System and method for identifying guests within a facility and delivering ordered items

Abstract

A delivery system can include one or more processors and a memory storing instructions, the instructions being 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 and associate the one or more attributes of the user with an order placed by the user. The delivery system can track one or more attributes of a user in one or more images over a period of time to identify the 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 remaining at one location for a period of time longer than a threshold time, create an association between the one or more attributes of the user and that location. The delivery system can then provide an instruction to deliver an item within the order to that location.

Description

Technical Field

[0001] 〔Cross - Reference to Related Applications〕 This application claims priority to U.S. Provisional Patent Application No. 63 / 347,404, filed May 31, 2022, entitled "SYSTEMS AND METHODS FOR LOCATING A GUEST IN A FACILITY FOR ORDER DELIVERY", the contents of which are hereby incorporated by reference in their entirety for all purposes.

Background Art

[0002] This section is intended to introduce the reader to various aspects of technologies that may be related to the various aspects of the technology described and / or claimed below. This discussion is believed to be helpful in showing the reader the background circumstances and facilitating a better understanding of the various aspects of the present disclosure. Accordingly, these descriptions should be read from the above perspective rather than as an admission of prior art.

[0003] In a restaurant or cafeteria facility, a guest may place an order at an ordering station (e.g., a kiosk, a register) and receive a card with a printed number. At a later point, a host can search for the card with the printed number (e.g., visually observing the card with the printed number on the table) and deliver the ordered items to the guest. In some cases, an output device (e.g., a buzzer) may be provided to the guest, or another output device (e.g., a mobile phone, a wall - mounted electronic display) may be used to instruct the output device to provide a notification indicating that the ordered items are ready at the pickup station. As a result, the guest can move to the pickup station to receive the ordered items. In some cases, the guest may also wait to be called by their name, which is a notification indicating that the ordered items are ready at the pickup station.

Summary of the Invention

[0004] The following summarizes some embodiments within the same scope as the subject matter of the original claims. These embodiments do not limit the scope of the claimed subject matter, but rather merely show an overview of possible forms of the subject matter. In fact, the present subject matter can include various forms that may be similar to or different from the embodiments shown below.

[0005] In one embodiment, a delivery system can include one or more processors and a memory storing instructions, the instructions being 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 can track one or more attributes of the user in one or more images over a period of time to identify the 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 remaining at one location for a period longer than a threshold time, create an association between the one or more attributes of the user and that location. The delivery system can then provide an instruction to deliver an item within the order to that location.

[0006] In one embodiment, a method of operating a delivery system can include using 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 associating the one or more attributes of the user with an order placed by the user. The method can use one or more processors to track one or more attributes of the user in one or more images over a period of time to identify the 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 remaining at that location for a period longer than a threshold time, create an association between the one or more attributes of the user and the location. The method can then provide an instruction to deliver an item within the order to the location.

[0007] In one embodiment, the delivery system includes one or more processors and a memory storing instructions that are 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 can also track one or more attributes of the user in one or more images over a period of time to identify the user's movement within the environment and create 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 remaining at one location for longer than a threshold time. The processor can 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 staff member associated with the environment.

[0008] These and other features, aspects, and advantages of the present disclosure will be better understood when the following detailed description is read with reference to the accompanying drawings in which like parts are designated with like reference numerals throughout.

Brief Description of the Drawings

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DETAILED DESCRIPTION OF THE INVENTION

[0010] When introducing elements of various embodiments of the present disclosure, articles such as "a", "an", and "the" are intended to mean that these elements exist one or two or three or more. The terms "comprising", "including", and "having" are intended to be inclusive and mean that additional elements other than the recited elements may exist. Also, references to "one embodiment" or "an embodiment" of the present disclosure are not to be construed as excluding the existence of additional embodiments that also include the described features.

[0011] Hereinafter, one or more specific embodiments of the present disclosure will be described. For the sake of brevity in explaining these embodiments, not all implementation features are described herein. It should be understood that in any such implementation development found in any engineering or design project, numerous implementation-specific decisions must be made to achieve a developer's specific objectives, such as compliance with system-related and business-related constraints that may vary depending on the implementation. Further, although such development efforts can be complex and time-consuming, they are to be understood as routine endeavors of design, fabrication, and manufacture for those skilled in the art who benefit from the present disclosure.

[0012] The present disclosure generally relates to systems and methods for receiving and delivering orders in an environment such as a dining environment. The dining environment can include various features such as vendors (e.g., restaurants, bakeries, ice cream stands) to provide a seamless and efficient dining experience for guests (e.g., customers), sellers (e.g., retail stores for clothing, accessories and / or souvenirs), stations (e.g., beverages, seasonings, tableware, hand sanitizers, trash cans), toilets and / or tables. A delivery system can be used to supplement or complement these dining environment features to receive orders from guests and facilitate the delivery of ordered items to the guests. In some embodiments, the delivery system can facilitate the delivery of ordered items to the guests in a passive manner (e.g., passive for the guests, the guests only need to walk from the place where they place the order to the table). For example, the delivery system can facilitate the delivery of ordered items to the guests without providing a physical object (e.g., a card printed with a number, a buzzer, a high frequency tag or reader) to the guests when receiving the order, and / or without giving a notification (e.g., calling the name, sound output, light output, tactile output, text message) to the guests. Further, in some embodiments, guests do not need to have or use a mobile phone for location tracking and / or linking to the order.

[0013] Guests can place orders advantageously for the vendor at the point-of-sale information management terminal. The order can include one or more items (e.g., food, toys, beverages). After placing the order, the guest can move within the dining environment and select a location (e.g., a table, a seat) for the dining experience. For example, the guest can sit at a table for the meal and wait for one or more items in the order to be delivered to that location. The delivery system can identify one or more attributes of the guest (e.g., while the guest is placing the order) and associate these one or more attributes with the order placed by the guest. The delivery system can receive image data of the dining environment and track one or more attributes within the dining environment. The delivery system can associate the order with the location by associating one or more attributes of the guest with that location after the guest makes a location selection such as sitting at a table. The delivery system can instruct the customer service staff (e.g., staff, employees) to deliver one or more items in the order to that location after the ordered items are completed (e.g., ready to deliver one or more items in the order to the guest).

[0014] In one embodiment, after a guest selects a location (e.g., a table, a seat), the guest may leave that location to visit a temporary location (e.g., a toilet, a station, a vendor, a hand sanitizing station). For example, a guest may sit at a table and then later walk to a vending machine to purchase a beverage. The delivery system can associate one or more attributes of the guest with the table and can determine whether leaving the table releases this association. The delivery system can determine whether arriving at the table forms an association and / or whether leaving the table releases the association, taking into account any of a variety of factors. These factors can include the respective times of arrival at the table, the respective times away from the table, the type of the temporary location, the actions or gestures the guest made at the table, the items placed on the table, and other guests present at the table. For example, a guest may leave an object (e.g., a water bottle) on a table and claim that the table is their table. Accordingly, the delivery system can determine that the guest may return to the table and can maintain the association between one or more attributes of the guest and the table.

[0015] On the other hand, a guest may also move from a first location (e.g., a first table) to a second location (e.g., a second table). For example, a guest may determine that the first table is too small. Accordingly, the guest may choose to move from the first table to the second table. The delivery system can determine that this movement of the guest from the first table to the second table is a break event. That is, the delivery system can release the association between one or more attributes of the guest and the first table. In one embodiment, the delivery system can then associate one or more attributes of the guest with the second table.

[0016] In one embodiment, the delivery system can associate one or more attributes of a guest with a location in response to the guest spending a time equal to or exceeding a threshold time (e.g., a dwell time) at that location. For example, if a guest has been sitting at a table for a time equal to or exceeding the threshold time, the delivery system can associate one or more attributes of the guest with that table. The threshold time can vary based on any of a variety of factors such as movements or gestures made by the guest at the table, items placed on the table, other guests present at the table, and the respective times spent at other locations visited between the point of sale and that location. For example, if a guest sits at a table, places a bag on the table, and starts playing with a mobile phone, this indicates an intention to stay at that table during a dining experience and can contribute to the delivery system shortening the threshold time (e.g., as compared to another guest standing beside the table, not placing a bag on the table, and / or continuously surveying the dining environment instead of playing with a mobile phone). In another example, a guest may stand beside a table while waiting for a previous table user to clear the table and leave. The delivery system can increase the threshold time due to the fact that the guest is in a standing position (e.g., as compared to another guest sitting at the table). As described above, when an ordered item is completed, the delivery system can instruct the host to deliver the ordered item to that location.

[0017] In some embodiments, a guest group may visit a dining environment. For example, a family unit may visit a dining environment for a family meal. The family unit may visit a vendor's point-of-sale information management terminal to place an order. A member of the family unit may place an order for all members of the family unit. The delivery system can identify one or more attributes of at least one member of the family unit (e.g., the member who placed the order). In another example, a guest group may visit a dining environment and place orders separately. Each member of the group may place their own order individually at the vendor's point-of-sale information management terminal. That is, members of the group may visit different vendors and / or place different orders at different terminals of the same vendor. The group may regroup or gather again at the same location within the dining environment (e.g., at one table) despite visiting separate point-of-sale information management terminals. The delivery system can track one or more attributes of each member as they move within the dining environment. For example, one member (e.g., the same or different from the member who placed the order) may claim ownership of a table for the group while other members of the group may visit a condiment station, a dish station, a toilet, or another vendor. The delivery system can determine that a member arriving at the table may be claiming ownership of the table for the group and thus associate the order with the table. Further, the delivery system can associate all orders of the group with the members at the table. Thus, the delivery system can provide instructions to deliver all orders of the group to the members at the table.

[0018] Embodiments of the present disclosure relate to a delivery system that utilizes computer vision technology to associate one or more attributes of a guest with an order placed by the guest. The delivery system can then associate the order with a location by using computer vision technology to identify the location of one or more attributes within the dining environment. The delivery system can track the movement of a guest within the dining environment based on one or more attributes. The attributes of the guest can be anonymous attributes such as hair color, clothing color, clothing items, gait, personal belongings, or accessories. That is, the attributes can be free of personally identifiable information (PII). The term PII can include information that directly identifies an individual (e.g., name, address, social security number, phone number), or data elements related to an individual (e.g., gender, race, date of birth, combination of geographic indicators). As described herein, the delivery system can identify one or more attributes of a guest after an order is completed, associate the one or more attributes with the order, track the one or more attributes within the dining environment to associate a location with the one or more attributes, and provide an instruction to deliver the ordered items to that location. In other words, the delivery system can associate an order with the location of a guest within the dining environment. Thus, the delivery system can facilitate the delivery of ordered items without using a specific type of visual indicator (e.g., a numbered card that can be reused by multiple guests over a period of time) provided to the guest within the dining environment for tracking purposes and / or notifications to the guest.

[0019] Based on these, FIG. 1 is a schematic diagram of an embodiment of a delivery system 10 that can be used in a dining environment 50 such as a food court, a food court, a cafeteria, a food truck park, or an amusement park. The dining environment 50 includes a space where a guest can visit a (single or plural) vendor 52, create an order at the (single or plural) point-of-sale information management terminal 54 of the (single or plural) vendor 52, select a (single or plural) table 58, visit a (single or plural) station 60, or otherwise move within the dining environment 50, such as a walkable area (e.g., a queue or a line). The dining environment 50 can include an entrance and an exit for guests to enter and leave the facility. The (single or plural) vendor 52 can include a restaurant, a food truck, a dessert shop (e.g., a bakery, an ice cream shop), or a beverage shop (e.g., a juice shop). The (single or plural) vendor 52 can include a (single or plural) point-of-sale information management terminal 54 that can receive an order from a guest. Each point-of-sale information management terminal 54 can be a kiosk and / or a mobile device such as a tablet. Note that at least one of the point-of-sale information management terminals 54 can include a mobile device such as a mobile phone that uses an application for communicating with a control system 64 or other vendor system owned / carried by one of the guests to place an order. Each point-of-sale information management terminal 54 can display the menu of the vendor 52 to enable the guest to complete a transaction (e.g., place an order) with the vendor 52. Also, the point-of-sale information management terminal 54 can operate by and / or include a customer service staff (e.g., a human customer service staff) who receives an order from a guest and creates the order using a mobile device.

[0020] The dining environment 50 can also include a guest area 56 where various guests can be seated. The guest area 56 can include a (single or multiple) table 58, and guests can sit or stand next to it during the dining experience. For example, guests can sit at the table 58 and have a meal. The (single or multiple) table 58 can include one or more chairs that can be movable chairs and / or fixed chairs (e.g., a picnic bench bolted or fixed to the ground or the (single or multiple) table 58, a metal chair, a wooden chair) that are not fixed to the ground or the (single or multiple) table 58, or can be accompanied by such chairs. The (single or multiple) station 60 can typically be a temporary location that guests visit before or after selecting the (single or multiple) table 58. The (single or multiple) station 60 can include a toilet, a vendor or retail store, a hand sanitizing station, a condiment station, a tableware station, a trash station, or a drink fountain, etc. For example, guests can visit the condiment station to obtain ketchup, barbecue sauce, salt, or pepper, etc. Guests may also go to the toilet before sitting at the (single or multiple) table 58. After the dining experience, guests clear the table, take any trash to the trash station, and then leave the dining environment 50.

[0021] In some embodiments, the dining environment 50 can include one or more cameras 62 that generate image data of the dining environment 50 (e.g., video data such as video data). The one or more cameras 62 can 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 can form a delivery system 10. The delivery system 10 generates and processes image data of the dining environment 50 during operation to identify one or more attributes of the guest. Further, the delivery system 10 can use machine learning algorithms or artificial intelligence to understand the position of the guest in the dining environment 50 and / or make predictions related thereto (e.g., whether an association between an order and a position should be established and / or whether an association between an order and a position should be removed). For example, the delivery system 10 can be trained to select a location and / or understand human behavior patterns of leaving a location using past and / or modeled data representing the dining environment 50.

[0022] The control system 64 can include a memory 66 and one or more processors 68 (e.g., processing circuits). The memory 66 can 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 disk drive, a solid state drive, or any other non-transitory computer-readable medium containing instructions for operating the delivery system. The memory 66 can also include a database of attributes (e.g., characteristics such as identifiable objects, movements, gestures, clothing colors, walking styles, head shapes, threshold times), maps (e.g., facility maps of the dining environment 50), human behavior patterns, historical data, machine learning algorithms, and / or other types of information for the control system 64. The processing circuit 68 can be configured to execute instructions. For example, the processing circuit 68 can 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.

[0023] Delivery system 10 can generate image data (by camera 62) and identify the orders placed by the guest at the point-of-sale information management terminal 54. Delivery system 10 can receive order indicators based on the image data and / or information provided to delivery system 10 by the point-of-sale information management terminal 54 (for example, control system 64 is communicatively coupled to one or more cameras 62 and / or the point-of-sale information management terminal 54 via a wireless or wired network). For example, delivery system 10 can process the image data to determine that the guest is interacting with the point-of-sale information management terminal 54 (for example, the guest is scrolling through the menu on the point-of-sale information management terminal 54) and / or that the guest has placed an order (for example, an order number / identifier is displayed on the display screen of the point-of-sale information management terminal 54 and captured in the image data). That is, after the payment information is entered, the point-of-sale information management terminal 54 can display on the display screen a text notifying the guest of the completed transaction. For example, the point-of-sale information management terminal can display "Thank you for ordering" or "Order number 123 is confirmed". Delivery system 10 can identify this screen within the image data to identify the order. In addition or alternatively, delivery system 10 can also receive from the point-of-sale information management terminal 54 information such as information that the guest has selected one or more items from the menu and / or information that the guest has entered payment information to place an order (for example, an order number / identifier has been communicated to control system 64). Delivery system 10 can associate a number or other identifier with the order. For example, the order can be "Order number 123". In one embodiment, delivery system 10 can also associate the time of order creation and / or the location of the point-of-sale information management terminal 54.

[0024] Delivery system 10 can identify one or more attributes of a guest during and / or at the time of an order placed by the guest, and can associate one or more attributes of the guest with the order. As further described in FIG. 3, the one or more attributes can be appearance indicators and can be free of personally identifiable information (PII). For example, delivery system 10 can identify the guest's hair color, the guest's relative height or size, the guest's head shape, the guest's gait, the color of the guest's clothing, and / or the guest's objects (e.g., personal possessions). The one or more attributes can be sufficient to enable differentiation between multiple different guests for tracking purposes within dining environment 50, but can be free of or not indicative of PII.

[0025] As further described in FIGS. 4 and 7, delivery system 10 can implement machine learning or computer vision techniques to understand human behavior patterns and associate one or more attributes of a guest with a location within dining environment 50. In this way, delivery system 10 can associate an order with the guest's location. For example, a guest can complete an order at point-of-sale terminal 54, select table 58, and wait for the ordered items. Delivery system 10 can associate one or more attributes of the guest with table 58 and provide an instruction to deliver the ordered items to table 58. In another example, a guest may select table 58 and go to station 60 leaving an object behind. Delivery system 10 can associate the guest with table 58 even though the guest has left. Delivery system 10 can identify an object of guest 80 on table 58 and determine that the guest may return.

[0026] In one embodiment, the delivery system 10 can be configured to provide an instruction (e.g., an audible instruction via a speaker and / or a visual instruction via a display) to the vendor(s) 52 to facilitate the delivery of the ordered item(s) to the guest. For example, the guest may move to the guest area 56 and sit at the table 58 after placing an order. The delivery system can store or access a map of the dining environment 50 (via the control system 64). The map can associate the objects within the dining environment 50 with respective identifiers such as letters, numbers, or shapes. For example, the tables 58 can be labeled with labels A - F respectively. In another example, the tables 58 can be labeled with letters and numbers such as A1, A2, A3, B1, B2, B3, etc. Further, seats at the tables 58 can be assigned letters, numbers, or both. For example, the instruction can be to provide the ordered item 123 to seat 3 at table A1, or to provide the ordered item 98 to seat J at table A1. Thus, the delivery system 10 can use the image data and / or the map to facilitate the delivery of the ordered items.

[0027] Delivery system 10 can passively update a map of the dining environment 50 to ensure accurate instructions. 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 guests. Delivery system 10 can also identify the configuration or orientation of table 58 and / or the state of table 58 to update the map of dining environment 50. For example, guests may attach one or more tables 58 together. Delivery system 10 can identify the attached tables in the image data and update a map that includes identifiers for each of the objects in dining environment 50. In another example, delivery system 10 can identify one or more unavailable (e.g., waiting for dishes to be cleared) tables from the image data and understand that future guests may not want to sit at one or more unavailable tables. In one example, delivery system 10 can output a notification to vendor 52 about the unavailable tables. In this way, delivery system 10 can understand the dining environment 50 in real time or near real time and provide accurate instructions for order delivery.

[0028] Note that the layout and arrangement of the dining environment in FIG. 1 are merely illustrative, and it should be understood that delivery system 10 can be used with any of a variety of dining environments arranged in any suitable form. Further, some components of delivery system 10 can be shared among vendors 52 and / or each component of delivery system 10 can be provided for each vendor (e.g., one or more cameras 62 to one vendor and one or more cameras 62 to another vendor). In fact, delivery system 10 can also share / communicate among multiple dining environments 50 or focus on a single, unique dining environment 50.

[0029] Based on these, FIG. 2 is an illustrative explanatory diagram of a guest 80 creating and placing an order 82 on the point-of-sale information management terminal 54 of a vendor 52. For example, the guest 80 can view a menu or list of items (such as products like food, toys, etc.) being sold by the vendor 52 on the point-of-sale information management terminal 54. Also, the guest 80 can select one or more items for purchase to create the order 82. The point-of-sale information management terminal 54 can be a kiosk, a self-checkout station, or the guest 80's mobile device, etc. For example, the kiosk can include a display screen that shows the menu of the provided vendor 52. The guest 80 can scroll through the menu and select one or more items for the order 82. The guest 80 can input payment (such as credit card information, cash, electronic transfer) to confirm the order 82. For example, the guest 80 can pass a credit card or debit card through the card reader of the kiosk, or insert cash into the kiosk. Thereafter, the kiosk can display confirmation messages such as "ORDER COMPLETED", "THANK YOU FOR YOUR ORDER", "ORDER NUMBER 123 CONFIRMED", etc. As described in this specification, the delivery system 10 can analyze image data (from one or more cameras 62) to identify the confirmation messages.

[0030] Based on these, the dining environment 50 can include two guests (e.g., the first guest 80a, the second guest 80b). The control system 64 may find it beneficial to distinguish between guests 80a, 80b in order to accurately deliver the ordered items. For example, guests 80a, 80b can visit the vendor 52 and / or the point-of-sale information management terminal 54. For example, the first guest 80a can visit the first point-of-sale information management terminal 54a to create a first order 82a. When the delivery system 10 confirms the first order 82a, it can identify one or more attributes of the first guest 80a and associate the attributes with the first point-of-sale information management terminal 54a and the first order 82a. Similarly, the delivery system 10 can associate one or more attributes of the second guest 80b with the second point-of-sale information management terminal 54b and / or the second order 82b. The delivery system 10 can track one or more attributes of the first guest 80a and the second guest 80b within the dining environment 50 for the delivery of the ordered items.

[0031] In some embodiments, the delivery system 10 can refrain from associating the first guest 80a and the second guest 80b as members of a group. For example, the delivery system 10 can recognize that the first guest 80a arrived before the second guest 80b. In another example, the delivery system 10 can identify the first guest 80a as already being a member of a group (e.g., without including the second guest 80b). In some embodiments, the delivery system 10 can associate the first guest 80a and the second guest 80b as members of the same group. For example, the first guest 80a and the second guest 80b can arrive at the dining environment 50 together (e.g., before placing orders 82a, 82b) and / or interact with each other at the dining environment 50. The first guest 80a and the second guest 80b can choose to create an order at the point-of-sale information management terminal 54. The delivery system 10 can also associate the first order 82a placed by the first guest 80a with the second order 82b placed by the second guest 80b. In this way, the delivery system 10 can have additional data points for determining the location of the group (e.g., to which orders 82a, 82b should be delivered). As will be further described with reference to FIG. 4, the first guest 80a and the second guest 80b may take different routes (e.g., paths) to arrive at a location.

[0032] FIG. 3 is an illustrative diagram of one or more attributes of guest 80 that the delivery system 10 of FIG. 1 can identify and / or use to track guest 80. The one or more attributes can be anonymized attributes or physical indicators rather than PII. For example, the delivery system 10 can identify hair color or hairstyle 90a, head shape 90b, clothing item shape and / or color 90c, accessories 90d, objects (e.g., personal belongings) 90e, or gait 90f.

[0033] For example, the delivery system 10 can identify the hair color 90a of the guest 80. The hair color 90a can include black, gray, white, brown, blonde, red, or combinations thereof. The hairstyle 90a can include braids, ponytails, bangs, or baldness / hair loss, etc. The head shape 90b can be the shape of the guest's face such as heart-shaped, square, oval, diamond, or triangular. The head shape 90b can also include head or face accessories such as glasses, hats, or piercings. In certain embodiments, the delivery system 10 can use the combination of hair color and / or hairstyle 90a and head shape 90b as one or more attributes of the guest 80. In addition to or instead of this, the delivery system 10 can also identify the color 90c of the guest 80's clothing. For example, the delivery system 10 can identify the color of the clothing, the pattern of the clothing, or the design on the clothing, etc. For example, the guest 80 may be wearing a shirt with a slogan, a cartoon character, or a graphic design.

[0034] In certain embodiments, the delivery system 10 can identify one or more accessories 90d of the guest 80. For example, the one or more accessories 90d can include earrings, necklaces, bracelets, rings, scarves, hair clips, necktie clips, belts, sunglasses, and / or other accessories worn by the guest 80. In the illustrated embodiment, the delivery system 10 can identify a charm necklace. In addition to or instead of this, the delivery system 10 can also identify one or more personal belongings 90e of the guest 80. The one or more personal belongings 90e can include handbags, telephones, wallets, jackets, backpacks, water bottles, and / or other objects carried by the guest 80. The one or more personal belongings 90e can also include baby strollers, walking sticks, wheelchairs, and / or other objects transported with the guest 80. For example, the delivery system 10 can identify a water bottle as a personal belonging 90e. As described herein, the delivery system 10 can track personal belongings 90e within the dining environment 50 to determine the location of the guest 80.

[0035] In some embodiments, delivery system 10 can track the gait 90f of guest 80. For example, delivery system 10 can recognize, distinguish, and associate the gait 90f with guest 80. For example, guest 80 may walk faster or slower than the average walking speed. In another example, the stride of guest 80 may be larger or smaller than the average stride. In another example, some guests 80 may have a unique gait, such as skipping, bouncing, running, or dragging their feet. Delivery system 10 can be trained (e.g., by artificial intelligence or machine learning) to identify the gait 90f of guest 80. One or more attributes 90 can also include the size of guest 80 (e.g., estimated absolute and / or relative size). For example, the guest may be a small child or a teenager who is smaller than other guests. In another example, the guest may be a basketball player and taller than other guests.

[0036] Delivery system 10 can track multiple attributes of guest 80 within dining environment 50. For example, delivery system 10 can track the size and gait 90f of guest 80. In one example, a taller guest may have a longer stride than a shorter guest. In another example, delivery system 10 can track hair color 90a, accessories 90d, and personal belongings 90e. Delivery system 10 can track any number and / or combination of attributes 90 of guest 80. Delivery system 10 can also efficiently track the attributes 90 that best distinguish guests by tracking different attributes 90 for different guests.

[0037] Based on these, FIG. 4 is an illustrative diagram of the delivery system 10 tracking the first guest 80a and the second guest 80b within the dining environment 50. For example, the first guest 80a can walk to the guest area 56, select the table 58a, visit the station 60, and then return to the table 58a to wait for the ordered items. As described herein, the delivery system 10 can identify and track one or more attributes 90 of the guests 80a, 80b within the dining environment 50 to provide instructions for the delivery of the ordered items.

[0038] For example, the first guest 80a can move along route 100 within the dining environment 50. The first guest 80a can enter the guest area 56 and visit the first table 58a as represented by point 102. The first guest 80a can spend a certain amount of time at the first table 58a to establish ownership of the first table 58a. The delivery system 10 can compare the time spent at the first table 58a with a threshold time. In response to the time spent at the first table 58a meeting or exceeding the threshold time, the delivery system 10 can associate one or more attributes of the first guest 80a with the first table 58a. Accordingly, the delivery system 10 can also associate the ordered item 82a with the first table 58a. In other words, the delivery system 10 can determine that the first table 58a is the location for the delivery of the ordered item 82a. The first guest 80a can leave the first table 58a and visit the station 60 as represented by point 104. The delivery system 10 can identify the station 60 as a temporary location (e.g., stored in or labeled in a database accessible to the delivery system 10). In another example, the first guest 80a can leave a personal possession 90e on the first table 58a to claim ownership of the table. The delivery system 10 can identify the personal possession 90 on the first table 58a as a claim to the table and associate one or more attributes 90 of the first guest 80a with the first table 58a. The delivery system 10 can refrain from disassociating the association between one or more attributes 90 of the first guest 80a or the ordered item 82a of the first guest 80a and the first table 58a. Thereafter, the first guest 80a can return to the table 58a as represented by point 106.

[0039] The second guest 80b can go directly to the second table 58b along route 108 at the same time or at another time. As represented by point 110, the second guest 80b arrives at the seat at the second table 58b and can initiate mobile device browsing, conversations with other guests at the second table 58b, and / or any other action / gesture / movement indicating an association with the second table 58b. The delivery system 10 can identify one or more attributes 90 of the second guest 80b. After the second guest 80b sits at the second table 58b, the delivery system 10 can associate one or more attributes 90 of the second guest 80b with the second table 58b. The delivery system 10 continuously monitors the image data to determine whether a release event has occurred, such as when the first guest 80a and / or the second guest 80b changes tables or leaves the dining environment 50. If no release event occurs, the delivery system 10 can give instructions to deliver the ordered items 82a, 82b to the first and second tables 58a, 58b respectively.

[0040] FIG. 5 is an illustrative diagram showing an instruction for the delivery system 10 to facilitate the delivery of the ordered item 82 to the location of the guest 80. For example, the guest 80 can place an order 82 at the point-of-sale information management terminal 54 of the vendor 52, and the delivery system 10 can identify one or more attributes of the guest 80. The vendor 52 can receive the order 82 and create the ordered item 82. After creating the order 82, the vendor 52 can indicate to the delivery system 10 the completion of the ordered item 82. The delivery system 10 can give an instruction to the vendor 52 indicating the location for the delivery of the ordered item 82 (e.g., based on the current association between the order 82 and the location through tracking one or more attributes 90 of the guest 80 who placed the order 82).

[0041] For example, guest 80 can place an order 82 for a children's meal including chicken nuggets and toys using the point-of-sale information management terminal 54. Vendor 52 can create the ordered items 82 in the kitchen. Delivery system 10 can give an instruction to deliver the ordered items 82 to the location of guest 80 after the ordered items 82 are completed (e.g., upon receiving an indication such as a user input indicating that the ordered items 82 are completed). On the other hand, it should be understood that delivery system 10 can also give an instruction to deliver the ordered items 82 to the location of guest 80 in response to the order 82 being associated with that location (e.g., the delivery system 10 associates the order 82 with that location when guest 80 sits at that location) (e.g., immediately upon association). In one embodiment, the ordered items 82 are delivered to table 58 after all items of the order 82 are ready. In other embodiments, each item of the order 82 is delivered as it is ready in order to maintain freshness.

[0042] Delivery system 10 can provide the position of guest 80 to the customer service staff of vendor 52. The customer service staff can be a person or an automated delivery system (e.g., a remotely controlled or autonomously controlled delivery vehicle). In certain embodiments, the person delivering the food can be a staff member of vendor 52. This person can receive an instruction indicating a table number / identifier, seat number / identifier, or a combination thereof within dining environment 50. For example, these instructions can include a string such as "Table 1, Seat 3 (TABLE1, SEAT3)" indicating the rightmost seat of the leftmost table. Delivery system 10 can output (e.g., display via a device at vendor 52 and / or carried by the customer service staff) a map of dining environment 50 where its position is labeled or otherwise highlighted. Thus, the customer service staff can use the map to move to that position. The map of dining environment 50 can be updated in real-time (e.g., substantially in real-time, near real-time) based on image data. Further, the map of dining environment 50 can include a schematic or an image (e.g., a still image or a video) based on the image data. In certain embodiments, one or more attributes 90 of guest 80 are not disclosed to (e.g., hidden from, unknown to) the customer service staff, guest 80 does not have a trackable item (e.g., a card with a printed number, a buzzer, an RFID tag) provided by vendor 52, and / or does not provide location data to delivery system 10 using its mobile device for the delivery of order 82. Instead, the customer service staff can deliver the items within order 82 based on the map and / or an identifier (singular or plural) representing the position (e.g., based only on these). In this way, the customer service staff is not prompted to grasp or visually confirm the attributes of guest 80 before delivering the ordered items 82 to its position, but can grasp the position designated for the delivery of order 82 placed by guest 80. In fact, it is not necessarily required for guest 80 to be present when the customer service staff finishes delivering order 82 placed by guest 80 to that position.Computer vision technology and / or algorithms can be accurate and reliable enough (e.g., via machine learning) to deliver order 82 without these additional steps or burdens on the customer service staff (e.g., without the customer service staff having to grasp and / or visually confirm the attributes of guest 80). Thus, the customer service staff can efficiently proceed to process the orders of other guests after taking order 82 to its location.

[0043] In some embodiments, the customer service staff delivering order item 82 can include an automated delivery system such as one or more robots, ground vehicles, aerial vehicles / drones, or any combination thereof. Thus, delivery system 10 can send a signal indicating the location to the automated delivery system to facilitate the delivery of order 82 to the location. The automated delivery system can receive and / or store a map of the dining environment 50, and delivery system 10 can provide a route from vendor 52 to that location. The automated delivery system can include one or more sensors (e.g., motion sensors) for identifying objects (e.g., people, carts, animals) within the dining environment 50. In some embodiments, machine learning, artificial intelligence, or computer vision capabilities can be used to program the automated delivery system to interpret and understand the dining environment 50. In this way, delivery system 10 can give an instruction to provide order item 82 to a location associated with one or more attributes 90 of guest 80.

[0044] FIG. 6 is an example method 120 of identifying one or more attributes 90 of a guest 80, associating one or more attributes 90 of the guest 80 with a location, and providing an ordered item 82 at a location within a dining environment 50. In block 122, the delivery system 10 can receive an input indicating that an order 82 is being placed by the guest 80. The delivery system 10 can be communicatively coupled to a point-of-sale information management terminal 54. That is, the point-of-sale information management terminal 54 can send a signal indicating completion of the order 82 to the delivery system 10 after the guest 80 has completed the order 82. The delivery system 10 can receive an order receipt or confirmation of the order 82, etc. The delivery system 10 can also identify the point-of-sale information management terminal 54 used by the guest 80 to create the order 82. The delivery system 10 can also assign a number or other identifier to the order 82. For example, the order number / identifier can be any combination of numbers or letters such as 123 or A2.

[0045] In another example, the delivery system 10 can receive image data from one or more cameras 62. The delivery system 10 can process the image data to receive an indication that an order 82 has been placed by a guest. For example, the delivery system 10 can process the image data to identify an order confirmation on the display of the point-of-sale management terminal 54 and / or to identify a guest 80 who is holding a payment method. The display of the point-of-sale management terminal 54 can display "THANK YOU FOR ORDERING" or "ORDER NUMBER 123 IS CONFIRMED". The delivery system 10 can 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 can identify that the guest 80 at the point-of-sale management terminal 54 is scrolling through the menu and adding one or more items to the cart. The delivery system 10 can understand that by adding items to the cart, there is a possibility that the guest 80 will check out and place an order 82. Accordingly, the delivery system 10 can analyze the image data over a period of time to receive an input indicating that an order 82 has been placed. For example, the delivery system 10 can identify image data indicating that the guest 80 is interacting with the point-of-sale management terminal 54 or reaching for their wallet to complete an order 82.

[0046] In some embodiments, although the delivery system 10 identifies the guest 80 at the point-of-sale management terminal 54, it may not receive an input indicating an order 82. For example, the guest 80 may scroll through the menu on the display of the point-of-sale management terminal 54. However, the guest 80 may also leave the point-of-sale management terminal 54 without completing an order 82. In another example, the guest 80 may leave without placing an order 82 after looking at the point-of-sale management terminal 54. Accordingly, the delivery system 10 can continue to process the image data for the order made by the guest 80 without separating or identifying the attributes of the guest 80 within the image data.

[0047] In block 124, delivery system 10 can generate and process image data of the dining environment 50 to identify guest 80. For example, one or more cameras 62 can generate image data, and control system 64 can process the image data to identify and / or separate the image data of guest 80. In one example, delivery system 10 can identify and separate the image data of guest 80 in response to guest 80 interacting with the point-of-sale information management system 54.

[0048] In block 126, delivery system 10 can identify one or more attributes 90 of guest 80 based on the separated image data. As described with reference to FIG. 3, the attributes 90 can be anonymous visual indicators. Delivery system 10 can identify one or more attributes 90 (e.g., a combination of attributes that sufficiently distinguish guest 80 from other guests) and use the one or more attributes 90 to track guest 80 within the dining environment 50. For example, guest 80 may be carrying a backpack, walking quickly, and wearing glasses. Additionally, another guest 80 may have blue hair, a cast on their leg, and / or be using a cane. Delivery system 10 can identify that the height or size of guest 80 is smaller than the average size. Delivery system 10 can estimate the height or size of guest 80 based on the characteristics of 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 table 58 within the dining environment 50). In this way, delivery system 10 can track guest 80 and other guests moving within the dining environment 50.

[0049] Note that it should be understood that the delivery system 10 can receive and analyze image data while the guest 80 is placing an order 82. For example, when the guest 80 starts interacting with the point-of-sale information management terminal 54 or otherwise steps differently through the stage that normally leads to the issuance of the order 82, the delivery system 10 can begin to identify one or more attributes 90 of the guest 80. In one embodiment, when the delivery system 10 identifies image data indicating that the guest 80 is interacting with the point-of-sale information management terminal 54 or reaching for their wallet to complete the order 82, it can start identifying and creating a profile of one or more attributes 90 of the guest 80.

[0050] In block 128, the delivery system 10 can associate one or more attributes of the guest 80 with the order 82. For example, the delivery system 10 can associate one or more attributes 90 such as the guest 80's backpack, fast walking pace, and glasses with the order 82.

[0051] In block 130, the delivery system 10 can monitor image data over a period of time to associate one or more attributes 90 of the guest 80 with a location. As described with reference to FIGS. 4 and 7, the guest 80 can take any of various routes before selecting the table 58. Therefore, it can be beneficial for the delivery system 10 to continuously monitor the image data to identify the location of the guest 80 within the dining environment 50 (e.g., the location for the delivery of the order 82). For example, the guest 80 may first select the table 58 and sit there, and then later leave the table 58 to visit the station 60. The guest 80 may also leave their backpack on the table 58 so that other guests can understand that they are claiming ownership of the table 58. Also, the delivery system 10 can identify the backpack on the table 58 as an indicator that the guest 80 intends to return to the table 58. In such a case, the delivery system 10 can associate one or more attributes 90 of the guest 80 with the table 58.

[0052] In block 132, delivery system 10 can give an instruction to deliver order 82 to that location. That is, delivery system 10 can give an instruction to deliver order 82 to that location to the customer service staff of vendor 52. For example, delivery system 10 can notify the customer service staff to take the ordered item 82 to table 58. Therefore, delivery system 10 can receive order 82 from guest 80, track guest 80 to determine a location for order delivery, and give an instruction to deliver order 82. It should be understood that method 120 can also be implemented simultaneously, at overlapping times, and / or at different times for multiple guests (for example, when multiple guests enter the dining environment 50 and place their respective orders over a certain period of time).

[0053] Method 120 can also be executed according to instructions stored on one or more tangible non-transitory machine-readable media, and / or by the processor or processing circuit of delivery system 10 described herein (via control system 64), or on another suitable controller. The blocks of method 120 can be executed in any suitable order. Further, some blocks of method 120 can be omitted, and / or other blocks can be added to method 120.

[0054] In one embodiment, a guest group may visit the dining environment 50 for a dining experience. The group may place a single order 82 together, or the group may split up and place multiple orders 82 at multiple point-of-sale information management terminals 54 and / or multiple vendors 52. After placing the (single or multiple) order 82, the group can head to the guest area 56 to wait for the (single or multiple) ordered items 82. FIG. 7 is an illustrative diagram of a delivery system 10 that tracks one or more attributes of a guest group 80 within the dining environment 50. For example, the guest group 80 can include a first guest 80a, a second guest 80b, and a third guest 80c. Each guest in the group may take a different route (e.g., path) within the dining environment 50, but the group can reach a final location. The final location can be a location where the guest group can have a dining experience or a location for the delivery of the ordered items.

[0055] Referring to FIG. 7, the third guest 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 may visit different locations within the dining environment 50 but can gather again at the first table 58a. Thus, the third guest 80c can wait for the other members of the group at the first table 58a. The third guest 80c can spend a time longer than the threshold time at the first table 58a. Thus, the delivery system 10 can associate the third guest 80c with the first table 58a. The delivery system 10 can further associate the other members of the group (e.g., the second guest 80b, the third guest 80c) with the first table 58a.

[0056] The first guest 80a can take route 152, stop by the second table 58b, start a conversation with one or more guests at the second table 58b, and then walk to the first table 58a. The time the first guest 80a stays at the second table 58b can be shorter than the threshold time, and thus the first guest 80a is not associated with the second table 58b. In certain cases, the first guest 80a may stay at the second table 58b for a time longer than the threshold time. In such a case, it is possible to either initially associate the first guest 80a with the second table 58b or consider other factors so that the first guest 80a is not associated with the second table 58b even for a time longer than the threshold time. For example, the delivery system 10 can determine that the first guest 80a should not be associated with the second table 58b based on the time spent at the second table 58b, the movements or gestures made by the first guest 80a at the second table 58b, and / or the other guests at the second table 58b. For example, the first guest 80a may bend at the knee or stand beside the second table 58b. The delivery system 10 can identify the posture of the first guest 80a as an indicator that the second table 58b can be a temporary location. The delivery system 10 can also consider group formation and the third guest 80c waiting at the first table 58a (for example, if the first guest 80a is part of a group and another member of the group is establishing their position, the threshold time and / or other factors used to establish an association with a different position from the first guest 80a can increase). Thus, the delivery system 10 can refrain from forming an association between the first guest 80a and the second table 58b. In practice, the first guest 80a may also leave the second table 58b and go to the first table 58a. If the first guest 80a is associated with the second table 58b, the delivery system 10 determines that a release event has occurred and releases the association between the first guest 80a and the second table 58b, and then can form another association between the first guest 80a and the first table 58a when sufficient information has been received.

[0057] The second guest 80b can take route 154 within the dining environment 50. The second guest 80b may visit a temporary location (e.g., station 60) before arriving at the first table 58a. For example, the second guest 80b may visit the toilet, the hand sanitizing station, the beverage station, or the condiment station. The delivery system 10 can identify station 60 as a temporary location rather than an order delivery location. Even if the second guest 80b spends more than a threshold time at station 60, the delivery system 10 can refrain from associating the second guest 80b with station 60. In this way, the delivery system 10 can distinguish areas of the dining environment 50.

[0058] The delivery system 10 can also passively update the map while tracking one or more attributes of the guests 80a, 80b, 80c within the dining environment 50. For example, the group of guests may attach the first table 58a to the third table 58c. The delivery system 10 can identify the movement and / or the change in configuration of the positions of the first table 58a and the third table 58c. The delivery system 10 can further identify the third guest 80c sitting at the first table 58a, and the first guest 80a and the second guest 80b sitting at the third table 58c. The delivery system 10 can update the map and give an indication of the update to the vendor 52. The delivery system 10 can also give an instruction to provide the ordered items based on the updated map. In this way, the delivery system 10 can give accurate order delivery instructions.

[0059] FIG. 8 is a method example 170 for determining whether a cancellation event has occurred by associating one or more attributes of guest 80 with a location. As described herein, guest 80 can arrive at a location by taking any one of a variety of different routes available within dining environment 50. For example, guest 80 may stop at a first location to talk to a friend, visit a condiment station, or stop at the toilet. Guest 80 may stop at a first location (e.g., a table, a seat) and stay at the first location for a certain period of time. If this time is longer than a threshold time, delivery system 10 can associate one or more attributes of guest 80 with the first location. However, guest 80 may also decide to move to a second location (e.g., a final location; an order delivery location). Therefore, delivery system 10 can determine whether a cancellation event has occurred and whether a new association can be formed with the second location. In this way, delivery system 10 can determine the location of guest 80 so as to accurately and efficiently facilitate the delivery of the ordered items.

[0060] In block 172, delivery system 10 can identify one or more attributes of guest 80 present at one location over a certain period of time. For example, delivery system 10 can identify guest 80 at the first table 58a and monitor the image data over a certain period of time. In one case, guest 80 may enter guest area 56 and sit at the first table 58a. Guest 80 may talk to another guest at the first table 58a, browse a mobile phone, or observe the dining environment 50. Delivery system 10 can identify these actions and correlate these actions with the intention of the guest to establish the first table 58a as a location for order delivery.

[0061] In block 174, delivery system 10 can determine whether the time spent at that location is longer than a threshold time. The threshold time can be the time when delivery system 10 determines, via machine learning or artificial intelligence, that guest 80 is claiming ownership of the first table 58a or is otherwise intending to establish the first table 58a as the location for order delivery. The threshold time can vary based on any of various factors such as the movements or gestures of the guest at the table, the items placed on the table, any other guests at the table, or the respective times spent at other locations. Delivery system 10 can start a clock or timer to monitor the image data and monitor the time that guest 80 spends at the first table 58a. In addition to or instead of this, delivery system 10 can also identify the timestamps within the image data. In this way, delivery system 10 can track the time that one or more attributes of guest 80 are present at the first table 58a and compare this time to the threshold time.

[0062] If the time spent at that location does not exceed the threshold time, delivery system 10 can disassociate guest 80 from that location. Guest 80 may leave the first table 58a after that time and walk to the second table 58b. Guest 80 may stand, sit, kneel, lean, or walk beside the second table 58b. For example, guest 80 may talk to another guest 80 present at the second table 58b and merge with other guests 80 at the second table 58b. Delivery system 10 can disassociate guest 80 from the first table 58 and monitor the image data over a period of time to determine whether it can associate guest 80 with the second table 58b. In such a case, method example 170 returns to block 172 and can identify one or more attributes of guest 80 present at the second table 58 over a period of time.

[0063] If the time spent at that location is longer than the threshold time, in block 176, delivery system 10 can associate one or more attributes of guest 80 with that location. For example, guest 80 may be sitting at the first table 58a waiting for the ordered item 82. In other words, guest 80 may claim ownership of the table for the dining experience. There may be cases where the time guest 80 spends at the first table while waiting for the ordered item 82 is longer than the threshold time. In such cases, delivery system 10 can associate one or more attributes of guest 80 with the first table 58a.

[0064] In block 178, delivery system 10 can determine whether a release event has occurred. In one scenario, guest 80 may leave the first table 58a, and then delivery system 10 can determine whether a release event has occurred. That is, delivery system 10 can determine whether the association can be released. Delivery system 10 can consider various factors to identify the release event, such as the respective times at the table, the respective times away from the table, the type of further location, the movements or gestures made by the guest at the table and / or the location away from the table, the personal belongings 90e left on the table, or other guests at the table.

[0065] If the release event does not occur, the delivery system 10 can return to block 176 and maintain the association between one or more attributes of the guest 80 and the first table 58a. For example, the guest 80 may visit the station 60 while leaving one or more personal belongings such as a water cylinder or a jacket on the first table 58a. The delivery system 10 can determine that there is a possibility that the guest 80 will return to the first table 58a as indicated by the personal belongings and the station 60. That is, the station 60 can be a temporary location. Therefore, the delivery system 10 can determine that there may be no release event occurring. In another example, the guest 80 may go to the second table 58b and talk to another guest 80. The guest 80 may be standing beside the second table 58b. The delivery system 10 can identify the standing posture of the guest 80 as an intention to return to the first table 58a. In such a case, the delivery system 10 can determine not to regard leaving the first table 58a as a release event. The method 170 returns to block 176, and the delivery system 10 can maintain the association between one or more attributes of the guest 80 and the first table 58a.

[0066] When a release event occurs, in block 180, delivery system 10 can release the association between one or more attributes and the location of guest 80. For example, guest 80 may sit at the second table 58b and start a conversation with other guests at the second table 58. Guest 80 may stay at the second table 58b for a time longer than the threshold time. Delivery system 10 can identify staying at the second table 58b as a release event. In another example, delivery system 10 can recognize that guest 80 may have completed the dining experience. For example, guest 80 may start packing personal belongings 90e and preparing to leave the dining environment 50. Also, guest 80 may clean the first table 58a by taking dishes, trays, boxes, etc. to the garbage station. In such cases, delivery system 10 can understand that guest 80 may intend to leave the dining environment 50. Delivery system 10 identifies guest 80 leaving the first table 58a as a release event and can release the association between one or more attributes and the location of guest 80. Therefore, delivery system 10 can accurately determine that it is appropriate to create and / or terminate the association with the location for order delivery by classifying some of the actions and / or combinations of actions taken by guest 80 via computer vision technology.

[0067] Method 170 can be stored on one or more tangible non-transitory machine-readable media and / or can be executed by a processor or processing circuit of the control system described above or on another suitable controller. The steps of method 170 can be executed in the order disclosed above or in any other suitable order. Further, some steps of the method can be omitted and / or other blocks can be added to method 170.

[0068] As used herein, "machine learning" and / or "computer vision" can mean algorithms and statistical models used by a computer system to perform a particular task, whether or not explicit instructions are used. For example, a machine learning process can generate a mathematical model based on a sample of clean data known as "training data" to make predictions or decisions without being explicitly programmed to perform the task. Delivery system 10 can generate a model (e.g., train and / or update, e.g., passively update) based on image data collected over a period of time. In this way, the model can improve over time based on new image data collected over time. For example, the model can receive image data to provide outputs related to creating and / or removing associations, and then use the image data to update and improve the model.

[0069] The use of PII should be well understood to comply with privacy policies and practices that are recognized as meeting or exceeding industry or government requirements for maintaining user privacy. In particular, personally identifiable information data should be managed and handled to minimize unintended or unauthorized access or use, and the nature of the permitted use should be clearly shown to the user.

[0070] Although only some features of the present invention have been illustrated and described herein, many modifications and changes will occur to those skilled in the art. Accordingly, it is to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the present invention. It should be understood that any feature illustrated and described with reference to FIGS. 1-5 can be combined in any suitable form.

[0071] The technology claimed in this specification refers to tangible objects and specific examples of a practical nature that reliably improve this technical field, and thus is not an abstract, intangible, or purely theoretical thing. Further, when any claim appended at the end of this specification includes one or more elements designated as "means for (performing) ... (function)" or "steps for (performing) ... (function)", such elements should be construed in accordance with 35 U.S.C. § 112(f). On the other hand, for any claim containing elements designated in any other form, such elements should not be construed in accordance with 35 U.S.C. § 112(f).

Explanation of Reference Numerals

[0072] 10 Delivery system 50 Dining environment 52 Vendor 54 POS 56 Guest area 58 Table 60 Station 62 Camera 64 Control system 66 Memory 68 Processor

Claims

1. It is a delivery system, One or more processors, Memory for storing instructions, The instruction is provided, 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 the order placed by the user, Tracking the one or more attributes of the user in the one or more images over a certain period of time to identify the user's movements within the environment, In response to the fact that the one or more attributes of the user in the one or more images remain in the same position for a longer period than a threshold time, an association is created between the one or more attributes of the user and the position; To give instructions to deliver the items in the aforementioned order to the aforementioned location, The one or more processors can perform the following actions: A delivery system characterized by the following features.

2. The aforementioned location includes a table or seat in a dining environment. The delivery system according to claim 1.

3. The instruction is executable by the one or more processors to cause the one or more processors to analyze the one or more images and determine that the order was made by the user. The delivery system according to claim 1.

4. The instruction is executable by the one or more processors to cause the one or more processors to determine that the order was placed by the user by analyzing the one or more images and identifying the order number on the display screen in the environment. The delivery system according to claim 3.

5. The aforementioned instruction is, Identifying one or more attributes of the user who is located at a further position for a longer period than the aforementioned threshold time, In response to the fact that one or more of the user's attributes exist in the further position for a longer period than the threshold time, the association between the one or more of the user's attributes and the position is released. The delivery system according to claim 1, wherein the one or more processors are capable of causing the one or more processors to perform the following actions.

6. The aforementioned instruction is, Identifying the one or more attributes of the user at a further location, By referring to the map of the environment, it is determined that the further location is a temporary location. In response to the fact that the aforementioned further position is the aforementioned temporary position, the association between the one or more attributes of the user and the aforementioned position is maintained. The delivery system according to claim 1, wherein the one or more processors are capable of causing the one or more processors to perform the following actions.

7. The one or more of the aforementioned attributes are anonymous attributes that do not provide the user's identity. The delivery system according to claim 1.

8. The aforementioned one or more attributes include hair color, hairstyle, clothing style, clothing color, accessories, objects carried by the user, gait, head shape, or a combination thereof. The delivery system according to claim 7.

9. The aforementioned instruction is, Identifying one or more attributes of further users within the one or more images captured by the one or more cameras, The further user's one or more attributes are associated with the additional order placed by the further user, Tracking the respective attributes of the further user in the one or more images over a certain period of time to identify the movements of each of the further user within the environment, In response to the fact that each of the one or more attributes of the further user in the one or more images remains in the position for a longer period than the threshold time, a further association is created between each of the one or more attributes of the further user and the position, To give instructions to deliver the items in the aforementioned order and each of the items in the aforementioned additional order to the aforementioned location, The delivery system according to claim 1, wherein the one or more processors are capable of causing the one or more processors to perform the following actions.

10. The aforementioned instruction is, In response to the coexistence of the aforementioned association and the further association over a certain period of time, the user and the further user are formed into a group, Receiving an indicator that the items in the aforementioned order and each of the items in the aforementioned further orders are ready for delivery to the aforementioned location at the delivery time, In response to the analysis of one or more attributes of the user in the one or more images and the one or more further attributes of the further user indicating that at least one of the user and the further user is present at the location at the delivery time, an instruction is given to deliver the items in the order and the respective items in the additional order to the location. The delivery system according to claim 9, wherein the one or more processors are capable of causing the one or more processors to perform the following actions.

11. The aforementioned instruction is, The instruction indicating the aforementioned location is given to the customer service staff in a way that they can see, Not providing the aforementioned one or more attributes to the customer service staff, The delivery system according to claim 1, wherein the one or more processors are capable of causing the one or more processors to perform the following actions.

12. A method for operating a delivery system, Using one or more processors, identify one or more attributes of a user in one or more images captured by one or more cameras, Using the one or more processors, associate the one or more attributes of the user with the orders placed by the user. Using the one or more processors, the one or more attributes of the user in the one or more images are tracked over a certain period of time to identify the user's movements within the environment. Using the one or more processors, in response to the one or more attributes of the user in the one or more images remaining in place for a longer period than a threshold time, an association is created between the one or more attributes of the user and the location; Using the one or more processors, instructions are given to deliver the items in the order to the location. A method characterized by including the following.

13. The process includes using the one or more processors to analyze the one or more images and determine that the order was placed by the user. The method according to claim 12.

14. Using the one or more processors, identify the one or more attributes of the user that are located at a further position for a longer period than the threshold time, Using the one or more processors, in response to the one or more attributes of the user existing at the further location for a longer period than the threshold time, the association between the one or more attributes of the user and the location is released. The method according to claim 12, including the method described in claim 12.

15. Using the one or more processors, identify the one or more attributes of the user at a further location. Using the one or more processors mentioned above, the system refers to the map of the environment and determines that the further location is a temporary location. Using the one or more processors, maintain the association between the one or more attributes of the user and the location in response that the further location is the temporary location. The method according to claim 12, including the method described in claim 12.

16. Using the one or more processors, the output of the one or more images and the one or more attributes of the user in the environment is blocked so as not to be disclosed to the staff in the environment. The method according to claim 12.

17. It is a delivery system, One or more processors, Memory for storing instructions, The instruction is provided, 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 the order placed by the user, Tracking the one or more attributes of the user in the one or more images over a certain period of time to identify the user's movements within the environment, In response to the fact that the one or more attributes of the user in the one or more images remain in the same position for a longer period than a threshold time, an association is created between the one or more attributes of the user and the position; To provide an output indicating the location in order so as to facilitate delivery of the items in the order to the location without displaying the one or more images to the personnel related to the environment, The one or more processors can perform the following actions: A delivery system characterized by the following features.

18. The output includes a map of the environment indicating the location. The delivery system according to claim 17.

19. The instruction is executable by the one or more processors to cause the one or more processors to update the map to represent the current position of each structure in the environment based on the image data. The delivery system according to claim 18.

20. The aforementioned instruction is, Identify the one or more attributes of the user at a further location, By referring to the map of the environment, it is determined that the further location is a temporary location. In response to the fact that the further location is the temporary location, maintain an association between one or more attributes of the user and the location. The delivery system according to claim 17.

21. It is a delivery system, One or more processors, Memory for storing instructions, The instruction is provided, Based on image data, identify two or more users who are placing a group order at the point-of-sale information management terminal, Based on the aforementioned image data, one or more attributes of the first user among the two or three or more users are identified. Associating the group order, which includes multiple items, placed by the first user with one or more of the first user's attributes, Over a certain period of time, based on the image data, an association is created between the one or more attributes of the first user and their location in the environment. Based on the association, instructions are given to deliver the multiple items of the group order to the location, The one or more processors can perform the following actions: A delivery system characterized by the following features.

22. The instruction is executable by the one or more processors to cause the one or more processors to give the instruction to deliver each of the items when each of the items is ready for delivery. The delivery system according to claim 21.

23. The instruction is executable by the one or more processors to cause the one or more processors to give the instruction to deliver the multiple items together based on all of the multiple items that are ready for delivery. The delivery system according to claim 21.

24. The aforementioned instruction is, Identifying one or more further attributes of the second user among the two or three or more users, The group order is associated with the one or more additional attributes of the second user and the one or more attributes of the first user, Tracking the one or more further attributes of the second user within the environment based on the image data over a certain period of time, The one or more processors can perform the following actions: The delivery system according to claim 21.

25. The instruction is executable by the one or more processors to cause the one or more processors to create the association based on both the one or more attributes of the first user and the one or more further attributes of the second user who remains in the position for longer than a threshold time. The delivery system according to claim 24.

26. The aforementioned instruction is, Based on the map of the environment, the presence of the first user's one or more attributes at a temporary location is identified. Based on the one or more additional attributes of the second user that remain in the position beyond a threshold time, a second association is created between the one or more additional attributes of the second user and the position. Creating the association based on the one or more attributes and the second association of the first user in the temporary location, The one or more processors can perform the following actions: The delivery system according to claim 24.

27. The one or more attributes mentioned above include the personal belongings of the first user. The aforementioned instruction is, Based on the image data, the personal belongings of the first user are identified at the location, Creating the association based on the personal item located at the aforementioned position, The one or more processors can perform the following actions: The delivery system according to claim 21.

28. It is a delivery system, One or more processors, Memory for storing instructions, The instruction is provided, Receiving a first order placed by a first user from a first point-of-sale information management terminal, Receiving a second order placed by a second user from a second point-of-sale information management terminal, Based on the determination that the first user and the second user are members of the same group, a group order is created that includes the first order and the second order. Based on image data, track one or more first attributes of the first user within the environment, Based on the aforementioned image data, one or more second attributes are tracked within the environment. Creating an association between the group order and the location within the environment based on the one or more first attributes, the one or more second attributes, or both; Based on the aforementioned association, instructions are given to deliver each item within the group order, The one or more processors can perform the following actions: A delivery system characterized by the following features.

29. The instruction is executable by one or more processors to cause one or more processors to determine that the first user and the second user are members of the group, based on the identification of the first user and the second user who arrived in the environment together based on the image data. The delivery system according to claim 28.

30. The aforementioned instruction is, Identifying the one or more first attributes of the first user at a temporary location within the environment based on the map of the environment, Based on the map of the environment, identify the one or more second attributes of the second user in the table, Creating the association between the one or more second attributes of the second user and the location, based on the one or more first attributes of the first user indicating that the first user is in the temporary location and the one or more second attributes of the second user indicating that the second user is attached to the table, wherein the location includes the table, and creating the association by doing so. The one or more processors can perform the following actions: The delivery system according to claim 28.

31. The instruction is executable by the one or more processors to cause the one or more processors to disassociate based on identifying the one or more first attributes of the first user that remain in the second table beyond a threshold time and the one or more second attributes of the second user that do not exist in the position. The delivery system according to claim 30.

32. The one or more first attributes of the first user include the first user's personal belongings. The aforementioned instruction is, Based on the image data, the one or more processors can perform the action of creating the association based on identifying the first user's personal item at the location, The delivery system according to claim 28.

33. The aforementioned instruction is, Based on the image data, a first table at the location based on the map of the environment is identified, Based on the image data over a certain period of time, identify the attached table including the first table and the second table at the location, Updating the map of the environment to include the attached table, The one or more processors can perform the following actions: The delivery system according to claim 28.

34. The instruction is executable by the one or more processors to cause the one or more processors to update the map of the environment based on identifying one or more tables in the environment that are occupied by one or more further users based on the image data. The delivery system according to claim 28.

35. The aforementioned instruction is, Receiving a third order placed by a third user from the first point-of-sale terminal, the second point-of-sale terminal, or the third point-of-sale terminal, Based on the aforementioned image data, the third user who arrived in the environment after the first and second users is identified, and it is determined that the third user is not a member of the group. The one or more processors can perform the following actions: The delivery system according to claim 28.

36. It is a method, The system receives group orders placed by one user within a user group, The processing system identifies one or more attributes of the user based on the image data, The processing system tracks one or more attributes of the user in the environment based on the image data over a certain period of time, Through the processing system, the group order is associated with its position in the environment based on the image data over a certain period of time. The processing system instructs the delivery of the group order to the location based on the association, A method characterized by including the following.

37. The method according to claim 36, wherein instructing the delivery of the group order via the processing system includes transmitting a location-indicating signal to an automated delivery system.

38. Instructing the delivery of the group order via the processing system includes instructing the display to show a map of the environment. To highlight the aforementioned location on the aforementioned map, The method according to claim 36, including the method described in claim 36.

39. The processing system tracks one or more additional attributes of further users within the user group in the environment based on the image data over a certain period of time, The processing system associates the group order with the position based on the one or more attributes of the user who remains in the position beyond the threshold time, and the one or more further attributes of any additional user who remains in the position beyond the threshold time. The method according to claim 36, including the method described in claim 36.

40. The processing system tracks one or more additional attributes of further users within the user group in the environment based on the image data over a certain period of time, The processing system associates the group order with the location based on the one or more attributes of the user who remains at the location beyond a threshold time, and the one or more further attributes of any additional user who remains at a temporary location. The method according to claim 36, including the method described in claim 36.

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