System for ordering operation processing using qr code attached to table and method for controlling the same

Abstract

The present disclosure relates to a server for processing order operations via a QR code attached to a table and a method for controlling the same. The server for processing order operations via a QR code attached to a table according to the present disclosure comprises a communication module, a memory storing at least one process for performing an order operation via a QR code attached to a table, and a processor that performs an order operation via a QR code attached to a table based on said at least one process. The processor responds to a request for a page that allows menu ordering via a QR code provided on a store table from a mobile terminal, transmits said page to the mobile terminal via said communication module, receives menu information selected via said page from the mobile terminal, and when prepayment processing is completed or a post-payment method is selected at the mobile terminal, transmits the selected menu information to the store terminal via said communication module.

Description

Technology Field

[0001] The present disclosure relates to a system for processing order operations via a QR code attached to a table and a method for controlling the same. More specifically, the present disclosure relates to a system comprising a mobile terminal, a server, and a store terminal for processing order operations via a QR code attached to a table, and a method for controlling the same. Background Technology

[0002] Generally, in establishments that serve beverages, food, etc., customers order from the menu or request service by calling a staff member directly or by using a bell.

[0003] Recently, methods such as installing kiosks to allow customers to place orders directly are being used to reduce the need for manpower while increasing customer convenience by minimizing face-to-face interactions with staff within the store.

[0004] According to such a system, there are inevitably limitations in providing the information that customers need.

[0005] In other words, selecting a menu requires various information, but there are difficulties, such as the fact that employees must spend a lot of time explaining it to customers.

[0006] Furthermore, from the customer's perspective, there is inevitably inconvenience in selecting a menu item, and there is also inconvenience in continuously requesting customer service.

[0007] Furthermore, while dedicated terminals capable of reading conventional barcodes could only be owned by product sellers, making it impossible for consumers to access information using barcodes, QR codes allow consumers to directly check product information with just a smartphone. The utility of QR codes has increased with the widespread adoption of smartphones. Smartphone users can easily obtain various types of information simply by downloading a free QR code scanning application and scanning the code with their device.

[0008] Korean Utility Model Registration No. 20-0349620 relates to a barcode ordering system and discloses configurations that allow a customer to place an order directly and perform automatic calculation using a barcode device having an order processing function on a barcode menu board.

[0009] However, in the aforementioned conventional technology, when using a barcode device, customers had to rely solely on photos displayed on the barcode menu for information regarding the products for sale, making it difficult to make accurate judgments. Consequently, there was a problem requiring the essential assistance of an employee to receive guidance on product information or how to use the barcode. In particular, because the same barcode or QR code was used for every table, there were issues with duplicate orders and complicated settlements even when the customer changed. The problem to be solved

[0010] The embodiment disclosed in this disclosure provides an order processing system via a QR code attached to a table and a control method thereof, which enables ordering and payment for a menu and processing selected menu information by utilizing a QR code attached to a table within a store in an optimized manner.

[0011] The problems that this disclosure aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by a person skilled in the art from the description below. means of solving the problem

[0012] A server for processing order operations via a QR code attached to a table according to the present disclosure for achieving the above-described technical problem comprises a communication module, a memory storing at least one process for performing order operations via a QR code attached to a table, and a processor that performs order operations via a QR code attached to a table based on said at least one process. The processor responds to a request for a page that allows menu ordering via a QR code provided on a store table from a mobile terminal, transmits said page to the mobile terminal via said communication module, receives menu information selected via said page from the mobile terminal, and when prepayment processing is completed or a post-payment method is selected at the mobile terminal, transmits the selected menu information to the store terminal via said communication module.

[0013] In an embodiment, when a QR code attached to a table is scanned through a mobile terminal, the processor can receive table information where the QR code was scanned and mobile terminal information from the mobile terminal through the communication module.

[0014] In an embodiment, the processor can transmit menu information selected through the mobile terminal to the store terminal in conjunction with the table information.

[0015] In an embodiment, the processor receives order status information regarding the selected menu information from the store terminal and can transmit the received order status information to the mobile terminal based on the mobile terminal information.

[0016] In an embodiment, the processor may include a pre-trained artificial intelligence model that takes as input at least one of table information where a QR code is scanned through the communication module, table usage patterns, order time, and dwell time, and outputs a recommended menu as an output value.

[0017] In an embodiment, the processor inputs table-by-table order pattern information by time period as an input value to the previously trained artificial intelligence model and receives future demand forecast information as an output value from the artificial intelligence model.

[0018] The processor receives user pattern information for selecting a menu through the page on the mobile terminal, and can dynamically change the user interface included in the page based on the received user pattern information.

[0019] In an embodiment, the processor can control the communication module to predict a cooking time based on at least one of the current kitchen situation, the menu preparation process, and past order history from the store terminal, and to transmit order status information including the predicted cooking time to the mobile terminal.

[0020] In an embodiment, the processor can process payment by integrating the first menu information and the second menu information after receiving the first menu information, table information, and mobile terminal information from the mobile terminal in a post-payment method and subsequently receiving the second menu information along with the same table information and mobile terminal information.

[0021] A control method for a server processing an order operation via a QR code attached to a table according to the present disclosure may include the steps of: responding to a request for a page that allows menu ordering via a QR code provided on a store table from a mobile terminal and transmitting said page to said mobile terminal via a communication module; receiving menu information selected via said page from said mobile terminal; and, when prepayment processing is completed or a post-payment method is selected from said mobile terminal, transmitting the selected menu information to a store terminal via said communication module.

[0022] In addition to this, a computer program stored on a computer-readable recording medium for implementing the present disclosure may be further provided.

[0023] In addition to this, a computer-readable recording medium for recording a computer program for implementing the present disclosure may be further provided. Effects of the invention

[0024] According to the means for solving the problem described above in the present disclosure, the present disclosure provides an optimized table ordering interface and a differentiated payment method based on prepayment and postpayment, thereby providing a method that makes additional orders more convenient when making postpayment and allows for payment integrated with prepayment.

[0025] The present disclosure can provide recommended menus close to user preferences based on the order time and dwell time of users per table using an artificial intelligence model, and can predict future demand based on order pattern information per table.

[0026] The effects of the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below. Brief explanation of the drawing

[0027] FIG. 1 is a conceptual diagram illustrating an order processing system via a QR code attached to a table according to the present disclosure. FIG. 2 is a block diagram showing an order processing server via a QR code attached to a table of the present disclosure. FIG. 3 is a block diagram showing a mobile terminal of the present disclosure. FIG. 4 is a flowchart illustrating a representative server control method of the present disclosure. FIG. 5 is a flowchart illustrating a control method of a system including a mobile terminal, a server, and a store terminal of the present disclosure. FIG. 6 is a conceptual diagram showing different order pages for each table of the present disclosure being displayed on a mobile terminal. Specific details for implementing the invention

[0028] Throughout this disclosure, the same reference numerals denote the same components. This disclosure does not describe all elements of the embodiments, and general content in the art to which this disclosure pertains or content that overlaps between embodiments is omitted. The terms 'part, module, component, block' as used in the specification may be implemented in software or hardware, and depending on the embodiments, a plurality of 'parts, modules, components, blocks' may be implemented as a single component, or a single 'part, module, component, block' may include a plurality of components.

[0029] Throughout the specification, when a part is described as being "connected" to another part, this includes not only cases where they are directly connected but also cases where they are indirectly connected, and indirect connections include connections made via a wireless communication network.

[0030] Furthermore, when it is stated that a part "includes" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0031] Throughout the specification, when it is stated that a component is located "on" another component, this includes not only cases where a component is in contact with another component, but also cases where another component exists between the two components.

[0032] The terms first, second, etc. are used to distinguish one component from another, and the components are not limited by the aforementioned terms.

[0033] Singular expressions include plural expressions unless there is an obvious exception in the context.

[0034] In each step, identification codes are used for convenience of explanation and do not describe the order of the steps; the steps may be performed differently from the specified order unless a specific order is clearly indicated in the context.

[0035] The operating principles and embodiments of the present disclosure will be described below with reference to the attached drawings.

[0036] In this specification, the term "device according to the present disclosure" includes all various devices capable of performing computational processing and providing results to a user. For example, the device according to the present disclosure may include all of a computer, a server device, and a portable terminal, or may be in the form of any one of these.

[0037] Here, the computer may include, for example, a notebook, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

[0038] The above server device is a server that processes information by communicating with an external device, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, and a web server.

[0039] The above portable terminal may include, for example, all types of handheld-based wireless communication devices such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, smartphones, etc., as well as wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMDs).

[0040] Functions related to artificial intelligence according to the present disclosure are operated through a processor and memory. The processor may be composed of one or more processors. In this case, the one or more processors may be general-purpose processors such as CPUs, APs, and DSPs (Digital Signal Processors), graphics-dedicated processors such as GPUs and VPUs (Vision Processing Units), or artificial intelligence-dedicated processors such as NPUs. The one or more processors control the processing of input data according to predefined operation rules or artificial intelligence models stored in memory. Alternatively, if the one or more processors are artificial intelligence-dedicated processors, the artificial intelligence-dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0041] The predefined rules of operation or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that a predefined rules of operation or artificial intelligence models configured to perform a desired characteristic (or objective) are created by a basic artificial intelligence model being trained using a number of training data by a learning algorithm. Such learning may be performed on the device itself where the artificial intelligence according to the present disclosure is executed, or it may be performed through a separate server and / or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

[0042] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values ​​and performs neural network operations through operations between the results of previous layers and the multiple weights. The multiple weights possessed by the multiple neural network layers can be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights may be updated so that the loss value or cost value obtained from the artificial intelligence model during the learning process is reduced or minimized. Artificial neural networks may include deep neural networks (DNNs), such as Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), Bidirectional Recurrent Deep Neural Networks (BRDNNs), or Deep Q-Networks, but are not limited to the examples mentioned above.

[0043] According to an exemplary embodiment of the present disclosure, a processor can implement artificial intelligence. Artificial intelligence refers to a machine learning method based on an artificial neural network that enables a machine to learn by mimicking human biological neurons. Methodologies of artificial intelligence can be classified according to the learning method into supervised learning, where input and output data are provided together as training data and the solution (output data) to the problem (input data) is predetermined; unsupervised learning, where only input data is provided without output data and the solution (output data) to the problem (input data) is not predetermined; and reinforcement learning, where a reward is given from an external environment whenever an action is taken from the current state, and learning proceeds in a direction that maximizes such reward. In addition, artificial intelligence methodologies can be classified according to the architecture, which is the structure of the learning model. The architectures of widely used deep learning technologies can be classified into Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Transformers, and Generative Adversarial Networks (GAN).

[0044] The device and system may include an artificial intelligence model. The artificial intelligence model may be a single model or may be implemented as multiple models. The artificial intelligence model may be composed of a neural network (or artificial neural network) and may include statistical learning algorithms that mimic biological neurons in machine learning and cognitive science. A neural network may refer to a model that possesses problem-solving capabilities by having artificial neurons (nodes) that form a network through synaptic connections and change the strength of synaptic connections through learning. The neurons of a neural network may include combinations of weights or biases. A neural network may include one or more layers composed of one or more neurons or nodes. For example, the device may include an input layer, a hidden layer, and an output layer. The neural network constituting the device can infer a result (output) to be predicted from an arbitrary input by changing the weights of the neurons through learning.

[0045] The processor can create neural networks, train or learn neural networks, perform computations based on received input data, generate information signals based on the results of the computation, or retrain neural networks. Neural network models may include, but are not limited to, various types of models such as Convolutional Neural Networks (CNN), Region with Convolutional Neural Networks (R-CNN), Region Proposal Networks (RPN), Recurrent Neural Networks (RNN), Stacking-based Deep Neural Networks (S-DNN), State-Space Dynamic Neural Networks (S-SDNN), Deconvolution Networks, Deep Belief Networks (DBN), Restructured Boltzmann Machines (RBM), Fully Convolutional Networks, Long Short-Term Memory Networks (LSTM), and Classification Networks, such as GoogleNet, AlexNet, and VGG Network. The processor may include one or more processors to perform computations according to neural network models. For example, a neural network is a deep neural network It may include a (Deep Neural Network).

[0046] Neural networks include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational Auto) Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Deep Belief Network), DCN (Deep Convolutional Network), DN (Deconvolutional Network), DCIGN (Deep Convolutional Inverse Graphics Network), GAN (Generative Adversarial Network), LSM (Liquid State Machine), ELM (Extreme Learning Machine), ESN (Echo It will be understood by a person skilled in the art that any neural network may be included, but is not limited to, State Network, Deep Residual Network, Differential Neural Computer, Neural Turning Machine, Capsule Network, Kohonen Network, and Attention Network.

[0047] According to exemplary embodiments of the present disclosure, the processor comprises a Convolutional Neural Network (CNN) such as GoogleNet, AlexNet, VGG Network, Region with Convolutional Neural Network (R-CNN), Region Proposal Network (RPN), Recurrent Neural Network (RNN), Stacking-based Deep Neural Network (S-DNN), State-Space Dynamic Neural Network (S-SDNN), Deconvolution Network, Deep Belief Network (DBN), Restricted Boltzmann Machine (RBM), Fully Convolutional Network, Long Short-Term Memory (LSTM) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT, SP-BERT, MRC / QA, Text Analysis, Dialog System, GPT-3, GPT-4 for Natural Language Processing, Visual Analytics, Visual Understanding, Video Synthesis for Vision Processing, Anomaly Detection, Prediction, Time-Series Forecasting, Optimization, Recommendation for ResNet Data Intelligence, Various artificial intelligence structures and algorithms, such as data creation, may be used, but are not limited thereto. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0048] FIG. 1 is a conceptual diagram showing an order processing system through a QR code attached to a table of the present disclosure.

[0049] A system according to the present disclosure may include a mobile terminal (200), a server (100), and a store terminal (300) for processing order operations via a QR code (400) (or NFC tag) attached to a table in a store.

[0050] The present disclosure may provide a table ordering and payment convergence system and a method of operating the same that integrally provides non-face-to-face menu information access using a QR code or NFC tag attached to a table, real-time order creation, pre / post-payment processing, and automatic order status notification functions.

[0051] To this end, the present disclosure may attach a sticker-type tag containing a uniquely identifiable QR code or NFC tag to each table so that a customer (or user) can identify the corresponding table through their mobile terminal (or smart device). In this case, the QR code or NFC tag is not limited to a sticker-type form, but may be in the form of a separate terminal so that the QR code or NFC tag can be changed by the control of a server (100) or a store terminal (300), and the QR code or NFC tag may be output on an output module of the terminal-type form.

[0052] The present disclosure enables a customer to access electronic menu information of a store in real time and check detailed information (price, description, image, allergy information, etc.) for each menu item by scanning a tag using the camera or NFC function of a mobile terminal.

[0053] For convenience of explanation, QR codes will be used as examples in this disclosure. However, parts described using QR codes in this disclosure may be replaced with NFC tags or applied by analogy in the same or similar manner.

[0054] The present disclosure can generate a digital order including menu items, quantity, special requests, etc. selected through a mobile terminal (200) and transmit it to a server (100).

[0055] The present disclosure may support customers in selecting a prepaid or postpaid payment method, and in the case of prepaid payment, it may carry out a safe and legal payment processing process utilizing an electronic financial business PG license.

[0056] In addition, the present disclosure may immediately and automatically transmit menu information (order details) to a store terminal (e.g., a printer or display device installed in the kitchen) after payment is completed to initiate the cooking process.

[0057] In addition, the present disclosure can notify the customer of order status information (e.g., received, cooking, ready) in real time via a notification message (e.g., notification message) to the customer's mobile terminal (200) when making a prepaid payment.

[0058] That is, the system according to the present disclosure can generate and manage QR codes, encode and manage NFC tag information, and design and provide a web or app-based user interface (UI) and user experience (UX) for customer smart devices. In particular, the system according to the present disclosure can maximize user accessibility by utilizing an intuitive interface similar to a delivery platform.

[0059] The system according to the present disclosure can design and implement a server-side architecture for efficiently managing order data and linking with a kitchen system, design and implement a PG system linkage module to support various payment methods (credit card, simple payment, etc.), and implement a method for linking with an external service (e.g., AlimTalk API) for sending notification messages.

[0060] FIG. 2 is a block diagram showing an order processing server via a QR code attached to a table of the present disclosure.

[0061] A server (100) for processing orders via a QR code attached to a table according to the present disclosure may include a communication module (100), a user input module (130), an interface module (140), a memory (170), and a processor (180).

[0062] The components illustrated in FIG. 2 are not essential for implementing the server (100) according to the present disclosure, so the server (100) described in this specification may have more or fewer components than the components listed above.

[0063] The communication module (110) may include one or more components that enable communication with an external device, for example, at least one of a broadcast receiving module, a wired communication module, a wireless communication module, a short-range communication module, and a location information module.

[0064] In addition to Wi-Fi modules and WiBro (Wireless broadband) modules, the wireless communication module may include wireless communication modules that support various wireless communication methods such as GSM (global System for Mobile Communication), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), UMTS (universal mobile telecommunications system), TDMA (Time Division Multiple Access), LTE (Long Term Evolution), 4G, 5G, and 6G.

[0065] The location information module is a module for obtaining the location (or current location) of the device according to the present disclosure, and representative examples thereof include a Global Positioning System (GPS) module or a Wireless Fidelity (WiFi) module. For example, if a GPS module is utilized, the location of the device can be obtained using signals sent from GPS satellites. As another example, if a Wi-Fi module is utilized, the location of the device can be obtained based on information from a Wireless Access Point (AP) that transmits or receives wireless signals from the Wi-Fi module. If necessary, the location information module may perform any of the functions of other modules of the communication unit to obtain data regarding the location of the device, either substituted or additionally. The location information module is a module used to obtain the location (or current location) of the device, and is not limited to a module that directly calculates or obtains the location of the device.

[0066] The user input module (130) is for receiving information from a user, and when information is input through the user input module, the processor can control the operation of the device to correspond to the input information. Such a user input module may include a hardware physical key (e.g., a button, dome switch, jog wheel, jog switch, etc. located on at least one of the front, rear, and side of the device) and a software touch key. As an example, the touch key may be composed of a virtual key, soft key, or visual key displayed on a touchscreen-type display unit through software processing, or may be composed of a touch key placed on a part other than the touchscreen. Meanwhile, the virtual key or visual key may be displayed on the touchscreen in various forms, and may be composed of, for example, a graphic, text, an icon, a video, or a combination thereof.

[0067] The interface module (140) serves as a passage for various types of external devices connected to the device. This interface module may include at least one of a wired / wireless headset port, an external charger port, a wired / wireless data port, a memory card port, a port for connecting a device equipped with an identification module (SIM), an audio I / O (Input / Output) port, a video I / O (Input / Output) port, and an earphone port. The device can perform appropriate control related to the external device connected to the interface module.

[0068] The memory (170) can store data supporting various functions of the device and programs for the operation of the processor, and can store input / output data (e.g., music files, still images, videos, etc.), and can store a number of application programs (or applications) running on the device, data for the operation of the device, and instructions. At least some of these application programs can be downloaded from an external server via wireless communication.

[0069] Additionally, the memory (170) may store at least one process (or task, operation, function, control method, process, data, algorithm, program, etc.) or processor for performing the method according to the present disclosure. Such at least one process may be performed under the control of the processor (180) and may refer to information used by the processor (180) to implement the method according to the present disclosure.

[0070] Such memory may include at least one type of storage medium among flash memory type, hard disk type, SSD type (Solid State Disk type), SSD type (Silicon Disk Drive type), multimedia card micro type, card type memory (e.g., SD or XD memory, etc.), RAM (random access memory; RAM), SRAM (static random access memory), ROM (read-only memory; ROM), EEPROM (electrically erasable programmable read-only memory), PROM (programmable read-only memory), magnetic memory, magnetic disk, and optical disk. Additionally, the memory may be a database that is separate from the device but connected via wired or wireless connection.

[0071] The processor (180) may be implemented with a memory that stores data for an algorithm or a program that reproduces the algorithm for controlling the operation of components within the device, and at least one processor (not shown) that performs the aforementioned operation using the data stored in the memory. In this case, the memory and the processor may each be implemented as separate chips. Alternatively, the memory and the processor may be implemented as a single chip.

[0072] The processor (180) may include an artificial intelligence model (181), a customer terminal interface module (182), an order processing module (184), a payment processing module (185), a kitchen integration module (186), and a notification provision module (187).

[0073] The artificial intelligence model (181), customer terminal interface module (182), order processing module (184), payment processing module (185), kitchen linkage module (186), and notification provision module (187) may be implemented as separate chips (or hardware modules), or may be implemented as software components in a block form within the processor (180).

[0074] The operation / function / control method performed by each of the artificial intelligence model (181), customer terminal interface module (182), order processing module (184), payment processing module (185), kitchen linkage module (186), and notification provision module (187) can be performed by the processor (180) or applied by analogy in the same / similar way by the processor (180).

[0075] The customer terminal interface module (182) can process menu information provision, order information reception, and prepaid and postpaid payments based on table identification tag information. When scanning the table identification tag, the customer terminal interface module (182) can check the current order status of the corresponding table and determine whether to add a new order or an existing order.

[0076] The system according to the present disclosure may have identification tags attached to the tables. Specifically, the server (100) may identify the tables through a customer mobile terminal (smart device) by attaching a tag in the form of a sticker (or display) containing a QR code or NFC tag that is uniquely identifiable to each table.

[0077] The mobile terminal (200) can allow customers to access electronic menu information in real time by scanning a tag using a smart device camera or NFC function, and can notify customers of electronic menu access and information by outputting detailed information (price, description, image, allergy information, etc.) for each menu item.

[0078] The order processing module (184) can manage orders per table by linking unique table identification information and order information, and can update order status information in real time. The order processing module (184) can generate a digital order including menu items, quantity, special requests, etc. selected through a smart device and transmit it to a server.

[0079] The payment processing module (185) is linked with the order processing unit to perform a PG license-based payment when a prepaid payment request is made, and can maintain a payment waiting state linked to table information when a postpaid payment request is made.

[0080] For example, the payment processing module (185) may support the customer in selecting a prepaid or postpaid payment method, and may include a safe and legal payment processing process utilizing an electronic financial business PG license when making a prepaid payment. The payment processing module (185) maintains a payment waiting state linked to table information when a postpaid payment request is made, and can subsequently process an integrated payment for accumulated order history. That is, the payment processing module (185) can process an integrated payment for accumulated order history when a postpaid payment request is made.

[0081] The kitchen linkage module (186) can receive confirmed menu information (order information) from the order processing module (185) and transmit it in real time to the store terminal (300) (kitchen system). Specifically, the kitchen linkage module (186) can initiate the cooking process by immediately and automatically transmitting the order details to a printer or digital display device in the kitchen after payment is completed. The kitchen linkage module (186) can also receive order information when post-payment is selected.

[0082] The notification providing module (187) can automatically send a notification message to the mobile terminal (200) (customer terminal) according to changes in the order status. The notification providing module (187) can send an order acceptance confirmation message when prepaid payment is completed. The notification providing module (187) can send a status notification message (or notification message) for each order processing stage to the mobile terminal (200) based on order status information transmitted from the store terminal (300) (kitchen system).

[0083] The notification providing module (187) can send step-by-step notification messages to the mobile terminal (200) based on information received from the store terminal, and can be linked with an external service (e.g., Notification Talk API) to send notifications.

[0084] A processor (180) (or central control unit) manages the above components in an integrated manner. It can control the customer terminal interface module (182), the payment processing module (184), the payment processing module (185), the kitchen linkage module (186), and the notification provision module (187).

[0085] FIG. 3 is a block diagram showing a mobile terminal of the present disclosure.

[0086] The mobile terminal (200) according to the present disclosure may refer to a customer smart device included in an order processing system via a QR code attached to a table.

[0087] Referring to FIG. 3, the mobile terminal (200) may include a communication module (210), a camera module (220), a user input module (230), an interface module (240), a display (250), a memory (270), and a processor (280).

[0088] The communication module (210), user input module (230), interface module (240), memory (270), and processor (280) can be applied in the same or similar way to the communication module (110), user input module (130), interface module (140), memory (170), and processor (180) described above in FIG. 2.

[0089] The camera module (120) processes image frames, such as still images or video, obtained by an image sensor in shooting mode. The processed image frames may be displayed on a display or stored in memory.

[0090] Meanwhile, if there are multiple camera modules, they may be arranged to form a matrix structure, and multiple image information having various angles or focal points may be input through the cameras forming such a matrix structure, and the cameras may also be arranged in a stereo structure to acquire left and right images for realizing a three-dimensional stereoscopic image.

[0091] The processor (280) can scan the QR code (400) attached (equipped) to the table through the camera module (120).

[0092] The display (250) displays (outputs) information processed by the device. For example, the display unit may display execution screen information of an application program (e.g., an application) running on the device, or UI (User Interface) and GUI (Graphic User Interface) information based on such execution screen information.

[0093] The display (250) can output a real-time preview image received through the camera module (220). The preview image may include a QR code attached to a table. When the QR code is scanned, the processor (280) can output a graphic object to the display (250) that allows selecting an order page link (URL) of the server (100) through the QR code.

[0094] When the above link is selected, the processor (280) connects to the server (100) and outputs the order page to the display (250), can receive menu information through the order page, and can proceed with the payment process.

[0095] Below, we will examine in more detail the table ordering and payment convergence system and its operation method, which comprehensively provide non-face-to-face menu information access using QR codes or NFC tags attached to tables, real-time order creation, pre / post-payment processing, and automatic order status notification functions.

[0096] FIG. 4 is a flowchart illustrating a representative server control method of the present disclosure.

[0097] A server (100) for processing an order operation via a QR code attached to a table according to the present disclosure may include a communication module (110), a memory (170) storing at least one process for performing an order operation via a QR code (400) attached to a table, and a processor (180) that performs an order operation via a QR code attached to a table based on the at least one process.

[0098] Referring to FIG. 4, the processor (180) responds to a request for a page that allows ordering a menu through a QR code provided on a store table at a mobile terminal, and transmits the page to the mobile terminal through the communication module (S410).

[0099] Afterward, the processor (180) can receive selected menu information from the mobile terminal (200) once the menu selection is completed through the page (S420).

[0100] Afterwards, when the prepayment processing is completed or the postpayment method is selected at the mobile terminal, the processor (180) can transmit the selected menu information to the store terminal (300) through the communication module (110) (S430).

[0101] FIG. 5 is a flowchart illustrating a control method of a system including a mobile terminal, a server, and a store terminal of the present disclosure.

[0102] Referring to FIG. 5, the mobile terminal (200) can scan a QR code provided on a table and request a menu page from the server (100) (S510). At this time, the mobile terminal (200) can transmit table information including the table number of the scanned QR code to the server (100).

[0103] The server (100) can identify a table number and transmit different menu pages to a mobile terminal (200) based on the identified table number.

[0104] FIG. 6 is a conceptual diagram showing different order pages for each table of the present disclosure being displayed on a mobile terminal.

[0105] As illustrated in FIG. 6(a), when the processor (180) receives a request for a page that allows menu ordering through a first QR code (600a) provided in a first table at the mobile terminal (200), it can transmit a first order page (610a) to the mobile terminal (200).

[0106] Additionally, when the processor (180) receives a request for a page that allows menu ordering through a second QR code (600b) provided in a second table different from the first table at the mobile terminal (200), it can transmit the second order page (610b), which is different from the first order page (610a), to the mobile terminal (200).

[0107] For example, the processor (180) may receive first table information when a page request is received via the first QR code (600a), and receive second table information when a page request is received via the second QR code (600b). In the memory (170) of the server (100), table location information may be stored for each table information.

[0108] The server (100) of the present disclosure can transmit different menu pages to a mobile terminal depending on the table location. For example, the processor (180) can transmit a menu page that allows coffee ordering to a mobile terminal when the first table is a table located on a terrace, and transmit a menu page that allows meal ordering to a mobile terminal when the second table is a table located inside a restaurant.

[0109] Meanwhile, the processor (180) can transmit a menu page containing multiple tabs distinguished by multiple categories within the menu page to a mobile terminal. At this time, when a request is received through a first QR code provided in a first table, the processor (180) can select a first tab (e.g., a tab where coffee menu orders are possible) and transmit it to the mobile terminal, and when a request is received through a second QR code provided in a second table, the processor (180) can select a second tab (e.g., a tab where meals can be ordered) and transmit it to the mobile terminal.

[0110] Returning to Fig. 5, when a menu page is received from the server (100), the mobile terminal (200) can complete the prepayment process or select post-payment after the menu is selected (S530). In this case, the mobile terminal can transmit order information to the server (100).

[0111] The server processor (180) can transmit order information to the store terminal (S540). The store terminal may be equipped with a communication module capable of communication and may include a printer or display capable of checking menu information. The store terminal may include the same or similar configuration as the mobile terminal (200) described in FIG. 3.

[0112] The store terminal can transmit order status information (e.g., received, cooking, ready) to the server (100) (S550). The processor (180) of the server (100) can transmit the order status information to the mobile terminal (200) so that it is displayed in the form of a notification window or on an order page (S560).

[0113] The processor (180) of the server (100) can receive information about the table where the QR code was scanned and information about the mobile terminal from the mobile terminal through the communication module when the QR code attached to the table is scanned through the mobile terminal.

[0114] The processor (180) can transmit menu information selected through the mobile terminal to the store terminal in conjunction with the table information.

[0115] The processor (180) can receive order status information regarding the selected menu information from the store terminal and transmit the received order status information to the mobile terminal based on the mobile terminal information.

[0116] Hereinafter, we will examine in more detail the function / operation / control method of the order processing server via the QR code attached to the table of the present disclosure. It will be obvious that the mobile terminal (200) and the store terminal (300) can operate to correspond (or respond) to the content described based on the server (100).

[0117] The processor (180) may include a pre-trained artificial intelligence model (181) that takes as input values ​​at least one of table information scanned by a QR code through the communication module, table usage patterns, order time, and dwell time, and outputs a recommended menu as an output value. The artificial intelligence model (181) may be the artificial intelligence model described in this specification.

[0118] For example, the artificial intelligence model (181) may be a supervised learning model that takes at least one of the table information where the QR code is scanned, the usage pattern per table, the order time, and the time spent as input values, and takes the correct answer recommendation menu as output values.

[0119] The processor (180) is a table context recognition and intelligent ordering system:

[0120] By utilizing a real-time table situation recognition algorithm, optimized services can be provided by analyzing usage patterns, order times, and dwell times for each table.

[0121] For example, the processor (180) can recommend a customized menu based on the table location, such as suggesting a differentiated menu based on the table location within the store. The processor (180) can suggest a differentiated menu based on the table location within the store (window, indoor, terrace, etc.).

[0122] For example, the processor (180) can calculate a table recommendation score through the following formula and determine a recommended menu based on the calculated table recommendation score.

[0123] Table Recommendation Score (T_s) = α × (Past Order Similarity) + β × (Table Location Fit) + γ × (Time of Day Popularity)

[0124] Here, α, β, and γ are weighting coefficients, which are automatically optimized based on store-specific data.

[0125] Meanwhile, the processor (180) can provide a hybrid authentication and security enhancement system. For example, the processor (180) can perform authentication by utilizing a multi-authentication protocol of a hybrid authentication method that combines the advantages of QR codes and NFC tags.

[0126] Additionally, the processor (180) can provide security based on one-time tokens with enhanced security through the generation of a unique one-time token for each order session.

[0127] Additionally, the processor (180) may perform digital signature-based order verification to ensure order integrity through cryptographic verification of order data. The following formulas may be used for session tokens and order verification.

[0128] Session Token(S_t) = Hash(Table ID + Timestamp + Random Number)

[0129] OrderVerification(O_v) = DigitalSign(OrderData + S_t)

[0130] Meanwhile, the artificial intelligence model (181) can be supervised to receive order pattern information by time period and table as input to the previously trained artificial intelligence model, and to output future demand prediction information from the artificial intelligence model.

[0131] The processor (180) can input order pattern information by time period and table by time period as an input value to the previously trained artificial intelligence model and receive future demand forecast information as an output value from the artificial intelligence model.

[0132] The processor (180) can provide an integrated store management interface.

[0133] The processor (180) can perform real-time table status monitoring to check the order status and payment status of all tables in the store at a glance, and can provide a smart table allocation algorithm that allocates tables by considering reservations, the current number of customers, order patterns, etc.

[0134] The processor (180) can perform dynamic menu management by adjusting menu availability in real time according to inventory status, time zone, and special events.

[0135] Table efficiency scores and inventory-based menu availability can be defined as follows.

[0136] Table Efficiency Score (T_e) = (Sales Contribution + Turnover + Customer Satisfaction) / 3

[0137] Inventory-based menu availability(M_a) = if(current_stock - estimated_consumption > safety_stock) then available else unavailable

[0138] Meanwhile, the processor (180) receives user pattern information for selecting a menu through the page on the mobile terminal and can dynamically change the user interface included in the page based on the received user pattern information.

[0139] The processor (180) can provide an adaptive user interface and a personalization engine. For example, the processor (180) can analyze user behavior patterns by analyzing menu navigation patterns, preferred categories, order speed, etc. in real time, and can provide a dynamic UI that automatically adjusts menu configuration, button placement, information display method, etc. according to user behavior.

[0140] The processor (180) can make intelligent menu recommendations considering personal preferences, seasonality, and inventory status using a customized menu recommendation algorithm, and the following formula may be used.

[0141] User Interest (I_u) = Σ(Category_Weight × View_Time × Interaction_Frequency)

[0142] Personalization Score (P_s) = (I_u + Purchase History + Time Zone Popularity) / Normalization Factor

[0143] Meanwhile, the processor (180) can control the communication module to predict the cooking time based on at least one of the current kitchen situation, the menu preparation process, and the past order history from the store terminal, and to transmit order status information including the predicted cooking time to the mobile terminal.

[0144] The processor (180) can provide an intelligent order status management and prediction system.

[0145] To this end, the processor (180) can predict an accurate cooking time based on the current kitchen situation, menu complexity, and past data using an order processing time prediction algorithm.

[0146] Additionally, the processor (180) can provide step-by-step notifications by analyzing the importance of each order status and sending notifications only at the necessary time (or a preset time).

[0147] In addition, the processor (180) can visualize the order status by representing the real-time order processing process in an intuitive graphic.

[0148] The function for determining the estimated cooking time and sending notifications can be defined as follows.

[0149] Estimated Cooking Time (E_t) = Base Cooking Time + (Kitchen Load Factor × Weight) + (Menu Complexity × Weight)

[0150] Notification_Send_Decision_Function(N_d) = if(Status_Importance > User_Set_Threshold) then Send_Notification else Hold_Notification

[0151] Meanwhile, the processor (180) can process the payment by integrating the first menu information and the second menu information after receiving the first menu information, table information, and mobile terminal information from the mobile terminal in a post-payment method, and then receiving the second menu information along with the same table information and mobile terminal information.

[0152] Meanwhile, the processor (180) can provide an efficient data synchronization and offline operation mechanism.

[0153] The processor (180) can perform gradual data synchronization by optimizing bandwidth by prioritizing the transmission of important data according to network conditions.

[0154] The processor (180) can provide an offline order queue that automatically synchronizes upon reconnection after receiving and temporarily storing orders even in the event of network connection problems.

[0155] The processor (180) can perform local data caching to improve response speed by caching frequently accessed menu information, images, etc. locally.

[0156] Synchronization priority and cache retention determination methods can be defined as follows.

[0157] Synchronization Priority (S_p) = (Data_Importance × 3) + (Change_Frequency × 2) + User_Request_Frequency

[0158] Decide to keep cache (C_d) = if((access_frequency × data_size) > threshold) then keep cache else clear cache

[0159] Meanwhile, the processor (180) can provide a biometric integrated payment system.

[0160] The processor (180) can support multiple biometric recognition by linking with various biometric recognition technologies such as fingerprint, face recognition, and iris scanning.

[0161] The processor (180) can perform tokenized biometric data processing to perform secure authentication using a one-time token instead of actual biometric data.

[0162] The processor (180) can provide a composite authentication process that can enhance security through a combination of biometric recognition and existing authentication methods.

[0163] The authentication confidence score and payment approval function can be defined as follows.

[0164] Authentication Reliability Score (A_s) = Σ(Authentication_Method_Weight × Authentication_Success)

[0165] Payment approval function(P_a) = if(A_s ≥ security_level_threshold) then approve else request_additional_authentication

[0166] Meanwhile, the processor (180) can provide an advanced order statistics and analysis system.

[0167] The processor (180) can perform real-time order pattern analysis that automatically analyzes order patterns by time period, table, and customer, and can perform prediction-based inventory management that can predict future demand and optimize inventory based on past order data.

[0168] The processor (180) may further include a customized promotion generation engine capable of making personalized discount and promotion suggestions using analyzed data.

[0169] The demand forecasting function and the forecast of promotion effects can be defined as follows.

[0170] Demand forecasting function(D_f) = Σ(Past_order_pattern × Seasonality_coefficient × Special_day_coefficient)

[0171] Promotion Effect Prediction (P_e) = (Expected Additional Sales - Discount Cost) × Customer Response Probability

[0172] Meanwhile, the processor (180) can provide a blockchain-based order-payment verification system.

[0173] The processor (180) can perform distributed ledger-based order recording to prevent tampering by recording order and payment history on the blockchain.

[0174] The processor (180) can perform an automatic settlement method utilizing a smart contract that automatically performs payment and settlement processing according to preset conditions.

[0175] The processor (180) can perform transparent order history management to provide an order history that both the customer and the store can trust.

[0176] The order block hash and smart contract execution conditions can be defined as follows.

[0177] Order Block Hash(O_h) = Hash(Previous_Block_Hash + Order_Data + Timestamp + Nonce)

[0178] Smart contract execution condition(S_c) = if(order_status == "Completed" && verification_success) then payment_execution else hold

[0179] According to the means for solving the problem described above in the present disclosure, the present disclosure provides an optimized table ordering interface and a differentiated payment method based on prepayment and postpayment, thereby providing a method that makes additional orders more convenient when making postpayment and allows for payment integrated with prepayment.

[0180] The present disclosure can provide recommended menus close to user preferences based on the order time and dwell time of users per table using an artificial intelligence model, and can predict future demand based on order pattern information per table.

[0181] The effects of the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below.

[0182] In the present disclosure, the operation / function / control method performed by the artificial intelligence model (182) can also be understood as being performed by the processor (180).

[0183] Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium that stores instructions executable by a computer. The instructions may be stored in the form of program code and, when executed by a processor, may generate a program module to perform the operation of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

[0184] Computer-readable recording media include all types of recording media that store instructions that can be decoded by a computer. Examples include ROM (Read Only Memory), RAM (Random Access Memory), magnetic tape, magnetic disk, flash memory, optical data storage devices, etc.

[0185] As described above, the disclosed embodiments have been explained with reference to the attached drawings. Those skilled in the art will understand that the present disclosure may be practiced in forms different from the disclosed embodiments without changing the technical spirit or essential features of the present disclosure. The disclosed embodiments are illustrative and should not be interpreted restrictively.

Claims

Claim 1 Communication module; memory storing at least one process for performing an order operation via a QR code attached to a table;The system includes a processor that performs an ordering operation via a QR code attached to a table based on at least one of the above processes, wherein the processor responds to a request for a page where a menu can be ordered via a QR code provided on a store table from a mobile terminal and transmits the page to the mobile terminal via the communication module, receives menu information selected via the page from the mobile terminal, and when prepayment processing is completed or a post-payment method is selected from the mobile terminal, transmits the selected menu information to a store terminal via the communication module, wherein the processor determines the location of the table based on a table number identified via the QR code, transmits a first page in which a tab allowing coffee menu ordering is activated among a plurality of tabs included in the page if the table is located on a terrace, and transmits a second page in which a tab allowing meal ordering is activated among the plurality of tabs if the table is located inside the store, and proposes a differentiated recommended menu according to the location of the table, and calculates a table recommendation score calculated by applying weights to past order similarity, table location suitability, and popularity by time of day, respectively, based on the following mathematical formula 1, and analyzes the menu search pattern on the mobile terminal in real time and the user according to the following mathematical formula 2 A server for processing order actions via a QR code attached to a table, characterized by calculating interest and a personalized score according to the following mathematical formula 3, and recommending an intelligent customized menu by combining the table recommendation score and the personalized score, wherein [Mathematical Formula 1] Table Recommendation Score = α × (Past Order Similarity) + β × (Table Location Suitability) + γ × (Popularity by Time of Day), where α, β, and γ are weighting coefficients automatically optimized through a real-time table situation recognition algorithm, [Mathematical Formula 2] User Interest = Σ(Category_Weight × View_Time × Interaction_Frequency), [Mathematical Formula 3] Personalized Score = (User Interest + Purchase_History + Time of Day Popularity) / Normalization_Coefficient. Claim 2 In claim 1, the processor is a server for processing orders via a QR code attached to a table, which receives table information where the QR code was scanned and mobile terminal information from the mobile terminal through the communication module when the QR code attached to the table is scanned through the mobile terminal. Claim 3 In claim 2, the processor is an order operation processing server via a QR code attached to a table, which transmits menu information selected through the mobile terminal in conjunction with the table information to the store terminal. Claim 4 In claim 3, the processor is an order operation processing server via a QR code attached to a table, which receives order status information regarding the selected menu information from the store terminal and transmits the received order status information to the mobile terminal based on the mobile terminal information. Claim 5 In claim 1, the processor comprises a pre-trained artificial intelligence model that takes as input value at least one of table information scanned by the QR code through the communication module, table-specific usage patterns, order time, and dwell time, and outputs a recommended menu as an output value. Claim 6 In claim 5, the processor is an order operation processing server via a QR code attached to a table, which inputs table-specific order pattern information by time period as an input value to the previously trained artificial intelligence model and receives future demand forecast information as an output value from the artificial intelligence model. Claim 7 In claim 1, the processor receives user pattern information for selecting a menu through the page on the mobile terminal, and dynamically changes the user interface included in the page based on the received user pattern information, an order operation processing server via a QR code attached to a table. Claim 8 In claim 1, the processor is an order operation processing server via a QR code attached to a table, which predicts a cooking time based on at least one of the current kitchen situation, the menu preparation process, and past order history from the store terminal, and controls the communication module to transmit order status information including the predicted cooking time to the mobile terminal. Claim 9 In claim 1, the processor is an order operation processing server via a QR code attached to a table, which processes payment by integrating the first menu information and the second menu information when, after receiving the first menu information, table information, and mobile terminal information from the mobile terminal in a post-payment method, the second menu information is additionally received along with the same table information and mobile terminal information. Claim 10 A step of transmitting said page to said mobile terminal via a communication module in response to a request for a page that allows ordering a menu via a QR code provided on a store table from a mobile terminal; a step of receiving menu information selected through said page on said mobile terminal; The method includes the step of transmitting selected menu information to a store terminal via the communication module when prepayment processing is completed or a postpayment method is selected at the mobile terminal, and the step of transmitting the page comprises determining the location of the table based on the table number identified through the QR code, transmitting a first page in which the tab allowing coffee menu ordering is activated among a plurality of tabs included in the page if the table is located on the terrace, and transmitting a second page in which the tab allowing meal ordering is activated among the plurality of tabs if the table is located inside the store, and proposing a differentiated recommended menu according to the location of the table, calculating a table recommendation score by applying weights to past order similarity, table location suitability, and popularity by time of day based on the following mathematical formula 1, analyzing the menu search pattern on the mobile terminal in real time to calculate user interest according to the following mathematical formula 2 and a personalization score according to the following mathematical formula 3, and recommending an intelligent customized menu by combining the table recommendation score and the personalization score, wherein [Mathematical Formula 1] Table Recommendation Score = α × (Past Order Similarity) + β × (Table Location Suitability) + γ × (Popularity by time period) where α, β, and γ are weighting coefficients automatically optimized through a real-time table situation recognition algorithm, [Equation 2] User Interest = Σ(Category_Weight × View_Time × Interaction_Frequency) [Equation 3] Personalization Score = (User Interest + Purchase_History + Time-Period_Popularity) / Normalization_Coefficient, a control method for a server processing order operations via a QR code attached to a table.

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