Method, computing device and non-transitory computer-readable recording medium for order processing

Abstract

The order processing method of the present invention is related to food delivery and is executed by at least one processor. It may include the following steps: determining at least one category from a plurality of categories for which an order needs to be categorized; determining a price threshold and a quantity threshold for each of the at least one category; receiving a target order from a specific store; determining whether the target order belongs to at least one category; and, in response to determining that the target order belongs to at least one category, classifying the target order as a large order or a regular order based on the price and quantity of the target order and the price threshold and quantity threshold of the category to which the target order belongs.

Description

Technical Field

[0001] This invention relates to an order processing method, a computing device, and a non-transitory computer-readable recording medium. Prior Technology

[0002] With the development of e-commerce, applications that enable users to search for and order goods or food on multiple online platforms are becoming increasingly popular. For example, users can install applications provided by service providers on electronic devices such as tablets, and then use these applications to easily search for and order goods or food on online platforms.

[0003] This type of online platform operates through a system that processes orders via delivery personnel to efficiently deliver ordered goods or food to the user's designated location. Upon receiving an order, the delivery person picks up the goods or food at a designated origin (e.g., a grocery store) and then delivers it to the user's chosen destination. Typically, delivery personnel utilize various modes of transportation, such as bicycles, motorcycles, and cars, and the platform considers multiple factors when matching delivery personnel with orders to allocate them.

[0004] However, a mismatch between a specific order and the delivery person's vehicle can reduce the efficiency of the delivery process. For example, if an order containing multiple food items is assigned to a delivery person using a bicycle, the delivery person may arrive at the grocery store to find that the quantity of food ordered is too large to carry on their own vehicle. In this case, the delivery person needs to cancel the order, resulting in unnecessary travel and waiting time. Furthermore, reassigning the order to a delivery person with suitable transportation may delay delivery completion time due to the additional time consumed.

[0005] As mentioned above, when the characteristics of an order do not match the delivery personnel's transportation vehicles, it not only leads to unnecessary movement and wasted time for the delivery personnel, but also reduces the efficiency of the overall delivery process. Therefore, there is a need for technological solutions that can further improve order processing efficiency and minimize delivery delays. Summary of the Invention

[0006] [The problem the invention aims to solve] In order to solve the problems described above, the object of the present invention is to provide an order processing method, a computing device, and a non-transitory computer-readable recording medium.

[0007] [Technical means to solve the problem] The present invention can be implemented in a variety of ways, including methods, apparatus (systems) and / or non-transitory computer-readable recording media containing computer-executable instructions.

[0008] An embodiment of the present invention relates to food delivery and is executed by at least one processor. The order processing method may include the following steps: determining at least one category from a plurality of categories for which orders need to be categorized; determining a price threshold and a quantity threshold for each of the at least one category; receiving a target order from a specific store; determining whether the target order belongs to at least one category; and, in response to determining that the target order belongs to at least one category, classifying the target order as a large order or a regular order based on the price and quantity of the target order and the price threshold and quantity threshold of the category to which the target order belongs.

[0009] According to one embodiment, the step of determining whether a target order belongs to at least one category may include the following steps: determining whether the target order belongs to a first category in at least one category, and classifying it as a large order or a regular order may include the following steps: comparing the price and quantity of the target order with a first price threshold and a first quantity threshold related to the first category, respectively; in response to determining that the price of the target order is greater than the first price threshold and the quantity of the target order is greater than the first quantity threshold, determining the target order as a large order; and in response to determining that the price of the target order is below the first price threshold or the quantity of the target order is below the first quantity threshold, determining the target order as a regular order.

[0010] According to one embodiment, the order processing method may further include the following steps: in response to classifying the target order as a large order, assigning the target order to a delivery person of a preset type.

[0011] According to one embodiment, the step of determining at least one category includes the following steps: calculating a first proportion of the number of order cancellations due to large orders to the total number of order cancellations based on multiple orders included in the database, according to multiple categories; and selecting at least one category from multiple categories based on the first proportion.

[0012] According to one embodiment, the step of selecting at least one category may include the following steps: selecting at least one category from a plurality of categories whose first proportion is above a first threshold.

[0013] According to one embodiment, the step of calculating the first ratio may include the following steps: if a delivery person who has accepted a specific order arrives at the store and has prepared the food related to the specific order, and the delivery person cancels the specific order, the cancellation of the specific order will be regarded as an order cancellation due to a large order.

[0014] According to one embodiment, the step of determining at least one category may further include the following steps: calculating a second proportion of large orders to the total orders according to multiple categories, and the step of selecting at least one category may include the following steps: selecting at least one category from multiple categories based on a first proportion and a second proportion.

[0015] According to one embodiment, the step of selecting at least one category may include the following steps: selecting at least one category from a plurality of categories whose first proportion is above a first threshold and whose second proportion is below a second threshold.

[0016] According to one embodiment, the step of determining the price threshold and the quantity threshold may include the following steps: determining a first price threshold and a first quantity threshold for a first category included in at least one category; and determining a second price threshold and a second quantity threshold for a second category included in at least one category, wherein the first category may be different from the second category, the first price threshold may be different from the second price threshold, and the first quantity threshold may be different from the second quantity threshold.

[0017] According to one embodiment, the step of determining the price threshold and the quantity threshold may include the following steps: determining a plurality of candidate price thresholds and a plurality of candidate quantity thresholds according to at least one category; and selecting the price threshold and the quantity threshold from the plurality of candidate price thresholds and candidate quantity thresholds according to at least one category.

[0018] According to one embodiment, the steps of determining multiple candidate price thresholds and multiple candidate quantity thresholds may include the following steps: for a first category in at least one category, normalizing the price and quantity of orders related to the first category; determining multiple candidate price thresholds corresponding to a preset percentile based on the normalized price; and determining multiple candidate quantity thresholds corresponding to the preset percentile based on the normalized quantity.

[0019] According to one embodiment, the steps of selecting a price threshold and a quantity threshold may include the following steps: creating multiple combinations based on multiple candidate price thresholds and multiple candidate quantity thresholds; calculating the expected on-time rate of orders for each of the multiple combinations; and selecting a price threshold and a quantity threshold from the multiple combinations based on the expected on-time rate.

[0020] According to one embodiment, the steps of selecting a price threshold and a quantity threshold may include the following steps: creating multiple combinations based on multiple candidate price thresholds and multiple candidate quantity thresholds; calculating the expected on-time rate and expected cancellation rate of orders for each of the multiple combinations; and selecting a price threshold and a quantity threshold from the multiple combinations based on the expected on-time rate and expected cancellation rate.

[0021] According to one embodiment, the steps of determining the price threshold and the quantity threshold may include the following steps: using an artificial intelligence model to obtain at least one category of price threshold and quantity threshold respectively. The artificial intelligence model may be a model that models the correlation between the input data set and the output data set by using a machine learning algorithm. The input data set contains price threshold information and quantity threshold information related to multiple orders within a preset period. The output data set contains the on-time rate and cancellation rate of multiple orders.

[0022] According to one embodiment, the order processing method may further include the following steps: collecting multiple orders; and updating at least one of at least one category, a price threshold, and a quantity threshold based on the collected multiple orders.

[0023] An embodiment of the present invention provides a non-transitory computer-readable recording medium that can record instructions for executing an order processing method on a computer.

[0024] According to an embodiment of the present invention, a computing device includes: a memory; and at least one processor connected to the memory for executing at least one computer-readable program included in the memory, the at least one program including instructions to perform the following steps: determining at least one category from a plurality of categories for which an order needs to be categorized; determining a price threshold and a quantity threshold for the at least one category respectively; receiving a target order from a specific store; determining whether the target order belongs to at least one category; and in response to determining that the target order belongs to at least one category, classifying the target order as a large order or a regular order based on the price and quantity of the target order and the price threshold and quantity threshold of the category to which the target order belongs.

[0025] [Comparison with the effectiveness of previous technologies] According to certain embodiments of the present invention, the efficiency reduction in the configuration process can be minimized by effectively matching the characteristics of the order with the delivery personnel's transportation vehicles. This not only prevents delivery personnel from accepting orders that are difficult to handle using their own vehicles, but also reduces unnecessary movement and waiting time.

[0026] According to certain embodiments of the present invention, orders can be immediately assigned to appropriate delivery personnel to prevent redistribution delays caused by order cancellations and to shorten delivery times. This allows for faster and more stable delivery services to users, improving the platform's operational efficiency.

[0027] According to some embodiments of the present invention, an optimal allocation method can be provided considering the characteristics of delivery personnel's transportation vehicles and orders to effectively utilize delivery personnel resources. This improves delivery personnel satisfaction, reduces platform operating costs, and increases profitability.

[0028] According to some embodiments of the present invention, for the received target orders, orders can be processed according to preset category price thresholds and quantity thresholds to reduce the cancellation rate caused by large orders and prevent unnecessary time consumption.

[0029] According to some embodiments of the present invention, when calculating the price threshold and the quantity threshold, the expected on-time rate and / or expected cancellation rate are taken into account, and large orders can be allocated based on the threshold that forms a compromise between reducing the cancellation rate and increasing delivery time.

[0030] The effects of this invention are not limited to those mentioned above. Those skilled in the art to which this invention pertains (hereinafter referred to as "skilled persons") can clearly understand other effects not mentioned through the description of the claims in the invention application. Simple Explanation of the Diagram

[0031] Hereinafter, several embodiments of the present invention will be described with reference to the accompanying drawings. In this process, similar reference numerals indicate similar structures, but are not limited thereto.

[0032] Figure 1 is an illustrative diagram of an order processing method according to an embodiment of the present invention.

[0033] Figure 2 is a schematic diagram of the connection structure of an information processing system according to an embodiment of the present invention for communicating with multiple user terminals in order to provide an order processing method.

[0034] Figure 3 is a block diagram of the internal structure of a user terminal and information processing system according to an embodiment of the present invention.

[0035] Figure 4 is an illustrative block diagram illustrating a method for determining a category, price threshold, and quantity threshold according to an embodiment of the present invention.

[0036] Figure 5 is a diagram illustrating a method for determining the category of a large order classification object according to an embodiment of the present invention.

[0037] Figure 6 is a diagram illustrating the steps of normalizing the order price and order quantity of a category in order to determine multiple candidate price thresholds and multiple candidate quantity thresholds according to an embodiment of the present invention.

[0038] Figure 7 is an illustration of information used to determine price thresholds and quantity thresholds according to an embodiment of the present invention.

[0039] Figure 8 is a diagram illustrating a method for determining a price threshold and a quantity threshold according to an embodiment of the present invention.

[0040] Figure 9 is a diagram illustrating a method for classifying target orders into large orders or ordinary orders according to an embodiment of the present invention.

[0041] Figure 10 is a flowchart of an order processing method according to an embodiment of the present invention. Implementation

[0042] The following is a detailed description of the specific contents used to implement the present invention with reference to the accompanying drawings. However, in the following description, specific descriptions of well-known functions or structures will be omitted when there is a possibility that they may unnecessarily obscure the spirit of the present invention.

[0043] In the accompanying drawings, the same or corresponding structural elements are given the same reference numerals. Furthermore, in describing the following embodiments, repeated descriptions related to the same or corresponding structural elements will be omitted. However, even if the description of structural elements is omitted, this does not mean that such structural elements are not included in any embodiment.

[0044] The advantages, features, and implementation methods of the disclosed embodiments become clear with reference to the embodiments described in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, and can be implemented through different methods. Furthermore, these embodiments are provided to ensure the completeness of this disclosure, and are only provided to enable those skilled in the art to fully understand the scope of the present invention.

[0045] In this specification, the terminology used will be briefly described and specific embodiments will be illustrated. While widely used terms have been selected as much as possible in consideration of the functionality of the invention, the terminology used herein may vary depending on the intent or convention of those skilled in the art, the emergence of new technologies, etc. Furthermore, in certain cases, terms arbitrarily chosen by the applicant may exist; in such cases, their meanings are detailed in the corresponding description of the invention. Therefore, the terminology used in this specification should be defined based on its meaning and the full text of this specification, and not limited to the name of the term.

[0046] In this specification, unless explicitly specified in the context as singular, singular expressions include plural expressions. Furthermore, unless explicitly specified in the context as plural, plural expressions include singular expressions. Throughout this specification, when indicating that a structural element includes any structural element, it means, unless specifically stated otherwise, that other structural elements may also be included, and not excluded.

[0047] In this specification, the terms "module" or "section" as used refer to a software structural element or a hardware structural element that performs a certain function. However, a "module" or "section" is not limited to software or hardware. A "module" or "section" may reside in an accessible storage medium, or may be reproduced on one or more processors. Thus, as an example, a "module" or "section" may include structural elements such as software structural elements, object-oriented software structural elements, class structural elements, and task structural elements, processes, functions, attributes, programs, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, lists, arrays, or variables. The functionality provided internally by structural elements and "modules" or "sections" may be formed by combining a smaller number of structural elements and "modules" or "sections," or further separated into additional structural elements and "modules" or "sections."

[0048] According to one embodiment of the present invention, a "module" or "unit" may be implemented by a processor and a memory. The term "processor" is broadly interpreted to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, state machines, etc. In many contexts, "processor" may also refer to application-specific integrated circuits (ASICs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), etc. For example, "processor" may also refer to a combination of processing devices such as a combination of a digital signal processor and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors combined with a digital signal processing chip, or any other combination of the aforementioned structures. Furthermore, "memory" is broadly interpreted to include any electronic component capable of storing electronic information. "Memory" may also refer to various types of processor-readable media such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic or tagged data storage devices, and registers. When the processor reads information from or writes information to memory, the memory and processor are in electronic communication. The memory integrated into the processor is in a state of electronic communication with the processor.

[0049] In the following embodiments, the terms “first”, “second”, “A”, “B”, “(a)”, “(b)”, etc., are used only to distinguish one structural element from another structural element, and the terms do not limit the nature, importance or order of the corresponding structural elements.

[0050] In the following embodiments, when a structural element is described as being "connected", "combined", or "linked" with another structural element, it indicates that the structural element can be directly connected or linked with another structural element. However, it can also be understood as the situation where other structural elements are "connected", "combined", or "linked" between the various structural elements.

[0051] In the following embodiments, the terms “comprises” and / or “comprising” as used mean that there is one or more other structural elements, steps, operations and / or devices among the mentioned structural elements, steps, operations and / or devices, without excluding additional possibilities.

[0052] Hereinafter, several embodiments of the present invention will be described in detail with reference to the accompanying drawings. Furthermore, several embodiments of the present invention will be described with reference to the accompanying drawings. Throughout the specification, the same reference numerals may denote the same structural elements.

[0053] Figure 1 is an illustrative diagram of an order processing method according to an embodiment of the present invention.

[0054] Referring to Figure 1, Example 10 of the order processing method is executed by the information processing system 130.

[0055] According to one embodiment, when user 100 is a customer, the customer can generate a delivery order for a specific product (e.g., food) using their own user terminal 110. Furthermore, the customer can generate a delivery order for a specific item using an application installed on the user terminal 110. In this specification, the delivery order for a specific item generated by the user terminal 110 can be considered equivalent to the target order 120 as the object of the order processing method.

[0056] The information processing system 130 can assign delivery jobs to delivery personnel. Delivery personnel can receive delivery jobs for specific items assigned as target orders 120 in a large order manner, performed according to the large order method. Furthermore, when the information processing system 130 assigns a target order as a regular order, delivery personnel can receive delivery jobs for specific items in a regular order manner.

[0057] An information processing system 130 according to an embodiment of the present invention can receive a target order 120 related to food delivery ordered by a user 100. The information processing system 130 then determines whether the target order 120 belongs to at least one category. The at least one category can be predetermined based on a menu that includes the target order 120. For example, as shown in FIG1, when the target order 120 is related to pizza, the information processing system 130 can determine whether the target order 120 belongs to the pizza category among multiple categories. However, it is not limited to this; when the target order 120 includes both pizza and fried chicken, the information processing system 130 can determine that the target order 120 belongs to the pizza and fried chicken categories respectively.

[0058] Subsequently, upon determining that target order 120 belongs to at least one category, in response, information processing system 130 can classify target order 120 as a large order or a regular order based on its price and quantity, as well as price and quantity thresholds for its category. Specifically, if information processing system 130 classifies target order 120 as a large order, the price and quantity of target order 120 may exceed preset price and quantity thresholds. The price and quantity thresholds for the target order's category can be statistically preset by information processing system 130 based on order prices and quantities pre-stored in the order database. Details regarding the setting of these thresholds will be explained later.

[0059] According to one embodiment, if the corresponding target order 120 is determined to be a large order, the information processing system 130 may assign the target order 120 to the delivery personnel 140 who are performing delivery according to the large order delivery method. In this case, the information processing system 130 may also choose not to assign the target order 120 to the delivery personnel 150 who are performing delivery according to the ordinary order delivery method. The delivery personnel 140 may be delivery personnel who possess or use transportation vehicles (e.g., cars) and are capable of performing delivery according to both ordinary order delivery and large order delivery methods. Correspondingly, the delivery personnel 150 may be delivery personnel who possess or use transportation vehicles (e.g., cars) and are unable to perform delivery according to the large order delivery method only.

[0060] As another example, if the target order 120 is determined not to be a large order, the information processing system 130 can assign the target order 120 as a regular order to the delivery personnel 150 who are delivering the order using the regular order delivery method. Later, the specific method by which the information processing system 130 classifies target orders and assigns them as large or regular orders to delivery personnel 140 and 150 will be described in detail with reference to FIG9.

[0061] According to one embodiment, the large order delivery method refers to delivery using transportation vehicles suitable for delivering large orders, such as automobiles. Conversely, the regular order delivery method refers to delivery using transportation vehicles unsuitable for delivering large orders, such as delivery personnel using electric scooters. However, it is not limited to these methods; both the large order delivery method and the regular order delivery method can include delivery using multiple transportation vehicles.

[0062] Figure 2 is a schematic diagram of the connection structure of an information processing system according to an embodiment of the present invention for communicating with multiple user terminals to provide an order processing method. The information processing system 230 may include a system (multiple information devices) capable of executing the order processing method. In one embodiment, the information processing system 230 may include a computer-executable program (e.g., a downloadable application) related to executing the order processing method and any computing device capable of storing, providing, and executing data. For example, it may include one or more server devices and / or databases, or one or more distributed computing devices and / or distributed databases based on cloud computing services. For example, the information processing system 230 may include additional systems (e.g., servers) for executing the order processing method.

[0063] The order processing method executed by the information processing system 230 can be provided to users through applications, web browsers, etc., installed on multiple user terminals 210_1, 210_2, and 210_3 respectively. The functions of the electronic device (e.g., electronic device 110 in FIG1) for executing the order processing method, as described with reference to FIG1, can be executed through the operation of user terminals 210_1, 210_2, and 210_3, or through the operation of the information processing system 230, or through the linkage between user terminals 210_1, 210_2, and 210_3 and the information processing system 230.

[0064] According to one embodiment, the users of multiple user terminals 210_1, 210_2, and 210_3 can be customers who place target orders, delivery personnel who receive target orders, and / or stores that receive orders for specific items related to the target orders. The users of the multiple user terminals 210_1, 210_2, and 210_3 can receive order processing methods executed by the information processing system 230. For example, if a customer sends a target order for a specific item to the information processing system 230 through user terminal 210_1, the information processing system 230 can classify the corresponding target order as a large order or a regular order. Then, the information processing system 230 can assign the classified target order to the delivery personnel through the user terminal 210_2 of the delivery personnel who are performing delivery work according to large orders or regular orders. Similarly, the information processing system 230 can notify the classified target orders through the store's user terminal 210_3 to determine whether the corresponding target order is equivalent to a large order or a regular order.

[0065] Multiple user terminals 210_1, 210_2, and 210_3 can communicate with the information processing system 230 through network 220. Network 220 enables communication between multiple user terminals 210_1, 210_2, and 210_3 and the information processing system 230. For example, network 220 can be implemented using wired networks such as Ethernet, power line communication networks, telephone line communication devices, and RS-serial communication, as well as wireless networks such as mobile communication networks, wireless LANs (WLAN), Wi-Fi, Bluetooth, and ZigBee, or combinations thereof, depending on the setup environment. The communication method is not limited; in addition to the communication methods that network 220 can include (e.g., mobile communication networks, wired internet, wireless internet, broadcast networks, satellite networks, etc.), it can also include short-range wireless communication between user terminals 210_1, 210_2, and 210_3.

[0066] For example, multiple user terminals 210_1, 210_2, and 210_3 can send data processing requests and instructions related to user requests for data processing to the information processing system 230 via the network 220, and the information processing system 230 can receive them.

[0067] In Figure 2, mobile phone terminal 210_1, tablet terminal 210_2, and personal computer (PC) terminal 210_3 are shown as examples of user terminals, but the model is not limited to these. User terminals 210_1, 210_2, and 210_3 can be any computing device capable of wired and / or wireless communication and capable of executing or installing data processing applications, etc. For example, user terminals may include smartphones, mobile phones, navigators, computers, laptops, digital broadcasting terminals, personal digital assistants (PDAs), portable multimedia players (PMPs), tablet computers, game consoles, wearable devices, Internet of Things (IoT) devices, virtual reality (VR) devices, augmented reality (AR) devices, object scanners, etc. Furthermore, although Figure 2 shows three user terminals 210_1, 210_2, and 210_3 communicating with the information processing system 230 via network 220, it is not limited to this, and the number of user terminals communicating with the information processing system 230 via network 220 can also be different.

[0068] Figure 3 is a block diagram illustrating the internal structure of a user terminal 210 and an information processing system 230 according to an embodiment of the present invention. The user terminal 210 can also be referred to as any computing device capable of executing applications and performing wired / wireless communication, such as the mobile phone terminal 210_1, tablet terminal 210_2, and PC terminal 210_3 shown in Figure 2. As shown, the user terminal 210 may include a memory 312, a processor 314, a communication module 316, and an input / output interface 318. Similarly, the information processing system 230 may include a memory 332, a processor 334, a communication module 336, and an input / output interface 338. As shown in Figure 3, the user terminal 210 and the information processing system 230 can send and receive information and / or data via network 220 using the respective communication modules 316 and 336. Furthermore, the input / output device 320 can input information and / or data to the user terminal 210 through the input / output interface 318, or output information and / or data generated by the user terminal 210.

[0069] The memories 312 and 332 may include any non-transitory computer-readable recording medium. According to one embodiment, the memories 312 and 332 may include non-volatile mass storage devices such as read-only memory (ROM), disk drives, solid-state drives (SSDs), or flash memory. As another example, non-volatile mass storage devices such as read-only memory, solid-state drives, flash memory, and disk drives, as additional permanent storage devices different from the memories, may be included within the user terminal 210 or the information processing system 230. Furthermore, the memories 312 and 332 may store an operating system and at least one program code (e.g., code for applications related to executing order processing methods).

[0070] Such software structural elements can be loaded from an additional computer-readable recording medium other than the memories 312 and 332. This additional computer-readable recording medium may include a recording medium that can be directly connected to the user terminal 210 and the information processing system 230, such as a floppy disk drive, magnetic disk, magnetic tape, DVD / CD-ROM drive, and / or memory card. As another example, software structural elements may also be loaded into memories 312 and 332 via communication modules 316 and 336, rather than a computer-readable recording medium. For example, at least one program may be loaded into memories 312 and 332 based on a file-installed computer program (e.g., an application related to executing an order processing method) provided by a developer or a file distribution system that distributes application installation files via network 220.

[0071] According to one embodiment, the memory 332 of the information processing system 230 may include an order database 332_1. The order database 332_1 may be a recording medium (e.g., a storage server) for storing data related to multiple orders pre-received by the information processing system 230 (e.g., all orders related to existing food orders). For example, the order database 332_1 may include, but is not limited to, information related to the number of order cancellations in the multiple orders related to the pre-stored food orders, the ratio of order cancellations to order cancellations due to large orders, and the ratio of orders to large orders in each order category. A method for classifying target orders based on information stored in the order database 332_1 will be described later.

[0072] Processors 314 and 334 can process computer program instructions by performing basic arithmetic, logic, and input / output operations. Instructions can be provided to processors 314 and 334 via memories 312 and 332 or communication modules 316 and 336. For example, processors 314 and 334 can execute instructions received based on program code stored in recording devices such as memories 312 and 332.

[0073] Communication modules 316 and 336 provide a structure or function that enables user terminal 210 and information processing system 230 to communicate with each other via network 220, and provide a structure or function that enables user terminal 210 and / or information processing system 230 to communicate with other user terminals or other systems (e.g., additional cloud systems, etc.). As an example, under the control of communication module 316, requests or data (e.g., data processing requests or data, etc.) generated by processor 314 of user terminal 210 based on program code stored in recording devices such as memory 312 can be transmitted to information processing system 230 via network 220. Conversely, user terminal 210 can receive control signals or instructions provided by processor 334 of information processing system 230 via communication module 316 through communication module 336 and network 220.

[0074] Input / output interface 318 refers to a device used to connect input / output device 320. As an example, input devices may include devices with audio and / or image sensors, such as cameras, keyboards, microphones, and mice, and output devices may include displays, speakers, haptic feedback devices, etc. As another example, input / output interface 318 can be an interface device for a device that integrates input and output functions, such as a touchscreen. For example, as the processor 314 of user terminal 210 executes computer program instructions loaded in memory 312, service screens based on information and / or data provided by information processing system 230 or other user terminals can be displayed on the monitor through input / output interface 318. In Figure 3, although input / output device 320 is not included in user terminal 210, it is not limited to this and can also be integrated with user terminal 210 as a single device. Furthermore, input / output interface 338 of information processing system 230 can be connected to information processing system 230, or it can be an interface device for input / output devices (not shown) that information processing system 230 may include. In Figure 3, although the input / output interfaces 318 and 338 are separate from the processors 314 and 334 as individual structural elements, this is not the case. The input / output interfaces 318 and 338 may also be included within the processors 314 and 334.

[0075] Compared to the structural elements in Figure 3, the user terminal 210 and information processing system 230 may include more structural elements. However, it is not necessary to explicitly show most of the existing technology structures. In one embodiment, the user terminal 210 may include at least a portion of the input / output devices 320. Furthermore, the user terminal 210 may also include other structural elements such as a transceiver, a Global Positioning System (GPS) module, a camera, various sensors, and a database. For example, if the user terminal 210 is a smartphone, it may include structural elements typically found in smartphones, such as an accelerometer, a gyroscope, a microphone module, a camera module, various physical buttons, buttons using a touchpad, output / output ports, and a vibrator for vibration.

[0076] According to one embodiment, the processor 314 of the user terminal 210 enables an application or web browser for executing an order processing method to function. In this case, program code associated with the corresponding application can be loaded into the memory 312 of the user terminal 210. During application operation, the processor 314 of the user terminal 210 can receive information and / or data provided from the input / output device 320 via the output / output interface 318, or receive information and / or data from the information processing system 230 via the communication module 316, process the received information and / or data, and store it in the memory 312. Furthermore, such information and / or data can be provided to the information processing system 230 via the communication module 316.

[0077] During application operation, processor 314 can input or receive selected voice data, text, images, and / or videos through input devices such as touchscreens, keyboards, cameras with audio sensors and / or image sensors, and microphones connected to input / output interface 318. It can store the received voice data, text, images, and / or videos in memory 312 or provide them to information processing system 230 via communication module 316 and network 220. In one embodiment, processor 314 receives user input via an input device and can provide data / requests corresponding to the received user input to information processing system 230 via network 220 and communication module 316.

[0078] The processor 314 of the user terminal 210 can transmit and output information and / or data to the input / output device 320 through the input / output interface 318. For example, the processor 314 of the user terminal 210 can output the processed information and / or data through the output device 320, such as a display output device (e.g., a touch screen, a monitor, etc.) or an audio output device (e.g., a speaker).

[0079] The processor 334 of the information processing system 230 can manage, process, and / or store information and / or data received from multiple user terminals 210 and / or multiple external systems. The information and / or data processed by the processor 334 can be provided to the user terminals 210 through the communication module 336 and the network 220.

[0080] Figure 4 is an illustrative block diagram illustrating a method 400 for determining a category, price threshold, and quantity threshold according to an embodiment of the present invention.

[0081] The order processing method of the present invention can be executed by at least one processor (hereinafter referred to as processor) (for example, at least one processor 334 of the information processing system 230 of FIG3).

[0082] Figure 4 illustrates an example of a method 400 for determining price and quantity thresholds according to various categories, according to an embodiment of the present invention, executed by a processor 334. Although an information processing system (e.g., the information processing system of Figure 3) capable of executing method 400 is not shown in Figure 4, the processor 334 can execute the corresponding method 400 within an information processing system.

[0083] Referring to Figure 4, the order database 332_1 can store multiple orders 410 related to existing food orders. The categories of these multiple orders 410 can be pre-determined by the processor 334. For example, the processor 334 prepares a pre-determined category directory (e.g., pizza, fried chicken, hamburgers, Korean food, etc.) and can determine the category by comparing keywords from the corresponding category directory with words extracted from the order details of the multiple orders 410. Based on this method, the processor 334 can determine all or some categories for the multiple orders 410 stored in the order database 332_1. Furthermore, the multiple orders 410 can include current order information for each order. For example, each order can include order price, quantity, cancellation reason, etc., but examples of order information are not limited to these.

[0084] According to one embodiment, processor 334 can determine the category of a large order to be judged. The process of determining the category of a large order to be judged will be described in detail later with reference to FIG5.

[0085] According to one embodiment, processor 334 can determine large order judgment thresholds (e.g., price thresholds and quantity thresholds) for categories 420 and 430 based on multiple orders 410. For example, processor 334 can utilize price information of multiple orders 410 stored in order database 332_1 to determine a first price threshold 422 for a first category 420. For example, processor 334 can utilize quantity information of multiple orders 410 stored in order database 332_1 to determine a second quantity threshold 434 for a second category 430. The method for determining each threshold 422, 424, 432, and 434 will be described in detail later with reference to Figures 6 to 8.

[0086] In this embodiment, the first category 420 and the second category 430 can be equivalent to the categories predetermined by the processor 334 as large order judgment objects; however, the number of categories is not limited to this.

[0087] According to one embodiment, processor 334 can classify target orders based on determined thresholds 422, 424, 432, and 434. In this case, processor 334 analyzes the order information of the target orders, and after confirming whether the target orders belong to each category 420 or 430, if it is determined that the target orders belong to at least one category (e.g., a first category or a second category), the price and quantity of the target orders can be compared with each threshold 422, 424, 432, and 434. The method for classifying target orders will be described in detail later with reference to FIG9.

[0088] Based on this structure, the processor can determine whether the received order is equivalent to a large order based on the statistics on the current stored orders.

[0089] Figure 5 is a diagram illustrating a method for determining the category of a large order classification object according to an embodiment of the present invention.

[0090] Figure 5 illustrates how a processor, according to an embodiment of the present invention, determines the category of a large order based on multiple order information categories. For this purpose, for multiple categories including a first category 420 and a second category 430, the processor can generate first information 510 related to order cancellation and second information 520 corresponding to the ratio of total orders to large orders. Furthermore, the first information 510 and / or the second information 520 can be generated based on order information pre-stored in a database (e.g., the order database in Figure 3).

[0091] According to one embodiment, the processor can calculate a first proportion of the number of order cancellations due to large orders to the total number of order cancellations, based on multiple orders included (or stored) in the order database and categorized by multiple orders. Furthermore, the processor can calculate the first proportion based on information related to the number of cancellations due to large orders among the multiple orders.

[0092] According to one embodiment, if a delivery person who has accepted a specific order arrives at the store and has prepared the food related to that specific order, and then cancels the specific order, the processor can treat the cancellation of the specific order as an order cancellation due to a large order. For example, the processor can store the information about the delivery person canceling the corresponding specific order as a large order cancellation in the order database, rather than a regular order cancellation. Furthermore, the corresponding specific order may include order information related to the large order cancellation.

[0093] According to one embodiment, the first ratio can be calculated based on first information 510, which can be based on the number of orders cancelled due to large orders. This can be a ratio for the processor to calculate the number of orders cancelled due to large orders divided by the total number of order cancellations for all orders in each category.

[0094] According to one embodiment, the processor can select at least one category from multiple categories as the category for determining large orders based on a first ratio. For example, if the total number of order cancellations in the first category 420 is 3500 and the number of large order cancellations is 1533, the processor can calculate the first ratio of the first category as 43.8%. As another example, if the total number of order cancellations in the second category 430 is 3780 and the number of large order cancellations is 2860, the first ratio of the second category can be calculated as 75.6%. Similarly, the processor can calculate the first ratio of order cancellations due to large orders to the total number of order cancellations for all categories belonging to multiple categories. Subsequently, after arranging the multiple categories in order of the highest calculated first ratio, the processor can select multiple categories in order of the highest first ratio. For example, as shown in FIG5, the processor can select the first category 420 and the second category 430 as categories. However, the number of categories is not limited to this; only one category can be selected, or more than three target categories can be selected.

[0095] According to one embodiment, the processor can set a first threshold that allows selection of categories from a calculated first proportion. For example, in this embodiment, if the processor sets the first threshold for the first proportion to be 50% or more, the processor can select only the second category whose first proportion satisfies the first threshold (50%) as the target category.

[0096] As an additional embodiment, the processor can calculate a second ratio of total orders to large orders according to multiple categories. To this end, the processor can generate second information 520 related to the large order ratio for each category. This information can be generated by the processor from order information related to multiple orders in each category, calculating the number of orders with corresponding large order information.

[0097] According to one embodiment, the processor can select multiple categories based on a first proportion related to large order cancellations and a second proportion related to the proportion of large orders. For example, the processor can select a category based on the value of the first proportion divided by the second proportion. For example, when the first proportion of the first category 420 is 43.8% and the second proportion is 3.5%, the processor can calculate the proportion relative number of the first category 420 as 12.514. And when the first proportion of the second category 430 is 75.6% and the second proportion is 5.7%, the processor can calculate the proportion relative number of the second category 430 as 13.263. The processor can calculate the proportion relative numbers of multiple categories in the same manner and can select more than one category in order of the higher proportion relative number.

[0098] Furthermore, in one embodiment, the processor may set a second threshold for the second category to select the category as the object of large order judgment. For example, the processor may set the second threshold for the second proportion of multiple categories to 5%. In this case, the processor may consider both the first threshold of the first proportion and the second threshold of the second proportion to select the target category. When the processor sets the first threshold of the first proportion to be above 40% and the threshold of the second proportion to be below 6%, the processor may select the first category 420 and the second category 430 as the target categories.

[0099] Based on this structure, the processor can reduce the cancellation rate of large orders in the selected category by choosing those with a low share of large orders as the category for large order cancellation.

[0100] Figure 6 is a diagram illustrating the steps of normalizing the order price and order quantity of a category in order to determine multiple candidate price thresholds and multiple candidate quantity thresholds according to an embodiment of the present invention.

[0101] Figure 6 is a diagram illustrating the steps of a processor in an embodiment of the present invention determining multiple candidate quantity and price thresholds based on normalized data of order price and order quantity during the execution of an order processing method. Figure 6 shows curves for normalizing the price and quantity of orders related to the first category. Furthermore, multiple candidate thresholds 612 and 622 corresponding to preset percentiles (%ile; percentile) are shown on each curve 610 and 620.

[0102] According to one embodiment, the processor can normalize the price and quantity data for all orders belonging to each category (e.g., the first or second category in Figure 5) that are considered as large orders. Furthermore, even if it is practically impossible to normalize the data for all orders, the processor can generate a normal distribution curve by assuming a normal distribution. Next, the processor can determine thresholds corresponding to the upper percentiles (e.g., set in descending order starting from the highest percentile value in Figure 6) in order to set candidate thresholds for price and quantity.

[0103] However, the processor's method for setting candidate thresholds is not limited to generating normal distribution curves. For example, the processor can generate a cumulative distribution function (CDF) for the actual price and quantity data of all orders in each category to calculate the percentile of each data point in the overall distribution. In this case, the processor can set candidate thresholds even if the individual price and quantity data correspond to asymmetric data that does not match a normal distribution curve (e.g., the curve's median slopes to the right).

[0104] According to one embodiment, the processor determines multiple candidate price thresholds and multiple candidate quantity thresholds according to categories, and can select price and quantity thresholds based on the corresponding multiple candidate thresholds. For example, the processor can determine multiple candidate price thresholds 612 from the price normal distribution curve 610 that correspond to percentiles decreasing at preset intervals (e.g., 1%ile) starting from 99%ile. Thus, the processor can determine candidate price thresholds corresponding to 99%ile, 98%ile, and 97%ile. For example, the processor can determine multiple candidate quantity thresholds 622 from the quantity normal distribution curve 620 that correspond to percentiles decreasing at preset intervals (e.g., 1%ile) starting from 99%ile. Thus, the processor can determine candidate quantity thresholds corresponding to 99%ile, 98%ile, and 97%ile.

[0105] However, the method by which the processor sets preset percentiles is not limited to Figure 6. For example, in the normalized order prices of the first category, the processor can set the percentile interval to 0.5%ile. In this case, each percentile corresponding to the candidate price threshold set by the processor can be equivalent to 99.5%ile, 99%ile, 98.5%ile, etc.

[0106] Figure 7 is an illustration of information used to determine price and quantity thresholds according to an embodiment of the present invention. Figure 8 is a diagram illustrating a method for determining price and quantity thresholds according to an embodiment of the present invention.

[0107] Figure 7 illustrates the price and quantity threshold setting information 700 used by the processor to calculate the price and quantity thresholds. Figure 8 illustrates the steps by which the processor generates price and quantity threshold data 820 from candidate price and candidate quantity combination data 810.

[0108] Information 700 in one embodiment of the present invention may include percentiles determined by the processor according to the embodiment illustrated in FIG6, multiple candidate price thresholds for a first category and a second category corresponding to the percentiles, and candidate quantity thresholds. Additionally, it may include information relating to the proportion of order quantities for each category's multiple candidate thresholds to the total number of orders.

[0109] According to one embodiment, the processor can calculate the percentage of preset percentiles for price and quantity in each order category, based on the percentage of orders in each category out of all orders. More specifically, the processor can calculate the percentage of preset percentiles as the expected cancellation rate for all orders. For example, if orders in the first category account for 5% of all orders, the processor can calculate the percentage of orders corresponding to preset percentiles (99%, 98%, and 97%) as 0.05%, 0.10%, and 0.15% of all orders, respectively. Similarly, in the case of the second category, the processor can calculate the percentage of orders corresponding to preset percentiles as 0.04%, 0.08%, and 0.12% of all orders, respectively.

[0110] The proportions calculated by the processor through the aforementioned embodiments can be used to subtract from the proportion of large orders in each category out of all orders. Figure 8 illustrates an example of the processor executing the corresponding method. For example, the processor can calculate the proportion of large orders in the first category out of all orders. If the proportion of large orders in the first category out of all orders is 0.8%, the processor can subtract the proportion (or expected cancellation rate) of the candidate price threshold corresponding to the 99th percentile out of all orders from 0.8%, i.e., subtract 0.05% from 0.8%. In this case, the processor can determine that the candidate price threshold corresponding to the 99th percentile has an impact on the expected large order cancellation rate of 0.05% across all orders. Furthermore, the processor's weight in selecting the candidate price (or quantity) threshold corresponding to the corresponding percentile as the price (or quantity) threshold increases with the increase in the absolute value of the reduced large order cancellation rate, but is not limited to this.

[0111] According to this method, the processor can generate data 810 related to all predetermined price and quantity candidate thresholds (e.g., price and quantity candidate thresholds in Figure 6).

[0112] According to one embodiment, the processor can create multiple combinations based on multiple candidate price thresholds and multiple candidate quantity thresholds. More specifically, the processor can create all combinations by pairing candidate price and candidate quantity thresholds corresponding to preset percentiles of each category, but is not limited thereto. As another example, the processor can generate threshold data by using a learned artificial neural network model to generate optimal combinations related to candidate price and quantity thresholds.

[0113] According to one embodiment, when generating threshold data 820, the processor not only considers the reduction rate of cancellation rate, but may also consider other factors (e.g., delayed arrival rate or on-time rate).

[0114] According to one embodiment, the processor can calculate the expected on-time rate for orders for multiple combinations separately. Furthermore, as shown in Figure 8, this can also be calculated based on the expected delayed arrival rate.

[0115] Furthermore, according to one embodiment, the processor can select price thresholds and quantity thresholds from multiple combinations based on the expected on-time rate associated with multiple order combinations. Figure 8 illustrates an example of a state in which threshold data 820 is generated from candidate price and candidate quantity combination data 810 using the expected cancellation rate and delayed arrival rate calculated according to the embodiment. Specifically, after selecting data to increase the expected cancellation rate, the processor can calculate candidate price thresholds and / or quantity thresholds before a sharp change in the expected on-time rate (or delayed arrival rate). For example, when the delayed arrival rate corresponding to the 99%ile candidate price threshold of the first category is 4.3% and the delayed arrival rate corresponding to the 98%ile candidate price threshold is 17.2%, the processor can select 75,600 Korean Won corresponding to data 812 corresponding to the first price threshold of 99%ile from the candidate price thresholds of the first category as the first price threshold 822.

[0116] According to the method of the described embodiment, the processor can select a price threshold and a quantity threshold. For example, the processor can select a first quantity threshold 824 corresponding to data 814 corresponding to a first quantity threshold. For example, the processor can select a second price threshold 826 corresponding to data 816 corresponding to a second price threshold from a second category. For example, the processor can select a second quantity threshold 828 corresponding to data 818 corresponding to a second quantity threshold.

[0117] In another embodiment, the processor may select the price and quantity thresholds using only the expected on-time rate. For example, the processor may select the price and quantity thresholds using a combination of candidate price and quantity thresholds prior to a sharp change in the delay arrival rate corresponding to each candidate price and quantity threshold.

[0118] Based on this structure, when calculating price and quantity thresholds, taking into account the expected on-time rate and / or expected cancellation rate, the processor can allocate large orders based on thresholds that form a compromise between reducing the cancellation rate and increasing delivery time.

[0119] According to one embodiment, the processor can utilize an artificial neural network model to obtain price and quantity thresholds. For example, the artificial neural network model can utilize machine learning algorithms. Specifically, the artificial intelligence model is a correlation model constructed to model the relationship between an input dataset and an output dataset (e.g., candidate price and candidate quantity combination data 810 in Figure 8, etc.). The input dataset contains price and quantity threshold information related to multiple orders within a preset period, and the output dataset contains the on-time rate and cancellation rate of multiple orders. In this case, the artificial intelligence model performs outlier detection on the input and output datasets, and can generate price and quantity threshold data based on data prior to the detected outliers (e.g., a sharp increase in delayed arrival rate).

[0120] Furthermore, according to one embodiment, the processor can obtain price and quantity thresholds using updated data. For example, the processor updates multiple collected quantities daily, and can update at least one of the price and quantity thresholds based on the updated order data. Thus, the processor can address the large order cancellation problem in a more flexible manner.

[0121] Figure 9 is a diagram illustrating a method for classifying target orders into large orders or ordinary orders according to an embodiment of the present invention.

[0122] Figure 9 illustrates an example of a method 900 in which a processor classifies received target orders into large orders and ordinary orders based on the category to which the corresponding target order belongs and the predetermined price and quantity thresholds for that category. The first category and the second category can be categories that the processor determines as target categories for determining large orders according to the embodiments described above (e.g., the category determination method in Figure 5). Furthermore, the first price threshold, the first quantity threshold, the second price threshold, and the second quantity threshold can be equivalent to thresholds determined by the processor according to the embodiments described above (e.g., the threshold data 820 in Figure 8).

[0123] According to one embodiment, the classification method 900 may begin with the step of receiving a target order (step S910). The processor may determine whether the received target order is an order related to a first category that is the target category (step S920). Similar to the embodiment described above, this can be performed by the processor comparing the keywords of the order details included in the target order with the keywords of the first category.

[0124] According to one embodiment, when the processor determines that the target order is related to a first category, the processor can determine whether the price and quantity of the target order are greater than a predetermined first price threshold and a first quantity threshold (step S930). If the price and quantity of the target order are greater than the predetermined first price threshold and first quantity threshold, the processor determines that the corresponding target order is a large order and can assign the corresponding target order to a delivery person of a preset type (step S940). For example, if the processor determines that the first price threshold is 75,000 Korean Won, the first quantity threshold is 5.4, the price of the target order is 87,000 Korean Won, and the quantity is 6, the processor can determine that the corresponding target order is a large order and assign the large order to a delivery person. In this case, the delivery person receiving the corresponding large order can be equivalent to a delivery person of a preset type suitable for delivering large orders. If the price of the target order is below the predetermined first price threshold or the quantity of the target order is below the first quantity threshold, the processor can determine that the corresponding target order is a regular order and assign the corresponding target order to a delivery person of a preset type (step S970).

[0125] According to one embodiment, when the target order does not match the order related to the first category, the processor can determine whether the target order is an order related to the second category (step S950).

[0126] According to one embodiment, the processor can determine whether the price and quantity of the target order are greater than a second price threshold and a second quantity threshold for a second category (step S960). If the price and quantity of the target order are greater than the predetermined second price threshold and second quantity threshold, the processor can assign the corresponding target order to a delivery person of a preset type (step S940). If the price of the target order is below the predetermined second price threshold or the quantity of the target order is below the second quantity threshold, the processor can determine that the corresponding target order is a regular order and assign the corresponding target order to a delivery person of a preset type (step S970).

[0127] When none of the target orders match the first or second category, the processor can assign the corresponding target orders as ordinary orders (step S970).

[0128] The process shown in Figure 9 and the accompanying description are merely examples. In other embodiments, they can be implemented in different ways. For example, the order of some steps may be changed, some steps may be omitted, some steps may be executed repeatedly, or some steps that are executed sequentially may be executed simultaneously.

[0129] Figure 10 is a flowchart of an order processing method according to an embodiment of the present invention.

[0130] The order processing method 1000 can be executed by at least one processor (hereinafter referred to as the processor) of a user terminal and / or information processing system. According to one embodiment, the order processing method 1000 may begin with the processor determining at least one category of orders requiring classification from a plurality of categories (step S1010). Wherein, in determining at least one target category, the processor may determine the at least one target category based on a first ratio or a second ratio of the number of order cancellations due to large orders to the total number of order cancellations and the ratio of total orders to large orders.

[0131] According to one embodiment, the processor can determine a price threshold and a quantity threshold for at least one category (step S1020). The processor can select a price threshold and a quantity threshold from multiple candidate price thresholds and multiple candidate quantity thresholds according to each target category.

[0132] According to one embodiment, the processor can receive a target order from a specific store (step S1030).

[0133] According to one embodiment, the processor can determine whether the target order belongs to at least one category (step S1040).

[0134] According to one embodiment, the processor can classify a target order into a large order or a regular order (step S1050). More specifically, in response to determining that a target order belongs to at least one category, the classification step S1050 can be performed based on the price and quantity of the target order and price and quantity thresholds of the list to which the target order belongs.

[0135] The described process and description are merely examples, and in some embodiments, they can be implemented in different ways. For example, in some embodiments, the order of the steps may be changed, or some steps may be executed repeatedly, or some steps may be omitted, or some steps may be added.

[0136] To enable a computer to execute the method, it can be provided by a computer program stored on a computer-readable recording medium. The medium can also be temporary storage for continuously storing, executing, or downloading a computer-executable program. Furthermore, the medium can be a variety of recording or storage units composed of one or more hardware components, and is not limited to media that directly access any computer system; it can also be distributed across a network. As examples, the medium includes magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floppy disks; and structures that store program instructions such as ROM, RAM, and flash memory. Furthermore, as yet another example, the medium can be a recording or storage medium managed by an application store that sells applications or a website or server that provides and sells various other software.

[0137] The methods, operations, or techniques of this invention can be implemented by various units. For example, the techniques can also be implemented by hardware, firmware, software, or a combination thereof. In relation to the disclosure of this invention, those skilled in the art should understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described can also be implemented by electronic hardware, computer software, or a combination thereof. To clearly illustrate this interchangeability between hardware and software, various exemplary structural elements, blocks, modules, circuits, and steps have been simply described above from a functional perspective. However, whether such functionality is implemented by hardware or software depends on the design requirements of the specific application and the overall system. Those skilled in the art can also implement the described functions in various ways for each specific application; therefore, such implementations should not be construed as departing from the scope of this invention.

[0138] In hardware examples, the processing unit for performing the technology may also be implemented within one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), graphics processing units (GPUs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions of the present invention, computers, or combinations thereof.

[0139] Therefore, the various exemplary logic blocks, modules, and circuits described in conjunction with this invention can also be implemented or executed by a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete logic gates or transistor logic, discrete hardware components, or any combination designed to perform the functions of this invention. The general-purpose processor can be a microprocessor; alternatively, the processor can also be any processor, controller, microcontroller, or state machine. The processor can also be implemented by a combination of computing devices, such as a digital signal processor, a microprocessor, multiple microprocessors, one or more microprocessors combined with a digital signal processing chip, or any other combination of structures.

[0140] In firmware and / or software instances, the technology may also be implemented by instructions stored on computer-readable media such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage devices. The instructions may be executed by more than one processor, or may cause the processor to perform the functions described in this invention according to a specific implementation.

[0141] When implemented in software, the technology can be used to store or transmit one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, including any medium that facilitates the transfer of a computer program from one place to another. Storage media can also be any available medium accessible to a computer. As a non-limiting example, such computer-readable media may include random access memory, read-only memory, electrically erasable programmable read-only memory, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other computer-accessible medium used to transfer or store prescribed program code in the form of instructions or data structures. Furthermore, any access can be suitably implemented by the computer-readable medium.

[0142] For example, if software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, stranded wire, digital subscriber line (DSL), or wireless technologies such as infrared, wireless, and microwave, then coaxial cable, fiber optic cable, stranded wire, digital subscriber line (DSL), or wireless technologies such as infrared, wireless, and microwave are included within the definition of media. In this application, the terms "disk" and "disc" include CDs, laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs, wherein disks typically reproduce magnetic data, while discs utilize lasers to reproduce optical data. The combinations described are also included within the scope of computer-readable media.

[0143] Software modules can reside within random access memory, flash memory, read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, cache, hard disk, removable disk, CD-ROM, or any known form of storage medium. As an example, the storage medium is connected to a processor so that the processor reads information from or records information to the storage medium. Alternatively, the storage medium can be integrated into the processor. The processor and storage medium can also reside within an application-specific integrated circuit (ASIC). The ASIC can also reside within a user terminal. Alternatively, the processor and storage medium can also exist as separate structural elements within the user terminal.

[0144] In the embodiments described above, although the currently disclosed main implementation method can be applied through one or more independent computer systems, the present invention is not limited thereto, and can also be implemented in any computing environment such as a network or a distributed computing environment. Furthermore, the main implementation method of the present invention can also be implemented by multiple processing chips or devices, and storage can be similarly affected by multiple devices. Such devices may also include personal computers, network servers, and portable devices.

[0145] While this specification describes content related to certain embodiments, those skilled in the art can make various modifications and alterations without departing from the scope of this invention. Furthermore, such modifications and alterations also fall within the scope of the appended patent applications.

[0146] 1000: Order Processing Method 100: Users 110: User Terminal 120: Target Orders 130: Information Processing System

Claims

1. An order processing method, executed by at least one processor, related to food delivery, wherein, The process includes the following steps: determining at least one category from multiple categories that requires order categorization; determining a price threshold and a quantity threshold for each of the at least one category; receiving a target order from a specific store; and determining whether the target order belongs to the at least one category. In response to determining that the target order belongs to at least one category, the target order is classified as a large order or a regular order based on the price and quantity of the target order and the price threshold and quantity threshold of the category to which the target order belongs. The step of determining the price threshold and the quantity threshold includes the following steps: determining multiple candidate price thresholds and multiple candidate quantity thresholds according to the at least one category; and selecting the price threshold and the quantity threshold from the multiple candidate price thresholds and the candidate quantity thresholds according to the at least one category. The step of selecting the price threshold and the quantity threshold includes the following steps: creating multiple combinations based on the multiple candidate price thresholds and the multiple candidate quantity thresholds; calculating the expected on-time rate for each of the multiple combinations; and selecting the price threshold and the quantity threshold from the multiple combinations based on the expected on-time rate.

2. As in request item 1, the order processing method, where, The step of determining whether the target order belongs to the at least one category includes the following steps: the step of determining whether the target order belongs to a first category in the at least one category and classifying it as the large order or the ordinary order includes the following steps: comparing the price and quantity of the target order with a first price threshold and a first quantity threshold related to the first category, respectively; in response to determining that the price of the target order is greater than the first price threshold and the quantity of the target order is greater than the first quantity threshold, classifying the target order as the large order; and in response to determining that the price of the target order is below the first price threshold or the quantity of the target order is below the first quantity threshold, classifying the target order as the ordinary order.

3. As in request item 1, the order processing method, where, It also includes the following steps: in response to classifying the target order as the large order, assigning the target order to a delivery person of a preset type.

4. As in request item 1, the order processing method, where, The step of determining the at least one category includes the following steps: calculating a first proportion of the number of order cancellations due to large orders to the total number of order cancellations based on multiple orders included in the database; and selecting at least one category from the multiple categories based on the first proportion.

5. As in request item 4, the order processing method, where, The step of selecting the at least one category includes the following steps: selecting the at least one category from the plurality of categories whose first proportion is above a first threshold.

6. As in request item 4, the order processing method, where, The step of calculating the first ratio includes the following steps: if the delivery person who accepted the specific order arrives at the store and has prepared the food related to the specific order, and the delivery person cancels the specific order, then the cancellation of the specific order is regarded as an order cancellation caused by the large order.

7. As in request item 4, the order processing method, where, The step of determining the at least one category further includes the following steps: calculating a second proportion of large orders to total orders according to the plurality of categories; the step of selecting the at least one category includes the following steps: selecting the at least one category from the plurality of categories based on the first proportion and the second proportion.

8. As in request item 7, the order processing method, wherein, The step of selecting the at least one category includes the following steps: selecting from the plurality of categories at least one category in which the first proportion is above a first threshold and the second proportion is below a second threshold.

9. As in request item 1, the order processing method, wherein, The steps of determining the price threshold and the quantity threshold include the following steps: determining a first price threshold and a first quantity threshold for a first category included in the at least one category; and determining a second price threshold and a second quantity threshold for a second category included in the at least one category, wherein the first category and the second category are different from each other, the first price threshold and the second price threshold are different from each other, and the first quantity threshold and the second quantity threshold are different from each other.

10. As in request item 1, the order processing method, wherein, The steps of determining the plurality of candidate price thresholds and the plurality of candidate quantity thresholds include the following steps: For a first category in the at least one category, normalize the price and quantity of orders related to the first category; Based on the normalized price, determine the plurality of candidate price thresholds corresponding to a preset percentile; and Based on the normalized quantity, determine the plurality of candidate quantity thresholds corresponding to the preset percentile.

11. As in request item 1, the order processing method, wherein, The step of selecting the price threshold and the quantity threshold from the plurality of combinations includes the following steps: calculating the expected cancellation rate of the order for each of the plurality of combinations; and selecting the price threshold and the quantity threshold from the plurality of combinations based on the expected on-time rate and the expected cancellation rate.

12. As in request item 1, the order processing method, wherein, The steps for determining the price threshold and the quantity threshold include the following steps: using an artificial intelligence model to obtain the price threshold and quantity threshold for at least one category respectively, wherein the artificial intelligence model is a model that uses a machine learning algorithm to model the correlation between the input data set and the output data set, wherein the input data set contains price threshold information and quantity threshold information related to multiple orders within a preset period, and the output data set contains the on-time rate and cancellation rate of the multiple orders.

13. As in request item 1, the order processing method, wherein, It also includes the following steps: collecting multiple orders; and updating at least one of the at least one category, the price threshold, and the quantity threshold based on the collected multiple orders.

14. A non-transitory computer-readable recording medium, wherein, The record contains instructions for performing any of the methods requested in item 1 to 13 on the computer.

15. A computing device, wherein, include: Memory; And at least one processor, connected to the memory, for executing at least one computer-readable program included in the memory, the at least one program including instructions to perform the following steps: determining at least one category from a plurality of categories that require categorized orders; determining a price threshold and a quantity threshold for the at least one category respectively; receiving a target order from a specific store; determining whether the target order belongs to the at least one category; In response to determining that the target order belongs to at least one category, the target order is classified as a large order or a regular order based on the price and quantity of the target order and the price threshold and quantity threshold of the category to which the target order belongs. The instruction to determine the price threshold and the quantity threshold includes the following instructions: determining multiple candidate price thresholds and multiple candidate quantity thresholds according to the at least one category; and selecting the price threshold and the quantity threshold from the multiple candidate price thresholds and the candidate quantity thresholds according to the at least one category. The step of selecting the price threshold and the quantity threshold includes the following steps: creating multiple combinations based on the multiple candidate price thresholds and the multiple candidate quantity thresholds; calculating the expected on-time rate for each of the multiple combinations; and selecting the price threshold and the quantity threshold from the multiple combinations based on the expected on-time rate.

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