 80a may spend a time period at first table 58a to establish ownership of first table 58a. Delivery system 10 may compare the time period at first table 58a with a threshold time period. In response to the time period at first table 58a meeting or exceeding the threshold time period, delivery system 10 may associate one or more attributes of first customer 80a with first table 58a. Therefore, delivery system 10 may also associate order 82a with first table 58a. In other words, delivery system 10 may determine that first table 58a is a location for delivery of order 82a. As represented by point 104, first customer 80a may leave first table 58a and visit station 60. Delivery system 10 may identify station 60 as a temporary location (e.g., as stored or marked in a database accessible by delivery system 10). In another example, the first customer 80a may leave a personal belonging 90e at the first table 58a to claim the table. The delivery system 10 may identify the personal belonging 90 on the first table 58a as a claim for the table, and the delivery system 10 may associate one or more attributes 90 of the first customer 80a with the first table 58a. The delivery system 10 may not interrupt the association of the one or more attributes 90 of the first customer 80a or the order 82a of the first customer 80a with the first table 58a. Then, as represented by point 106, the first customer 80a may return to the table 58a.

[0046] At the same time or another time, the second customer 80b may go directly to the second table 58b along the route 108. As represented by point 110, the second customer 80b may be seated at the second table 58b and begin browsing the mobile device, talking to other customers at the second table 58b, and / or taking some other action / gesture / movement indicating an association with the second table 58b. The delivery system 10 may identify one or more attributes 90 of the second customer 80b. After the second customer 80b is seated at the second table 58b, the delivery system 10 may associate one or more attributes 90 of the second customer 80b with the second table 58b. The delivery system 10 may continuously monitor the image data to determine whether an interruption event occurs, such as whether the first customer 80a and / or the second customer 80b switches tables, leaves the dining environment 50, etc. If the interruption event does not occur, the delivery system 10 may provide instructions for delivering the orders 82a, 82b to the first and second tables 58a, 58b, respectively.

[0047] Figure 5 82 to a location of a customer 80. For example, a customer 80 may place an order 82 at a point of sale terminal 54 of a vendor 52, and the delivery system 10 may identify one or more attributes of the customer 80. The vendor 52 may receive the order 82 and create the order 82. After the vendor 52 prepares the order 82, the vendor 52 may indicate completion of the order 82 to the delivery system 10. The delivery system 10 may provide instructions to the vendor 52 indicating a location for delivery of the order 82 (e.g., based on a current association of a location with the order 82, via tracking one or more attributes 90 of the customer 80 who placed the order 82).

[0048] For example, customer 80 may use point of sale terminal 54 to place order 82 for a children's meal, including chicken nuggets and toys. Vendor 52 may create order 82 in the kitchen. After order 82 is completed (e.g., upon receiving an indication (such as user input) that order 82 is completed), delivery system 10 may provide instructions for delivering order 82 to the location of customer 80. However, it should also be appreciated that delivery system 10 may provide instructions for delivering order 82 to the location of customer 80 in response to (e.g., as long as) order 82 is associated with a location (e.g., customer 80 is seated at the location in a manner that causes delivery system 10 to associate order 82 with the location). In embodiments, all items of order 82 may be made before order 82 is delivered to table 58. In other embodiments, each item of order 82 is delivered as it is made to ensure freshness.

[0049] The delivery system 10 can provide the location of the customer 80 to the server of the supplier 52. The server can be an individual or an automated delivery system (e.g., a remotely controlled or autonomously controlled delivery vehicle). In an embodiment, the individual delivering the food can be a staff member of the supplier 52. The individual can receive instructions indicating table numbers / identifiers, seat numbers / identifiers, or a combination thereof within the dining environment 50. For example, the instruction can include a string of text, such as "table 1, seat 3", which indicates the leftmost table and the rightmost seat. The delivery system 10 can output (e.g., via the supplier 52 and / or displayed by a device carried by the server) a map of the dining environment 50, wherein the location is marked on the map or otherwise highlighted. In this way, the server can use the map to travel to the location. The map of the dining environment 50 can be updated in real time (e.g., substantially real time, near real time) based on the image data. In addition, the map of the dining environment 50 may include a schematic diagram or image (e.g., a still or moving image) based on the image data. In an embodiment, one or more attributes 90 of the customer 80 are not disclosed to the server (e.g., hidden from the server; not known by the server), the customer 80 does not have any trackable items provided by the supplier 52 (e.g., no card with a printed number, no buzzer, no RFID tag), and / or the customer 80 does not use its own mobile device to provide location data to the delivery system 10 for delivery of the order 82. Alternatively, the server may deliver the items in the order 82 to the location based on (e.g., based only on) a map and / or (one or more) identifiers of the location. In this way, the server may not know any attributes of the customer 80 and may not be prompted to visually confirm any attributes of the customer 80 prior to delivery of the order 82 to the location, but instead the server may know the location designated for delivery of the order 82 placed by the customer 80. In fact, the customer 80 does not necessarily need to be present in order for the server to complete the delivery of the order 82 placed by the customer 80 to the location. Computer vision techniques and / or algorithms (e.g., via machine learning) may be sufficiently accurate and reliable to deliver order 82 without these additional steps or additional burden on the server (e.g., without the server needing to know and / or visually confirm the attributes of customer 80). Accordingly, the server can bring order 82 to the location and then efficiently proceed with processing other customers' orders.

[0050] In an embodiment, the server delivering the order 82 may include an automated delivery system, such as one or more robots, ground vehicles, air vehicles / drones, or any combination thereof. As such, the delivery system 10 may transmit a signal indicating a location to the automated delivery system to facilitate delivery of the order 82 to the location. The automated delivery system may receive and / or store a map of the dining environment 50, and the delivery system 10 may provide a route from the supplier 52 to the location. The automated delivery system may include one or more sensors (e.g., motion sensors) to identify objects (e.g., people, shopping carts, animals) within the dining environment 50. In an embodiment, the automated delivery system may be programmed using machine learning, artificial intelligence, or computer vision capabilities to interpret and understand the dining environment 50. In this way, the delivery system 10 may provide instructions to provide the order 82 to a location associated with one or more attributes 90 of the customer 80.

[0051] Figure 6 1 is an example method 120 for identifying one or more attributes 90 of a customer 80, associating one or more attributes 90 of the customer 80 with a location, and delivering an order 82 to a location within a dining environment 50. At block 122, the delivery system 10 may receive input indicating that the order 82 was placed by the customer 80. The delivery system 10 may be communicatively coupled to a point-of-sale terminal 54. That is, after the customer 80 completes the order 82, the point-of-sale terminal 54 may transmit a signal indicating the completion of the order 82 to the delivery system 10. The delivery system 10 may receive a receipt of the order 82, a confirmation of the order 82, etc. The delivery system 10 may also identify the point-of-sale terminal 54 used by the customer 80 to create the order 82. The delivery system 10 may also assign a number or other identifier to the order 82. For example, the order number / identifier may be any combination of numbers or letters, such as 123 or A2.

[0052] In another example, the delivery system 10 may receive image data from one or more cameras 62. The delivery system 10 may process the image data to receive an indication that the order 82 is issued by the customer. For example, the delivery system 10 may process the image data and identify an order confirmation on a display of the point-of-sale terminal 54 and / or identify the customer 80 holding a payment method. The display of the point-of-sale terminal 54 may read "Thank you for placing your order" or "Order number 123 confirmation". The delivery system 10 may extract the order number or other identifier of the order 82 from the image data (e.g., via text processing). In another example, the delivery system 10 may identify a customer 80 scrolling through a menu at the point-of-sale terminal 54 and adding one or more items to a shopping cart. The delivery system 10 may understand that adding items to the shopping cart may cause the customer 80 to check out and place the order 82. As such, the delivery system 10 may analyze the image data over time to receive input indicating that the order 82 is issued. For example, the delivery system 10 may identify image data indicating that the customer 80 interacts with the point-of-sale terminal 54 or reaches for a wallet to complete the order 82.

[0053] In an embodiment, delivery system 10 may identify customer 80 at point of sale terminal 54, but may not receive input indicating order 82. For example, customer 80 may scroll through menus on a display of point of sale terminal 54. However, customer 80 may walk away from point of sale terminal 54 without completing order 82. In another example, customer 80 may view point of sale terminal 54 and walk away without placing order 82. As such, delivery system 10 may not isolate or identify attributes of customer 80 in the image data, and may continue to process image data for orders placed by customer 80.

[0054] At block 124, the delivery system 10 may generate and process image data of the dining environment 50 and identify the customer 80. For example, the one or more cameras 62 may generate image data, and the control system 64 may process the image data to identify and / or isolate the image data of the customer 80. In an example, in response to the customer 80 interacting with the point of sale system 54, the delivery system 10 may identify and isolate the image data of the customer 80.

[0055] At block 126, the delivery system 10 may identify one or more attributes 90 of the customer 80 based on the isolated image data. Figure 3As described, the attribute 90 can be an anonymous, appearance indicator. The delivery system 10 can identify one or more attributes 90 (e.g., a combination of attributes sufficient to distinguish the customer 80 from other customers) and use the one or more attributes 90 to track the customer 80 within the dining environment 50. For example, the customer 80 may be carrying a backpack, has a fast gait, and wears glasses. In addition, another customer 80 may have blue hair, a plaster cast on his leg, and / or use crutches. The delivery system 10 can identify that the height or size of the customer 80 is smaller than the average size. The delivery system 10 can estimate the height or size of the customer 80 based on the characteristics of the camera 62 (e.g., field of view, position relative to the dining environment 50) and / or the characteristics of the dining environment 50 (e.g., the size of the table 58 within the dining environment 50). In this way, the delivery system 10 can track the customer 80 and other customers traveling within the dining environment 50.

[0056] It should be appreciated that the delivery system 10 may receive and analyze image data while the order 82 is being placed by the customer 80. For example, the delivery system 10 may begin identifying one or more attributes 90 of the customer 80 when the customer 80 initiates an interaction with the point-of-sale terminal 54 or otherwise takes steps that typically result in the order 82 being placed. In one embodiment, the delivery system 10 may identify image data indicating that the customer 80 interacts with the point-of-sale terminal 54 or reaches out to grab a wallet to complete the order 82, and then the delivery system 10 may begin identifying and creating a profile of the one or more attributes 90 of the customer 80.

[0057] At block 128, the delivery system 10 may associate one or more attributes of the customer 80 with the order 82. For example, the delivery system 10 may associate one or more attributes 90 of the customer 80, such as a backpack, a fast gait, and glasses, with the order 82.

[0058] At block 130, the delivery system 10 may monitor the image data over time to associate one or more attributes 90 of the customer 80 with a location. Figure 4 and Figure 7 As described, customer 80 may take any of a variety of routes before selecting table 58. As such, it may be beneficial for delivery system 10 to continuously monitor image data to identify the location of customer 80 within dining environment 50 (e.g., the location of delivery of order 82). For example, customer 80 may first select and sit down at table 58, then leave table 58 to access station 60. Customer 80 may leave his backpack at table 58 so that other customers may understand that table 58 is required. Additionally, delivery system 10 may identify the backpack on table 58 as an indicator that customer 80 intends to return to table 58. As such, delivery system 10 may associate one or more attributes 90 of customer 80 with table 58.

[0059] At block 132, the delivery system 10 may provide instructions to deliver the order 82 to the location. That is, the delivery system 10 may provide instructions to the server of the vendor 52 to deliver the order 82 to the location. For example, the delivery system 10 may provide a notification to the server to bring the order 82 to the table 58. Accordingly, the delivery system 10 may receive the order 82 from the customer 80, track the customer 80 to determine the location of the order delivery, and provide instructions for delivering the order 82. It should be appreciated that the method 120 may be performed for multiple customers at the same time, at overlapping times, and / or at different times (e.g., when multiple customers enter the dining environment 50 and place corresponding orders over time).

[0060] The method 120 may be performed according to instructions stored on one or more tangible, non-transitory, machine-readable media, and / or may be executed by a processor or processing circuitry of the delivery system 10 described herein (via the control system 64) or on another suitable controller. The blocks of the method 120 may be performed in any suitable order. In addition, certain blocks of the method 120 may be omitted and / or other blocks may be added to the method 120.

[0061] In an embodiment, a group of customers may visit the dining environment 50 for a dining experience. The group may stay together and place one order 82, or the group may separate and place multiple orders 82 at multiple point of sale terminals 54 and / or multiple vendors 52. After placing the order(s) 82, the group may proceed to the customer area 56 to wait for the order(s) 82. Figure 7 is an example illustration of a delivery system 10 that tracks one or more attributes of the group of customers 80 within a dining environment 50. For example, the group of customers 80 may include a first customer 80a, a second customer 80b, and a third customer 80c. Each customer of the group may take a different route (e.g., path) within the dining environment 50, but the group may end up at a final location. The final location may be a location where the group of customers may have their dining experience, or an order delivery location.

[0062] refer to Figure 7, the third customer 80c can take route 150 and go directly to the first table 58a. The first table 58a can be the final location of the group. For example, the group can visit different locations within the dining environment 50, but the group can regroup at the first table 58a. As such, the third customer 80c can wait for other members of the group at the first table 58a. The third customer 80c can spend a time period greater than the threshold time period at the first table 58a. As such, the delivery system 10 can associate the third customer 80c with the first table 58a. The delivery system 10 can further associate other members of the group (e.g., the second customer 80b, the third customer 80c) with the first table 58a.

[0063] The first customer 80a may take route 152 and stop at the second table 58b, start a conversation with one or more customers at the second table 58b, and then walk to the first table 58a. The first customer 80a may stay at the second table 58b for a time period less than the threshold time period, and therefore the first customer 80a is not associated with the second table 58b. In an example, the first customer 80a may stay at the second table 58b for a time period longer than the threshold time period. In such cases, the first customer 80a may initially be associated with the second table 58b, or other factors may be considered so that the first customer 80a is not associated with the second table 58b even if the time period is longer than the threshold time period. For example, the delivery system 10 may determine that the first customer 80a should not be associated with the second table 58b based on the time period spent at the second table 58b, the movement or gesture made by the first customer 80a at the second table 58b, and / or other customers at the second table 58b. For example, the first customer 80a may kneel or stand next to the second table 58b. The delivery system 10 may identify the body posture of the first customer 80a as an indication that the second table 58b may be a temporary location. The delivery system 10 may also factor in the formation of a group and the third customer 80c waiting at the first table 58a (e.g., if the first customer 80a is part of the group and another member of the group has established its location, the threshold time and / or other factors utilized so that the first customer 80a can establish an association with a different location may be increased). Therefore, the delivery system 10 may not form an association between the first customer 80a and the second table 58b. In fact, the first customer 80a may leave the second table 58b and go to the first table 58a. If the first customer 80a is associated with the second table 58b, the delivery system 10 may determine that an interruption event has occurred, and then the association between the first customer 80a and the second table 58b may be interrupted, and then another association may be formed between the first customer 80a and the first table 58a when sufficient information is received.

[0064] The second customer 80b may take a route 154 within the dining environment 50. The second customer 80b may visit a temporary location (e.g., station 60) before arriving at the first table 58a. For example, the second customer 80b may visit a rest room, a hand sanitizer station, a beverage station, or a condiment station. The delivery system 10 may identify the station 60 as a temporary location rather than an order delivery location. Even if the second customer 80b spends a time period greater than a threshold time period at the station 60, the delivery system 10 may not associate the second customer 80b with the station 60. In this way, the delivery system 10 can distinguish between areas of the dining environment 50.

[0065] While tracking one or more attributes of customers 80a, 80b, 80c within the dining environment 50, the delivery system 10 can also passively update the map. For example, the group of customers can combine the first table 58a with the third table 58c. The delivery system 10 can identify that the first table 58a and the third table 58c move positions and / or change configurations. The delivery system 10 can further identify the third customer 80c sitting at the first table 58a, and the first customer 80a and the second customer 80b sitting at the third table 58c. The delivery system 10 can update the map and provide updated instructions to the supplier 52. The delivery system 10 can also provide instructions to provide orders based on the updated map. In this way, the delivery system 10 can provide accurate instructions for order delivery.

[0066] Figure 8 170 is an example method for associating one or more attributes of a customer 80 with a location and determining whether an interruption event has occurred. As described herein, a customer 80 may take any of a variety of different routes available within a dining environment 50 to arrive at a location. For example, a customer 80 may stop at a first location to chat with a friend, visit a condiment station, stop at a lounge, etc. A customer 80 may stop at a first location (e.g., a table, a seat) and stay at the first location for a time period. If the time period is greater than a threshold time period, the delivery system 10 may associate one or more attributes of the customer 80 with the first location. However, the customer 80 may decide to move to a second location (e.g., a final location; an order delivery location). As such, the delivery system 10 may determine whether an interruption event has occurred and whether a new association may be formed with the second location. In this way, the delivery system 10 may determine the location of the customer 80 to accurately and efficiently facilitate the delivery of the order.

[0067] At block 172, the delivery system 10 may identify one or more attributes of the customer 80 that was present at the location for a period of time. For example, the delivery system 10 may identify the customer 80 at the first table 58a and may monitor the image data over time. In an example, the customer 80 may enter the customer area 56 and sit at the first table 58a. The customer 80 may be talking to another customer at the first table 58a, browsing his phone, observing the dining environment 50, etc. The delivery system 10 may identify these behaviors and correlate these behaviors with the customer's intent to establish the first table 58a as its location for order delivery.

[0068] At box 174, the delivery system 10 can determine whether the time period spent at the location is greater than a threshold time period. The threshold time period can be an amount of time determined by the delivery system 10 via machine learning or artificial intelligence that can indicate that the customer 80 requires the first table 58a or otherwise intends to establish the first table 58a as its location for order delivery. The threshold time period can vary based on any of a variety of factors, such as movements or gestures made by the customer at the table, any items placed on the table, any other customers at the table, corresponding times spent at other locations, etc. In order to monitor the image data, the delivery system 10 can start a clock or timer to monitor the time period that the customer 80 can spend at the first table 58a. Additionally or alternatively, the delivery system 10 can identify a timestamp within the image data. As such, the delivery system 10 can track the time period that one or more attributes of the customer 80 can exist at the first table 58a and compare the time period to the threshold time period.

[0069] If the time spent at the location is not greater than the threshold time period, the delivery system 10 may not associate the customer 80 with the location. After the time period, the customer 80 may leave the first table 58a and walk to the second table 58b. The customer 80 may stand, sit, kneel, lean against, or walk past the second table 58b. For example, the customer 80 may talk with another customer 80 located at the second table 58b and join other customers 80 at the second table 58b. The delivery system 10 may not associate the customer 80 with the first table 58, and monitor the image data over time to determine whether the customer 80 can be associated with the second table 58b. As such, the example method 170 may return to box 172 and identify one or more attributes of the customer 80 who was present at the second table 58 for the time period.

[0070] If the time period spent at the location is greater than the threshold time period, then at block 176, the delivery system 10 may associate one or more attributes of the customer 80 with the location. For example, the customer 80 may be seated at the first table 58a waiting for his order 82. In other words, the customer 80 may have requested the table for his dining experience. The time period that the customer 80 spent waiting for his order 82 at the first table may be greater than the threshold time period. As such, the delivery system 10 may associate one or more attributes of the customer 80 with the first table 58a.

[0071] At block 178, the delivery system 10 may determine whether an interruption event has occurred. In an example, the customer 80 may leave the first table 58a, and the delivery system 10 may determine whether an interruption event has occurred. That is, the delivery system 10 may determine whether the association is interruptible. To identify an interruption event, the delivery system 10 may consider a variety of factors, such as corresponding time at the table, corresponding time away from the table, type of additional location, movement or gestures by customers at and / or away from the table, personal belongings 90e left at the table, other customers at the table, etc.

[0072] If the interruption event does not occur, the delivery system 10 can return to box 176 and maintain the association of one or more attributes of the customer 80 with the first table 58a. For example, the customer 80 can leave one or more personal belongings (such as a water bottle or a jacket) at the first table 58a and visit the station 60. The delivery system 10 can determine that the personal belongings and the station 60 can indicate that the customer 80 returns to the first table 58a. That is, the station 60 can be a temporary location. As such, the delivery system 10 can determine that the interruption event may not occur. In another example, the customer 80 can go to the second table 58b and talk with other customers 80. The customer 80 can stand next to the second table 58b. The delivery system 10 can identify the standing posture of the customer 80 as an intention to return to the first table 58a. As such, the delivery system 10 may not determine that leaving the first table 58a is an interruption event. The method 170 can return to box 176, and the delivery system 10 can maintain the association of one or more attributes of the customer 80 with the first table 58a.

[0073] If an interruption event occurs, at box 180, the delivery system 10 can interrupt the association between one or more attributes of the customer 80 and the location. For example, the customer 80 can sit at the second table 58b and start a conversation with other customers at the second table 58. The customer 80 can stay at the second table 58b for a time period greater than the threshold time period. The delivery system 10 can identify the stay at the second table 58b as an interruption event. In another example, the delivery system 10 can recognize that the customer 80 may have completed the dining experience. For example, the customer 80 can pack his personal belongings 90e and prepare to leave the dining environment 50. The customer 80 can also clean the first table 58a by taking the plates, trays, boxes, etc. to the garbage station. As such, the delivery system 10 can understand that the customer 80 may be planning to leave the dining environment 50. The delivery system 10 can identify the customer 80 leaving the first table 58a as an interruption event and interrupt the association between one or more attributes of the customer 80 and the location. Accordingly, delivery system 10 may classify certain actions and / or combinations of actions taken by customer 80 via computer vision techniques, which may enable delivery system 10 to accurately determine that it is appropriate to create and / or end an association with a location for order delivery.

[0074] The method 170 may be stored on one or more tangible, non-transitory, machine-readable media, and / or may be executed by a processor or processing circuit system of the control system described above or executed on another suitable controller. The steps of the method 170 may be performed in the order disclosed above or in any other suitable order. In addition, certain steps of the method may be omitted and / or other blocks may be added to the method 170.

[0075] As used herein, 'machine learning' and / or 'computer vision' may refer to algorithms and statistical models used by computer systems to perform specific tasks with or without the use of explicit instructions. For example, a machine learning process may generate a mathematical model based on a sample of clean data, referred to as "training data," to make predictions or decisions without being explicitly programmed to perform a task. The delivery system 10 may generate (e.g., train and / or update (such as passively update)) a model based on image data collected over time. In this way, the model may be improved over time based on new image data collected over time. For example, a model may receive image data to provide output related to creating and / or interrupting associations, and the image data may then also be used to update and refine the model.

[0076] It is well known that the use of PII should be subject to privacy policies and practices that are generally recognized to meet or exceed industry or government requirements for maintaining the privacy of users. In particular, personally identifiable information data should be managed and processed to minimize unintentional or unauthorized access or use, and the nature of authorized use should be clearly indicated to users.

[0077] Although only certain features of the present invention have been illustrated and described herein, many modifications and changes will occur to those skilled in the art. It is to be understood, therefore, that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention. Figure 1-5 Any features shown and described may be combined in any suitable manner.

[0078] The technology presented and claimed herein is cited and applied to substantial objects and specific examples that demonstrably improve the practical nature of the art, and as such is not abstract, intangible, or purely theoretical. In addition, if any claim appended at the end of this specification contains one or more elements designated as "means for (performing) ... (function)" or "step for (performing) ... (function)", it is intended that such elements are to be interpreted in accordance with 35 U.S.C. 112(f). However, for any claim containing elements designated in any other manner, it is intended that such elements are not to be interpreted in accordance with 35 U.S.C. 112(f).

Claims

1. A delivery system comprising: one or more processors; a memory storing instructions executable by the one or more processors to cause the one or more processors to: identifying one or more attributes of a user in one or more images captured by one or more cameras; associating the one or more attributes of the user with an order placed by the user; tracking the one or more attributes of the user in the one or more images over time to identify movement of the user within an environment; In response to the one or more attributes of the user in the one or more images remaining at a location for greater than a threshold time, creating an association between the one or more attributes of the user and the location; as well as Instructions are provided for delivering the items in the order to the location.

2. The delivery system of claim 1, wherein the location comprises a table or seat in a dining environment.

3. The delivery system of claim 1, wherein the instructions are executable by the one or more processors to cause the one or more processors to analyze the one or more images to determine that the order was placed by the user.

4. A delivery system according to claim 3, wherein the instructions are executable by the one or more processors to cause the one or more processors to analyze the one or more images to identify an order number on a display screen in the environment to thereby determine that the order was placed by the user.

5. The delivery system of claim 1, wherein the instructions are executable by the one or more processors to cause the one or more processors to: identifying the one or more attributes of the user at additional locations for greater than the threshold time; and In response to the one or more attributes of the user being greater than the threshold time at the additional location, breaking the association between the one or more attributes of the user and the location.

6. The delivery system of claim 1, wherein the instructions are executable by the one or more processors to cause the one or more processors to: identifying the one or more attributes of the user at an additional location; referencing a map of the environment to determine that the additional location is a temporary location; and In response to the additional location being the temporary location, maintaining the association between the one or more attributes of the user and the location.

7. The delivery system of claim 1, wherein the one or more attributes are anonymous attributes that do not provide an identification of the user.

8. The delivery system of claim 7, wherein the one or more attributes include: Hair color, hairstyle, clothing item shape, clothing item color, accessories, objects carried by the user, gait, head shape, or any combination thereof.

9. The delivery system of claim 1, wherein the instructions are executable by the one or more processors to cause the one or more processors to: identifying one or more respective attributes of additional users in the one or more images captured by the one or more cameras; associating the one or more respective attributes of the additional user with additional orders placed by the additional user; tracking the one or more respective attributes of the additional user in the one or more images over time to identify respective movements of the additional user within the environment; responsive to the one or more corresponding attributes of the additional user in the one or more images remaining at the location for greater than the threshold time, creating an additional association between the one or more corresponding attributes of the additional user and the location; as well as Instructions are provided for delivering the items in the order and the corresponding items in the additional orders to the location.

10. The delivery system of claim 9, wherein the instructions are executable by the one or more processors to cause the one or more processors to: responsive to the association and the additional association existing together within a time period, forming the user and the additional user into a group; receiving an indication that the items in the order and the corresponding items in the additional orders are ready for delivery to the location at a delivery time; and In response to an analysis of the one or more attributes of the user and the one or more additional attributes of the additional user in the one or more images indicating that at least one of the user and the additional users was present at the location at the delivery time, providing the instructions for delivering the items in the order and the corresponding items in the additional order to the location.

11. The delivery system of claim 1 , wherein the instructions are executable by the one or more processors to cause the one or more processors to: providing said instructions indicating said location for visualization by a facilitator; and The one or more attributes are withheld from the server.

12. A method of operating a delivery system, the method comprising: identifying, using one or more processors, one or more attributes of a user in one or more images captured by one or more cameras; associating, using the one or more processors, the one or more attributes of the user with an order placed by the user; tracking, using the one or more processors, the one or more attributes of the user in the one or more images over time to identify movement of the user within an environment; creating, using the one or more processors, an association between the one or more attributes of the user and the location in response to the one or more attributes of the user in the one or more images being at a location for greater than a threshold time; as well as Using the one or more processors, instructions are provided to deliver items in the order to the location.

13. The method of claim 12, comprising analyzing, using the one or more processors, the one or more images to determine that the order was placed by the user.

14. The method according to claim 12, comprising: identifying, using the one or more processors, the one or more attributes of the user at additional locations for greater than the threshold time; as well as Using the one or more processors, in response to the one or more attributes of the user being greater than the threshold time at the additional location, breaking the association between the one or more attributes of the user and the location.

15. The method according to claim 12, comprising: identifying, using the one or more processors, the one or more attributes of the user at an additional location; referencing, using the one or more processors, a map of the environment to determine that the additional location is a temporary location; as well as In response to the additional location being the temporary location, using the one or more processors, maintaining the association between the one or more attributes of the user and the location.

16. The method of claim 12, comprising using the one or more processors to block output of the one or more images and the one or more attributes of the user in the environment so that the one or more images and the one or more attributes are not disclosed to persons in the environment.

17. A delivery system comprising: one or more processors; a memory storing instructions executable by the one or more processors to cause the one or more processors to: identifying one or more attributes of a user in one or more images captured by one or more cameras; associating the one or more attributes of the user with an order placed by the user; tracking the one or more attributes of the user in the one or more images over time to identify movement of the user within an environment; In response to the one or more attributes of the user in the one or more images remaining at a location for greater than a threshold time, creating an association between the one or more attributes of the user and the location; as well as An output is provided that indicates the location to facilitate delivery of items in the order to the location without displaying the one or more images to a person associated with the environment.

18. The delivery system of claim 17, wherein the output comprises a map indicating the environment of the location.

19. The delivery system of claim 18, wherein the instructions are executable by the one or more processors to cause the one or more processors to update the map to represent respective current locations of structures in the environment based on the image data.

20. The delivery system of claim 17, wherein: The instructions identify the one or more attributes of the user at an additional location; referencing a map of the environment to determine that the additional location is a temporary location; as well as In response to the additional location being the temporary location, maintaining the association between the one or more attributes of the user and the location.

Images