System

Abstract

A system is provided.SOLUTION: The system includes a means for managing a seat reservation using the generated artificial intelligence, a means for allowing a user to reserve a seat online, a means for analyzing a congestion situation of a cafe in real time, and a means for managing reservation information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Many cafe customers today spend long hours at cafes due to remote work or schoolwork. However, this often leads to issues with seating availability, waiting times for orders, and staff allocation. These issues not only reduce customer comfort but also hinder efficient operation of cafes. There is also an increasing need to respond to peak demand times and provide services customized for each user. There is a need to solve these issues and improve cafe profitability and the quality of customer service while allowing cafe customers to spend time comfortably. [Means for solving the problem]

[0005] This invention utilizes the generated AI to enable so-called cafe refugees, such as freelancers, remote workers, and students, to spend time comfortably in cafes, while at the same time improving the profitability of cafes and the quality of customer service. Specifically, it provides the following means:

[0006] The generated AI will be used to manage seat reservations, providing users with a means to reserve seats online and a means to analyze the cafe's occupancy status in real time, allowing users to reserve seats in advance and cafes to optimize seat usage.

[0007] The generated artificial intelligence is used to analyze a user's past ordering history and provide a means to suggest personalized menu items for each user, thereby speeding up the ordering process and reducing waiting times.

[0008] The generated AI is used to analyze customer visit data, identify peak times based on the visit data, and provide a means to optimize staff shifts according to the peak times, thereby ensuring efficient staff allocation and faster service.

[0009] Each table will have a touchscreen, providing a way for customers to order and pay, as well as a way to calculate and display the total after an order is completed, reducing wait times and increasing table turnover.

[0010] These measures allow cafe customers to spend time in a comfortable environment, and allow the cafe to operate efficiently and profitably.

[0011] "Generated artificial intelligence" is software that has the ability to analyze user behavior patterns and store visit data and automatically perform specific tasks.

[0012] The "means for managing seat reservations" is a system that receives online reservation requests from users and reserves available seats.

[0013] The "means of analyzing the congestion situation of a cafe in real time" is a system that monitors the number of customers in a cafe and the seat usage status in real time and analyzes the data.

[0014] The "means for analyzing the user's past order history" is a system that collects data on orders placed by the user in the past and identifies preferences and patterns based on that data.

[0015] The "means for proposing personalized menus" is a system that automatically proposes menus suitable for a user based on the user's order history and preferences.

[0016] The "means for analyzing customer visit data" is a system that collects data on customers visiting the cafe and analyzes it to identify patterns and trends.

[0017] The "means for identifying peak times" is a system that identifies specific time periods when the number of customers visiting a store is concentrated, based on store visit data.

[0018] "Means for optimizing staff shifts" refers to a system that adjusts staff work schedules optimally according to the number of customers visiting the store, such as during peak times.

[0019] "A means for users to order and pay by installing a touch screen" refers to a system in which users can directly order from the menu and make payments using a touch screen installed on each table.

[0020] The "means for calculating and displaying the total amount after an order is completed" is a system that automatically calculates the total amount of the order placed by the user and displays it on the touch screen. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0023] First, the terms used in the following description will be explained.

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] A specific embodiment for implementing this invention will now be described. This invention utilizes the generated artificial intelligence to implement the following four main functions in order to provide a comfortable and efficient environment for cafe users and cafe management.

[0043] 1. Seat reservation and management system

[0044] The server manages seat reservations within the cafe. Users can check seat availability online and request a reservation. The server analyzes the congestion situation in real time and reserves an available seat. The reservation information includes the user ID, reservation time, and reserved seat number. This allows users to reserve a seat in advance and allows the cafe to optimize seat usage.

[0045] For example, if a user requests a reservation online for "2 hours from 08:00", the server checks the seat availability for that time period and provides an available seat. The user can then start working at that seat.

[0046] 2. Customized Order Process

[0047] The server analyzes the user's past order history and generates a personalized menu, allowing the user to quickly order items that suit their preferences. For example, if User A frequently ordered "latte" in the past, the server will recommend "latte" the next time the user orders.

[0048] For example, when User B visits a store, the server recommends "cappuccino" based on the user's previous order history, allowing User B to place the order quickly. This reduces waiting time and makes the ordering process smoother.

[0049] 3. Peak time analysis and staff shift optimization

[0050] The server analyzes customer visit data to identify peak and off-peak times, and then optimizes staff shifts based on this information. The server allocates more staff during peak times and fewer staff during off-peak times.

[0051] For example, if data analysis reveals that peak hours are between 9:00 and 11:00, shift A (8:00 and 12:00) can be staffed with more staff during this time period, resulting in faster service and improved customer satisfaction.

[0052] 4. Introducing smart tables

[0053] Each table is equipped with a touchscreen, which users can use to order and pay. The order is immediately sent to the server and added to the order list. Payment is also processed on the touchscreen, and the total amount is displayed. This allows users to complete all operations without leaving their table.

[0054] For example, if user C uses the touchscreen to order a sandwich and coffee, the order is immediately sent to the server and added to the order list. When paying, the total amount is displayed on the touchscreen, allowing user C to complete the payment immediately.

[0055] These features allow cafe customers to spend their time comfortably and efficiently, and cafes can improve their profitability and customer service quality. This invention is extremely useful in modern cafe culture, and brings great benefits to both customers and cafe operators.

[0056] The processing flow will be explained below.

[0057] Seat reservation and management system

[0058] Step 1:

[0059] Users search online for available seats at cafes and enter the desired date and time.

[0060] Step 2:

[0061] The server checks the seat availability in the system based on the provided date and time. The server analyzes the seat availability in real time.

[0062] Step 3:

[0063] The server presents the available seats to the user, who then selects the seat they desire.

[0064] Step 4:

[0065] After the user selects a seat, the server marks the seat as reserved and saves the reservation information in a database.

[0066] Step 5:

[0067] The server sends the reservation confirmation information (reservation number, seat number, date and time) to the user, who receives the reservation confirmation.

[0068] Customized Order Process

[0069] Step 1:

[0070] A user places an order at a cafe by selecting the items they want to order via a touchscreen or an application.

[0071] Step 2:

[0072] The server checks the user's past order history and analyzes the user's preferences and purchasing patterns based on the past order data.

[0073] Step 3:

[0074] Based on the analysis results, the server proposes a personalized menu suitable for the user, and the user decides to order from the proposed menu.

[0075] Step 4:

[0076] Once the user places an order, the server sends it to the kitchen or barista, along with the order details (items, quantities, special requests, etc.).

[0077] Step 5:

[0078] The server stores the user's new order information in a database as an order history, which will improve personalized suggestions next time.

[0079] Peak time analysis and staff shift optimization

[0080] Step 1:

[0081] The server collects data on past visits to the cafe, including information such as the time of visit, the number of customers, and the items purchased.

[0082] Step 2:

[0083] The server analyzes the collected visitor data, identifies peak times (time periods with the highest number of customers), and uses data analysis algorithms to analyze visitor patterns.

[0084] Step 3:

[0085] The server optimizes staff shifts based on peak time information, deploying more staff during peak times and fewer staff during off-peak times.

[0086] Step 4:

[0087] The server notifies the staff of the optimized shift schedule, and the staff work according to the new shift schedule.

[0088] Step 5:

[0089] The server continuously collects actual store visit data and dynamically adjusts shift schedules, allowing for flexible response to fluctuations in peak times.

[0090] Introducing smart tables

[0091] Step 1:

[0092] The user sits at a table in a cafe and checks the menu using a touchscreen mounted on the table.

[0093] Step 2:

[0094] The user places an order via the touchscreen, where the order is selected and each item is added to the order list.

[0095] Step 3:

[0096] When the user confirms the order, the terminal (touch screen) sends the order information to the server, which receives the order information.

[0097] Step 4:

[0098] The server sends the order to the kitchen or barista, including the order details (item, quantity, special requests, etc.).

[0099] Step 5:

[0100] When the product ordered by the user is ready, a notification will appear on the touchscreen, and the user can confirm the notification and receive the product.

[0101] Step 6:

[0102] After the user has finished their meal, they pay on the touchscreen, and the server calculates the total amount and displays it on the touchscreen.

[0103] Step 7:

[0104] Once the user has completed the payment, the server resets the order list and prepares it for a new order.

[0105] Example 1

[0106] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0107] Traditional cafes often lack efficiency in many areas, such as seat reservations, personalized orders, and staff shift management. In particular, systems to ensure users' comfort in the cafe are inadequate, making it difficult to manage crowds and speed up user orders.

[0108] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0109] In this invention, the server includes means for managing seat reservations using the generated artificial intelligence, means for users to reserve seats online, means for analyzing the cafe's congestion status in real time, means for managing reservation information, means for generating personalized menus based on users' past ordering history, and means for ordering and paying using the touch screen of the smart table, thereby enabling efficient seat management, providing personalized service to users, and efficient operation even when the cafe is crowded.

[0110] "Generated artificial intelligence" is an intelligent system that analyzes users' past behavior and the situation within the cafe, and is used to manage seats and optimize orders.

[0111] "Seat reservation" refers to the act of a user reserving a seat in a cafe online in advance.

[0112] "Cafe congestion status" refers to the number of customers in the cafe and the number of available seats shown in real time.

[0113] "Reservation information" refers to data relating to a seat reservation, including a user ID, reservation time, and seat number.

[0114] A "personalized menu" refers to a special menu that is individually provided based on a user's past ordering history and preferences.

[0115] A "smart table" is a table installed in a cafe that has touchscreen functionality and is a device that allows users to order and pay.

[0116] "Touchscreen" refers to an input device that a user can operate by touching the screen.

[0117] "Order history" refers to a record of orders a user has made at a cafe in the past.

[0118] "Peak time" refers to the time period when the number of customers in the cafe is particularly high.

[0119] "Staff shift optimization" refers to making adjustments to deploy staff most effectively based on store visit data and peak time analysis.

[0120] MODE FOR CARRYING OUT THE INVENTION

[0121] This invention utilizes the generated artificial intelligence to implement the following four main functions in order to provide a comfortable and efficient environment for cafe users and cafe operators.

[0122] 1. Seat reservation and management system

[0123] The server manages seat reservations within the cafe. Users access a dedicated reservation system online and request a seat reservation by entering the desired date and time. The server analyzes the congestion situation in real time based on data from sensors installed on the seats within the cafe and reserves an available seat. Reservation information, including the user ID, reservation time, and reserved seat number, is saved in a database.

[0124] For example, if a user requests a reservation online for "2 hours from 08:00", the server checks the availability of seats during that time slot and provides an available seat. A confirmation message is sent to the user, and the user can begin working at that seat.

[0125] Example prompt sentence:

[0126] "I'd like to reserve a seat at a cafe for two hours starting at 8 o'clock."

[0127] 2. Customized Order Process

[0128] The server analyzes the user's past order history and generates a personalized menu using a generative AI model. This generative AI model creates a recommendation list taking into account the user's preferences and order frequency. When the user visits the cafe, they can view the personalized menu on their device (tablet or smartphone).

[0129] For example, if user A has frequently ordered "latte" in the past, the server will display a screen recommending "latte" the next time the user orders, allowing the user to complete their order smoothly.

[0130] Example prompt sentence:

[0131] I'd like to order the same drink as last time.

[0132] 3. Peak time analysis and staff shift optimization

[0133] The server collects store visit data and uses generative AI models to identify peak and off-peak times, leveraging information from point-of-sale systems and sensor data. Once peak times are identified, the server generates recommendations to optimize staff shifts for those times.

[0134] For example, if an analysis of customer visit data reveals that peak hours are between 9:00 and 11:00, the server will create a shift plan that allocates more staff to these hours, thereby speeding up service and improving customer satisfaction.

[0135] Example prompt sentence:

[0136] "I'd like you to analyze whether the number of customers is high or low between 9:00 and 11:00."

[0137] 4. Introducing smart tables

[0138] Each table in the cafe is equipped with a touchscreen, which customers can use to order and pay. When a customer enters their order on the touchscreen, the information is sent to the server in real time and added to the order list. Payment is also processed through the touchscreen, where the total amount is displayed and payment can be completed.

[0139] For example, if User C uses the touchscreen to order a sandwich and coffee, this order information is immediately sent to the server and notified to the kitchen staff. When paying, the total amount is displayed on the touchscreen, and User C can complete the payment on the spot.

[0140] Example prompt sentence:

[0141] "I want to order a sandwich and coffee from the touchscreen at my table."

[0142] These features allow cafe customers to spend their time comfortably and efficiently, and cafe operators can improve their profitability and customer service quality. This invention is extremely useful in modern cafe culture, and brings great benefits to both customers and cafe operators.

[0143] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0144] Seat reservation and management system

[0145] Step 1: Check seat availability

[0146] The server collects data from sensors installed on the seats and checks the seat availability in real time. It takes the sensor data as input and stores the status of each seat as "vacant" or "occupied" in a database.

[0147] Specific behavior:

[0148] If the sensor reports the occupancy of seat A as "vacant", the server updates the status as "vacant" in the database.

[0149] Input: Seat status data from sensors

[0150] Output: Updated seat state database

[0151] Step 2: Accepting a booking request

[0152] A user accesses the online reservation system using a terminal, inputs the desired date and time, and submits a reservation request. The reservation request input on the terminal is sent to the server.

[0153] Specific behavior:

[0154] When a user enters a reservation for "2 hours from 8:00" and presses the send button, the information is sent to the server.

[0155] Input: Reservation request information from user (date and time)

[0156] Output: Booking request accepted on server side

[0157] Step 3: Secure and confirm your seat

[0158] The server searches the database for available seats for the specified date and time based on the received reservation request. If an available seat is found, the server confirms the reservation and sends a confirmation message to the user.

[0159] Specific behavior:

[0160] The server searches the database and confirms that seat B is available and the reservation is confirmed, after which a confirmation message is sent to the user.

[0161] Input: Database search based on reservation request

[0162] Output: Confirmed reservation information and a confirmation message

[0163] Customized Order Process

[0164] Step 1: Analyze your order history

[0165] The server retrieves past order history from the database and uses a generative AI model to generate a personalized menu for each user. The order history data is used as input and analyzed using the AI ​​model to create a recommendation list.

[0166] Specific behavior:

[0167] The server analyzes User A's past order history and determines that he frequently orders "lattes."

[0168] Input: User's order history data

[0169] Output: A personalized menu recommendation list

[0170] Step 2: Offer a personalized menu

[0171] When the user checks the menu on the terminal, the server displays the generated personalized menu. The user checks the menu provided to the terminal.

[0172] Specific behavior:

[0173] The server displays a recommendation menu including latte on User A's device.

[0174] Input: Personalized Menu Recommendation List

[0175] Output: Personalization menu displayed on device

[0176] Peak time analysis and staff shift optimization

[0177] Step 1: Collect store visit data

[0178] The server collects data on the number of customers and time periods from the POS system and sensors, and stores the collected data in a database.

[0179] Specific behavior:

[0180] The server retrieves the number of customers from 9:00 to 11:00 from the POS system.

[0181] Input: POS system and sensor data

[0182] Output: Updated store visit database

[0183] Step 2: Data analysis and shift optimization

[0184] The server analyzes the collected store visit data using a generative AI model to identify peak and off-peak times, and generates suggestions for optimizing staff shifts based on the identified peak times.

[0185] Specific behavior:

[0186] The server analyzes that "9:00 to 11:00" is the peak time and generates shift suggestions.

[0187] Input: Store visit data

[0188] Output: Peak time information and shift optimization suggestions

[0189] Introducing smart tables

[0190] Step 1: Enter your order on the touchscreen

[0191] Users input their orders using a touchscreen installed on the smart table, and the information is sent to the server in real time.

[0192] Specific behavior:

[0193] User C orders a "sandwich" and a "coffee" on the touchscreen.

[0194] Input: User's order information

[0195] Output: Order information sent to the server

[0196] Step 2: Add to order list

[0197] The server processes the received order information and adds it to the order list, which is then notified to the kitchen staff.

[0198] Specific behavior:

[0199] The server adds "sandwich" and "coffee" to the order list and notifies the kitchen.

[0200] Input: User's order information

[0201] Output: Updated order list and notification to the kitchen

[0202] Step 3: Payment Processing

[0203] The user pays using the touchscreen, and the server processes the payment information and displays the total amount.

[0204] Specific behavior:

[0205] User C enters credit card information on the touchscreen and completes the payment.

[0206] Input: User's payment information

[0207] Output: Total amount and payment completion status

[0208] (Application example 1)

[0209] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0210] In today's cafe operations, it is extremely important for users to be able to smoothly reserve seats, quickly place personalized orders, and optimize staff shifts during peak times. However, existing systems have difficulty comprehensively resolving these issues. In particular, the lack of integration with smart devices and real-time service quality maintenance reduces user experience and operational efficiency. Therefore, a comprehensive system is needed to improve cafe operational efficiency and user satisfaction.

[0211] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0212] In this invention, the server includes means for managing seat reservations using the generated artificial intelligence, means for allowing users to reserve seats online, means for analyzing the cafe's congestion status in real time, means for managing orders and payments using smart devices, means for analyzing peak times and optimizing staff shifts, means for integrating and managing order histories through smart devices, and means for maintaining the quality of service provided using smart devices, thereby enabling cafe customers to smoothly reserve seats and quickly order from personalized menus, and further improving overall operational efficiency and service quality through the integrated smart device system.

[0213] "Generated artificial intelligence" is an AI system developed to analyze user behavior and cafe operation data, and make individual service suggestions and optimize operations.

[0214] The "means for managing seat reservations" is a system that allows users to reserve seats at cafes online in advance and efficiently manages the reservation information.

[0215] "Means for users to reserve seats online" refers to an application or web service that allows users to check information about available seats at a cafe via the Internet and confirm reservations.

[0216] "A means for analyzing the congestion status of a cafe in real time" is a system that instantly analyzes data such as the number of customers and seat usage status to determine the current level of congestion in the cafe.

[0217] "Means for managing orders and payments using smart devices" refers to a system that allows users to easily place orders and pay using digital devices such as tablets and smartphones.

[0218] The "means of analyzing peak times and optimizing staff shifts" is a system that analyzes store visit data, identifies particularly busy times, and efficiently allocates staff shifts according to those times.

[0219] "Means for integrated management of order history via smart devices" is a system that centrally manages a user's past order history on a digital device and provides personalized services.

[0220] "Means for maintaining the quality of services provided using smart devices" refers to a system for ensuring consistency in customer service using smart devices and increasing customer satisfaction.

[0221] This invention is a system for improving the operational efficiency of cafes and increasing customer satisfaction. This system uses generated artificial intelligence to perform various management and optimization functions.

[0222] Specifically, the following measures will be implemented:

[0223] 1. Seat reservation and management system

[0224] The server allows users to reserve seats online and manages the reservation information. It is possible to analyze the cafe's congestion status in real time and provide information on available seats. This allows users to reserve seats from their smartphones and enjoy a comfortable stay in a reserved seat.

[0225] Example: When a user requests a reservation for "1 hour from 10:00" in the app, the server checks the availability of seats during that time period and reserves seat number 42.

[0226] 2. Customized Order Process

[0227] The server uses the generated AI to analyze the user's past order history and propose personalized menus, allowing users to quickly order items that suit their preferences and reduce waiting times. Order history is managed in an integrated manner through smart devices, making it possible to make personalized suggestions based on past order data.

[0228] Example: Based on User A's past order history, "Latte" and "New Product 1" are recommended.

[0229] 3. Peak time analysis and staff shift optimization

[0230] The server analyzes customer visit data and identifies peak times. Based on this information, it optimizes staff shifts and increases the efficiency of cafe operations. This allows staff to be appropriately allocated according to peak times, enabling the maintenance of service quality.

[0231] Example: The server identifies "08:00-10:00" as peak time and allocates more staff during this time period.

[0232] 4. Integrated management using smart devices

[0233] Users can use smart devices such as smartphones and tablets to reserve seats, place orders, and make payments at the cafe. The server integrates order history and seat information by linking with these smart devices, providing users with seamless service.

[0234] Example: A user places an order using the touchscreen on a smart table and completes payment through a smartphone app.

[0235] These functions are realized by the following hardware and software.

[0236] Hardware: Smartphones, tablets, touchscreens (smart tables)

[0237] Software: Python, Flask, SQLite for database management and AI analysis

[0238] The generative AI model is used to analyze user behavior and cafe operation data to make individual service suggestions and optimize operations.

[0239] Example prompt sentence:

[0240] Please recommend frequently ordered menu items based on User A's past order history.

[0241] + Past Order History:

[0242] latté

[0243] cappuccino

[0244] latté

[0245] + Recommendation results:

[0246] "Latte" (because it's frequently ordered)

[0247] "New product 1"

[0248] "New product 2"

[0249] This allows cafe customers to smoothly reserve seats, quickly order from personalized menus, and improve overall operational efficiency and service quality through an integrated smart device system.

[0250] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0251] Step 1:

[0252] A user makes a seat reservation request using a smartphone. The input includes the user ID, desired date and time, and seat type. The server receives this, checks the seat information in the database in real time, and checks whether there is availability on the specified date and time. If there is availability, the seat is reserved and the information is returned to the user's device. The output is the reservation confirmation information.

[0253] Step 2:

[0254] A user browses past order history to place a customized order on a smart device. The server retrieves the order history from the database based on the user ID and inputs it into a generative AI model. The generative AI model analyzes the order history based on the prompt text and suggests a personalized menu for each user. The input is the user ID, and the output is a list of recommended menu items.

[0255] Step 3:

[0256] The user decides what to order from the proposed menu and sends it to the server via their smart device. The server receives the order information, records it in an order database, and notifies the kitchen in real time. At the same time, payment information is generated and displayed on the user's device. The input is the order information, and the output is payment information and an order confirmation notification.

[0257] Step 4:

[0258] Cafe staff open the shift management screen and check the peak time analysis data from the server. The server analyzes customer visit data from past data sets, identifies peak times, and proposes appropriate shift allocation. Staff adjust their shifts based on the proposal. The input is past customer visit data, and the output is peak times and proposed shift allocation.

[0259] Step 5:

[0260] The user checks the order history and seat reservation information on a smart device. The server manages this data in real time and makes it accessible to the user at any time. The input is the user ID, and the output is the integrated order history and seat reservation information.

[0261] Step 6:

[0262] After placing an order, the user completes payment on their smart device. The payment information is sent to the server, and the payment process is carried out. Once payment is complete, a confirmation notice is sent to the user's device. The input is payment information, and the output is a payment confirmation notice.

[0263] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0264] A specific embodiment for implementing this invention will now be described. This invention utilizes the generated artificial intelligence and emotion engine to implement the following five main functions in order to provide a comfortable and efficient environment for cafe users and cafe management.

[0265] 1. Seat reservation and management system

[0266] The server manages seat reservations within the cafe. Users can check seat availability online and request a reservation. The server analyzes the congestion situation in real time and reserves an available seat. The reservation information includes the user ID, reservation time, and reserved seat number. This allows users to reserve a seat in advance and allows the cafe to optimize seat usage.

[0267] For example, if a user requests a reservation online for "2 hours from 08:00", the server checks the seat availability for that time period and provides an available seat. The user can then start working at that seat.

[0268] 2. Customized Order Process

[0269] The server analyzes the user's past order history and generates a personalized menu, allowing the user to quickly order items that suit their preferences. For example, if User A frequently ordered "latte" in the past, the server will recommend "latte" the next time the user orders.

[0270] For example, when User B visits a store, the server recommends "cappuccino" based on the user's previous order history, allowing User B to place the order quickly. This reduces waiting time and makes the ordering process smoother.

[0271] 3. Peak time analysis and staff shift optimization

[0272] The server analyzes customer visit data to identify peak and off-peak times, and then optimizes staff shifts based on this information. The server allocates more staff during peak times and fewer staff during off-peak times.

[0273] For example, if data analysis reveals that peak hours are between 9:00 and 11:00, shift A (8:00 and 12:00) can be staffed with more staff during this time period, resulting in faster service and improved customer satisfaction.

[0274] 4. Introducing smart tables

[0275] Each table is equipped with a touchscreen, which users can use to order and pay. The order is immediately sent to the server and added to the order list. Payment is also processed on the touchscreen, and the total amount is displayed. This allows users to complete all operations without leaving their table.

[0276] For example, if user C uses the touchscreen to order a sandwich and coffee, the order is immediately sent to the server and added to the order list. When paying, the total amount is displayed on the touchscreen, allowing user C to complete the payment immediately.

[0277] 5. Introducing the Emotion Engine

[0278] The emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotions in real time. The server then uses this emotional data to optimize the user experience. For example, if the user is feeling stressed, it will provide relaxation menus and suggestions.

[0279] For example, if User D visits a cafe and the emotion engine detects stress from User D's facial expression while placing an order on the touchscreen, the server will suggest a "special menu for relaxation." User D can accept this suggestion and spend time in a relaxing environment.

[0280] These features allow cafe customers to spend their time comfortably and efficiently, and cafes can improve their profitability and customer service quality. This invention is extremely useful in modern cafe culture, and brings great benefits to both customers and cafe operators.

[0281] The processing flow will be explained below.

[0282] Seat reservation and management system

[0283] Step 1:

[0284] Users search online for available seats at cafes and enter the desired date and time.

[0285] Step 2:

[0286] The server checks the seat availability in the system based on the provided date and time. The server analyzes the seat availability in real time.

[0287] Step 3:

[0288] The server presents the available seats to the user, who then selects the seat they desire.

[0289] Step 4:

[0290] After the user selects a seat, the server marks the seat as reserved and saves the reservation information in a database.

[0291] Step 5:

[0292] The server sends the reservation confirmation information (reservation number, seat number, date and time) to the user, who receives the reservation confirmation.

[0293] Customized Order Process

[0294] Step 1:

[0295] A user places an order at a cafe by selecting the items they want to order via a touchscreen or an application.

[0296] Step 2:

[0297] The server checks the user's past order history and analyzes the user's preferences and purchasing patterns based on the past order data.

[0298] Step 3:

[0299] Based on the analysis results, the server proposes a personalized menu suitable for the user, and the user decides to order from the proposed menu.

[0300] Step 4:

[0301] Once the user places an order, the server sends it to the kitchen or barista, along with the order details (items, quantities, special requests, etc.).

[0302] Step 5:

[0303] The server stores the user's new order information in a database as an order history, which will improve personalized suggestions next time.

[0304] Peak time analysis and staff shift optimization

[0305] Step 1:

[0306] The server collects data on past visits to the cafe, including information such as the time of visit, the number of customers, and the items purchased.

[0307] Step 2:

[0308] The server analyzes the collected visitor data, identifies peak times (time periods with the highest number of customers), and uses data analysis algorithms to analyze visitor patterns.

[0309] Step 3:

[0310] The server optimizes staff shifts based on peak time information, deploying more staff during peak times and fewer staff during off-peak times.

[0311] Step 4:

[0312] The server notifies the staff of the optimized shift schedule, and the staff work according to the new shift schedule.

[0313] Step 5:

[0314] The server continuously collects actual store visit data and dynamically adjusts shift schedules, allowing for flexible response to fluctuations in peak times.

[0315] Introducing smart tables

[0316] Step 1:

[0317] The user sits at a table in a cafe and checks the menu using a touchscreen mounted on the table.

[0318] Step 2:

[0319] The user places an order via the touchscreen, where the order is selected and each item is added to the order list.

[0320] Step 3:

[0321] When the user confirms the order, the terminal (touch screen) sends the order information to the server, which receives the order information.

[0322] Step 4:

[0323] The server sends the order to the kitchen or barista, including the order details (item, quantity, special requests, etc.).

[0324] Step 5:

[0325] When the product ordered by the user is ready, a notification will appear on the touchscreen, and the user can confirm the notification and receive the product.

[0326] Step 6:

[0327] After the user has finished their meal, they pay on the touchscreen, and the server calculates the total amount and displays it on the touchscreen.

[0328] Step 7:

[0329] Once the user has completed the payment, the server resets the order list and prepares it for a new order.

[0330] Introducing the Emotion Engine

[0331] Step 1:

[0332] When a user enters a cafe, the device's camera captures the user's face, and the emotion engine recognizes their facial expressions and evaluates their emotional state in real time.

[0333] Step 2:

[0334] The server receives data from the emotion engine and analyzes the user's emotional state, determining emotional states such as stress, happiness, fatigue, etc.

[0335] Step 3:

[0336] When a user places an order on a touchscreen, the server uses emotional data to suggest personalized menu items, such as relaxing drinks or healthy foods to reduce stress.

[0337] Step 4:

[0338] It offers special offers and services based on the user's emotional state. If the user is feeling stressed, it may offer special promotions or discounts.

[0339] Step 5:

[0340] The server continuously collects user emotional data and dynamically adjusts the quality of service to optimize the user experience.

[0341] In this way, the system combined with the emotion engine provides personalized services according to the user's needs and emotional state, resulting in a more comfortable and satisfying cafe experience.

[0342] Example 2

[0343] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0344] Cafe operations face a wide range of challenges, including seat reservation management, order efficiency, peak time analysis, staff shift management, and even service optimization based on customer sentiment. Conventional systems have difficulty addressing these challenges in a unified manner, resulting in a decline in customer satisfaction and the operational efficiency of the cafe. The objective of this invention is to provide a system that solves these challenges and provides a comfortable and efficient cafe experience.

[0345] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for managing seat reservations using generated artificial intelligence, a means for allowing users to reserve seats online, a means for analyzing the cafe's congestion status in real time, a means for managing reservation information, a means for ordering and paying using a touch screen, and a means for analyzing user emotions and making suggestions using an emotion engine. This makes it possible to improve the efficiency of cafe management and user satisfaction.

[0346] "Generated artificial intelligence" refers to machine learning models that are trained to automatically collect and analyze data and deliver optimal results for a specific purpose.

[0347] "Means for managing seat reservations" refers to technology that supports the process of users checking seat availability online and making appropriate reservations.

[0348] "Means by which users can reserve seats online" refers to an interface that allows users to reserve seats remotely via devices such as a web browser or smartphone app.

[0349] "Means for analyzing the congestion status of a cafe in real time" refers to a system that analyzes data such as current seat occupancy status and order status in real time to determine the congestion status of a cafe.

[0350] "Means for managing reservation information" refers to a system that stores and manages user reservation details (user ID, reservation time, seat number, etc.) in a database and allows them to be updated and referenced as needed.

[0351] "Means for ordering and paying using a touchscreen" refers to technology that allows users to order and pay for cafe menu items using a touchscreen terminal installed on each table.

[0352] An "emotion engine" refers to an algorithm that analyzes emotions from a user's facial expressions and voice, and then provides appropriate responses and suggestions based on that emotional data.

[0353] "Means for analyzing user emotions and making suggestions" refers to technology that analyzes emotional data obtained by an emotion engine and suggests optimal services and products based on the user's current state.

[0354] "Means for analyzing past order history" refers to technology for analyzing a user's past order data and understanding the user's preferences and trends.

[0355] "Means for suggesting personalized menus" refers to a system for displaying and suggesting menus optimized for each individual user based on past order history and preference data.

[0356] "Means for analyzing customer visit data" refers to technology that collects and analyzes information about visits to a cafe (time, date, number of people, etc.) and identifies patterns of customer behavior.

[0357] "Means for identifying peak times" refers to a system for analyzing store visit data to identify the times when the cafe is most heavily used.

[0358] "Means for optimizing staff shifts" refers to technology that optimizes staff work schedules based on data on peak and off-peak times, achieving efficient personnel deployment.

[0359] MODE FOR CARRYING OUT THE INVENTION

[0360] A specific embodiment for implementing this invention will now be described. This invention utilizes the generated artificial intelligence and emotion engine to implement the following five main functions in order to provide a comfortable and efficient environment for cafe users and cafe management.

[0361] Seat reservation and management system

[0362] The server manages seat reservations within the cafe. Users check seat availability online and request a reservation. Specifically, the user opens the seat reservation page using a web browser or smartphone app and enters the desired time and necessary information. The device then sends this information to the server via the HTTPS protocol. The server accesses the database to check seat availability for the specified time slot and, if there is availability, confirms the reservation information.

[0363] For example, if a user requests a reservation online for "2 hours from 08:00," the server checks availability for that time slot and provides an available seat, allowing the user to begin working at that seat.

[0364] Customized Order Process

[0365] The server analyzes the user's past order history and generates a personalized menu. When a user opens the order page on their smartphone or tablet, the device queries the server for their past order history. The server retrieves the order history from the database and generates a recommended menu using a generative AI model.

[0366] For example, if user A has frequently ordered "latte" in the past, the server can recommend "latte" the next time user A orders, allowing user A to complete the order quickly.

[0367] Peak time analysis and staff shift optimization

[0368] The server analyzes visitor data and identifies peak and off-peak times. The server collects visitor data from the POS system and ordering system and analyzes peak and off-peak times for specific time periods. Data analysis uses aggregation functions and histograms.

[0369] For example, if data analysis reveals that peak hours are between 9:00 and 11:00, shift A (8:00 and 12:00) will be staffed with more staff during this time period to speed up service.

[0370] Introducing smart tables

[0371] Each table is equipped with a touchscreen, which users use to order and pay. Specifically, users use the touchscreen to view the cafe menu, select the items they want, and confirm their order. The terminal instantly transmits the order information to the server, and the total amount is displayed when payment is made.

[0372] For example, if user C uses the touchscreen to order a sandwich and coffee, the order is immediately sent to the server and added to the order list. When paying, the total amount is displayed on the touchscreen, allowing the user to complete the payment immediately.

[0373] Introducing the Emotion Engine

[0374] The emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotions in real time. The camera and microphone installed on the device capture the user's facial expressions and voice, which are then analyzed by the emotion engine. The server then uses the data sent from the emotion engine to suggest appropriate services and products.

[0375] For example, if User D visits a cafe and the emotion engine detects stress in User D's facial expression while placing an order on the touchscreen, the server will suggest a "special menu for relaxation." User D can accept this suggestion and spend time in a relaxing environment.

[0376] Example prompts to input to the generative AI model

[0377] Prompt text (seat reservation and management system): In a cafe seat reservation system, what happens when a user goes online and requests to reserve a seat for 2 hours starting at 08:00?

[0378] Prompt statement (customized ordering process): If User A has frequently ordered lattes in the past, what menu item will they see the next time they order?

[0379] Prompt (Peak time analysis and staff shift optimization): If the cafe's peak time is 09:00-11:00, what will be the staff shifts?

[0380] Prompt (Smart Table Introduction): What happens when a user orders a sandwich and coffee using the touchscreen?

[0381] Prompt (introduction of emotion engine): If the emotion engine detects that the user is stressed, how will the server respond?

[0382] Based on the above explanation, this system can provide cafe users with a comfortable and efficient environment and also realize effective operation management for cafe operators.

[0383] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0384] Seat reservation and management system

[0385] Step 1:

[0386] A user opens a seat reservation page online. As input, the user enters the desired reservation time (e.g., 2 hours from 08:00) and user ID into the reservation form. This sends a seat reservation request to the system.

[0387] Step 2:

[0388] The terminal sends the information entered by the user to the server. The terminal uses the HTTPS protocol to send the entered reservation information (user ID, desired reservation time) to the server. The accuracy of the data is maintained by properly formatting the input data before sending it.

[0389] Step 3:

[0390] The server checks seat availability. The server executes an SQL query against the database to retrieve seat availability for the specified time slot. The availability data returned from the database becomes the input for the server.

[0391] Step 4:

[0392] The server confirms and manages the reservation. If there are available seats in the specified time slot, the server saves the reservation information in the database and confirms the reservation. The reservation information (user ID, reservation time, seat number) is the output data of the server. If there are no available seats, an error message is generated.

[0393] Step 5:

[0394] The terminal receives the server's output data and displays the reservation confirmation information or an error message to the user. Specifically, the reservation number and seat number are displayed on the screen, allowing the user to confirm that the reservation has been confirmed.

[0395] Customized Order Process

[0396] Step 1:

[0397] The user opens the order page. As input, the user launches the ordering app and accesses the order page, which allows the system to prepare an order.

[0398] Step 2:

[0399] The terminal queries the server for past order history. Using the user ID as input, it requests past order data via the REST API. The terminal sends the input data to the server.

[0400] Step 3:

[0401] The server analyzes the order history. It retrieves the user's past order history from the database and analyzes it using a generative AI model. The past order data becomes the input data for the server.

[0402] Step 4:

[0403] The server generates a personalized recommended menu. Based on the generative AI model, it lists the most suitable menu items for the user. The recommended menu data becomes the server's output data.

[0404] Step 5:

[0405] The terminal displays the recommended menu to the user. The terminal receives output data from the server and displays the recommended menu on the screen. This allows the user to quickly select products and confirm their order.

[0406] Peak time analysis and staff shift optimization

[0407] Step 1:

[0408] The server collects store visit data. As input, it periodically obtains store visit data (time, number of people, order details) from the POS system and ordering system. This allows the server to obtain basic data for shift optimization.

[0409] Step 2:

[0410] The server analyzes the visitor data to identify peak and off-peak times. It aggregates the data and performs statistical analysis to analyze visitor patterns during specific time periods. The aggregated data is the input data, and the analysis results are the output data.

[0411] Step 3:

[0412] The server generates an optimal shift schedule. Based on the results of peak and off-peak times, it runs an algorithm to optimize staff shift schedules. The optimized shift schedule is the output data.

[0413] Step 4:

[0414] The device notifies staff of shift information. It receives optimized shift schedule data from the server and sends push notifications to each staff member's device (smartphone or tablet). The notification data is the input, and the displayed shift information is the output.

[0415] Step 5:

[0416] Staff work based on shifts. According to the notified shift information, staff perform their duties according to their respective working hours. This allows for efficient service provision during peak times.

[0417] Introducing smart tables

[0418] Step 1:

[0419] The user operates the touchscreen of the smart table. As input, the user browses the menu and selects the desired item on the touchscreen. This allows the user to prepare an order.

[0420] Step 2:

[0421] The terminal sends the order information to the server. The selected order information is formatted and sent to the server. The order items and user ID are included as input data.

[0422] Step 3:

[0423] The server processes the order information, receives the order information, adds the order to a list in the order processing system, and forwards the order to the kitchen. The processed order information is the output data.

[0424] Step 4:

[0425] The terminal prompts the user to complete the payment process. After the order is confirmed, a payment screen is displayed, allowing the user to select a payment method. The payment method and amount are included as input data.

[0426] Step 5:

[0427] The user completes the payment. The payment procedure is completed on the touch screen using the selected payment method. The payment completion is sent to the server, and the transaction data becomes the output data.

[0428] Introducing the Emotion Engine

[0429] Step 1:

[0430] The device captures the user's facial expressions and voice using a camera and microphone. The capture device takes the user's facial movements and tone of voice as input, which prepares the device for sentiment analysis.

[0431] Step 2:

[0432] The device sends the captured data to the emotion engine. The captured data is sent to the emotion engine in real time. The input data includes facial expressions and voice characteristics.

[0433] Step 3:

[0434] The emotion engine analyzes the emotion data. It analyzes the received data and identifies the user's emotion (e.g., happiness, stress, anger). The analysis results are output data.

[0435] Step 4:

[0436] The server generates suggestions based on the emotion data. Based on the data from the emotion engine, it generates suggestions to optimize the user experience (such as special menus or relaxation suggestions). The suggested data is the output.

[0437] Step 5:

[0438] The device displays the suggestions to the user, receives the suggestion data, and displays it on the touch screen, allowing the user to accept the appropriate suggestion.

[0439] Through the above processing steps, the system can provide efficient and comfortable service to cafe patrons.

[0440] (Application example 2)

[0441] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0442] Traditional cafe operations have not been able to efficiently manage seating or improve customer satisfaction. In particular, it is difficult to grasp the congestion situation of customers and provide personalized service based on each customer's order history. There is also a lack of means to manage staff shifts and analyze customer sentiment in real time to optimize the experience. As a result, customers are likely to have a low-satisfaction experience, and cafe operators also face challenges in providing efficient service.

[0443] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for managing seat reservations using the generated artificial intelligence, means for allowing users to reserve seats online, means for analyzing the cafe's congestion status in real time, means for managing reservation information, means for analyzing the user's facial expressions and voice in real time and recognizing emotions, means for optimizing the user experience based on the recognized emotion data, means for users to easily place orders using a smartphone or a head-mounted display, and means for providing analyzed peak time information to a manager. This enables efficient seat management and improved customer satisfaction.

[0444] "Generated artificial intelligence" is an artificial intelligence system that is automatically generated based on data and designed to perform specific tasks.

[0445] "Means for managing seat reservations" refers to a function that tracks and manages seat reservations in the cafe in real time and allows users to reserve seats online.

[0446] "A means for analyzing the congestion situation of a cafe in real time" is a system that collects and analyzes data on the flow of people within a cafe in real time to grasp the congestion situation.

[0447] "Means for managing reservation information" refers to a function that allows users to save and update data related to seat reservations and provide that information as needed.

[0448] "Means for analyzing users' facial expressions and voice in real time and recognizing their emotions" refers to technology that analyzes the facial expressions and tone of voice of cafe patrons to recognize their current emotional state.

[0449] The "means for optimizing user experience based on recognized emotional data" is a system for personalizing services at a cafe and providing an optimal experience by taking into account the recognized emotional state of the user.

[0450] "Means for users to easily place orders using a smartphone or head-mounted display" refers to technology that allows users to easily order products using a mobile device or head-mounted display.

[0451] The "means for providing analyzed peak time information to the administrator" is a system that identifies peak and off-peak times at the cafe through data analysis and provides that information to the cafe operator.

[0452] To implement this invention, the following system is required.

[0453] First, the server uses the generated artificial intelligence (AI) to manage seat reservations in the cafe in real time. An interface is provided so that users can reserve seats online. When a user submits a reservation request, the server analyzes the cafe's congestion status in real time and reserves an available seat. The reservation information includes the user ID, reservation time, and reserved seat number.

[0454] The server also uses the generated AI to analyze the user's past order history and propose a personalized menu for each user, allowing users to place orders more quickly using their smartphones or head-mounted displays.

[0455] The server also analyzes the cafe's visitor data to identify peak times. Based on the identified peak time information, it becomes possible to optimize staff shifts. This peak time information is also provided to managers for efficient shift management and improved customer service.

[0456] The system also includes an AI engine that analyzes the user's facial expressions and voice in real time to recognize emotions. For example, it uses the smartphone camera and head-mounted display sensors to analyze the user's facial expressions and tone of voice. Based on the obtained emotional data, the server provides functions to optimize the user experience. For example, if the user is feeling stressed, it will suggest a menu that will help them relax.

[0457] These processes are performed using the following specific software and hardware. Server-side processing mainly uses Flask (a web application framework) and Firebase (database management). Face recognition and emotion analysis use OpenCV (a face recognition library) and EmotionRecognizer (a specific emotion recognition library). The user interface is provided via a smartphone application and a head-mounted display.

[0458] A concrete example is given below. Consider a scenario in which a user visits a cafe and tries to check a seat reservation on their smartphone. The user requests a reservation for "two hours from 8:00," and the server checks the seat availability for that time period and provides an available seat. At that time, the server analyzes the user's facial expression, and if the user appears stressed, it suggests a "special menu to help them relax."

[0459] An example prompt is:

[0460] "I'm at the cafe and I'm feeling a bit stressed. Can you suggest a menu item that will help me relax?"

[0461] In this way, users can have a comfortable and personalized cafe experience.

[0462] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0463] Step 1:

[0464] A user accesses a cafe's seat reservation system using a smartphone or head-mounted display.

[0465] Input: User ID, desired reservation time

[0466] Output: Show seat availability

[0467] Specific operation: The server receives a reservation request from a user, checks the current seat availability from the database, and displays available seats to the user in real time.

[0468] Step 2:

[0469] The user reserves the desired vacant seat.

[0470] Input: Seat number, reservation time

[0471] Output: Reservation confirmation information

[0472] Specific operation: The server saves the user's selected seat number and reservation time in the database and notifies the user that the reservation is confirmed. The reservation information includes the user ID, reservation time, and reserved seat number.

[0473] Step 3:

[0474] The server analyzes the user's past ordering history and generates a personalized menu.

[0475] Input: User ID, past order history

[0476] Output: personalized menu

[0477] How it works: The server uses the generative AI model to analyze the user's past order data. Based on the results, it generates a menu tailored to the user's preferences and presents it to the user.

[0478] Step 4:

[0479] The user places an order using a smartphone or a head-mounted display.

[0480] Enter: personalized menu

[0481] Output: Order confirmation information

[0482] Specific operation: The user selects from the presented personalized menu and enters an order. The server receives the order, generates an order confirmation, and notifies the user.

[0483] Step 5:

[0484] The server analyzes visit data and identifies peak times.

[0485] Input: Store visit data

[0486] Output: Peak time information

[0487] Specific operation: The server analyzes the collected visitor data and identifies peak times when the number of visitors is high during a specific time period. This information is used to optimize staff shifts.

[0488] Step 6:

[0489] Servers optimize staff shifts according to peak times.

[0490] Input: Peak time information, current shift information

[0491] Output: Optimized shift schedule

[0492] Specific operation: The server generates a new shift schedule that optimizes staff allocation based on peak time information and current staff shift information, and provides the generated shift schedule to the manager.

[0493] Step 7:

[0494] The user's facial expressions and voice are captured by sensors on a smartphone or head-mounted display, and their emotions are analyzed.

[0495] Input: Captured facial and voice data

[0496] Output: Recognized emotion data

[0497] How it works: The device captures the user's facial expressions and voice data, which are then analyzed by a generative AI model. The analysis results identify the user's current emotional state.

[0498] Step 8:

[0499] The server provides a service that optimizes the user experience based on the recognized emotion data.

[0500] Input: Recognized emotion data

[0501] Output: Optimized service (e.g., relaxation menu suggestions)

[0502] Specific operation: Based on the recognized emotion data, the server provides services that optimize the user experience, such as suggesting relaxation menus to users who are feeling stressed.

[0503] Example prompt sentence:

[0504] "I'm at the cafe and I'm feeling a bit stressed. Can you suggest a menu item that will help me relax?"

[0505] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0506] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0507] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0508] [Second embodiment]

[0509] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0510] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0511] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0512] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0513] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0514] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0515] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0516] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0517] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0518] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0519] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0520] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0521] A specific embodiment for implementing this invention will now be described. This invention utilizes the generated artificial intelligence to implement the following four main functions in order to provide a comfortable and efficient environment for cafe users and cafe management.

[0522] 1. Seat reservation and management system

[0523] The server manages seat reservations within the cafe. Users can check seat availability online and request a reservation. The server analyzes the congestion situation in real time and reserves an available seat. The reservation information includes the user ID, reservation time, and reserved seat number. This allows users to reserve a seat in advance and allows the cafe to optimize seat usage.

[0524] For example, if a user requests a reservation online for "2 hours from 08:00", the server checks the seat availability for that time period and provides an available seat. The user can then start working at that seat.

[0525] 2. Customized Order Process

[0526] The server analyzes the user's past order history and generates a personalized menu, allowing the user to quickly order items that suit their preferences. For example, if User A frequently ordered "latte" in the past, the server will recommend "latte" the next time the user orders.

[0527] For example, when User B visits a store, the server recommends "cappuccino" based on the user's previous order history, allowing User B to place the order quickly. This reduces waiting time and makes the ordering process smoother.

[0528] 3. Peak time analysis and staff shift optimization

[0529] The server analyzes customer visit data to identify peak and off-peak times, and then optimizes staff shifts based on this information. The server allocates more staff during peak times and fewer staff during off-peak times.

[0530] For example, if data analysis reveals that peak hours are between 9:00 and 11:00, shift A (8:00 and 12:00) can be staffed with more staff during this time period, resulting in faster service and improved customer satisfaction.

[0531] 4. Introducing smart tables

[0532] Each table is equipped with a touchscreen, which users can use to order and pay. The order is immediately sent to the server and added to the order list. Payment is also processed on the touchscreen, and the total amount is displayed. This allows users to complete all operations without leaving their table.

[0533] For example, if user C uses the touchscreen to order a sandwich and coffee, the order is immediately sent to the server and added to the order list. When paying, the total amount is displayed on the touchscreen, allowing user C to complete the payment immediately.

[0534] These features allow cafe customers to spend their time comfortably and efficiently, and cafes can improve their profitability and customer service quality. This invention is extremely useful in modern cafe culture, and brings great benefits to both customers and cafe operators.

[0535] The processing flow will be explained below.

[0536] Seat reservation and management system

[0537] Step 1:

[0538] Users search online for available seats at cafes and enter the desired date and time.

[0539] Step 2:

[0540] The server checks the seat availability in the system based on the provided date and time. The server analyzes the seat availability in real time.

[0541] Step 3:

[0542] The server presents the available seats to the user, who then selects the seat they desire.

[0543] Step 4:

[0544] After the user selects a seat, the server marks the seat as reserved and saves the reservation information in a database.

[0545] Step 5:

[0546] The server sends the reservation confirmation information (reservation number, seat number, date and time) to the user, who receives the reservation confirmation.

[0547] Customized Order Process

[0548] Step 1:

[0549] A user places an order at a cafe by selecting the items they want to order via a touchscreen or an application.

[0550] Step 2:

[0551] The server checks the user's past order history and analyzes the user's preferences and purchasing patterns based on the past order data.

[0552] Step 3:

[0553] Based on the analysis results, the server proposes a personalized menu suitable for the user, and the user decides to order from the proposed menu.

[0554] Step 4:

[0555] Once the user places an order, the server sends it to the kitchen or barista, along with the order details (items, quantities, special requests, etc.).

[0556] Step 5:

[0557] The server stores the user's new order information in a database as an order history, which will improve personalized suggestions next time.

[0558] Peak time analysis and staff shift optimization

[0559] Step 1:

[0560] The server collects data on past visits to the cafe, including information such as the time of visit, the number of customers, and the items purchased.

[0561] Step 2:

[0562] The server analyzes the collected visitor data, identifies peak times (time periods with the highest number of customers), and uses data analysis algorithms to analyze visitor patterns.

[0563] Step 3:

[0564] The server optimizes staff shifts based on peak time information, deploying more staff during peak times and fewer staff during off-peak times.

[0565] Step 4:

[0566] The server notifies the staff of the optimized shift schedule, and the staff work according to the new shift schedule.

[0567] Step 5:

[0568] The server continuously collects actual store visit data and dynamically adjusts shift schedules, allowing for flexible response to fluctuations in peak times.

[0569] Introducing smart tables

[0570] Step 1:

[0571] The user sits at a table in a cafe and checks the menu using a touchscreen mounted on the table.

[0572] Step 2:

[0573] The user places an order via the touchscreen, where the order is selected and each item is added to the order list.

[0574] Step 3:

[0575] When the user confirms the order, the terminal (touch screen) sends the order information to the server, which receives the order information.

[0576] Step 4:

[0577] The server sends the order to the kitchen or barista, including the order details (item, quantity, special requests, etc.).

[0578] Step 5:

[0579] When the product ordered by the user is ready, a notification will appear on the touchscreen, and the user can confirm the notification and receive the product.

[0580] Step 6:

[0581] After the user has finished their meal, they pay on the touchscreen, and the server calculates the total amount and displays it on the touchscreen.

[0582] Step 7:

[0583] Once the user has completed the payment, the server resets the order list and prepares it for a new order.

[0584] Example 1

[0585] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0586] Traditional cafes often lack efficiency in many areas, such as seat reservations, personalized orders, and staff shift management. In particular, systems to ensure users' comfort in the cafe are inadequate, making it difficult to manage crowds and speed up user orders.

[0587] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0588] In this invention, the server includes means for managing seat reservations using the generated artificial intelligence, means for users to reserve seats online, means for analyzing the cafe's congestion status in real time, means for managing reservation information, means for generating personalized menus based on users' past ordering history, and means for ordering and paying using the touch screen of the smart table, thereby enabling efficient seat management, providing personalized service to users, and efficient operation even when the cafe is crowded.

[0589] "Generated artificial intelligence" is an intelligent system that analyzes users' past behavior and the situation within the cafe, and is used to manage seats and optimize orders.

[0590] "Seat reservation" refers to the act of a user reserving a seat in a cafe online in advance.

[0591] "Cafe congestion status" refers to the number of customers in the cafe and the number of available seats shown in real time.

[0592] "Reservation information" refers to data relating to a seat reservation, including a user ID, reservation time, and seat number.

[0593] A "personalized menu" refers to a special menu that is individually provided based on a user's past ordering history and preferences.

[0594] A "smart table" is a table installed in a cafe that has touchscreen functionality and is a device that allows users to order and pay.

[0595] "Touchscreen" refers to an input device that a user can operate by touching the screen.

[0596] "Order history" refers to a record of orders a user has made at a cafe in the past.

[0597] "Peak time" refers to the time period when the number of customers in the cafe is particularly high.

[0598] "Staff shift optimization" refers to making adjustments to deploy staff most effectively based on store visit data and peak time analysis.

[0599] MODE FOR CARRYING OUT THE INVENTION

[0600] This invention utilizes the generated artificial intelligence to implement the following four main functions in order to provide a comfortable and efficient environment for cafe users and cafe operators.

[0601] 1. Seat reservation and management system

[0602] The server manages seat reservations within the cafe. Users access a dedicated reservation system online and request a seat reservation by entering the desired date and time. The server analyzes the congestion situation in real time based on data from sensors installed on the seats within the cafe and reserves an available seat. Reservation information, including the user ID, reservation time, and reserved seat number, is saved in a database.

[0603] For example, if a user requests a reservation online for "2 hours from 08:00", the server checks the availability of seats during that time slot and provides an available seat. A confirmation message is sent to the user, and the user can begin working at that seat.

[0604] Example prompt sentence:

[0605] "I'd like to reserve a seat at a cafe for two hours starting at 8 o'clock."

[0606] 2. Customized Order Process

[0607] The server analyzes the user's past order history and generates a personalized menu using a generative AI model. This generative AI model creates a recommendation list taking into account the user's preferences and order frequency. When the user visits the cafe, they can view the personalized menu on their device (tablet or smartphone).

[0608] For example, if user A has frequently ordered "latte" in the past, the server will display a screen recommending "latte" the next time the user orders, allowing the user to complete their order smoothly.

[0609] Example prompt sentence:

[0610] I'd like to order the same drink as last time.

[0611] 3. Peak time analysis and staff shift optimization

[0612] The server collects store visit data and uses generative AI models to identify peak and off-peak times, leveraging information from point-of-sale systems and sensor data. Once peak times are identified, the server generates recommendations to optimize staff shifts for those times.

[0613] For example, if an analysis of customer visit data reveals that peak hours are between 9:00 and 11:00, the server will create a shift plan that allocates more staff to these hours, thereby speeding up service and improving customer satisfaction.

[0614] Example prompt sentence:

[0615] "I'd like you to analyze whether the number of customers is high or low between 9:00 and 11:00."

[0616] 4. Introducing smart tables

[0617] Each table in the cafe is equipped with a touchscreen, which customers can use to order and pay. When a customer enters their order on the touchscreen, the information is sent to the server in real time and added to the order list. Payment is also processed through the touchscreen, where the total amount is displayed and payment can be completed.

[0618] For example, if User C uses the touchscreen to order a sandwich and coffee, this order information is immediately sent to the server and notified to the kitchen staff. When paying, the total amount is displayed on the touchscreen, and User C can complete the payment on the spot.

[0619] Example prompt sentence:

[0620] "I want to order a sandwich and coffee from the touchscreen at my table."

[0621] These features allow cafe customers to spend their time comfortably and efficiently, and cafe operators can improve their profitability and customer service quality. This invention is extremely useful in modern cafe culture, and brings great benefits to both customers and cafe operators.

[0622] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0623] Seat reservation and management system

[0624] Step 1: Check seat availability

[0625] The server collects data from sensors installed on the seats and checks the seat availability in real time. It takes the sensor data as input and stores the status of each seat as "vacant" or "occupied" in a database.

[0626] Specific behavior:

[0627] If the sensor reports the occupancy of seat A as "vacant", the server updates the status as "vacant" in the database.

[0628] Input: Seat status data from sensors

[0629] Output: Updated seat state database

[0630] Step 2: Accepting a booking request

[0631] A user accesses the online reservation system using a terminal, inputs the desired date and time, and submits a reservation request. The reservation request input on the terminal is sent to the server.

[0632] Specific behavior:

[0633] When a user enters a reservation for "2 hours from 8:00" and presses the send button, the information is sent to the server.

[0634] Input: Reservation request information from user (date and time)

[0635] Output: Booking request accepted on server side

[0636] Step 3: Secure and confirm your seat

[0637] The server searches the database for available seats for the specified date and time based on the received reservation request. If an available seat is found, the server confirms the reservation and sends a confirmation message to the user.

[0638] Specific behavior:

[0639] The server searches the database and confirms that seat B is available and the reservation is confirmed, after which a confirmation message is sent to the user.

[0640] Input: Database search based on reservation request

[0641] Output: Confirmed reservation information and a confirmation message

[0642] Customized Order Process

[0643] Step 1: Analyze your order history

[0644] The server retrieves past order history from the database and uses a generative AI model to generate a personalized menu for each user. The order history data is used as input and analyzed using the AI ​​model to create a recommendation list.

[0645] Specific behavior:

[0646] The server analyzes User A's past order history and determines that he frequently orders "lattes."

[0647] Input: User's order history data

[0648] Output: A personalized menu recommendation list

[0649] Step 2: Offer a personalized menu

[0650] When the user checks the menu on the terminal, the server displays the generated personalized menu. The user checks the menu provided to the terminal.

[0651] Specific behavior:

[0652] The server displays a recommendation menu including latte on User A's device.

[0653] Input: Personalized Menu Recommendation List

[0654] Output: Personalization menu displayed on device

[0655] Peak time analysis and staff shift optimization

[0656] Step 1: Collect store visit data

[0657] The server collects data on the number of customers and time periods from the POS system and sensors, and stores the collected data in a database.

[0658] Specific behavior:

[0659] The server retrieves the number of customers from 9:00 to 11:00 from the POS system.

[0660] Input: POS system and sensor data

[0661] Output: Updated store visit database

[0662] Step 2: Data analysis and shift optimization

[0663] The server analyzes the collected store visit data using a generative AI model to identify peak and off-peak times, and generates suggestions for optimizing staff shifts based on the identified peak times.

[0664] Specific behavior:

[0665] The server analyzes that "9:00 to 11:00" is the peak time and generates shift suggestions.

[0666] Input: Store visit data

[0667] Output: Peak time information and shift optimization suggestions

[0668] Introducing smart tables

[0669] Step 1: Enter your order on the touchscreen

[0670] Users input their orders using a touchscreen installed on the smart table, and the information is sent to the server in real time.

[0671] Specific behavior:

[0672] User C orders a "sandwich" and a "coffee" on the touchscreen.

[0673] Input: User's order information

[0674] Output: Order information sent to the server

[0675] Step 2: Add to order list

[0676] The server processes the received order information and adds it to the order list, which is then notified to the kitchen staff.

[0677] Specific behavior:

[0678] The server adds "sandwich" and "coffee" to the order list and notifies the kitchen.

[0679] Input: User's order information

[0680] Output: Updated order list and notification to the kitchen

[0681] Step 3: Payment Processing

[0682] The user pays using the touchscreen, and the server processes the payment information and displays the total amount.

[0683] Specific behavior:

[0684] User C enters credit card information on the touchscreen and completes the payment.

[0685] Input: User's payment information

[0686] Output: Total amount and payment completion status

[0687] (Application example 1)

[0688] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0689] In today's cafe operations, it is extremely important for users to be able to smoothly reserve seats, quickly place personalized orders, and optimize staff shifts during peak times. However, existing systems have difficulty comprehensively resolving these issues. In particular, the lack of integration with smart devices and real-time service quality maintenance reduces user experience and operational efficiency. Therefore, a comprehensive system is needed to improve cafe operational efficiency and user satisfaction.

[0690] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0691] In this invention, the server includes means for managing seat reservations using the generated artificial intelligence, means for allowing users to reserve seats online, means for analyzing the cafe's congestion status in real time, means for managing orders and payments using smart devices, means for analyzing peak times and optimizing staff shifts, means for integrating and managing order histories through smart devices, and means for maintaining the quality of service provided using smart devices, thereby enabling cafe customers to smoothly reserve seats and quickly order from personalized menus, and further improving overall operational efficiency and service quality through the integrated smart device system.

[0692] "Generated artificial intelligence" is an AI system developed to analyze user behavior and cafe operation data, and make individual service suggestions and optimize operations.

[0693] The "means for managing seat reservations" is a system that allows users to reserve seats at cafes online in advance and efficiently manages the reservation information.

[0694] "Means for users to reserve seats online" refers to an application or web service that allows users to check information about available seats at a cafe via the Internet and confirm reservations.

[0695] "A means for analyzing the congestion status of a cafe in real time" is a system that instantly analyzes data such as the number of customers and seat usage status to determine the current level of congestion in the cafe.

[0696] "Means for managing orders and payments using smart devices" refers to a system that allows users to easily place orders and pay using digital devices such as tablets and smartphones.

[0697] The "means of analyzing peak times and optimizing staff shifts" is a system that analyzes store visit data, identifies particularly busy times, and efficiently allocates staff shifts according to those times.

[0698] "Means for integrated management of order history via smart devices" is a system that centrally manages a user's past order history on a digital device and provides personalized services.

[0699] "Means for maintaining the quality of services provided using smart devices" refers to a system for ensuring consistency in customer service using smart devices and increasing customer satisfaction.

[0700] This invention is a system for improving the operational efficiency of cafes and increasing customer satisfaction. This system uses generated artificial intelligence to perform various management and optimization functions.

[0701] Specifically, the following measures will be implemented:

[0702] 1. Seat reservation and management system

[0703] The server allows users to reserve seats online and manages the reservation information. It is possible to analyze the cafe's congestion status in real time and provide information on available seats. This allows users to reserve seats from their smartphones and enjoy a comfortable stay in a reserved seat.

[0704] Example: When a user requests a reservation for "1 hour from 10:00" in the app, the server checks the availability of seats during that time period and reserves seat number 42.

[0705] 2. Customized Order Process

[0706] The server uses the generated AI to analyze the user's past order history and propose personalized menus, allowing users to quickly order items that suit their preferences and reduce waiting times. Order history is managed in an integrated manner through smart devices, making it possible to make personalized suggestions based on past order data.

[0707] Example: Based on User A's past order history, "Latte" and "New Product 1" are recommended.

[0708] 3. Peak time analysis and staff shift optimization

[0709] The server analyzes customer visit data and identifies peak times. Based on this information, it optimizes staff shifts and increases the efficiency of cafe operations. This allows staff to be appropriately allocated according to peak times, enabling the maintenance of service quality.

[0710] Example: The server identifies "08:00-10:00" as peak time and allocates more staff during this time period.

[0711] 4. Integrated management using smart devices

[0712] Users can use smart devices such as smartphones and tablets to reserve seats, place orders, and make payments at the cafe. The server integrates order history and seat information by linking with these smart devices, providing users with seamless service.

[0713] Example: A user places an order using the touchscreen on a smart table and completes payment through a smartphone app.

[0714] These functions are realized by the following hardware and software.

[0715] Hardware: Smartphones, tablets, touchscreens (smart tables)

[0716] Software: Python, Flask, SQLite for database management and AI analysis

[0717] The generative AI model is used to analyze user behavior and cafe operation data to make individual service suggestions and optimize operations.

[0718] Example prompt sentence:

[0719] Please recommend frequently ordered menu items based on User A's past order history.

[0720] + Past Order History:

[0721] latté

[0722] cappuccino

[0723] latté

[0724] + Recommendation results:

[0725] "Latte" (because it's frequently ordered)

[0726] "New product 1"

[0727] "New product 2"

[0728] This allows cafe customers to smoothly reserve seats, quickly order from personalized menus, and improve overall operational efficiency and service quality through an integrated smart device system.

[0729] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0730] Step 1:

[0731] A user makes a seat reservation request using a smartphone. The input includes the user ID, desired date and time, and seat type. The server receives this, checks the seat information in the database in real time, and checks whether there is availability on the specified date and time. If there is availability, the seat is reserved and the information is returned to the user's device. The output is the reservation confirmation information.

[0732] Step 2:

[0733] A user browses past order history to place a customized order on a smart device. The server retrieves the order history from the database based on the user ID and inputs it into a generative AI model. The generative AI model analyzes the order history based on the prompt text and suggests a personalized menu for each user. The input is the user ID, and the output is a list of recommended menu items.

[0734] Step 3:

[0735] The user decides what to order from the proposed menu and sends it to the server via their smart device. The server receives the order information, records it in an order database, and notifies the kitchen in real time. At the same time, payment information is generated and displayed on the user's device. The input is the order information, and the output is payment information and an order confirmation notification.

[0736] Step 4:

[0737] Cafe staff open the shift management screen and check the peak time analysis data from the server. The server analyzes customer visit data from past data sets, identifies peak times, and proposes appropriate shift allocation. Staff adjust their shifts based on the proposal. The input is past customer visit data, and the output is peak times and proposed shift allocation.

[0738] Step 5:

[0739] The user checks the order history and seat reservation information on a smart device. The server manages this data in real time and makes it accessible to the user at any time. The input is the user ID, and the output is the integrated order history and seat reservation information.

[0740] Step 6:

[0741] After placing an order, the user completes payment on their smart device. The payment information is sent to the server, and the payment process is carried out. Once payment is complete, a confirmation notice is sent to the user's device. The input is payment information, and the output is a payment confirmation notice.

[0742] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0743] A specific embodiment for implementing this invention will now be described. This invention utilizes the generated artificial intelligence and emotion engine to implement the following five main functions in order to provide a comfortable and efficient environment for cafe users and cafe management.

[0744] 1. Seat reservation and management system

[0745] The server manages seat reservations within the cafe. Users can check seat availability online and request a reservation. The server analyzes the congestion situation in real time and reserves an available seat. The reservation information includes the user ID, reservation time, and reserved seat number. This allows users to reserve a seat in advance and allows the cafe to optimize seat usage.

[0746] For example, if a user requests a reservation online for "2 hours from 08:00", the server checks the seat availability for that time period and provides an available seat. The user can then start working at that seat.

[0747] 2. Customized Order Process

[0748] The server analyzes the user's past order history and generates a personalized menu, allowing the user to quickly order items that suit their preferences. For example, if User A frequently ordered "latte" in the past, the server will recommend "latte" the next time the user orders.

[0749] For example, when User B visits a store, the server recommends "cappuccino" based on the user's previous order history, allowing User B to place the order quickly. This reduces waiting time and makes the ordering process smoother.

[0750] 3. Peak time analysis and staff shift optimization

[0751] The server analyzes customer visit data to identify peak and off-peak times, and then optimizes staff shifts based on this information. The server allocates more staff during peak times and fewer staff during off-peak times.

[0752] For example, if data analysis reveals that peak hours are between 9:00 and 11:00, shift A (8:00 and 12:00) can be staffed with more staff during this time period, resulting in faster service and improved customer satisfaction.

[0753] 4. Introducing smart tables

[0754] Each table is equipped with a touchscreen, which users can use to order and pay. The order is immediately sent to the server and added to the order list. Payment is also processed on the touchscreen, and the total amount is displayed. This allows users to complete all operations without leaving their table.

[0755] For example, if user C uses the touchscreen to order a sandwich and coffee, the order is immediately sent to the server and added to the order list. When paying, the total amount is displayed on the touchscreen, allowing user C to complete the payment immediately.

[0756] 5. Introducing the Emotion Engine

[0757] The emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotions in real time. The server then uses this emotional data to optimize the user experience. For example, if the user is feeling stressed, it will provide relaxation menus and suggestions.

[0758] For example, if User D visits a cafe and the emotion engine detects stress from User D's facial expression while placing an order on the touchscreen, the server will suggest a "special menu for relaxation." User D can accept this suggestion and spend time in a relaxing environment.

[0759] These features allow cafe customers to spend their time comfortably and efficiently, and cafes can improve their profitability and customer service quality. This invention is extremely useful in modern cafe culture, and brings great benefits to both customers and cafe operators.

[0760] The processing flow will be explained below.

[0761] Seat reservation and management system

[0762] Step 1:

[0763] Users search online for available seats at cafes and enter the desired date and time.

[0764] Step 2:

[0765] The server checks the seat availability in the system based on the provided date and time. The server analyzes the seat availability in real time.

[0766] Step 3:

[0767] The server presents the available seats to the user, who then selects the seat they desire.

[0768] Step 4:

[0769] After the user selects a seat, the server marks the seat as reserved and saves the reservation information in a database.

[0770] Step 5:

[0771] The server sends the reservation confirmation information (reservation number, seat number, date and time) to the user, who receives the reservation confirmation.

[0772] Customized Order Process

[0773] Step 1:

[0774] A user places an order at a cafe by selecting the items they want to order via a touchscreen or an application.

[0775] Step 2:

[0776] The server checks the user's past order history and analyzes the user's preferences and purchasing patterns based on the past order data.

[0777] Step 3:

[0778] Based on the analysis results, the server proposes a personalized menu suitable for the user, and the user decides to order from the proposed menu.

[0779] Step 4:

[0780] Once the user places an order, the server sends it to the kitchen or barista, along with the order details (items, quantities, special requests, etc.).

[0781] Step 5:

[0782] The server stores the user's new order information in a database as an order history, which will improve personalized suggestions next time.

[0783] Peak time analysis and staff shift optimization

[0784] Step 1:

[0785] The server collects data on past visits to the cafe, including information such as the time of visit, the number of customers, and the items purchased.

[0786] Step 2:

[0787] The server analyzes the collected visitor data, identifies peak times (time periods with the highest number of customers), and uses data analysis algorithms to analyze visitor patterns.

[0788] Step 3:

[0789] The server optimizes staff shifts based on peak time information, deploying more staff during peak times and fewer staff during off-peak times.

[0790] Step 4:

[0791] The server notifies the staff of the optimized shift schedule, and the staff work according to the new shift schedule.

[0792] Step 5:

[0793] The server continuously collects actual store visit data and dynamically adjusts shift schedules, allowing for flexible response to fluctuations in peak times.

[0794] Introducing smart tables

[0795] Step 1:

[0796] The user sits at a table in a cafe and checks the menu using a touchscreen mounted on the table.

[0797] Step 2:

[0798] The user places an order via the touchscreen, where the order is selected and each item is added to the order list.

[0799] Step 3:

[0800] When the user confirms the order, the terminal (touch screen) sends the order information to the server, which receives the order information.

[0801] Step 4:

[0802] The server sends the order to the kitchen or barista, including the order details (item, quantity, special requests, etc.).

[0803] Step 5:

[0804] When the product ordered by the user is ready, a notification will appear on the touchscreen, and the user can confirm the notification and receive the product.

[0805] Step 6:

[0806] After the user has finished their meal, they pay on the touchscreen, and the server calculates the total amount and displays it on the touchscreen.

[0807] Step 7:

[0808] Once the user has completed the payment, the server resets the order list and prepares it for a new order.

[0809] Introducing the Emotion Engine

[0810] Step 1:

[0811] When a user enters a cafe, the device's camera captures the user's face, and the emotion engine recognizes their facial expressions and evaluates their emotional state in real time.

[0812] Step 2:

[0813] The server receives data from the emotion engine and analyzes the user's emotional state, determining emotional states such as stress, happiness, fatigue, etc.

[0814] Step 3:

[0815] When a user places an order on a touchscreen, the server uses emotional data to suggest personalized menu items, such as relaxing drinks or healthy foods to reduce stress.

[0816] Step 4:

[0817] It offers special offers and services based on the user's emotional state. If the user is feeling stressed, it may offer special promotions or discounts.

[0818] Step 5:

[0819] The server continuously collects user emotional data and dynamically adjusts the quality of service to optimize the user experience.

[0820] In this way, the system combined with the emotion engine provides personalized services according to the user's needs and emotional state, resulting in a more comfortable and satisfying cafe experience.

[0821] Example 2

[0822] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0823] Cafe operations face a wide range of challenges, including seat reservation management, order efficiency, peak time analysis, staff shift management, and even service optimization based on customer sentiment. Conventional systems have difficulty addressing these challenges in a unified manner, resulting in a decline in customer satisfaction and the operational efficiency of the cafe. The objective of this invention is to provide a system that solves these challenges and provides a comfortable and efficient cafe experience.

[0824] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for managing seat reservations using generated artificial intelligence, a means for allowing users to reserve seats online, a means for analyzing the cafe's congestion status in real time, a means for managing reservation information, a means for ordering and paying using a touch screen, and a means for analyzing user emotions and making suggestions using an emotion engine. This makes it possible to improve the efficiency of cafe management and user satisfaction.

[0825] "Generated artificial intelligence" refers to machine learning models that are trained to automatically collect and analyze data and deliver optimal results for a specific purpose.

[0826] "Means for managing seat reservations" refers to technology that supports the process of users checking seat availability online and making appropriate reservations.

[0827] "Means by which users can reserve seats online" refers to an interface that allows users to reserve seats remotely via devices such as a web browser or smartphone app.

[0828] "Means for analyzing the congestion status of a cafe in real time" refers to a system that analyzes data such as current seat occupancy status and order status in real time to determine the congestion status of a cafe.

[0829] "Means for managing reservation information" refers to a system that stores and manages user reservation details (user ID, reservation time, seat number, etc.) in a database and allows them to be updated and referenced as needed.

[0830] "Means for ordering and paying using a touchscreen" refers to technology that allows users to order and pay for cafe menu items using a touchscreen terminal installed on each table.

[0831] An "emotion engine" refers to an algorithm that analyzes emotions from a user's facial expressions and voice, and then provides appropriate responses and suggestions based on that emotional data.

[0832] "Means for analyzing user emotions and making suggestions" refers to technology that analyzes emotional data obtained by an emotion engine and suggests optimal services and products based on the user's current state.

[0833] "Means for analyzing past order history" refers to technology for analyzing a user's past order data and understanding the user's preferences and trends.

[0834] "Means for suggesting personalized menus" refers to a system for displaying and suggesting menus optimized for each individual user based on past order history and preference data.

[0835] "Means for analyzing customer visit data" refers to technology that collects and analyzes information about visits to a cafe (time, date, number of people, etc.) and identifies patterns of customer behavior.

[0836] "Means for identifying peak times" refers to a system for analyzing store visit data to identify the times when the cafe is most heavily used.

[0837] "Means for optimizing staff shifts" refers to technology that optimizes staff work schedules based on data on peak and off-peak times, achieving efficient personnel deployment.

[0838] MODE FOR CARRYING OUT THE INVENTION

[0839] A specific embodiment for implementing this invention will now be described. This invention utilizes the generated artificial intelligence and emotion engine to implement the following five main functions in order to provide a comfortable and efficient environment for cafe users and cafe management.

[0840] Seat reservation and management system

[0841] The server manages seat reservations within the cafe. Users check seat availability online and request a reservation. Specifically, the user opens the seat reservation page using a web browser or smartphone app and enters the desired time and necessary information. The device then sends this information to the server via the HTTPS protocol. The server accesses the database to check seat availability for the specified time slot and, if there is availability, confirms the reservation information.

[0842] For example, if a user requests a reservation online for "2 hours from 08:00," the server checks availability for that time slot and provides an available seat, allowing the user to begin working at that seat.

[0843] Customized Order Process

[0844] The server analyzes the user's past order history and generates a personalized menu. When a user opens the order page on their smartphone or tablet, the device queries the server for their past order history. The server retrieves the order history from the database and generates a recommended menu using a generative AI model.

[0845] For example, if user A has frequently ordered "latte" in the past, the server can recommend "latte" the next time user A orders, allowing user A to complete the order quickly.

[0846] Peak time analysis and staff shift optimization

[0847] The server analyzes visitor data and identifies peak and off-peak times. The server collects visitor data from the POS system and ordering system and analyzes peak and off-peak times for specific time periods. Data analysis uses aggregation functions and histograms.

[0848] For example, if data analysis reveals that peak hours are between 9:00 and 11:00, shift A (8:00 and 12:00) will be staffed with more staff during this time period to speed up service.

[0849] Introducing smart tables

[0850] Each table is equipped with a touchscreen, which users use to order and pay. Specifically, users use the touchscreen to view the cafe menu, select the items they want, and confirm their order. The terminal instantly transmits the order information to the server, and the total amount is displayed when payment is made.

[0851] For example, if user C uses the touchscreen to order a sandwich and coffee, the order is immediately sent to the server and added to the order list. When paying, the total amount is displayed on the touchscreen, allowing the user to complete the payment immediately.

[0852] Introducing the Emotion Engine

[0853] The emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotions in real time. The camera and microphone installed on the device capture the user's facial expressions and voice, which are then analyzed by the emotion engine. The server then uses the data sent from the emotion engine to suggest appropriate services and products.

[0854] For example, if User D visits a cafe and the emotion engine detects stress in User D's facial expression while placing an order on the touchscreen, the server will suggest a "special menu for relaxation." User D can accept this suggestion and spend time in a relaxing environment.

[0855] Example prompts to input to the generative AI model

[0856] Prompt text (seat reservation and management system): In a cafe seat reservation system, what happens when a user goes online and requests to reserve a seat for 2 hours starting at 08:00?

[0857] Prompt statement (customized ordering process): If User A has frequently ordered lattes in the past, what menu item will they see the next time they order?

[0858] Prompt (Peak time analysis and staff shift optimization): If the cafe's peak time is 09:00-11:00, what will be the staff shifts?

[0859] Prompt (Smart Table Introduction): What happens when a user orders a sandwich and coffee using the touchscreen?

[0860] Prompt (introduction of emotion engine): If the emotion engine detects that the user is stressed, how will the server respond?

[0861] Based on the above explanation, this system can provide cafe users with a comfortable and efficient environment and also realize effective operation management for cafe operators.

[0862] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0863] Seat reservation and management system

[0864] Step 1:

[0865] A user opens a seat reservation page online. As input, the user enters the desired reservation time (e.g., 2 hours from 08:00) and user ID into the reservation form. This sends a seat reservation request to the system.

[0866] Step 2:

[0867] The terminal sends the information entered by the user to the server. The terminal uses the HTTPS protocol to send the entered reservation information (user ID, desired reservation time) to the server. The accuracy of the data is maintained by properly formatting the input data before sending it.

[0868] Step 3:

[0869] The server checks seat availability. The server executes an SQL query against the database to retrieve seat availability for the specified time slot. The availability data returned from the database becomes the input for the server.

[0870] Step 4:

[0871] The server confirms and manages the reservation. If there are available seats in the specified time slot, the server saves the reservation information in the database and confirms the reservation. The reservation information (user ID, reservation time, seat number) is the output data of the server. If there are no available seats, an error message is generated.

[0872] Step 5:

[0873] The terminal receives the server's output data and displays the reservation confirmation information or an error message to the user. Specifically, the reservation number and seat number are displayed on the screen, allowing the user to confirm that the reservation has been confirmed.

[0874] Customized Order Process

[0875] Step 1:

[0876] The user opens the order page. As input, the user launches the ordering app and accesses the order page, which allows the system to prepare an order.

[0877] Step 2:

[0878] The terminal queries the server for past order history. Using the user ID as input, it requests past order data via the REST API. The terminal sends the input data to the server.

[0879] Step 3:

[0880] The server analyzes the order history. It retrieves the user's past order history from the database and analyzes it using a generative AI model. The past order data becomes the input data for the server.

[0881] Step 4:

[0882] The server generates a personalized recommended menu. Based on the generative AI model, it lists the most suitable menu items for the user. The recommended menu data becomes the server's output data.

[0883] Step 5:

[0884] The terminal displays the recommended menu to the user. The terminal receives output data from the server and displays the recommended menu on the screen. This allows the user to quickly select products and confirm their order.

[0885] Peak time analysis and staff shift optimization

[0886] Step 1:

[0887] The server collects store visit data. As input, it periodically obtains store visit data (time, number of people, order details) from the POS system and ordering system. This allows the server to obtain basic data for shift optimization.

[0888] Step 2:

[0889] The server analyzes the visitor data to identify peak and off-peak times. It aggregates the data and performs statistical analysis to analyze visitor patterns during specific time periods. The aggregated data is the input data, and the analysis results are the output data.

[0890] Step 3:

[0891] The server generates an optimal shift schedule. Based on the results of peak and off-peak times, it runs an algorithm to optimize staff shift schedules. The optimized shift schedule is the output data.

[0892] Step 4:

[0893] The device notifies staff of shift information. It receives optimized shift schedule data from the server and sends push notifications to each staff member's device (smartphone or tablet). The notification data is the input, and the displayed shift information is the output.

[0894] Step 5:

[0895] Staff work based on shifts. According to the notified shift information, staff perform their duties according to their respective working hours. This allows for efficient service provision during peak times.

[0896] Introducing smart tables

[0897] Step 1:

[0898] The user operates the touchscreen of the smart table. As input, the user browses the menu and selects the desired item on the touchscreen. This allows the user to prepare an order.

[0899] Step 2:

[0900] The terminal sends the order information to the server. The selected order information is formatted and sent to the server. The order items and user ID are included as input data.

[0901] Step 3:

[0902] The server processes the order information, receives the order information, adds the order to a list in the order processing system, and forwards the order to the kitchen. The processed order information is the output data.

[0903] Step 4:

[0904] The terminal prompts the user to complete the payment process. After the order is confirmed, a payment screen is displayed, allowing the user to select a payment method. The payment method and amount are included as input data.

[0905] Step 5:

[0906] The user completes the payment. The payment procedure is completed on the touch screen using the selected payment method. The payment completion is sent to the server, and the transaction data becomes the output data.

[0907] Introducing the Emotion Engine

[0908] Step 1:

[0909] The device captures the user's facial expressions and voice using a camera and microphone. The capture device takes the user's facial movements and tone of voice as input, which prepares the device for sentiment analysis.

[0910] Step 2:

[0911] The device sends the captured data to the emotion engine. The captured data is sent to the emotion engine in real time. The input data includes facial expressions and voice characteristics.

[0912] Step 3:

[0913] The emotion engine analyzes the emotion data. It analyzes the received data and identifies the user's emotion (e.g., happiness, stress, anger). The analysis results are output data.

[0914] Step 4:

[0915] The server generates suggestions based on the emotion data. Based on the data from the emotion engine, it generates suggestions to optimize the user experience (such as special menus or relaxation suggestions). The suggested data is the output.

[0916] Step 5:

[0917] The device displays the suggestions to the user, receives the suggestion data, and displays it on the touch screen, allowing the user to accept the appropriate suggestion.

[0918] Through the above processing steps, the system can provide efficient and comfortable service to cafe patrons.

[0919] (Application example 2)

[0920] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0921] Traditional cafe operations have not been able to efficiently manage seating or improve customer satisfaction. In particular, it is difficult to grasp the congestion situation of customers and provide personalized service based on each customer's order history. There is also a lack of means to manage staff shifts and analyze customer sentiment in real time to optimize the experience. As a result, customers are likely to have a low-satisfaction experience, and cafe operators also face challenges in providing efficient service.

[0922] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for managing seat reservations using the generated artificial intelligence, means for allowing users to reserve seats online, means for analyzing the cafe's congestion status in real time, means for managing reservation information, means for analyzing the user's facial expressions and voice in real time and recognizing emotions, means for optimizing the user experience based on the recognized emotion data, means for users to easily place orders using a smartphone or a head-mounted display, and means for providing analyzed peak time information to a manager. This enables efficient seat management and improved customer satisfaction.

[0923] "Generated artificial intelligence" is an artificial intelligence system that is automatically generated based on data and designed to perform specific tasks.

[0924] "Means for managing seat reservations" refers to a function that tracks and manages seat reservations in the cafe in real time and allows users to reserve seats online.

[0925] "A means for analyzing the congestion situation of a cafe in real time" is a system that collects and analyzes data on the flow of people within a cafe in real time to grasp the congestion situation.

[0926] "Means for managing reservation information" refers to a function that allows users to save and update data related to seat reservations and provide that information as needed.

[0927] "Means for analyzing users' facial expressions and voice in real time and recognizing their emotions" refers to technology that analyzes the facial expressions and tone of voice of cafe patrons to recognize their current emotional state.

[0928] The "means for optimizing user experience based on recognized emotional data" is a system for personalizing services at a cafe and providing an optimal experience by taking into account the recognized emotional state of the user.

[0929] "Means for users to easily place orders using a smartphone or head-mounted display" refers to technology that allows users to easily order products using a mobile device or head-mounted display.

[0930] The "means for providing analyzed peak time information to the administrator" is a system that identifies peak and off-peak times at the cafe through data analysis and provides that information to the cafe operator.

[0931] To implement this invention, the following system is required.

[0932] First, the server uses the generated artificial intelligence (AI) to manage seat reservations in the cafe in real time. An interface is provided so that users can reserve seats online. When a user submits a reservation request, the server analyzes the cafe's congestion status in real time and reserves an available seat. The reservation information includes the user ID, reservation time, and reserved seat number.

[0933] The server also uses the generated AI to analyze the user's past order history and propose a personalized menu for each user, allowing users to place orders more quickly using their smartphones or head-mounted displays.

[0934] The server also analyzes the cafe's visitor data to identify peak times. Based on the identified peak time information, it becomes possible to optimize staff shifts. This peak time information is also provided to managers for efficient shift management and improved customer service.

[0935] The system also includes an AI engine that analyzes the user's facial expressions and voice in real time to recognize emotions. For example, it uses the smartphone camera and head-mounted display sensors to analyze the user's facial expressions and tone of voice. Based on the obtained emotional data, the server provides functions to optimize the user experience. For example, if the user is feeling stressed, it will suggest a menu that will help them relax.

[0936] These processes are performed using the following specific software and hardware. Server-side processing mainly uses Flask (a web application framework) and Firebase (database management). Face recognition and emotion analysis use OpenCV (a face recognition library) and EmotionRecognizer (a specific emotion recognition library). The user interface is provided via a smartphone application and a head-mounted display.

[0937] A concrete example is given below. Consider a scenario in which a user visits a cafe and tries to check a seat reservation on their smartphone. The user requests a reservation for "two hours from 8:00," and the server checks the seat availability for that time period and provides an available seat. At that time, the server analyzes the user's facial expression, and if the user appears stressed, it suggests a "special menu to help them relax."

[0938] An example prompt is:

[0939] "I'm at the cafe and I'm feeling a bit stressed. Can you suggest a menu item that will help me relax?"

[0940] In this way, users can have a comfortable and personalized cafe experience.

[0941] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0942] Step 1:

[0943] A user accesses a cafe's seat reservation system using a smartphone or head-mounted display.

[0944] Input: User ID, desired reservation time

[0945] Output: Show seat availability

[0946] Specific operation: The server receives a reservation request from a user, checks the current seat availability from the database, and displays available seats to the user in real time.

[0947] Step 2:

[0948] The user reserves the desired vacant seat.

[0949] Input: Seat number, reservation time

[0950] Output: Reservation confirmation information

[0951] Specific operation: The server saves the user's selected seat number and reservation time in the database and notifies the user that the reservation is confirmed. The reservation information includes the user ID, reservation time, and reserved seat number.

[0952] Step 3:

[0953] The server analyzes the user's past ordering history and generates a personalized menu.

[0954] Input: User ID, past order history

[0955] Output: personalized menu

[0956] How it works: The server uses the generative AI model to analyze the user's past order data. Based on the results, it generates a menu tailored to the user's preferences and presents it to the user.

[0957] Step 4:

[0958] The user places an order using a smartphone or a head-mounted display.

[0959] Enter: personalized menu

[0960] Output: Order confirmation information

[0961] Specific operation: The user selects from the presented personalized menu and enters an order. The server receives the order, generates an order confirmation, and notifies the user.

[0962] Step 5:

[0963] The server analyzes visit data and identifies peak times.

[0964] Input: Store visit data

[0965] Output: Peak time information

[0966] Specific operation: The server analyzes the collected visitor data and identifies peak times when the number of visitors is high during a specific time period. This information is used to optimize staff shifts.

[0967] Step 6:

[0968] Servers optimize staff shifts according to peak times.

[0969] Input: Peak time information, current shift information

[0970] Output: Optimized shift schedule

[0971] Specific operation: The server generates a new shift schedule that optimizes staff allocation based on peak time information and current staff shift information, and provides the generated shift schedule to the manager.

[0972] Step 7:

[0973] The user's facial expressions and voice are captured by sensors on a smartphone or head-mounted display, and their emotions are analyzed.

[0974] Input: Captured facial and voice data

[0975] Output: Recognized emotion data

[0976] How it works: The device captures the user's facial expressions and voice data, which are then analyzed by a generative AI model. The analysis results identify the user's current emotional state.

[0977] Step 8:

[0978] The server provides a service that optimizes the user experience based on the recognized emotion data.

[0979] Input: Recognized emotion data

[0980] Output: Optimized service (e.g., relaxation menu suggestions)

[0981] Specific operation: Based on the recognized emotion data, the server provides services that optimize the user experience, such as suggesting relaxation menus to users who are feeling stressed.

[0982] Example prompt sentence:

[0983] "I'm at the cafe and I'm feeling a bit stressed. Can you suggest a menu item that will help me relax?"

[0984] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0985] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0986] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0987] [Third embodiment]

[0988] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0989] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0990] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0991] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0992] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0993] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0994] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0995] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0996] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0997] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0998] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0999] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1000] A specific embodiment for implementing this invention will now be described. This invention utilizes the generated artificial intelligence to implement the following four main functions in order to provide a comfortable and efficient environment for cafe users and cafe management.

[1001] 1. Seat reservation and management system

[1002] The server manages seat reservations within the cafe. Users can check seat availability online and request a reservation. The server analyzes the congestion situation in real time and reserves an available seat. The reservation information includes the user ID, reservation time, and reserved seat number. This allows users to reserve a seat in advance and allows the cafe to optimize seat usage.

[1003] For example, if a user requests a reservation online for "2 hours from 08:00", the server checks the seat availability for that time period and provides an available seat. The user can then start working at that seat.

[1004] 2. Customized Order Process

[1005] The server analyzes the user's past order history and generates a personalized menu, allowing the user to quickly order items that suit their preferences. For example, if User A frequently ordered "latte" in the past, the server will recommend "latte" the next time the user orders.

[1006] For example, when User B visits a store, the server recommends "cappuccino" based on the user's previous order history, allowing User B to place the order quickly. This reduces waiting time and makes the ordering process smoother.

[1007] 3. Peak time analysis and staff shift optimization

[1008] The server analyzes customer visit data to identify peak and off-peak times, and then optimizes staff shifts based on this information. The server allocates more staff during peak times and fewer staff during off-peak times.

[1009] For example, if data analysis reveals that peak hours are between 9:00 and 11:00, shift A (8:00 and 12:00) can be staffed with more staff during this time period, resulting in faster service and improved customer satisfaction.

[1010] 4. Introducing smart tables

[1011] Each table is equipped with a touchscreen, which users can use to order and pay. The order is immediately sent to the server and added to the order list. Payment is also processed on the touchscreen, and the total amount is displayed. This allows users to complete all operations without leaving their table.

[1012] For example, if user C uses the touchscreen to order a sandwich and coffee, the order is immediately sent to the server and added to the order list. When paying, the total amount is displayed on the touchscreen, allowing user C to complete the payment immediately.

[1013] These features allow cafe customers to spend their time comfortably and efficiently, and cafes can improve their profitability and customer service quality. This invention is extremely useful in modern cafe culture, and brings great benefits to both customers and cafe operators.

[1014] The processing flow will be explained below.

[1015] Seat reservation and management system

[1016] Step 1:

[1017] Users search online for available seats at cafes and enter the desired date and time.

[1018] Step 2:

[1019] The server checks the seat availability in the system based on the provided date and time. The server analyzes the seat availability in real time.

[1020] Step 3:

[1021] The server presents the available seats to the user, who then selects the seat they desire.

[1022] Step 4:

[1023] After the user selects a seat, the server marks the seat as reserved and saves the reservation information in a database.

[1024] Step 5:

[1025] The server sends the reservation confirmation information (reservation number, seat number, date and time) to the user, who receives the reservation confirmation.

[1026] Customized Order Process

[1027] Step 1:

[1028] A user places an order at a cafe by selecting the items they want to order via a touchscreen or an application.

[1029] Step 2:

[1030] The server checks the user's past order history and analyzes the user's preferences and purchasing patterns based on the past order data.

[1031] Step 3:

[1032] Based on the analysis results, the server proposes a personalized menu suitable for the user, and the user decides to order from the proposed menu.

[1033] Step 4:

[1034] Once the user places an order, the server sends it to the kitchen or barista, along with the order details (items, quantities, special requests, etc.).

[1035] Step 5:

[1036] The server stores the user's new order information in a database as an order history, which will improve personalized suggestions next time.

[1037] Peak time analysis and staff shift optimization

[1038] Step 1:

[1039] The server collects data on past visits to the cafe, including information such as the time of visit, the number of customers, and the items purchased.

[1040] Step 2:

[1041] The server analyzes the collected visitor data, identifies peak times (time periods with the highest number of customers), and uses data analysis algorithms to analyze visitor patterns.

[1042] Step 3:

[1043] The server optimizes staff shifts based on peak time information, deploying more staff during peak times and fewer staff during off-peak times.

[1044] Step 4:

[1045] The server notifies the staff of the optimized shift schedule, and the staff work according to the new shift schedule.

[1046] Step 5:

[1047] The server continuously collects actual store visit data and dynamically adjusts shift schedules, allowing for flexible response to fluctuations in peak times.

[1048] Introducing smart tables

[1049] Step 1:

[1050] The user sits at a table in a cafe and checks the menu using a touchscreen mounted on the table.

[1051] Step 2:

[1052] The user places an order via the touchscreen, where the order is selected and each item is added to the order list.

[1053] Step 3:

[1054] When the user confirms the order, the terminal (touch screen) sends the order information to the server, which receives the order information.

[1055] Step 4:

[1056] The server sends the order to the kitchen or barista, including the order details (item, quantity, special requests, etc.).

[1057] Step 5:

[1058] When the product ordered by the user is ready, a notification will appear on the touchscreen, and the user can confirm the notification and receive the product.

[1059] Step 6:

[1060] After the user has finished their meal, they pay on the touchscreen, and the server calculates the total amount and displays it on the touchscreen.

[1061] Step 7:

[1062] Once the user has completed the payment, the server resets the order list and prepares it for a new order.

[1063] Example 1

[1064] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1065] Traditional cafes often lack efficiency in many areas, such as seat reservations, personalized orders, and staff shift management. In particular, systems to ensure users' comfort in the cafe are inadequate, making it difficult to manage crowds and speed up user orders.

[1066] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1067] In this invention, the server includes means for managing seat reservations using the generated artificial intelligence, means for users to reserve seats online, means for analyzing the cafe's congestion status in real time, means for managing reservation information, means for generating personalized menus based on users' past ordering history, and means for ordering and paying using the touch screen of the smart table, thereby enabling efficient seat management, providing personalized service to users, and efficient operation even when the cafe is crowded.

[1068] "Generated artificial intelligence" is an intelligent system that analyzes users' past behavior and the situation within the cafe, and is used to manage seats and optimize orders.

[1069] "Seat reservation" refers to the act of a user reserving a seat in a cafe online in advance.

[1070] "Cafe congestion status" refers to the number of customers in the cafe and the number of available seats shown in real time.

[1071] "Reservation information" refers to data relating to a seat reservation, including a user ID, reservation time, and seat number.

[1072] A "personalized menu" refers to a special menu that is individually provided based on a user's past ordering history and preferences.

[1073] A "smart table" is a table installed in a cafe that has touchscreen functionality and is a device that allows users to order and pay.

[1074] "Touchscreen" refers to an input device that a user can operate by touching the screen.

[1075] "Order history" refers to a record of orders a user has made at a cafe in the past.

[1076] "Peak time" refers to the time period when the number of customers in the cafe is particularly high.

[1077] "Staff shift optimization" refers to making adjustments to deploy staff most effectively based on store visit data and peak time analysis.

[1078] MODE FOR CARRYING OUT THE INVENTION

[1079] This invention utilizes the generated artificial intelligence to implement the following four main functions in order to provide a comfortable and efficient environment for cafe users and cafe operators.

[1080] 1. Seat reservation and management system

[1081] The server manages seat reservations within the cafe. Users access a dedicated reservation system online and request a seat reservation by entering the desired date and time. The server analyzes the congestion situation in real time based on data from sensors installed on the seats within the cafe and reserves an available seat. Reservation information, including the user ID, reservation time, and reserved seat number, is saved in a database.

[1082] For example, if a user requests a reservation online for "2 hours from 08:00", the server checks the availability of seats during that time slot and provides an available seat. A confirmation message is sent to the user, and the user can begin working at that seat.

[1083] Example prompt sentence:

[1084] "I'd like to reserve a seat at a cafe for two hours starting at 8 o'clock."

[1085] 2. Customized Order Process

[1086] The server analyzes the user's past order history and generates a personalized menu using a generative AI model. This generative AI model creates a recommendation list taking into account the user's preferences and order frequency. When the user visits the cafe, they can view the personalized menu on their device (tablet or smartphone).

[1087] For example, if user A has frequently ordered "latte" in the past, the server will display a screen recommending "latte" the next time the user orders, allowing the user to complete their order smoothly.

[1088] Example prompt sentence:

[1089] I'd like to order the same drink as last time.

[1090] 3. Peak time analysis and staff shift optimization

[1091] The server collects store visit data and uses generative AI models to identify peak and off-peak times, leveraging information from point-of-sale systems and sensor data. Once peak times are identified, the server generates recommendations to optimize staff shifts for those times.

[1092] For example, if an analysis of customer visit data reveals that peak hours are between 9:00 and 11:00, the server will create a shift plan that allocates more staff to these hours, thereby speeding up service and improving customer satisfaction.

[1093] Example prompt sentence:

[1094] "I'd like you to analyze whether the number of customers is high or low between 9:00 and 11:00."

[1095] 4. Introducing smart tables

[1096] Each table in the cafe is equipped with a touchscreen, which customers can use to order and pay. When a customer enters their order on the touchscreen, the information is sent to the server in real time and added to the order list. Payment is also processed through the touchscreen, where the total amount is displayed and payment can be completed.

[1097] For example, if User C uses the touchscreen to order a sandwich and coffee, this order information is immediately sent to the server and notified to the kitchen staff. When paying, the total amount is displayed on the touchscreen, and User C can complete the payment on the spot.

[1098] Example prompt sentence:

[1099] "I want to order a sandwich and coffee from the touchscreen at my table."

[1100] These features allow cafe customers to spend their time comfortably and efficiently, and cafe operators can improve their profitability and customer service quality. This invention is extremely useful in modern cafe culture, and brings great benefits to both customers and cafe operators.

[1101] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1102] Seat reservation and management system

[1103] Step 1: Check seat availability

[1104] The server collects data from sensors installed on the seats and checks the seat availability in real time. It takes the sensor data as input and stores the status of each seat as "vacant" or "occupied" in a database.

[1105] Specific behavior:

[1106] If the sensor reports the occupancy of seat A as "vacant", the server updates the status as "vacant" in the database.

[1107] Input: Seat status data from sensors

[1108] Output: Updated seat state database

[1109] Step 2: Accepting a booking request

[1110] A user accesses the online reservation system using a terminal, inputs the desired date and time, and submits a reservation request. The reservation request input on the terminal is sent to the server.

[1111] Specific behavior:

[1112] When a user enters a reservation for "2 hours from 8:00" and presses the send button, the information is sent to the server.

[1113] Input: Reservation request information from user (date and time)

[1114] Output: Booking request accepted on server side

[1115] Step 3: Secure and confirm your seat

[1116] The server searches the database for available seats for the specified date and time based on the received reservation request. If an available seat is found, the server confirms the reservation and sends a confirmation message to the user.

[1117] Specific behavior:

[1118] The server searches the database and confirms that seat B is available and the reservation is confirmed, after which a confirmation message is sent to the user.

[1119] Input: Database search based on reservation request

[1120] Output: Confirmed reservation information and a confirmation message

[1121] Customized Order Process

[1122] Step 1: Analyze your order history

[1123] The server retrieves past order history from the database and uses a generative AI model to generate a personalized menu for each user. The order history data is used as input and analyzed using the AI ​​model to create a recommendation list.

[1124] Specific behavior:

[1125] The server analyzes User A's past order history and determines that he frequently orders "lattes."

[1126] Input: User's order history data

[1127] Output: A personalized menu recommendation list

[1128] Step 2: Offer a personalized menu

[1129] When the user checks the menu on the terminal, the server displays the generated personalized menu. The user checks the menu provided to the terminal.

[1130] Specific behavior:

[1131] The server displays a recommendation menu including latte on User A's device.

[1132] Input: Personalized Menu Recommendation List

[1133] Output: Personalization menu displayed on device

[1134] Peak time analysis and staff shift optimization

[1135] Step 1: Collect store visit data

[1136] The server collects data on the number of customers and time periods from the POS system and sensors, and stores the collected data in a database.

[1137] Specific behavior:

[1138] The server retrieves the number of customers from 9:00 to 11:00 from the POS system.

[1139] Input: POS system and sensor data

[1140] Output: Updated store visit database

[1141] Step 2: Data analysis and shift optimization

[1142] The server analyzes the collected store visit data using a generative AI model to identify peak and off-peak times, and generates suggestions for optimizing staff shifts based on the identified peak times.

[1143] Specific behavior:

[1144] The server analyzes that "9:00 to 11:00" is the peak time and generates shift suggestions.

[1145] Input: Store visit data

[1146] Output: Peak time information and shift optimization suggestions

[1147] Introducing smart tables

[1148] Step 1: Enter your order on the touchscreen

[1149] Users input their orders using a touchscreen installed on the smart table, and the information is sent to the server in real time.

[1150] Specific behavior:

[1151] User C orders a "sandwich" and a "coffee" on the touchscreen.

[1152] Input: User's order information

[1153] Output: Order information sent to the server

[1154] Step 2: Add to order list

[1155] The server processes the received order information and adds it to the order list, which is then notified to the kitchen staff.

[1156] Specific behavior:

[1157] The server adds "sandwich" and "coffee" to the order list and notifies the kitchen.

[1158] Input: User's order information

[1159] Output: Updated order list and notification to the kitchen

[1160] Step 3: Payment Processing

[1161] The user pays using the touchscreen, and the server processes the payment information and displays the total amount.

[1162] Specific behavior:

[1163] User C enters credit card information on the touchscreen and completes the payment.

[1164] Input: User's payment information

[1165] Output: Total amount and payment completion status

[1166] (Application example 1)

[1167] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1168] In today's cafe operations, it is extremely important for users to be able to smoothly reserve seats, quickly place personalized orders, and optimize staff shifts during peak times. However, existing systems have difficulty comprehensively resolving these issues. In particular, the lack of integration with smart devices and real-time service quality maintenance reduces user experience and operational efficiency. Therefore, a comprehensive system is needed to improve cafe operational efficiency and user satisfaction.

[1169] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1170] In this invention, the server includes means for managing seat reservations using the generated artificial intelligence, means for allowing users to reserve seats online, means for analyzing the cafe's congestion status in real time, means for managing orders and payments using smart devices, means for analyzing peak times and optimizing staff shifts, means for integrating and managing order histories through smart devices, and means for maintaining the quality of service provided using smart devices, thereby enabling cafe customers to smoothly reserve seats and quickly order from personalized menus, and further improving overall operational efficiency and service quality through the integrated smart device system.

[1171] "Generated artificial intelligence" is an AI system developed to analyze user behavior and cafe operation data, and make individual service suggestions and optimize operations.

[1172] The "means for managing seat reservations" is a system that allows users to reserve seats at cafes online in advance and efficiently manages the reservation information.

[1173] "Means for users to reserve seats online" refers to an application or web service that allows users to check information about available seats at a cafe via the Internet and confirm reservations.

[1174] "A means for analyzing the congestion status of a cafe in real time" is a system that instantly analyzes data such as the number of customers and seat usage status to determine the current level of congestion in the cafe.

[1175] "Means for managing orders and payments using smart devices" refers to a system that allows users to easily place orders and pay using digital devices such as tablets and smartphones.

[1176] The "means of analyzing peak times and optimizing staff shifts" is a system that analyzes store visit data, identifies particularly busy times, and efficiently allocates staff shifts according to those times.

[1177] "Means for integrated management of order history via smart devices" is a system that centrally manages a user's past order history on a digital device and provides personalized services.

[1178] "Means for maintaining the quality of services provided using smart devices" refers to a system for ensuring consistency in customer service using smart devices and increasing customer satisfaction.

[1179] This invention is a system for improving the operational efficiency of cafes and increasing customer satisfaction. This system uses generated artificial intelligence to perform various management and optimization functions.

[1180] Specifically, the following measures will be implemented:

[1181] 1. Seat reservation and management system

[1182] The server allows users to reserve seats online and manages the reservation information. It is possible to analyze the cafe's congestion status in real time and provide information on available seats. This allows users to reserve seats from their smartphones and enjoy a comfortable stay in a reserved seat.

[1183] Example: When a user requests a reservation for "1 hour from 10:00" in the app, the server checks the availability of seats during that time period and reserves seat number 42.

[1184] 2. Customized Order Process

[1185] The server uses the generated AI to analyze the user's past order history and propose personalized menus, allowing users to quickly order items that suit their preferences and reduce waiting times. Order history is managed in an integrated manner through smart devices, making it possible to make personalized suggestions based on past order data.

[1186] Example: Based on User A's past order history, "Latte" and "New Product 1" are recommended.

[1187] 3. Peak time analysis and staff shift optimization

[1188] The server analyzes customer visit data and identifies peak times. Based on this information, it optimizes staff shifts and increases the efficiency of cafe operations. This allows staff to be appropriately allocated according to peak times, enabling the maintenance of service quality.

[1189] Example: The server identifies "08:00-10:00" as peak time and allocates more staff during this time period.

[1190] 4. Integrated management using smart devices

[1191] Users can use smart devices such as smartphones and tablets to reserve seats, place orders, and make payments at the cafe. The server integrates order history and seat information by linking with these smart devices, providing users with seamless service.

[1192] Example: A user places an order using the touchscreen on a smart table and completes payment through a smartphone app.

[1193] These functions are realized by the following hardware and software.

[1194] Hardware: Smartphones, tablets, touchscreens (smart tables)

[1195] Software: Python, Flask, SQLite for database management and AI analysis

[1196] The generative AI model is used to analyze user behavior and cafe operation data to make individual service suggestions and optimize operations.

[1197] Example prompt sentence:

[1198] Please recommend frequently ordered menu items based on User A's past order history.

[1199] + Past Order History:

[1200] latté

[1201] cappuccino

[1202] latté

[1203] + Recommendation results:

[1204] "Latte" (because it's frequently ordered)

[1205] "New product 1"

[1206] "New product 2"

[1207] This allows cafe customers to smoothly reserve seats, quickly order from personalized menus, and improve overall operational efficiency and service quality through an integrated smart device system.

[1208] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1209] Step 1:

[1210] A user makes a seat reservation request using a smartphone. The input includes the user ID, desired date and time, and seat type. The server receives this, checks the seat information in the database in real time, and checks whether there is availability on the specified date and time. If there is availability, the seat is reserved and the information is returned to the user's device. The output is the reservation confirmation information.

[1211] Step 2:

[1212] A user browses past order history to place a customized order on a smart device. The server retrieves the order history from the database based on the user ID and inputs it into a generative AI model. The generative AI model analyzes the order history based on the prompt text and suggests a personalized menu for each user. The input is the user ID, and the output is a list of recommended menu items.

[1213] Step 3:

[1214] The user decides what to order from the proposed menu and sends it to the server via their smart device. The server receives the order information, records it in an order database, and notifies the kitchen in real time. At the same time, payment information is generated and displayed on the user's device. The input is the order information, and the output is payment information and an order confirmation notification.

[1215] Step 4:

[1216] Cafe staff open the shift management screen and check the peak time analysis data from the server. The server analyzes customer visit data from past data sets, identifies peak times, and proposes appropriate shift allocation. Staff adjust their shifts based on the proposal. The input is past customer visit data, and the output is peak times and proposed shift allocation.

[1217] Step 5:

[1218] The user checks the order history and seat reservation information on a smart device. The server manages this data in real time and makes it accessible to the user at any time. The input is the user ID, and the output is the integrated order history and seat reservation information.

[1219] Step 6:

[1220] After placing an order, the user completes payment on their smart device. The payment information is sent to the server, and the payment process is carried out. Once payment is complete, a confirmation notice is sent to the user's device. The input is payment information, and the output is a payment confirmation notice.

[1221] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1222] A specific embodiment for implementing this invention will now be described. This invention utilizes the generated artificial intelligence and emotion engine to implement the following five main functions in order to provide a comfortable and efficient environment for cafe users and cafe management.

[1223] 1. Seat reservation and management system

[1224] The server manages seat reservations within the cafe. Users can check seat availability online and request a reservation. The server analyzes the congestion situation in real time and reserves an available seat. The reservation information includes the user ID, reservation time, and reserved seat number. This allows users to reserve a seat in advance and allows the cafe to optimize seat usage.

[1225] For example, if a user requests a reservation online for "2 hours from 08:00", the server checks the seat availability for that time period and provides an available seat. The user can then start working at that seat.

[1226] 2. Customized Order Process

[1227] The server analyzes the user's past order history and generates a personalized menu, allowing the user to quickly order items that suit their preferences. For example, if User A frequently ordered "latte" in the past, the server will recommend "latte" the next time the user orders.

[1228] For example, when User B visits a store, the server recommends "cappuccino" based on the user's previous order history, allowing User B to place the order quickly. This reduces waiting time and makes the ordering process smoother.

[1229] 3. Peak time analysis and staff shift optimization

[1230] The server analyzes customer visit data to identify peak and off-peak times, and then optimizes staff shifts based on this information. The server allocates more staff during peak times and fewer staff during off-peak times.

[1231] For example, if data analysis reveals that peak hours are between 9:00 and 11:00, shift A (8:00 and 12:00) can be staffed with more staff during this time period, resulting in faster service and improved customer satisfaction.

[1232] 4. Introducing smart tables

[1233] Each table is equipped with a touchscreen, which users can use to order and pay. The order is immediately sent to the server and added to the order list. Payment is also processed on the touchscreen, and the total amount is displayed. This allows users to complete all operations without leaving their table.

[1234] For example, if user C uses the touchscreen to order a sandwich and coffee, the order is immediately sent to the server and added to the order list. When paying, the total amount is displayed on the touchscreen, allowing user C to complete the payment immediately.

[1235] 5. Introducing the Emotion Engine

[1236] The emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotions in real time. The server then uses this emotional data to optimize the user experience. For example, if the user is feeling stressed, it will provide relaxation menus and suggestions.

[1237] For example, if User D visits a cafe and the emotion engine detects stress from User D's facial expression while placing an order on the touchscreen, the server will suggest a "special menu for relaxation." User D can accept this suggestion and spend time in a relaxing environment.

[1238] These features allow cafe customers to spend their time comfortably and efficiently, and cafes can improve their profitability and customer service quality. This invention is extremely useful in modern cafe culture, and brings great benefits to both customers and cafe operators.

[1239] The processing flow will be explained below.

[1240] Seat reservation and management system

[1241] Step 1:

[1242] Users search online for available seats at cafes and enter the desired date and time.

[1243] Step 2:

[1244] The server checks the seat availability in the system based on the provided date and time. The server analyzes the seat availability in real time.

[1245] Step 3:

[1246] The server presents the available seats to the user, who then selects the seat they desire.

[1247] Step 4:

[1248] After the user selects a seat, the server marks the seat as reserved and saves the reservation information in a database.

[1249] Step 5:

[1250] The server sends the reservation confirmation information (reservation number, seat number, date and time) to the user, who receives the reservation confirmation.

[1251] Customized Order Process

[1252] Step 1:

[1253] A user places an order at a cafe by selecting the items they want to order via a touchscreen or an application.

[1254] Step 2:

[1255] The server checks the user's past order history and analyzes the user's preferences and purchasing patterns based on the past order data.

[1256] Step 3:

[1257] Based on the analysis results, the server proposes a personalized menu suitable for the user, and the user decides to order from the proposed menu.

[1258] Step 4:

[1259] Once the user places an order, the server sends it to the kitchen or barista, along with the order details (items, quantities, special requests, etc.).

[1260] Step 5:

[1261] The server stores the user's new order information in a database as an order history, which will improve personalized suggestions next time.

[1262] Peak time analysis and staff shift optimization

[1263] Step 1:

[1264] The server collects data on past visits to the cafe, including information such as the time of visit, the number of customers, and the items purchased.

[1265] Step 2:

[1266] The server analyzes the collected visitor data, identifies peak times (time periods with the highest number of customers), and uses data analysis algorithms to analyze visitor patterns.

[1267] Step 3:

[1268] The server optimizes staff shifts based on peak time information, deploying more staff during peak times and fewer staff during off-peak times.

[1269] Step 4:

[1270] The server notifies the staff of the optimized shift schedule, and the staff work according to the new shift schedule.

[1271] Step 5:

[1272] The server continuously collects actual store visit data and dynamically adjusts shift schedules, allowing for flexible response to fluctuations in peak times.

[1273] Introducing smart tables

[1274] Step 1:

[1275] The user sits at a table in a cafe and checks the menu using a touchscreen mounted on the table.

[1276] Step 2:

[1277] The user places an order via the touchscreen, where the order is selected and each item is added to the order list.

[1278] Step 3:

[1279] When the user confirms the order, the terminal (touch screen) sends the order information to the server, which receives the order information.

[1280] Step 4:

[1281] The server sends the order to the kitchen or barista, including the order details (item, quantity, special requests, etc.).

[1282] Step 5:

[1283] When the product ordered by the user is ready, a notification will appear on the touchscreen, and the user can confirm the notification and receive the product.

[1284] Step 6:

[1285] After the user has finished their meal, they pay on the touchscreen, and the server calculates the total amount and displays it on the touchscreen.

[1286] Step 7:

[1287] Once the user has completed the payment, the server resets the order list and prepares it for a new order.

[1288] Introducing the Emotion Engine

[1289] Step 1:

[1290] When a user enters a cafe, the device's camera captures the user's face, and the emotion engine recognizes their facial expressions and evaluates their emotional state in real time.

[1291] Step 2:

[1292] The server receives data from the emotion engine and analyzes the user's emotional state, determining emotional states such as stress, happiness, fatigue, etc.

[1293] Step 3:

[1294] When a user places an order on a touchscreen, the server uses emotional data to suggest personalized menu items, such as relaxing drinks or healthy foods to reduce stress.

[1295] Step 4:

[1296] It offers special offers and services based on the user's emotional state. If the user is feeling stressed, it may offer special promotions or discounts.

[1297] Step 5:

[1298] The server continuously collects user emotional data and dynamically adjusts the quality of service to optimize the user experience.

[1299] In this way, the system combined with the emotion engine provides personalized services according to the user's needs and emotional state, resulting in a more comfortable and satisfying cafe experience.

[1300] Example 2

[1301] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1302] Cafe operations face a wide range of challenges, including seat reservation management, order efficiency, peak time analysis, staff shift management, and even service optimization based on customer sentiment. Conventional systems have difficulty addressing these challenges in a unified manner, resulting in a decline in customer satisfaction and the operational efficiency of the cafe. The objective of this invention is to provide a system that solves these challenges and provides a comfortable and efficient cafe experience.

[1303] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for managing seat reservations using generated artificial intelligence, a means for allowing users to reserve seats online, a means for analyzing the cafe's congestion status in real time, a means for managing reservation information, a means for ordering and paying using a touch screen, and a means for analyzing user emotions and making suggestions using an emotion engine. This makes it possible to improve the efficiency of cafe management and user satisfaction.

[1304] "Generated artificial intelligence" refers to machine learning models that are trained to automatically collect and analyze data and deliver optimal results for a specific purpose.

[1305] "Means for managing seat reservations" refers to technology that supports the process of users checking seat availability online and making appropriate reservations.

[1306] "Means by which users can reserve seats online" refers to an interface that allows users to reserve seats remotely via devices such as a web browser or smartphone app.

[1307] "Means for analyzing the congestion status of a cafe in real time" refers to a system that analyzes data such as current seat occupancy status and order status in real time to determine the congestion status of a cafe.

[1308] "Means for managing reservation information" refers to a system that stores and manages user reservation details (user ID, reservation time, seat number, etc.) in a database and allows them to be updated and referenced as needed.

[1309] "Means for ordering and paying using a touchscreen" refers to technology that allows users to order and pay for cafe menu items using a touchscreen terminal installed on each table.

[1310] An "emotion engine" refers to an algorithm that analyzes emotions from a user's facial expressions and voice, and then provides appropriate responses and suggestions based on that emotional data.

[1311] "Means for analyzing user emotions and making suggestions" refers to technology that analyzes emotional data obtained by an emotion engine and suggests optimal services and products based on the user's current state.

[1312] "Means for analyzing past order history" refers to technology for analyzing a user's past order data and understanding the user's preferences and trends.

[1313] "Means for suggesting personalized menus" refers to a system for displaying and suggesting menus optimized for each individual user based on past order history and preference data.

[1314] "Means for analyzing customer visit data" refers to technology that collects and analyzes information about visits to a cafe (time, date, number of people, etc.) and identifies patterns of customer behavior.

[1315] "Means for identifying peak times" refers to a system for analyzing store visit data to identify the times when the cafe is most heavily used.

[1316] "Means for optimizing staff shifts" refers to technology that optimizes staff work schedules based on data on peak and off-peak times, achieving efficient personnel deployment.

[1317] MODE FOR CARRYING OUT THE INVENTION

[1318] A specific embodiment for implementing this invention will now be described. This invention utilizes the generated artificial intelligence and emotion engine to implement the following five main functions in order to provide a comfortable and efficient environment for cafe users and cafe management.

[1319] Seat reservation and management system

[1320] The server manages seat reservations within the cafe. Users check seat availability online and request a reservation. Specifically, the user opens the seat reservation page using a web browser or smartphone app and enters the desired time and necessary information. The device then sends this information to the server via the HTTPS protocol. The server accesses the database to check seat availability for the specified time slot and, if there is availability, confirms the reservation information.

[1321] For example, if a user requests a reservation online for "2 hours from 08:00," the server checks availability for that time slot and provides an available seat, allowing the user to begin working at that seat.

[1322] Customized Order Process

[1323] The server analyzes the user's past order history and generates a personalized menu. When a user opens the order page on their smartphone or tablet, the device queries the server for their past order history. The server retrieves the order history from the database and generates a recommended menu using a generative AI model.

[1324] For example, if user A has frequently ordered "latte" in the past, the server can recommend "latte" the next time user A orders, allowing user A to complete the order quickly.

[1325] Peak time analysis and staff shift optimization

[1326] The server analyzes visitor data and identifies peak and off-peak times. The server collects visitor data from the POS system and ordering system and analyzes peak and off-peak times for specific time periods. Data analysis uses aggregation functions and histograms.

[1327] For example, if data analysis reveals that peak hours are between 9:00 and 11:00, shift A (8:00 and 12:00) will be staffed with more staff during this time period to speed up service.

[1328] Introducing smart tables

[1329] Each table is equipped with a touchscreen, which users use to order and pay. Specifically, users use the touchscreen to view the cafe menu, select the items they want, and confirm their order. The terminal instantly transmits the order information to the server, and the total amount is displayed when payment is made.

[1330] For example, if user C uses the touchscreen to order a sandwich and coffee, the order is immediately sent to the server and added to the order list. When paying, the total amount is displayed on the touchscreen, allowing the user to complete the payment immediately.

[1331] Introducing the Emotion Engine

[1332] The emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotions in real time. The camera and microphone installed on the device capture the user's facial expressions and voice, which are then analyzed by the emotion engine. The server then uses the data sent from the emotion engine to suggest appropriate services and products.

[1333] For example, if User D visits a cafe and the emotion engine detects stress in User D's facial expression while placing an order on the touchscreen, the server will suggest a "special menu for relaxation." User D can accept this suggestion and spend time in a relaxing environment.

[1334] Example prompts to input to the generative AI model

[1335] Prompt text (seat reservation and management system): In a cafe seat reservation system, what happens when a user goes online and requests to reserve a seat for 2 hours starting at 08:00?

[1336] Prompt statement (customized ordering process): If User A has frequently ordered lattes in the past, what menu item will they see the next time they order?

[1337] Prompt (Peak time analysis and staff shift optimization): If the cafe's peak time is 09:00-11:00, what will be the staff shifts?

[1338] Prompt (Smart Table Introduction): What happens when a user orders a sandwich and coffee using the touchscreen?

[1339] Prompt (introduction of emotion engine): If the emotion engine detects that the user is stressed, how will the server respond?

[1340] Based on the above explanation, this system can provide cafe users with a comfortable and efficient environment and also realize effective operation management for cafe operators.

[1341] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1342] Seat reservation and management system

[1343] Step 1:

[1344] A user opens a seat reservation page online. As input, the user enters the desired reservation time (e.g., 2 hours from 08:00) and user ID into the reservation form. This sends a seat reservation request to the system.

[1345] Step 2:

[1346] The terminal sends the information entered by the user to the server. The terminal uses the HTTPS protocol to send the entered reservation information (user ID, desired reservation time) to the server. The accuracy of the data is maintained by properly formatting the input data before sending it.

[1347] Step 3:

[1348] The server checks seat availability. The server executes an SQL query against the database to retrieve seat availability for the specified time slot. The availability data returned from the database becomes the input for the server.

[1349] Step 4:

[1350] The server confirms and manages the reservation. If there are available seats in the specified time slot, the server saves the reservation information in the database and confirms the reservation. The reservation information (user ID, reservation time, seat number) is the output data of the server. If there are no available seats, an error message is generated.

[1351] Step 5:

[1352] The terminal receives the server's output data and displays the reservation confirmation information or an error message to the user. Specifically, the reservation number and seat number are displayed on the screen, allowing the user to confirm that the reservation has been confirmed.

[1353] Customized Order Process

[1354] Step 1:

[1355] The user opens the order page. As input, the user launches the ordering app and accesses the order page, which allows the system to prepare an order.

[1356] Step 2:

[1357] The terminal queries the server for past order history. Using the user ID as input, it requests past order data via the REST API. The terminal sends the input data to the server.

[1358] Step 3:

[1359] The server analyzes the order history. It retrieves the user's past order history from the database and analyzes it using a generative AI model. The past order data becomes the input data for the server.

[1360] Step 4:

[1361] The server generates a personalized recommended menu. Based on the generative AI model, it lists the most suitable menu items for the user. The recommended menu data becomes the server's output data.

[1362] Step 5:

[1363] The terminal displays the recommended menu to the user. The terminal receives output data from the server and displays the recommended menu on the screen. This allows the user to quickly select products and confirm their order.

[1364] Peak time analysis and staff shift optimization

[1365] Step 1:

[1366] The server collects store visit data. As input, it periodically obtains store visit data (time, number of people, order details) from the POS system and ordering system. This allows the server to obtain basic data for shift optimization.

[1367] Step 2:

[1368] The server analyzes the visitor data to identify peak and off-peak times. It aggregates the data and performs statistical analysis to analyze visitor patterns during specific time periods. The aggregated data is the input data, and the analysis results are the output data.

[1369] Step 3:

[1370] The server generates an optimal shift schedule. Based on the results of peak and off-peak times, it runs an algorithm to optimize staff shift schedules. The optimized shift schedule is the output data.

[1371] Step 4:

[1372] The device notifies staff of shift information. It receives optimized shift schedule data from the server and sends push notifications to each staff member's device (smartphone or tablet). The notification data is the input, and the displayed shift information is the output.

[1373] Step 5:

[1374] Staff work based on shifts. According to the notified shift information, staff perform their duties according to their respective working hours. This allows for efficient service provision during peak times.

[1375] Introducing smart tables

[1376] Step 1:

[1377] The user operates the touchscreen of the smart table. As input, the user browses the menu and selects the desired item on the touchscreen. This allows the user to prepare an order.

[1378] Step 2:

[1379] The terminal sends the order information to the server. The selected order information is formatted and sent to the server. The order items and user ID are included as input data.

[1380] Step 3:

[1381] The server processes the order information, receives the order information, adds the order to a list in the order processing system, and forwards the order to the kitchen. The processed order information is the output data.

[1382] Step 4:

[1383] The terminal prompts the user to complete the payment process. After the order is confirmed, a payment screen is displayed, allowing the user to select a payment method. The payment method and amount are included as input data.

[1384] Step 5:

[1385] The user completes the payment. The payment procedure is completed on the touch screen using the selected payment method. The payment completion is sent to the server, and the transaction data becomes the output data.

[1386] Introducing the Emotion Engine

[1387] Step 1:

[1388] The device captures the user's facial expressions and voice using a camera and microphone. The capture device takes the user's facial movements and tone of voice as input, which prepares the device for sentiment analysis.

[1389] Step 2:

[1390] The device sends the captured data to the emotion engine. The captured data is sent to the emotion engine in real time. The input data includes facial expressions and voice characteristics.

[1391] Step 3:

[1392] The emotion engine analyzes the emotion data. It analyzes the received data and identifies the user's emotion (e.g., happiness, stress, anger). The analysis results are output data.

[1393] Step 4:

[1394] The server generates suggestions based on the emotion data. Based on the data from the emotion engine, it generates suggestions to optimize the user experience (such as special menus or relaxation suggestions). The suggested data is the output.

[1395] Step 5:

[1396] The device displays the suggestions to the user, receives the suggestion data, and displays it on the touch screen, allowing the user to accept the appropriate suggestion.

[1397] Through the above processing steps, the system can provide efficient and comfortable service to cafe patrons.

[1398] (Application example 2)

[1399] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1400] Traditional cafe operations have not been able to efficiently manage seating or improve customer satisfaction. In particular, it is difficult to grasp the congestion situation of customers and provide personalized service based on each customer's order history. There is also a lack of means to manage staff shifts and analyze customer sentiment in real time to optimize the experience. As a result, customers are likely to have a low-satisfaction experience, and cafe operators also face challenges in providing efficient service.

[1401] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for managing seat reservations using the generated artificial intelligence, means for allowing users to reserve seats online, means for analyzing the cafe's congestion status in real time, means for managing reservation information, means for analyzing the user's facial expressions and voice in real time and recognizing emotions, means for optimizing the user experience based on the recognized emotion data, means for users to easily place orders using a smartphone or a head-mounted display, and means for providing analyzed peak time information to a manager. This enables efficient seat management and improved customer satisfaction.

[1402] "Generated artificial intelligence" is an artificial intelligence system that is automatically generated based on data and designed to perform specific tasks.

[1403] "Means for managing seat reservations" refers to a function that tracks and manages seat reservations in the cafe in real time and allows users to reserve seats online.

[1404] "A means for analyzing the congestion situation of a cafe in real time" is a system that collects and analyzes data on the flow of people within a cafe in real time to grasp the congestion situation.

[1405] "Means for managing reservation information" refers to a function that allows users to save and update data related to seat reservations and provide that information as needed.

[1406] "Means for analyzing users' facial expressions and voice in real time and recognizing their emotions" refers to technology that analyzes the facial expressions and tone of voice of cafe patrons to recognize their current emotional state.

[1407] The "means for optimizing user experience based on recognized emotional data" is a system for personalizing services at a cafe and providing an optimal experience by taking into account the recognized emotional state of the user.

[1408] "Means for users to easily place orders using a smartphone or head-mounted display" refers to technology that allows users to easily order products using a mobile device or head-mounted display.

[1409] The "means for providing analyzed peak time information to the administrator" is a system that identifies peak and off-peak times at the cafe through data analysis and provides that information to the cafe operator.

[1410] To implement this invention, the following system is required.

[1411] First, the server uses the generated artificial intelligence (AI) to manage seat reservations in the cafe in real time. An interface is provided so that users can reserve seats online. When a user submits a reservation request, the server analyzes the cafe's congestion status in real time and reserves an available seat. The reservation information includes the user ID, reservation time, and reserved seat number.

[1412] The server also uses the generated AI to analyze the user's past order history and propose a personalized menu for each user, allowing users to place orders more quickly using their smartphones or head-mounted displays.

[1413] The server also analyzes the cafe's visitor data to identify peak times. Based on the identified peak time information, it becomes possible to optimize staff shifts. This peak time information is also provided to managers for efficient shift management and improved customer service.

[1414] The system also includes an AI engine that analyzes the user's facial expressions and voice in real time to recognize emotions. For example, it uses the smartphone camera and head-mounted display sensors to analyze the user's facial expressions and tone of voice. Based on the obtained emotional data, the server provides functions to optimize the user experience. For example, if the user is feeling stressed, it will suggest a menu that will help them relax.

[1415] These processes are performed using the following specific software and hardware. Server-side processing mainly uses Flask (a web application framework) and Firebase (database management). Face recognition and emotion analysis use OpenCV (a face recognition library) and EmotionRecognizer (a specific emotion recognition library). The user interface is provided via a smartphone application and a head-mounted display.

[1416] A concrete example is given below. Consider a scenario in which a user visits a cafe and tries to check a seat reservation on their smartphone. The user requests a reservation for "two hours from 8:00," and the server checks the seat availability for that time period and provides an available seat. At that time, the server analyzes the user's facial expression, and if the user appears stressed, it suggests a "special menu to help them relax."

[1417] An example prompt is:

[1418] "I'm at the cafe and I'm feeling a bit stressed. Can you suggest a menu item that will help me relax?"

[1419] In this way, users can have a comfortable and personalized cafe experience.

[1420] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1421] Step 1:

[1422] A user accesses a cafe's seat reservation system using a smartphone or head-mounted display.

[1423] Input: User ID, desired reservation time

[1424] Output: Show seat availability

[1425] Specific operation: The server receives a reservation request from a user, checks the current seat availability from the database, and displays available seats to the user in real time.

[1426] Step 2:

[1427] The user reserves the desired vacant seat.

[1428] Input: Seat number, reservation time

[1429] Output: Reservation confirmation information

[1430] Specific operation: The server saves the user's selected seat number and reservation time in the database and notifies the user that the reservation is confirmed. The reservation information includes the user ID, reservation time, and reserved seat number.

[1431] Step 3:

[1432] The server analyzes the user's past ordering history and generates a personalized menu.

[1433] Input: User ID, past order history

[1434] Output: personalized menu

[1435] How it works: The server uses the generative AI model to analyze the user's past order data. Based on the results, it generates a menu tailored to the user's preferences and presents it to the user.

[1436] Step 4:

[1437] The user places an order using a smartphone or a head-mounted display.

[1438] Enter: personalized menu

[1439] Output: Order confirmation information

[1440] Specific operation: The user selects from the presented personalized menu and enters an order. The server receives the order, generates an order confirmation, and notifies the user.

[1441] Step 5:

[1442] The server analyzes visit data and identifies peak times.

[1443] Input: Store visit data

[1444] Output: Peak time information

[1445] Specific operation: The server analyzes the collected visitor data and identifies peak times when the number of visitors is high during a specific time period. This information is used to optimize staff shifts.

[1446] Step 6:

[1447] Servers optimize staff shifts according to peak times.

[1448] Input: Peak time information, current shift information

[1449] Output: Optimized shift schedule

[1450] Specific operation: The server generates a new shift schedule that optimizes staff allocation based on peak time information and current staff shift information, and provides the generated shift schedule to the manager.

[1451] Step 7:

[1452] The user's facial expressions and voice are captured by sensors on a smartphone or head-mounted display, and their emotions are analyzed.

[1453] Input: Captured facial and voice data

[1454] Output: Recognized emotion data

[1455] How it works: The device captures the user's facial expressions and voice data, which are then analyzed by a generative AI model. The analysis results identify the user's current emotional state.

[1456] Step 8:

[1457] The server provides a service that optimizes the user experience based on the recognized emotion data.

[1458] Input: Recognized emotion data

[1459] Output: Optimized service (e.g., relaxation menu suggestions)

[1460] Specific operation: Based on the recognized emotion data, the server provides services that optimize the user experience, such as suggesting relaxation menus to users who are feeling stressed.

[1461] Example prompt sentence:

[1462] "I'm at the cafe and I'm feeling a bit stressed. Can you suggest a menu item that will help me relax?"

[1463] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1464] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1465] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1466] [Fourth embodiment]

[1467] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1468] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1469] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1470] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1471] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1472] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1473] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1474] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1475] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1476] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1477] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1478] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1479] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1480] A specific embodiment for implementing this invention will now be described. This invention utilizes the generated artificial intelligence to implement the following four main functions in order to provide a comfortable and efficient environment for cafe users and cafe management.

[1481] 1. Seat reservation and management system

[1482] The server manages seat reservations within the cafe. Users can check seat availability online and request a reservation. The server analyzes the congestion situation in real time and reserves an available seat. The reservation information includes the user ID, reservation time, and reserved seat number. This allows users to reserve a seat in advance and allows the cafe to optimize seat usage.

[1483] For example, if a user requests a reservation online for "2 hours from 08:00", the server checks the seat availability for that time period and provides an available seat. The user can then start working at that seat.

[1484] 2. Customized Order Process

[1485] The server analyzes the user's past order history and generates a personalized menu, allowing the user to quickly order items that suit their preferences. For example, if User A frequently ordered "latte" in the past, the server will recommend "latte" the next time the user orders.

[1486] For example, when User B visits a store, the server recommends "cappuccino" based on the user's previous order history, allowing User B to place the order quickly. This reduces waiting time and makes the ordering process smoother.

[1487] 3. Peak time analysis and staff shift optimization

[1488] The server analyzes customer visit data to identify peak and off-peak times, and then optimizes staff shifts based on this information. The server allocates more staff during peak times and fewer staff during off-peak times.

[1489] For example, if data analysis reveals that peak hours are between 9:00 and 11:00, shift A (8:00 and 12:00) can be staffed with more staff during this time period, resulting in faster service and improved customer satisfaction.

[1490] 4. Introducing smart tables

[1491] Each table is equipped with a touchscreen, which users can use to order and pay. The order is immediately sent to the server and added to the order list. Payment is also processed on the touchscreen, and the total amount is displayed. This allows users to complete all operations without leaving their table.

[1492] For example, if user C uses the touchscreen to order a sandwich and coffee, the order is immediately sent to the server and added to the order list. When paying, the total amount is displayed on the touchscreen, allowing user C to complete the payment immediately.

[1493] These features allow cafe customers to spend their time comfortably and efficiently, and cafes can improve their profitability and customer service quality. This invention is extremely useful in modern cafe culture, and brings great benefits to both customers and cafe operators.

[1494] The processing flow will be explained below.

[1495] Seat reservation and management system

[1496] Step 1:

[1497] Users search online for available seats at cafes and enter the desired date and time.

[1498] Step 2:

[1499] The server checks the seat availability in the system based on the provided date and time. The server analyzes the seat availability in real time.

[1500] Step 3:

[1501] The server presents the available seats to the user, who then selects the seat they desire.

[1502] Step 4:

[1503] After the user selects a seat, the server marks the seat as reserved and saves the reservation information in a database.

[1504] Step 5:

[1505] The server sends the reservation confirmation information (reservation number, seat number, date and time) to the user, who receives the reservation confirmation.

[1506] Customized Order Process

[1507] Step 1:

[1508] A user places an order at a cafe by selecting the items they want to order via a touchscreen or an application.

[1509] Step 2:

[1510] The server checks the user's past order history and analyzes the user's preferences and purchasing patterns based on the past order data.

[1511] Step 3:

[1512] Based on the analysis results, the server proposes a personalized menu suitable for the user, and the user decides to order from the proposed menu.

[1513] Step 4:

[1514] Once the user places an order, the server sends it to the kitchen or barista, along with the order details (items, quantities, special requests, etc.).

[1515] Step 5:

[1516] The server stores the user's new order information in a database as an order history, which will improve personalized suggestions next time.

[1517] Peak time analysis and staff shift optimization

[1518] Step 1:

[1519] The server collects data on past visits to the cafe, including information such as the time of visit, the number of customers, and the items purchased.

[1520] Step 2:

[1521] The server analyzes the collected visitor data, identifies peak times (time periods with the highest number of customers), and uses data analysis algorithms to analyze visitor patterns.

[1522] Step 3:

[1523] The server optimizes staff shifts based on peak time information, deploying more staff during peak times and fewer staff during off-peak times.

[1524] Step 4:

[1525] The server notifies the staff of the optimized shift schedule, and the staff work according to the new shift schedule.

[1526] Step 5:

[1527] The server continuously collects actual store visit data and dynamically adjusts shift schedules, allowing for flexible response to fluctuations in peak times.

[1528] Introducing smart tables

[1529] Step 1:

[1530] The user sits at a table in a cafe and checks the menu using a touchscreen mounted on the table.

[1531] Step 2:

[1532] The user places an order via the touchscreen, where the order is selected and each item is added to the order list.

[1533] Step 3:

[1534] When the user confirms the order, the terminal (touch screen) sends the order information to the server, which receives the order information.

[1535] Step 4:

[1536] The server sends the order to the kitchen or barista, including the order details (item, quantity, special requests, etc.).

[1537] Step 5:

[1538] When the product ordered by the user is ready, a notification will appear on the touchscreen, and the user can confirm the notification and receive the product.

[1539] Step 6:

[1540] After the user has finished their meal, they pay on the touchscreen, and the server calculates the total amount and displays it on the touchscreen.

[1541] Step 7:

[1542] Once the user has completed the payment, the server resets the order list and prepares it for a new order.

[1543] Example 1

[1544] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1545] Traditional cafes often lack efficiency in many areas, such as seat reservations, personalized orders, and staff shift management. In particular, systems to ensure users' comfort in the cafe are inadequate, making it difficult to manage crowds and speed up user orders.

[1546] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1547] In this invention, the server includes means for managing seat reservations using the generated artificial intelligence, means for users to reserve seats online, means for analyzing the cafe's congestion status in real time, means for managing reservation information, means for generating personalized menus based on users' past ordering history, and means for ordering and paying using the touch screen of the smart table, thereby enabling efficient seat management, providing personalized service to users, and efficient operation even when the cafe is crowded.

[1548] "Generated artificial intelligence" is an intelligent system that analyzes users' past behavior and the situation within the cafe, and is used to manage seats and optimize orders.

[1549] "Seat reservation" refers to the act of a user reserving a seat in a cafe online in advance.

[1550] "Cafe congestion status" refers to the number of customers in the cafe and the number of available seats shown in real time.

[1551] "Reservation information" refers to data relating to a seat reservation, including a user ID, reservation time, and seat number.

[1552] A "personalized menu" refers to a special menu that is individually provided based on a user's past ordering history and preferences.

[1553] A "smart table" is a table installed in a cafe that has touchscreen functionality and is a device that allows users to order and pay.

[1554] "Touchscreen" refers to an input device that a user can operate by touching the screen.

[1555] "Order history" refers to a record of orders a user has made at a cafe in the past.

[1556] "Peak time" refers to the time period when the number of customers in the cafe is particularly high.

[1557] "Staff shift optimization" refers to making adjustments to deploy staff most effectively based on store visit data and peak time analysis.

[1558] MODE FOR CARRYING OUT THE INVENTION

[1559] This invention utilizes the generated artificial intelligence to implement the following four main functions in order to provide a comfortable and efficient environment for cafe users and cafe operators.

[1560] 1. Seat reservation and management system

[1561] The server manages seat reservations within the cafe. Users access a dedicated reservation system online and request a seat reservation by entering the desired date and time. The server analyzes the congestion situation in real time based on data from sensors installed on the seats within the cafe and reserves an available seat. Reservation information, including the user ID, reservation time, and reserved seat number, is saved in a database.

[1562] For example, if a user requests a reservation online for "2 hours from 08:00", the server checks the availability of seats during that time slot and provides an available seat. A confirmation message is sent to the user, and the user can begin working at that seat.

[1563] Example prompt sentence:

[1564] "I'd like to reserve a seat at a cafe for two hours starting at 8 o'clock."

[1565] 2. Customized Order Process

[1566] The server analyzes the user's past order history and generates a personalized menu using a generative AI model. This generative AI model creates a recommendation list taking into account the user's preferences and order frequency. When the user visits the cafe, they can view the personalized menu on their device (tablet or smartphone).

[1567] For example, if user A has frequently ordered "latte" in the past, the server will display a screen recommending "latte" the next time the user orders, allowing the user to complete their order smoothly.

[1568] Example prompt sentence:

[1569] I'd like to order the same drink as last time.

[1570] 3. Peak time analysis and staff shift optimization

[1571] The server collects store visit data and uses generative AI models to identify peak and off-peak times, leveraging information from point-of-sale systems and sensor data. Once peak times are identified, the server generates recommendations to optimize staff shifts for those times.

[1572] For example, if an analysis of customer visit data reveals that peak hours are between 9:00 and 11:00, the server will create a shift plan that allocates more staff to these hours, thereby speeding up service and improving customer satisfaction.

[1573] Example prompt sentence:

[1574] "I'd like you to analyze whether the number of customers is high or low between 9:00 and 11:00."

[1575] 4. Introducing smart tables

[1576] Each table in the cafe is equipped with a touchscreen, which customers can use to order and pay. When a customer enters their order on the touchscreen, the information is sent to the server in real time and added to the order list. Payment is also processed through the touchscreen, where the total amount is displayed and payment can be completed.

[1577] For example, if User C uses the touchscreen to order a sandwich and coffee, this order information is immediately sent to the server and notified to the kitchen staff. When paying, the total amount is displayed on the touchscreen, and User C can complete the payment on the spot.

[1578] Example prompt sentence:

[1579] "I want to order a sandwich and coffee from the touchscreen at my table."

[1580] These features allow cafe customers to spend their time comfortably and efficiently, and cafe operators can improve their profitability and customer service quality. This invention is extremely useful in modern cafe culture, and brings great benefits to both customers and cafe operators.

[1581] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1582] Seat reservation and management system

[1583] Step 1: Check seat availability

[1584] The server collects data from sensors installed on the seats and checks the seat availability in real time. It takes the sensor data as input and stores the status of each seat as "vacant" or "occupied" in a database.

[1585] Specific behavior:

[1586] If the sensor reports the occupancy of seat A as "vacant", the server updates the status as "vacant" in the database.

[1587] Input: Seat status data from sensors

[1588] Output: Updated seat state database

[1589] Step 2: Accepting a booking request

[1590] A user accesses the online reservation system using a terminal, inputs the desired date and time, and submits a reservation request. The reservation request input on the terminal is sent to the server.

[1591] Specific behavior:

[1592] When a user enters a reservation for "2 hours from 8:00" and presses the send button, the information is sent to the server.

[1593] Input: Reservation request information from user (date and time)

[1594] Output: Booking request accepted on server side

[1595] Step 3: Secure and confirm your seat

[1596] The server searches the database for available seats for the specified date and time based on the received reservation request. If an available seat is found, the server confirms the reservation and sends a confirmation message to the user.

[1597] Specific behavior:

[1598] The server searches the database and confirms that seat B is available and the reservation is confirmed, after which a confirmation message is sent to the user.

[1599] Input: Database search based on reservation request

[1600] Output: Confirmed reservation information and a confirmation message

[1601] Customized Order Process

[1602] Step 1: Analyze your order history

[1603] The server retrieves past order history from the database and uses a generative AI model to generate a personalized menu for each user. The order history data is used as input and analyzed using the AI ​​model to create a recommendation list.

[1604] Specific behavior:

[1605] The server analyzes User A's past order history and determines that he frequently orders "lattes."

[1606] Input: User's order history data

[1607] Output: A personalized menu recommendation list

[1608] Step 2: Offer a personalized menu

[1609] When the user checks the menu on the terminal, the server displays the generated personalized menu. The user checks the menu provided to the terminal.

[1610] Specific behavior:

[1611] The server displays a recommendation menu including latte on User A's device.

[1612] Input: Personalized Menu Recommendation List

[1613] Output: Personalization menu displayed on device

[1614] Peak time analysis and staff shift optimization

[1615] Step 1: Collect store visit data

[1616] The server collects data on the number of customers and time periods from the POS system and sensors, and stores the collected data in a database.

[1617] Specific behavior:

[1618] The server retrieves the number of customers from 9:00 to 11:00 from the POS system.

[1619] Input: POS system and sensor data

[1620] Output: Updated store visit database

[1621] Step 2: Data analysis and shift optimization

[1622] The server analyzes the collected store visit data using a generative AI model to identify peak and off-peak times, and generates suggestions for optimizing staff shifts based on the identified peak times.

[1623] Specific behavior:

[1624] The server analyzes that "9:00 to 11:00" is the peak time and generates shift suggestions.

[1625] Input: Store visit data

[1626] Output: Peak time information and shift optimization suggestions

[1627] Introducing smart tables

[1628] Step 1: Enter your order on the touchscreen

[1629] Users input their orders using a touchscreen installed on the smart table, and the information is sent to the server in real time.

[1630] Specific behavior:

[1631] User C orders a "sandwich" and a "coffee" on the touchscreen.

[1632] Input: User's order information

[1633] Output: Order information sent to the server

[1634] Step 2: Add to order list

[1635] The server processes the received order information and adds it to the order list, which is then notified to the kitchen staff.

[1636] Specific behavior:

[1637] The server adds "sandwich" and "coffee" to the order list and notifies the kitchen.

[1638] Input: User's order information

[1639] Output: Updated order list and notification to the kitchen

[1640] Step 3: Payment Processing

[1641] The user pays using the touchscreen, and the server processes the payment information and displays the total amount.

[1642] Specific behavior:

[1643] User C enters credit card information on the touchscreen and completes the payment.

[1644] Input: User's payment information

[1645] Output: Total amount and payment completion status

[1646] (Application example 1)

[1647] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1648] In today's cafe operations, it is extremely important for users to be able to smoothly reserve seats, quickly place personalized orders, and optimize staff shifts during peak times. However, existing systems have difficulty comprehensively resolving these issues. In particular, the lack of integration with smart devices and real-time service quality maintenance reduces user experience and operational efficiency. Therefore, a comprehensive system is needed to improve cafe operational efficiency and user satisfaction.

[1649] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1650] In this invention, the server includes means for managing seat reservations using the generated artificial intelligence, means for allowing users to reserve seats online, means for analyzing the cafe's congestion status in real time, means for managing orders and payments using smart devices, means for analyzing peak times and optimizing staff shifts, means for integrating and managing order histories through smart devices, and means for maintaining the quality of service provided using smart devices, thereby enabling cafe customers to smoothly reserve seats and quickly order from personalized menus, and further improving overall operational efficiency and service quality through the integrated smart device system.

[1651] "Generated artificial intelligence" is an AI system developed to analyze user behavior and cafe operation data, and make individual service suggestions and optimize operations.

[1652] The "means for managing seat reservations" is a system that allows users to reserve seats at cafes online in advance and efficiently manages the reservation information.

[1653] "Means for users to reserve seats online" refers to an application or web service that allows users to check information about available seats at a cafe via the Internet and confirm reservations.

[1654] "A means for analyzing the congestion status of a cafe in real time" is a system that instantly analyzes data such as the number of customers and seat usage status to determine the current level of congestion in the cafe.

[1655] "Means for managing orders and payments using smart devices" refers to a system that allows users to easily place orders and pay using digital devices such as tablets and smartphones.

[1656] The "means of analyzing peak times and optimizing staff shifts" is a system that analyzes store visit data, identifies particularly busy times, and efficiently allocates staff shifts according to those times.

[1657] "Means for integrated management of order history via smart devices" is a system that centrally manages a user's past order history on a digital device and provides personalized services.

[1658] "Means for maintaining the quality of services provided using smart devices" refers to a system for ensuring consistency in customer service using smart devices and increasing customer satisfaction.

[1659] This invention is a system for improving the operational efficiency of cafes and increasing customer satisfaction. This system uses generated artificial intelligence to perform various management and optimization functions.

[1660] Specifically, the following measures will be implemented:

[1661] 1. Seat reservation and management system

[1662] The server allows users to reserve seats online and manages the reservation information. It is possible to analyze the cafe's congestion status in real time and provide information on available seats. This allows users to reserve seats from their smartphones and enjoy a comfortable stay in a reserved seat.

[1663] Example: When a user requests a reservation for "1 hour from 10:00" in the app, the server checks the availability of seats during that time period and reserves seat number 42.

[1664] 2. Customized Order Process

[1665] The server uses the generated AI to analyze the user's past order history and propose personalized menus, allowing users to quickly order items that suit their preferences and reduce waiting times. Order history is managed in an integrated manner through smart devices, making it possible to make personalized suggestions based on past order data.

[1666] Example: Based on User A's past order history, "Latte" and "New Product 1" are recommended.

[1667] 3. Peak time analysis and staff shift optimization

[1668] The server analyzes customer visit data and identifies peak times. Based on this information, it optimizes staff shifts and increases the efficiency of cafe operations. This allows staff to be appropriately allocated according to peak times, enabling the maintenance of service quality.

[1669] Example: The server identifies "08:00-10:00" as peak time and allocates more staff during this time period.

[1670] 4. Integrated management using smart devices

[1671] Users can use smart devices such as smartphones and tablets to reserve seats, place orders, and make payments at the cafe. The server integrates order history and seat information by linking with these smart devices, providing users with seamless service.

[1672] Example: A user places an order using the touchscreen on a smart table and completes payment through a smartphone app.

[1673] These functions are realized by the following hardware and software.

[1674] Hardware: Smartphones, tablets, touchscreens (smart tables)

[1675] Software: Python, Flask, SQLite for database management and AI analysis

[1676] The generative AI model is used to analyze user behavior and cafe operation data to make individual service suggestions and optimize operations.

[1677] Example prompt sentence:

[1678] Please recommend frequently ordered menu items based on User A's past order history.

[1679] + Past Order History:

[1680] latté

[1681] cappuccino

[1682] latté

[1683] + Recommendation results:

[1684] "Latte" (because it's frequently ordered)

[1685] "New product 1"

[1686] "New product 2"

[1687] This allows cafe customers to smoothly reserve seats, quickly order from personalized menus, and improve overall operational efficiency and service quality through an integrated smart device system.

[1688] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1689] Step 1:

[1690] A user makes a seat reservation request using a smartphone. The input includes the user ID, desired date and time, and seat type. The server receives this, checks the seat information in the database in real time, and checks whether there is availability on the specified date and time. If there is availability, the seat is reserved and the information is returned to the user's device. The output is the reservation confirmation information.

[1691] Step 2:

[1692] A user browses past order history to place a customized order on a smart device. The server retrieves the order history from the database based on the user ID and inputs it into a generative AI model. The generative AI model analyzes the order history based on the prompt text and suggests a personalized menu for each user. The input is the user ID, and the output is a list of recommended menu items.

[1693] Step 3:

[1694] The user decides what to order from the proposed menu and sends it to the server via their smart device. The server receives the order information, records it in an order database, and notifies the kitchen in real time. At the same time, payment information is generated and displayed on the user's device. The input is the order information, and the output is payment information and an order confirmation notification.

[1695] Step 4:

[1696] Cafe staff open the shift management screen and check the peak time analysis data from the server. The server analyzes customer visit data from past data sets, identifies peak times, and proposes appropriate shift allocation. Staff adjust their shifts based on the proposal. The input is past customer visit data, and the output is peak times and proposed shift allocation.

[1697] Step 5:

[1698] The user checks the order history and seat reservation information on a smart device. The server manages this data in real time and makes it accessible to the user at any time. The input is the user ID, and the output is the integrated order history and seat reservation information.

[1699] Step 6:

[1700] After placing an order, the user completes payment on their smart device. The payment information is sent to the server, and the payment process is carried out. Once payment is complete, a confirmation notice is sent to the user's device. The input is payment information, and the output is a payment confirmation notice.

[1701] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1702] A specific embodiment for implementing this invention will now be described. This invention utilizes the generated artificial intelligence and emotion engine to implement the following five main functions in order to provide a comfortable and efficient environment for cafe users and cafe management.

[1703] 1. Seat reservation and management system

[1704] The server manages seat reservations within the cafe. Users can check seat availability online and request a reservation. The server analyzes the congestion situation in real time and reserves an available seat. The reservation information includes the user ID, reservation time, and reserved seat number. This allows users to reserve a seat in advance and allows the cafe to optimize seat usage.

[1705] For example, if a user requests a reservation online for "2 hours from 08:00", the server checks the seat availability for that time period and provides an available seat. The user can then start working at that seat.

[1706] 2. Customized Order Process

[1707] The server analyzes the user's past order history and generates a personalized menu, allowing the user to quickly order items that suit their preferences. For example, if User A frequently ordered "latte" in the past, the server will recommend "latte" the next time the user orders.

[1708] For example, when User B visits a store, the server recommends "cappuccino" based on the user's previous order history, allowing User B to place the order quickly. This reduces waiting time and makes the ordering process smoother.

[1709] 3. Peak time analysis and staff shift optimization

[1710] The server analyzes customer visit data to identify peak and off-peak times, and then optimizes staff shifts based on this information. The server allocates more staff during peak times and fewer staff during off-peak times.

[1711] For example, if data analysis reveals that peak hours are between 9:00 and 11:00, shift A (8:00 and 12:00) can be staffed with more staff during this time period, resulting in faster service and improved customer satisfaction.

[1712] 4. Introducing smart tables

[1713] Each table is equipped with a touchscreen, which users can use to order and pay. The order is immediately sent to the server and added to the order list. Payment is also processed on the touchscreen, and the total amount is displayed. This allows users to complete all operations without leaving their table.

[1714] For example, if user C uses the touchscreen to order a sandwich and coffee, the order is immediately sent to the server and added to the order list. When paying, the total amount is displayed on the touchscreen, allowing user C to complete the payment immediately.

[1715] 5. Introducing the Emotion Engine

[1716] The emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotions in real time. The server then uses this emotional data to optimize the user experience. For example, if the user is feeling stressed, it will provide relaxation menus and suggestions.

[1717] For example, if User D visits a cafe and the emotion engine detects stress from User D's facial expression while placing an order on the touchscreen, the server will suggest a "special menu for relaxation." User D can accept this suggestion and spend time in a relaxing environment.

[1718] These features allow cafe customers to spend their time comfortably and efficiently, and cafes can improve their profitability and customer service quality. This invention is extremely useful in modern cafe culture, and brings great benefits to both customers and cafe operators.

[1719] The processing flow will be explained below.

[1720] Seat reservation and management system

[1721] Step 1:

[1722] Users search online for available seats at cafes and enter the desired date and time.

[1723] Step 2:

[1724] The server checks the seat availability in the system based on the provided date and time. The server analyzes the seat availability in real time.

[1725] Step 3:

[1726] The server presents the available seats to the user, who then selects the seat they desire.

[1727] Step 4:

[1728] After the user selects a seat, the server marks the seat as reserved and saves the reservation information in a database.

[1729] Step 5:

[1730] The server sends the reservation confirmation information (reservation number, seat number, date and time) to the user, who receives the reservation confirmation.

[1731] Customized Order Process

[1732] Step 1:

[1733] A user places an order at a cafe by selecting the items they want to order via a touchscreen or an application.

[1734] Step 2:

[1735] The server checks the user's past order history and analyzes the user's preferences and purchasing patterns based on the past order data.

[1736] Step 3:

[1737] Based on the analysis results, the server proposes a personalized menu suitable for the user, and the user decides to order from the proposed menu.

[1738] Step 4:

[1739] Once the user places an order, the server sends it to the kitchen or barista, along with the order details (items, quantities, special requests, etc.).

[1740] Step 5:

[1741] The server stores the user's new order information in a database as an order history, which will improve personalized suggestions next time.

[1742] Peak time analysis and staff shift optimization

[1743] Step 1:

[1744] The server collects data on past visits to the cafe, including information such as the time of visit, the number of customers, and the items purchased.

[1745] Step 2:

[1746] The server analyzes the collected visitor data, identifies peak times (time periods with the highest number of customers), and uses data analysis algorithms to analyze visitor patterns.

[1747] Step 3:

[1748] The server optimizes staff shifts based on peak time information, deploying more staff during peak times and fewer staff during off-peak times.

[1749] Step 4:

[1750] The server notifies the staff of the optimized shift schedule, and the staff work according to the new shift schedule.

[1751] Step 5:

[1752] The server continuously collects actual store visit data and dynamically adjusts shift schedules, allowing for flexible response to fluctuations in peak times.

[1753] Introducing smart tables

[1754] Step 1:

[1755] The user sits at a table in a cafe and checks the menu using a touchscreen mounted on the table.

[1756] Step 2:

[1757] The user places an order via the touchscreen, where the order is selected and each item is added to the order list.

[1758] Step 3:

[1759] When the user confirms the order, the terminal (touch screen) sends the order information to the server, which receives the order information.

[1760] Step 4:

[1761] The server sends the order to the kitchen or barista, including the order details (item, quantity, special requests, etc.).

[1762] Step 5:

[1763] When the product ordered by the user is ready, a notification will appear on the touchscreen, and the user can confirm the notification and receive the product.

[1764] Step 6:

[1765] After the user has finished their meal, they pay on the touchscreen, and the server calculates the total amount and displays it on the touchscreen.

[1766] Step 7:

[1767] Once the user has completed the payment, the server resets the order list and prepares it for a new order.

[1768] Introducing the Emotion Engine

[1769] Step 1:

[1770] When a user enters a cafe, the device's camera captures the user's face, and the emotion engine recognizes their facial expressions and evaluates their emotional state in real time.

[1771] Step 2:

[1772] The server receives data from the emotion engine and analyzes the user's emotional state, determining emotional states such as stress, happiness, fatigue, etc.

[1773] Step 3:

[1774] When a user places an order on a touchscreen, the server uses emotional data to suggest personalized menu items, such as relaxing drinks or healthy foods to reduce stress.

[1775] Step 4:

[1776] It offers special offers and services based on the user's emotional state. If the user is feeling stressed, it may offer special promotions or discounts.

[1777] Step 5:

[1778] The server continuously collects user emotional data and dynamically adjusts the quality of service to optimize the user experience.

[1779] In this way, the system combined with the emotion engine provides personalized services according to the user's needs and emotional state, resulting in a more comfortable and satisfying cafe experience.

[1780] Example 2

[1781] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1782] Cafe operations face a wide range of challenges, including seat reservation management, order efficiency, peak time analysis, staff shift management, and even service optimization based on customer sentiment. Conventional systems have difficulty addressing these challenges in a unified manner, resulting in a decline in customer satisfaction and the operational efficiency of the cafe. The objective of this invention is to provide a system that solves these challenges and provides a comfortable and efficient cafe experience.

[1783] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for managing seat reservations using generated artificial intelligence, a means for allowing users to reserve seats online, a means for analyzing the cafe's congestion status in real time, a means for managing reservation information, a means for ordering and paying using a touch screen, and a means for analyzing user emotions and making suggestions using an emotion engine. This makes it possible to improve the efficiency of cafe management and user satisfaction.

[1784] "Generated artificial intelligence" refers to machine learning models that are trained to automatically collect and analyze data and deliver optimal results for a specific purpose.

[1785] "Means for managing seat reservations" refers to technology that supports the process of users checking seat availability online and making appropriate reservations.

[1786] "Means by which users can reserve seats online" refers to an interface that allows users to reserve seats remotely via devices such as a web browser or smartphone app.

[1787] "Means for analyzing the congestion status of a cafe in real time" refers to a system that analyzes data such as current seat occupancy status and order status in real time to determine the congestion status of a cafe.

[1788] "Means for managing reservation information" refers to a system that stores and manages user reservation details (user ID, reservation time, seat number, etc.) in a database and allows them to be updated and referenced as needed.

[1789] "Means for ordering and paying using a touchscreen" refers to technology that allows users to order and pay for cafe menu items using a touchscreen terminal installed on each table.

[1790] An "emotion engine" refers to an algorithm that analyzes emotions from a user's facial expressions and voice, and then provides appropriate responses and suggestions based on that emotional data.

[1791] "Means for analyzing user emotions and making suggestions" refers to technology that analyzes emotional data obtained by an emotion engine and suggests optimal services and products based on the user's current state.

[1792] "Means for analyzing past order history" refers to technology for analyzing a user's past order data and understanding the user's preferences and trends.

[1793] "Means for suggesting personalized menus" refers to a system for displaying and suggesting menus optimized for each individual user based on past order history and preference data.

[1794] "Means for analyzing customer visit data" refers to technology that collects and analyzes information about visits to a cafe (time, date, number of people, etc.) and identifies patterns of customer behavior.

[1795] "Means for identifying peak times" refers to a system for analyzing store visit data to identify the times when the cafe is most heavily used.

[1796] "Means for optimizing staff shifts" refers to technology that optimizes staff work schedules based on data on peak and off-peak times, achieving efficient personnel deployment.

[1797] MODE FOR CARRYING OUT THE INVENTION

[1798] A specific embodiment for implementing this invention will now be described. This invention utilizes the generated artificial intelligence and emotion engine to implement the following five main functions in order to provide a comfortable and efficient environment for cafe users and cafe management.

[1799] Seat reservation and management system

[1800] The server manages seat reservations within the cafe. Users check seat availability online and request a reservation. Specifically, the user opens the seat reservation page using a web browser or smartphone app and enters the desired time and necessary information. The device then sends this information to the server via the HTTPS protocol. The server accesses the database to check seat availability for the specified time slot and, if there is availability, confirms the reservation information.

[1801] For example, if a user requests a reservation online for "2 hours from 08:00," the server checks availability for that time slot and provides an available seat, allowing the user to begin working at that seat.

[1802] Customized Order Process

[1803] The server analyzes the user's past order history and generates a personalized menu. When a user opens the order page on their smartphone or tablet, the device queries the server for their past order history. The server retrieves the order history from the database and generates a recommended menu using a generative AI model.

[1804] For example, if user A has frequently ordered "latte" in the past, the server can recommend "latte" the next time user A orders, allowing user A to complete the order quickly.

[1805] Peak time analysis and staff shift optimization

[1806] The server analyzes visitor data and identifies peak and off-peak times. The server collects visitor data from the POS system and ordering system and analyzes peak and off-peak times for specific time periods. Data analysis uses aggregation functions and histograms.

[1807] For example, if data analysis reveals that peak hours are between 9:00 and 11:00, shift A (8:00 and 12:00) will be staffed with more staff during this time period to speed up service.

[1808] Introducing smart tables

[1809] Each table is equipped with a touchscreen, which users use to order and pay. Specifically, users use the touchscreen to view the cafe menu, select the items they want, and confirm their order. The terminal instantly transmits the order information to the server, and the total amount is displayed when payment is made.

[1810] For example, if user C uses the touchscreen to order a sandwich and coffee, the order is immediately sent to the server and added to the order list. When paying, the total amount is displayed on the touchscreen, allowing the user to complete the payment immediately.

[1811] Introducing the Emotion Engine

[1812] The emotion engine analyzes the user's facial expressions and tone of voice to recognize their emotions in real time. The camera and microphone installed on the device capture the user's facial expressions and voice, which are then analyzed by the emotion engine. The server then uses the data sent from the emotion engine to suggest appropriate services and products.

[1813] For example, if User D visits a cafe and the emotion engine detects stress in User D's facial expression while placing an order on the touchscreen, the server will suggest a "special menu for relaxation." User D can accept this suggestion and spend time in a relaxing environment.

[1814] Example prompts to input to the generative AI model

[1815] Prompt text (seat reservation and management system): In a cafe seat reservation system, what happens when a user goes online and requests to reserve a seat for 2 hours starting at 08:00?

[1816] Prompt statement (customized ordering process): If User A has frequently ordered lattes in the past, what menu item will they see the next time they order?

[1817] Prompt (Peak time analysis and staff shift optimization): If the cafe's peak time is 09:00-11:00, what will be the staff shifts?

[1818] Prompt (Smart Table Introduction): What happens when a user orders a sandwich and coffee using the touchscreen?

[1819] Prompt (introduction of emotion engine): If the emotion engine detects that the user is stressed, how will the server respond?

[1820] Based on the above explanation, this system can provide cafe users with a comfortable and efficient environment and also realize effective operation management for cafe operators.

[1821] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1822] Seat reservation and management system

[1823] Step 1:

[1824] A user opens a seat reservation page online. As input, the user enters the desired reservation time (e.g., 2 hours from 08:00) and user ID into the reservation form. This sends a seat reservation request to the system.

[1825] Step 2:

[1826] The terminal sends the information entered by the user to the server. The terminal uses the HTTPS protocol to send the entered reservation information (user ID, desired reservation time) to the server. The accuracy of the data is maintained by properly formatting the input data before sending it.

[1827] Step 3:

[1828] The server checks seat availability. The server executes an SQL query against the database to retrieve seat availability for the specified time slot. The availability data returned from the database becomes the input for the server.

[1829] Step 4:

[1830] The server confirms and manages the reservation. If there are available seats in the specified time slot, the server saves the reservation information in the database and confirms the reservation. The reservation information (user ID, reservation time, seat number) is the output data of the server. If there are no available seats, an error message is generated.

[1831] Step 5:

[1832] The terminal receives the server's output data and displays the reservation confirmation information or an error message to the user. Specifically, the reservation number and seat number are displayed on the screen, allowing the user to confirm that the reservation has been confirmed.

[1833] Customized Order Process

[1834] Step 1:

[1835] The user opens the order page. As input, the user launches the ordering app and accesses the order page, which allows the system to prepare an order.

[1836] Step 2:

[1837] The terminal queries the server for past order history. Using the user ID as input, it requests past order data via the REST API. The terminal sends the input data to the server.

[1838] Step 3:

[1839] The server analyzes the order history. It retrieves the user's past order history from the database and analyzes it using a generative AI model. The past order data becomes the input data for the server.

[1840] Step 4:

[1841] The server generates a personalized recommended menu. Based on the generative AI model, it lists the most suitable menu items for the user. The recommended menu data becomes the server's output data.

[1842] Step 5:

[1843] The terminal displays the recommended menu to the user. The terminal receives output data from the server and displays the recommended menu on the screen. This allows the user to quickly select products and confirm their order.

[1844] Peak time analysis and staff shift optimization

[1845] Step 1:

[1846] The server collects store visit data. As input, it periodically obtains store visit data (time, number of people, order details) from the POS system and ordering system. This allows the server to obtain basic data for shift optimization.

[1847] Step 2:

[1848] The server analyzes the visitor data to identify peak and off-peak times. It aggregates the data and performs statistical analysis to analyze visitor patterns during specific time periods. The aggregated data is the input data, and the analysis results are the output data.

[1849] Step 3:

[1850] The server generates an optimal shift schedule. Based on the results of peak and off-peak times, it runs an algorithm to optimize staff shift schedules. The optimized shift schedule is the output data.

[1851] Step 4:

[1852] The device notifies staff of shift information. It receives optimized shift schedule data from the server and sends push notifications to each staff member's device (smartphone or tablet). The notification data is the input, and the displayed shift information is the output.

[1853] Step 5:

[1854] Staff work based on shifts. According to the notified shift information, staff perform their duties according to their respective working hours. This allows for efficient service provision during peak times.

[1855] Introducing smart tables

[1856] Step 1:

[1857] The user operates the touchscreen of the smart table. As input, the user browses the menu and selects the desired item on the touchscreen. This allows the user to prepare an order.

[1858] Step 2:

[1859] The terminal sends the order information to the server. The selected order information is formatted and sent to the server. The order items and user ID are included as input data.

[1860] Step 3:

[1861] The server processes the order information, receives the order information, adds the order to a list in the order processing system, and forwards the order to the kitchen. The processed order information is the output data.

[1862] Step 4:

[1863] The terminal prompts the user to complete the payment process. After the order is confirmed, a payment screen is displayed, allowing the user to select a payment method. The payment method and amount are included as input data.

[1864] Step 5:

[1865] The user completes the payment. The payment procedure is completed on the touch screen using the selected payment method. The payment completion is sent to the server, and the transaction data becomes the output data.

[1866] Introducing the Emotion Engine

[1867] Step 1:

[1868] The device captures the user's facial expressions and voice using a camera and microphone. The capture device takes the user's facial movements and tone of voice as input, which prepares the device for sentiment analysis.

[1869] Step 2:

[1870] The device sends the captured data to the emotion engine. The captured data is sent to the emotion engine in real time. The input data includes facial expressions and voice characteristics.

[1871] Step 3:

[1872] The emotion engine analyzes the emotion data. It analyzes the received data and identifies the user's emotion (e.g., happiness, stress, anger). The analysis results are output data.

[1873] Step 4:

[1874] The server generates suggestions based on the emotion data. Based on the data from the emotion engine, it generates suggestions to optimize the user experience (such as special menus or relaxation suggestions). The suggested data is the output.

[1875] Step 5:

[1876] The device displays the suggestions to the user, receives the suggestion data, and displays it on the touch screen, allowing the user to accept the appropriate suggestion.

[1877] Through the above processing steps, the system can provide efficient and comfortable service to cafe patrons.

[1878] (Application example 2)

[1879] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1880] Traditional cafe operations have not been able to efficiently manage seating or improve customer satisfaction. In particular, it is difficult to grasp the congestion situation of customers and provide personalized service based on each customer's order history. There is also a lack of means to manage staff shifts and analyze customer sentiment in real time to optimize the experience. As a result, customers are likely to have a low-satisfaction experience, and cafe operators also face challenges in providing efficient service.

[1881] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for managing seat reservations using the generated artificial intelligence, means for allowing users to reserve seats online, means for analyzing the cafe's congestion status in real time, means for managing reservation information, means for analyzing the user's facial expressions and voice in real time and recognizing emotions, means for optimizing the user experience based on the recognized emotion data, means for users to easily place orders using a smartphone or a head-mounted display, and means for providing analyzed peak time information to a manager. This enables efficient seat management and improved customer satisfaction.

[1882] "Generated artificial intelligence" is an artificial intelligence system that is automatically generated based on data and designed to perform specific tasks.

[1883] "Means for managing seat reservations" refers to a function that tracks and manages seat reservations in the cafe in real time and allows users to reserve seats online.

[1884] "A means for analyzing the congestion situation of a cafe in real time" is a system that collects and analyzes data on the flow of people within a cafe in real time to grasp the congestion situation.

[1885] "Means for managing reservation information" refers to a function that allows users to save and update data related to seat reservations and provide that information as needed.

[1886] "Means for analyzing users' facial expressions and voice in real time and recognizing their emotions" refers to technology that analyzes the facial expressions and tone of voice of cafe patrons to recognize their current emotional state.

[1887] The "means for optimizing user experience based on recognized emotional data" is a system for personalizing services at a cafe and providing an optimal experience by taking into account the recognized emotional state of the user.

[1888] "Means for users to easily place orders using a smartphone or head-mounted display" refers to technology that allows users to easily order products using a mobile device or head-mounted display.

[1889] The "means for providing analyzed peak time information to the administrator" is a system that identifies peak and off-peak times at the cafe through data analysis and provides that information to the cafe operator.

[1890] To implement this invention, the following system is required.

[1891] First, the server uses the generated artificial intelligence (AI) to manage seat reservations in the cafe in real time. An interface is provided so that users can reserve seats online. When a user submits a reservation request, the server analyzes the cafe's congestion status in real time and reserves an available seat. The reservation information includes the user ID, reservation time, and reserved seat number.

[1892] The server also uses the generated AI to analyze the user's past order history and propose a personalized menu for each user, allowing users to place orders more quickly using their smartphones or head-mounted displays.

[1893] The server also analyzes the cafe's visitor data to identify peak times. Based on the identified peak time information, it becomes possible to optimize staff shifts. This peak time information is also provided to managers for efficient shift management and improved customer service.

[1894] The system also includes an AI engine that analyzes the user's facial expressions and voice in real time to recognize emotions. For example, it uses the smartphone camera and head-mounted display sensors to analyze the user's facial expressions and tone of voice. Based on the obtained emotional data, the server provides functions to optimize the user experience. For example, if the user is feeling stressed, it will suggest a menu that will help them relax.

[1895] These processes are performed using the following specific software and hardware. Server-side processing mainly uses Flask (a web application framework) and Firebase (database management). Face recognition and emotion analysis use OpenCV (a face recognition library) and EmotionRecognizer (a specific emotion recognition library). The user interface is provided via a smartphone application and a head-mounted display.

[1896] A concrete example is given below. Consider a scenario in which a user visits a cafe and tries to check a seat reservation on their smartphone. The user requests a reservation for "two hours from 8:00," and the server checks the seat availability for that time period and provides an available seat. At that time, the server analyzes the user's facial expression, and if the user appears stressed, it suggests a "special menu to help them relax."

[1897] An example prompt is:

[1898] "I'm at the cafe and I'm feeling a bit stressed. Can you suggest a menu item that will help me relax?"

[1899] In this way, users can have a comfortable and personalized cafe experience.

[1900] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1901] Step 1:

[1902] A user accesses a cafe's seat reservation system using a smartphone or head-mounted display.

[1903] Input: User ID, desired reservation time

[1904] Output: Show seat availability

[1905] Specific operation: The server receives a reservation request from a user, checks the current seat availability from the database, and displays available seats to the user in real time.

[1906] Step 2:

[1907] The user reserves the desired vacant seat.

[1908] Input: Seat number, reservation time

[1909] Output: Reservation confirmation information

[1910] Specific operation: The server saves the user's selected seat number and reservation time in the database and notifies the user that the reservation is confirmed. The reservation information includes the user ID, reservation time, and reserved seat number.

[1911] Step 3:

[1912] The server analyzes the user's past ordering history and generates a personalized menu.

[1913] Input: User ID, past order history

[1914] Output: personalized menu

[1915] How it works: The server uses the generative AI model to analyze the user's past order data. Based on the results, it generates a menu tailored to the user's preferences and presents it to the user.

[1916] Step 4:

[1917] The user places an order using a smartphone or a head-mounted display.

[1918] Enter: personalized menu

[1919] Output: Order confirmation information

[1920] Specific operation: The user selects from the presented personalized menu and enters an order. The server receives the order, generates an order confirmation, and notifies the user.

[1921] Step 5:

[1922] The server analyzes visit data and identifies peak times.

[1923] Input: Store visit data

[1924] Output: Peak time information

[1925] Specific operation: The server analyzes the collected visitor data and identifies peak times when the number of visitors is high during a specific time period. This information is used to optimize staff shifts.

[1926] Step 6:

[1927] Servers optimize staff shifts according to peak times.

[1928] Input: Peak time information, current shift information

[1929] Output: Optimized shift schedule

[1930] Specific operation: The server generates a new shift schedule that optimizes staff allocation based on peak time information and current staff shift information, and provides the generated shift schedule to the manager.

[1931] Step 7:

[1932] The user's facial expressions and voice are captured by sensors on a smartphone or head-mounted display, and their emotions are analyzed.

[1933] Input: Captured facial and voice data

[1934] Output: Recognized emotion data

[1935] How it works: The device captures the user's facial expressions and voice data, which are then analyzed by a generative AI model. The analysis results identify the user's current emotional state.

[1936] Step 8:

[1937] The server provides a service that optimizes the user experience based on the recognized emotion data.

[1938] Input: Recognized emotion data

[1939] Output: Optimized service (e.g., relaxation menu suggestions)

[1940] Specific operation: Based on the recognized emotion data, the server provides services that optimize the user experience, such as suggesting relaxation menus to users who are feeling stressed.

[1941] Example prompt sentence:

[1942] "I'm at the cafe and I'm feeling a bit stressed. Can you suggest a menu item that will help me relax?"

[1943] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1944] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1945] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1946] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1947] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1948] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1949] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1950] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1951] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1952] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1953] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1954] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1955] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1956] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1957] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1958] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1959] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1960] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1961] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1962] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1963] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1964] The following is further disclosed regarding the above embodiment.

[1965] (Claim 1)

[1966] a means for managing seat reservations using the generated artificial intelligence;

[1967] a means for users to reserve seats online;

[1968] A means of analyzing the cafe's congestion status in real time,

[1969] A system including a means for managing reservation information.

[1970] (Claim 2)

[1971] means for analyzing the user's past order history using the generated artificial intelligence;

[1972] A means for suggesting a personalized menu for each user;

[1973] 10. The system of claim 1, further comprising means for expediting ordering based on the suggested menu.

[1974] (Claim 3)

[1975] A means for analyzing store visit data using the generated artificial intelligence;

[1976] A means for identifying peak times based on store visit data;

[1977] 10. The system of claim 1, further comprising means for optimizing staff shifts according to peak times.

[1978] (Claim 4)

[1979] Each table will have a touchscreen for users to order and pay.

[1980] A means to manage orders using a touchscreen;

[1981] 10. The system of claim 1, further comprising means for calculating and displaying a total amount after an order is completed.

[1982] "Example 1"

[1983] (Claim 1)

[1984] a means for managing seat reservations using the generated artificial intelligence;

[1985] a means for users to reserve seats online;

[1986] A means of analyzing the cafe's congestion status in real time,

[1987] a means for managing reservation information;

[1988] means for generating a personalized menu based on a user's past ordering history;

[1989] The system includes a means to order and pay using the touchscreen on the smart table.

[1990] (Claim 2)

[1991] means for analyzing the user's past order history using the generated artificial intelligence;

[1992] A means for suggesting a personalized menu for each user;

[1993] 10. The system of claim 1, further comprising means for expediting ordering based on the suggested menu.

[1994] (Claim 3)

[1995] A means for analyzing store visit data using the generated artificial intelligence;

[1996] A means for identifying peak times based on store visit data;

[1997] 10. The system of claim 1, further comprising means for optimizing staff shifts according to peak times.

[1998] "Application Example 1"

[1999] (Claim 1)

[2000] a means for managing seat reservations using the generated artificial intelligence;

[2001] a means for users to reserve seats online;

[2002] A means of analyzing the cafe's congestion status in real time,

[2003] a means for managing orders and payments using a smart device;

[2004] A system that includes a means to analyze peak times and optimize staff shifts.

[2005] (Claim 2)

[2006] means for analyzing the user's past order history using the generated artificial intelligence;

[2007] A means for suggesting a personalized menu for each user;

[2008] A means of expediting ordering based on a suggested menu;

[2009] The system according to claim 1, further comprising means for integrating and managing order history throu...

Claims

1. a means for managing seat reservations using the generated artificial intelligence; a means for users to reserve seats online; A means of analyzing the cafe's congestion status in real time, A system including a means for managing reservation information.

2. means for analyzing the user's past order history using the generated artificial intelligence; A means for suggesting a personalized menu for each user; 10. The system of claim 1, further comprising means for expediting ordering based on the suggested menu.

3. A means for analyzing store visit data using the generated artificial intelligence; A means for identifying peak times based on store visit data; 2. The system of claim 1, further comprising means for optimizing staff shifts according to peak times.

4. Each table will have a touchscreen for users to order and pay. A means to manage orders using a touchscreen; 2. The system according to claim 1, further comprising means for calculating and displaying a total amount after an order is completed.

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