system

Abstract

The system according to this embodiment aims to improve the operational efficiency and ensure consistency in quality for restaurants, thereby enhancing customer satisfaction. [Solution] The system according to this embodiment comprises a reception unit, a processing unit, a proposal unit, a support unit, and an analysis unit. The reception unit receives customer orders and payments. The processing unit processes the orders and payments received by the reception unit. The proposal unit proposes the optimal reservation schedule using the customer's reservation history and data. The support unit provides support to employees as an assistant to their work. The analysis unit analyzes customer preferences and order history to propose the optimal menu.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

[0007] The system according to this embodiment can improve the operational efficiency and ensure consistency in quality for restaurants, thereby enhancing customer satisfaction. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment][[ID=⑨]] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The Smart Manager Assistant, according to an embodiment of the present invention, is a service in which AI handles all tasks that can be replaced by humans in the operations of restaurants and eateries, and also makes decisions. The Smart Manager Assistant aims to improve the operational efficiency and quality consistency of restaurants. Specifically, the Smart Manager Assistant takes customer orders and payments via smartphone, and the AI ​​receives and processes them. The AI ​​utilizes customer reservation history and data to propose the optimal reservation schedule, thereby reducing waiting times. Furthermore, the AI ​​supports operations as an assistant to employees, reducing errors and waiting times. The AI ​​analyzes customer preferences and order history and proposes the optimal menu, thereby improving customer satisfaction. The unique value provided by the Smart Manager Assistant is quality consistency, operational efficiency, and a smooth restaurant experience. For example, the Smart Manager Assistant improves customer satisfaction by reducing human errors and inconsistencies and ensuring quality consistency. Employees can improve operational efficiency and reduce their workload with the support of AI, allowing them to focus on more productive work. In this way, the Smart Manager Assistant can improve the operational efficiency and quality consistency of restaurants and improve customer satisfaction.

[0029] The smart store manager assistant according to this embodiment comprises a reception unit, a processing unit, a proposal unit, a support unit, and an analysis unit. The reception unit receives customer orders and payments. Customer orders and payments include, but are not limited to, online orders, credit card payments, and electronic money payments. The reception unit provides, for example, a web interface for receiving online orders. The reception unit also includes a payment gateway for processing credit card payments. Furthermore, the reception unit includes a QR code (registered trademark) scanner for receiving electronic money payments. For example, the reception unit receives orders and payments when customers enter their orders and credit card information through the web interface. The processing unit processes the orders and payments received by the reception unit. For example, the processing unit verifies the order details and checks the inventory status. The processing unit also verifies the payment information and approves the payment. Furthermore, the processing unit transmits the order details to the kitchen and starts cooking. For example, the processing unit verifies the order details, confirms the order if the items are in stock, and automatically processes the payment. The proposal unit uses the customer's reservation history and data to propose the optimal reservation schedule. The Proposal Department, for example, analyzes past reservation data to suggest the optimal reservation time. The Proposal Department also proposes the optimal reservation schedule considering the customer's preferred time and congestion level. Furthermore, the Proposal Department proposes the optimal reservation plan based on the customer's preferences and past usage history. For example, the Proposal Department analyzes past reservation data to suggest the time slot most convenient for the customer. The Support Department assists employees with their work. For example, the Support Department assists with order confirmation and food delivery. The Support Department also assists with inventory management and customer service. Furthermore, the Support Department assists with data entry and report creation. For example, the Support Department reduces the burden on employees by confirming orders and assisting with food delivery. The Analysis Department analyzes customer preferences and order history to suggest the optimal menu. For example, the Analysis Department uses data mining techniques to analyze customer preferences. The Analysis Department also uses machine learning algorithms to analyze customer order history. Furthermore, the Analysis Department proposes the optimal menu considering customer attribute information.For example, the analysis department analyzes a customer's past order history and suggests a menu tailored to their preferences. This allows the smart store manager assistant according to the embodiment to efficiently handle customer orders and payments, suggest reservation schedules, provide operational support, and suggest menus.

[0030] The reception desk accepts customer orders and payments. Customer orders and payments include, but are not limited to, online orders, credit card payments, and electronic money payments. For example, the reception desk provides a web interface for accepting online orders. Specifically, the web interface is user-friendly and designed to allow customers to easily enter their orders. Customers can select products from a menu, specify quantities, and confirm their orders. The reception desk also has a payment gateway for processing credit card payments. The payment gateway is highly secure and can safely handle customers' credit card information. Furthermore, the reception desk has a QR code scanner for accepting electronic money payments. The QR code scanner allows customers to easily complete payments by simply scanning a QR code with their smartphone. For example, the reception desk accepts orders and payments when customers enter their orders and credit card information through the web interface. This allows the reception desk to meet diverse customer needs and provide a smooth ordering and payment process. Furthermore, the reception desk can manage order and payment data in real time, improving the overall efficiency of the system. For example, inventory information is automatically updated as soon as an order is confirmed, preventing stockouts. Furthermore, the reception desk can save customers' order history and refer to it when they place future orders. This allows the reception desk to enhance customer convenience and contribute to acquiring repeat customers.

[0031] The processing unit handles orders and payments received by the reception department. For example, the processing unit verifies order details and checks inventory status. Specifically, once order details are entered into the system, the processing unit automatically accesses the inventory database to check if the ordered items are in stock. The processing unit also verifies payment information and authorizes the payment. Payment information is verified in cooperation with credit card companies and electronic money service providers, and the order is only confirmed after approval is received. Furthermore, the processing unit transmits the order details to the kitchen and begins cooking. For example, the processing unit verifies the order details, confirms the order if the items are in stock, and automatically processes the payment. Once the order is confirmed, the order details are displayed on a display or printer in the kitchen, allowing cooking staff to respond quickly. This streamlines the process from order to cooking start, enabling the processing unit to provide customers with faster service. In addition, the processing unit manages order priorities and can efficiently process multiple orders even when they come in simultaneously. For example, by starting cooking in the optimal order based on the order details and cooking time, the overall waiting time can be reduced. The processing unit can also monitor the progress of orders in real time and make adjustments as needed. This allows the processing unit to provide high-quality service to customers and improve their satisfaction.

[0032] The proposal department utilizes customer booking history and data to suggest the optimal booking schedule. For example, the proposal department analyzes past booking data to suggest the best booking time. Specifically, the proposal department retrieves customers' past booking history from the database and analyzes usage frequency and time-of-day trends. The proposal department also considers the customer's preferred time and congestion levels to suggest the optimal booking schedule. For example, if a customer wants to book during a specific time slot, the proposal department checks the congestion level for that time slot in real time and suggests the best booking time. Furthermore, the proposal department suggests the best booking plan based on the customer's preferences and past usage history. For example, the proposal department analyzes past booking data and suggests the time slot most convenient for the customer. This allows the proposal department to provide personalized booking suggestions to customers and improve customer convenience. In addition, the proposal department can use AI to analyze booking data and make more sophisticated suggestions. For example, it can use machine learning algorithms to predict customer booking patterns and suggest the best booking time. The proposal department can also collect customer feedback and continuously improve the accuracy of its suggestions. This allows the proposal department to always provide customers with the best booking suggestions and improve customer satisfaction.

[0033] The support department assists employees with their work. For example, the support department supports order confirmation and food delivery. Specifically, after an order is confirmed, the support department verifies the order details and communicates them to the cooking staff. When the food is ready, they notify the serving staff to ensure prompt service. The support department also supports inventory management and customer service. For example, if inventory is low, the support department automatically issues an alert to prompt replenishment. The support department also responds quickly to customer inquiries and complaints to resolve problems. Furthermore, the support department supports data entry and report creation. For example, by automatically collecting sales data and customer data and creating reports, it enables management to quickly understand the situation. This allows the support department to reduce the burden on employees and improve operational efficiency. In addition, the support department can utilize AI to support its operations. For example, it can provide a chatbot that automatically responds to customer inquiries using natural language processing technology. It can also use machine learning algorithms to manage inventory and forecast sales, and propose optimal inventory replenishment and sales strategies. This allows the support department to efficiently assist employees with their work and contribute to optimizing store operations.

[0034] The analytics department analyzes customer preferences and order history to propose the most suitable menu. For example, the analytics department uses data mining techniques to analyze customer preferences. Specifically, it retrieves past order history and attribute information from a database and uses data mining techniques to extract customer preferences and trends. The analytics department also analyzes customer order history using machine learning algorithms. For example, it predicts menu items that customers will like based on the menu items they have ordered in the past and how often they have ordered them. Furthermore, the analytics department proposes the most suitable menu considering customer attribute information. For example, it proposes the most suitable menu for a customer based on attribute information such as age, gender, and preferences. This allows the analytics department to provide personalized menu suggestions to customers and improve customer satisfaction. In addition, the analytics department can use AI to analyze data and make more sophisticated menu suggestions. For example, it uses deep learning techniques to learn customer order patterns and predict menu items that customers will like with high accuracy. The analytics department can also collect customer feedback and continuously improve the accuracy of its suggestions. This allows the analytics department to always provide the most suitable menu suggestions to customers and improve customer satisfaction. Furthermore, the analytics department can analyze sales and inventory data from across the entire store and propose optimal menu configurations and pricing. This allows the analytics department to contribute to more efficient store operations and increased sales.

[0035] The reception desk can accept orders and payments via smartphone. For example, the reception desk can provide a dedicated smartphone application, allowing customers to place orders and make payments through the app. For instance, when a customer selects dishes using the smartphone app and confirms their order, the reception desk can receive the order and automatically process the payment using AI. The reception desk can also offer a payment method using QR codes. For example, a customer can scan a QR code with their smartphone to complete the payment. Furthermore, the reception desk can also accept orders via voice input using a smartphone. For example, when a customer speaks their order into their smartphone, the AI ​​analyzes the voice and accepts the order. This improves customer convenience by allowing orders and payments to be made via smartphone. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk can input order data obtained through the smartphone app into a generating AI, and have the generating AI perform order analysis and payment processing.

[0036] The processing unit can confirm orders received by the reception unit and automatically process payments. For example, the processing unit can verify order details and check inventory status. For example, the processing unit can verify order details, confirm the order if inventory is available, and automatically process the payment. The processing unit can also verify payment information and approve payments. For example, the processing unit can verify credit card information and approve payments. Furthermore, the processing unit can transmit order details to the kitchen and begin cooking. For example, the processing unit can transmit order details to the kitchen and begin cooking. This reduces order errors and waiting times by confirming orders and processing payments automatically. Some or all of the above processes in the processing unit may be performed using AI, for example, or not using AI. For example, the processing unit can input order data received by the reception unit into a generating AI and have the generating AI perform order details verification and payment processing.

[0037] The suggestion department can utilize customer reservation history and data to propose the optimal reservation schedule. For example, the suggestion department can analyze past reservation data and propose the optimal reservation time. For example, the suggestion department can analyze past reservation data and propose the time slot that is most convenient for the customer. The suggestion department can also propose the optimal reservation schedule considering the customer's preferred time and congestion level. For example, the suggestion department can propose the optimal reservation time considering the customer's preferred time and congestion level. Furthermore, the suggestion department can propose the optimal reservation plan based on the customer's preferences and past usage history. For example, the suggestion department can propose the optimal reservation plan based on the customer's preferences and past usage history. By proposing the optimal reservation schedule, waiting times are reduced and customer convenience is improved. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can input customer reservation history and data into a generating AI and have the generating AI produce a suggestion for the optimal reservation schedule.

[0038] The support department can assist employees with their work, reducing errors and waiting times. For example, the support department can assist with order confirmation and food delivery. The support department can also assist with inventory management and customer service. Furthermore, the support department can assist with data entry and report creation. By assisting employees with their work, this improves operational efficiency and reduces their workload. Some or all of the processes described above in the support department may be performed using AI, or not. For example, the support department can input order confirmation and food delivery into a generating AI and have the generating AI perform the work support.

[0039] The analysis department can analyze customer preferences and order history and propose the optimal menu. For example, the analysis department can analyze customer preferences using data mining techniques and propose the optimal menu. The analysis department can also analyze customer order history using machine learning algorithms and propose the optimal menu. Furthermore, the analysis department can propose the optimal menu considering customer attribute information. For example, the analysis department considers customer attribute information and proposes the optimal menu. As a result, customer satisfaction improves by analyzing customer preferences and order history. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input customer preferences and order history into a generating AI and have the generating AI propose the optimal menu.

[0040] The reception desk can analyze a customer's past order history and select the optimal reception method. For example, the reception desk can automatically display menus that the customer has frequently ordered in the past as suggestions. The reception desk can also prioritize suggesting ordering methods (voice, text, etc.) that the customer has used in the past. The reception desk can also predict and suggest menus that the customer will use at a specific time of day based on their past order history. This allows the reception desk to select the optimal reception method by analyzing the customer's past order history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the customer's past order history data into a generating AI and have the generating AI select the optimal reception method.

[0041] The reception desk can filter orders based on the customer's current situation and areas of interest. For example, if a customer is health-conscious, the reception desk will prioritize displaying low-calorie or organic menu items. The reception desk can also suggest allergen-free menu items if a customer has allergies. The reception desk can also prioritize displaying menu items related to a particular dish if a customer is interested in that dish. This allows for more appropriate order taking by filtering based on the customer's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the customer's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0042] The reception desk can prioritize accepting orders that are highly relevant by considering the customer's geographical location. For example, if the customer is nearby, the AI ​​will prioritize processing that order. The reception desk can also postpone orders if the customer is far away. The reception desk can also prioritize displaying menus related to a specific area if the customer is in that area. This allows for the priority acceptance of highly relevant orders by considering the customer's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's geographical location data into a generating AI and have the generating AI prioritize accepting highly relevant orders.

[0043] The reception desk can analyze a customer's social media activity when taking an order and accept relevant orders. For example, if a customer mentions a particular dish on social media, the reception desk can prioritize suggesting that dish. The reception desk can also prioritize displaying the menu of a restaurant if a customer follows that restaurant on social media. The reception desk can also suggest menus related to an event if a customer participates in that event on social media. In this way, relevant orders can be accepted by analyzing a customer's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input customer social media activity data into a generating AI and have the generating AI take relevant orders.

[0044] The processing unit can adjust the level of detail in order processing based on the importance of the order. For example, in the case of an important order, the processing unit can have the AI ​​perform a detailed check and process it accurately. The processing unit can also have the AI ​​process general orders quickly. The processing unit can also have the AI ​​process special orders by following special procedures. This allows for efficient order processing by adjusting the level of detail in order processing based on the importance of the order. Some or all of the processing described above in the processing unit may be performed using AI, or not using AI. For example, the processing unit can input order importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in processing.

[0045] The processing unit can apply different processing algorithms depending on the order category when processing an order. For example, in the case of a drink order, the processing unit can apply an algorithm that allows the AI ​​to process it quickly. The processing unit can also apply an algorithm that allows the AI ​​to perform detailed verification in the case of a meal order. The processing unit can also apply an algorithm that allows the AI ​​to perform detailed verification in the case of a dessert order. In this way, efficient order processing becomes possible by applying different processing algorithms depending on the order category. Some or all of the above processing in the processing unit may be performed using AI, or not using AI. For example, the processing unit can input order category data into a generating AI and have the generating AI execute the application of different processing algorithms.

[0046] The processing unit can determine the processing priority based on when the order was submitted. For example, the processing unit can prioritize orders submitted earlier. The processing unit can also prioritize orders with approaching deadlines. The processing unit can also prioritize orders submitted during specific time periods. This enables efficient order processing by determining the processing priority based on when the order was submitted. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input order submission time data into a generating AI and have the generating AI perform the processing priority determination.

[0047] The processing unit can adjust the order of processing based on the relevance of orders during order processing. For example, the processing unit can prioritize processing highly relevant orders based on the customer's past order history. The processing unit can also prioritize processing highly relevant orders based on the customer's current situation. The processing unit can also prioritize processing highly relevant orders based on the customer's areas of interest. This allows for efficient order processing by adjusting the order of processing based on the relevance of orders. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input order relevance data into a generating AI and have the generating AI perform the processing order adjustment.

[0048] The suggestion unit can make optimal suggestions by referring to past reservation data when proposing a reservation schedule. For example, the suggestion unit can propose an optimal reservation schedule based on the customer's past reservation times. The suggestion unit can also propose a reservation schedule that avoids congestion based on the customer's past reservation history. The suggestion unit can also analyze the customer's past reservation history and propose the most efficient reservation schedule. In this way, the optimal reservation schedule can be proposed by referring to past reservation data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past reservation data into a generating AI and have the generating AI execute the proposal of an optimal reservation schedule.

[0049] The suggestion unit can make suggestions considering customer attribute information when proposing a reservation schedule. For example, the suggestion unit can propose the optimal reservation schedule based on the customer's age and gender. The suggestion unit can also propose the optimal reservation schedule based on the customer's occupation and lifestyle. The suggestion unit can also propose the optimal reservation schedule based on the customer's hobbies and interests. By considering customer attribute information, a more appropriate reservation schedule can be proposed. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input customer attribute information into a generating AI and have the generating AI execute the proposal of the optimal reservation schedule.

[0050] The suggestion unit can make optimal suggestions by considering the customer's geographical location when proposing a reservation schedule. For example, if the customer is nearby, the AI ​​will prioritize suggesting that reservation. The suggestion unit can also postpone suggesting a reservation if the customer is far away. The suggestion unit can also prioritize suggesting reservation schedules related to a specific area if the customer is in that area. This allows the suggestion unit to propose the optimal reservation schedule by considering the customer's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the customer's geographical location data into a generating AI and have the generating AI propose the optimal reservation schedule.

[0051] The suggestion unit can analyze the customer's social media activity when suggesting reservation schedules. For example, if the customer mentions a specific event on social media, the suggestion unit can suggest a reservation schedule related to that event. The suggestion unit can also prioritize suggesting reservation schedules for restaurants that the customer follows on social media. The suggestion unit can also suggest reservation schedules related to dishes that the customer is interested in on social media. In this way, relevant reservation schedules can be suggested by analyzing the customer's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the customer's social media activity data into a generating AI and have the generating AI perform the task of suggesting relevant reservation schedules.

[0052] The support department can select the optimal support method by referring to an employee's past work history when providing business support. For example, the support department can use AI to suggest the optimal support method based on the employee's past work history. The support department can also prioritize support for specific tasks based on an employee's past work history. The support department can also analyze an employee's past work history and suggest efficient support methods. This allows the support department to select the optimal support method by referring to an employee's past work history. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input employee past work history data into a generating AI and have the generating AI select the optimal support method.

[0053] The support department can provide support while considering employee attribute information. For example, the support department can propose the most suitable support method based on the employee's age and experience. The support department can also prioritize support for specific tasks based on the employee's job responsibilities. The support department can also propose efficient support methods based on the employee's skills and abilities. This allows for more appropriate work support by considering employee attribute information. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input employee attribute information into a generating AI and have the generating AI propose the most suitable support method.

[0054] The support department can select the optimal support method by considering the geographical location of employees when providing support for their work. For example, if an employee is nearby, the AI ​​will prioritize supporting that task. The support department can also postpone tasks if the employee is far away. The support department can also prioritize supporting tasks related to a specific area if the employee is in that area. This allows the support department to select the optimal support method by considering the geographical location of employees. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input employee geographical location data into a generating AI and have the generating AI select the optimal support method.

[0055] The support department can analyze employees' social media activity to provide support when assisting with their work. For example, if an employee mentions a particular task on social media, the support department can prioritize supporting that task. The support department can also prioritize supporting tasks related to a particular skill if an employee indicates on social media that they possess that skill. The support department can also prioritize supporting tasks related to a particular project if an employee indicates on social media that they are participating in that project. In this way, by analyzing employees' social media activity, the support department can support relevant tasks. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input employee social media activity data into a generating AI and have the generating AI perform support for relevant tasks.

[0056] The analysis department can make optimal menu suggestions by referring to past order data. For example, the analysis department can suggest the optimal menu based on the menus the customer has ordered in the past. The analysis department can also suggest menus that avoid crowds based on the customer's past order history. The analysis department can also analyze the customer's past order history and suggest the most efficient menu. In this way, the optimal menu can be suggested by referring to past order data. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input past order data into a generation AI and have the generation AI perform the optimal menu suggestion.

[0057] The analysis department can make menu suggestions while considering customer attribute information. For example, the analysis department can suggest the optimal menu based on the customer's age and gender. The analysis department can also suggest the optimal menu based on the customer's occupation and lifestyle. The analysis department can also suggest the optimal menu based on the customer's hobbies and interests. By considering customer attribute information, more appropriate menu suggestions become possible. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input customer attribute information into a generating AI and have the generating AI suggest the optimal menu.

[0058] The analysis unit can make optimal menu suggestions by considering the customer's geographical location. For example, if the customer is nearby, the AI ​​will prioritize suggesting that menu item. The analysis unit can also postpone suggesting that menu item if the customer is far away. The analysis unit can also prioritize suggesting menu items related to a specific area if the customer is in that area. This allows the system to suggest the optimal menu by considering the customer's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the customer's geographical location data into a generating AI and have the generating AI suggest the optimal menu.

[0059] The analytics department can analyze a customer's social media activity when suggesting menus. For example, if a customer mentions a particular dish on social media, the analytics department will prioritize suggesting that dish. The analytics department can also prioritize suggesting menu items from a restaurant if the customer follows that restaurant on social media. The analytics department can also suggest menu items related to an event if the customer participates in that event on social media. In this way, relevant menu items can be suggested by analyzing the customer's social media activity. Some or all of the above processing in the analytics department may be performed using AI, or not. For example, the analytics department can input customer social media activity data into a generating AI and have the generating AI suggest relevant menu items.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can monitor customers' health conditions and suggest the most suitable menu based on those conditions. For example, the reception desk can use data obtained from customers' smartwatches or fitness trackers to understand their current health status. If a customer has high blood pressure, it can suggest a low-sodium menu. If a customer has diabetes, it can prioritize displaying low-carbohydrate menus. Furthermore, if a customer is on a diet, it can suggest a low-calorie menu. This enables the suggestion of the most suitable menu according to the customer's health condition, leading to improved customer satisfaction.

[0062] The processing unit can provide real-time feedback on customer orders. For example, immediately after an order is received, the processing unit can send a confirmation message to the customer. It can also send a notification to the customer when the order has been relayed to the kitchen and cooking has begun. Furthermore, the processing unit can send a completion notification to the customer when the order is finished, allowing them to track the order's progress in real time. This allows customers to check the order's progress in real time, providing them with peace of mind.

[0063] The proposal department can analyze past customer feedback and suggest the optimal booking schedule. For example, based on feedback previously provided by customers, the proposal department can identify the times and services that customers were satisfied with. It can also suggest avoiding times and services that customers were dissatisfied with. Furthermore, the proposal department can use customer feedback to identify areas for service improvement and suggest better booking schedules. This allows for the suggestion of more satisfying booking schedules by leveraging past customer feedback.

[0064] The support department can assign tasks according to employees' skill levels. For example, the support department can evaluate employees' skill levels and assign appropriate tasks. New employees can be assigned simpler tasks, while experienced employees can be assigned more complex ones. The support department can also provide training programs to support employee skill development. Furthermore, the support department can monitor the progress of tasks according to employees' skill levels and provide support as needed. This enables optimal task assignment based on employee skill levels, improving work efficiency.

[0065] The analytics department can make cross-sell and up-sell suggestions based on customer purchase history. For example, the analytics department can analyze products a customer has previously purchased and suggest related products. If a customer orders pasta, it can suggest salads and desserts. Similarly, if a customer purchases items in a specific price range, it can suggest items in a higher price range. Furthermore, the analytics department can suggest specific promotions and discounts based on customer purchase history. This allows for cross-sell and up-sell suggestions utilizing customer purchase history, ultimately increasing sales.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception area accepts customer orders and payments. This includes online orders, credit card payments, and electronic money payments. The reception area is equipped with a web interface for accepting online orders, a payment gateway for processing credit card payments, and a QR code scanner for accepting electronic money payments. Step 2: The processing unit processes the orders and payments received by the reception unit. For example, it verifies the order details, checks inventory status, verifies and approves payment information, and transmits the order details to the kitchen to begin cooking. Step 3: The proposal department uses customer booking history and data to suggest the optimal booking schedule. For example, they analyze past booking data and suggest the best booking time considering the customer's preferred time and congestion levels. Step 4: The support department assists employees with their work. For example, they help with order confirmation, food service, inventory management, customer service, data entry, and report creation. Step 5: The analysis department analyzes customer preferences and order history to propose the optimal menu. For example, they use data mining techniques and machine learning algorithms to analyze customer preferences and order history, and propose the optimal menu considering customer attribute information.

[0068] (Example of form 2) The Smart Manager Assistant, according to an embodiment of the present invention, is a service in which AI handles all tasks that can be replaced by humans in the operations of restaurants and eateries, and also makes decisions. The Smart Manager Assistant aims to improve the operational efficiency and quality consistency of restaurants. Specifically, the Smart Manager Assistant takes customer orders and payments via smartphone, and the AI ​​receives and processes them. The AI ​​utilizes customer reservation history and data to propose the optimal reservation schedule, thereby reducing waiting times. Furthermore, the AI ​​supports operations as an assistant to employees, reducing errors and waiting times. The AI ​​analyzes customer preferences and order history and proposes the optimal menu, thereby improving customer satisfaction. The unique value provided by the Smart Manager Assistant is quality consistency, operational efficiency, and a smooth restaurant experience. For example, the Smart Manager Assistant improves customer satisfaction by reducing human errors and inconsistencies and ensuring quality consistency. Employees can improve operational efficiency and reduce their workload with the support of AI, allowing them to focus on more productive work. In this way, the Smart Manager Assistant can improve the operational efficiency and quality consistency of restaurants and improve customer satisfaction.

[0069] The smart store manager assistant according to this embodiment comprises a reception unit, a processing unit, a proposal unit, a support unit, and an analysis unit. The reception unit receives customer orders and payments. Customer orders and payments include, but are not limited to, online orders, credit card payments, and electronic money payments. The reception unit provides, for example, a web interface for receiving online orders. The reception unit also includes a payment gateway for processing credit card payments. Furthermore, the reception unit includes a QR code scanner for receiving electronic money payments. For example, the reception unit receives orders and payments when customers enter their orders and credit card information through the web interface. The processing unit processes the orders and payments received by the reception unit. The processing unit verifies order details and checks inventory status. The processing unit also verifies payment information and approves payments. Furthermore, the processing unit transmits order details to the kitchen and starts cooking. For example, the processing unit verifies order details, confirms the order if inventory is available, and automatically processes the payment. The proposal unit utilizes customer reservation history and data to propose the optimal reservation schedule. The Proposal Department, for example, analyzes past reservation data to suggest the optimal reservation time. The Proposal Department also proposes the optimal reservation schedule considering the customer's preferred time and congestion level. Furthermore, the Proposal Department proposes the optimal reservation plan based on the customer's preferences and past usage history. For example, the Proposal Department analyzes past reservation data to suggest the time slot most convenient for the customer. The Support Department assists employees with their work. For example, the Support Department assists with order confirmation and food delivery. The Support Department also assists with inventory management and customer service. Furthermore, the Support Department assists with data entry and report creation. For example, the Support Department reduces the burden on employees by confirming orders and assisting with food delivery. The Analysis Department analyzes customer preferences and order history to suggest the optimal menu. For example, the Analysis Department uses data mining techniques to analyze customer preferences. The Analysis Department also uses machine learning algorithms to analyze customer order history. Furthermore, the Analysis Department proposes the optimal menu considering customer attribute information.For example, the analysis department analyzes a customer's past order history and suggests a menu tailored to their preferences. This allows the smart store manager assistant according to the embodiment to efficiently handle customer orders and payments, suggest reservation schedules, provide operational support, and suggest menus.

[0070] The reception desk accepts customer orders and payments. Customer orders and payments include, but are not limited to, online orders, credit card payments, and electronic money payments. For example, the reception desk provides a web interface for accepting online orders. Specifically, the web interface is user-friendly and designed to allow customers to easily enter their orders. Customers can select products from a menu, specify quantities, and confirm their orders. The reception desk also has a payment gateway for processing credit card payments. The payment gateway is highly secure and can safely handle customers' credit card information. Furthermore, the reception desk has a QR code scanner for accepting electronic money payments. The QR code scanner allows customers to easily complete payments by simply scanning a QR code with their smartphone. For example, the reception desk accepts orders and payments when customers enter their orders and credit card information through the web interface. This allows the reception desk to meet diverse customer needs and provide a smooth ordering and payment process. Furthermore, the reception desk can manage order and payment data in real time, improving the overall efficiency of the system. For example, inventory information is automatically updated as soon as an order is confirmed, preventing stockouts. Furthermore, the reception desk can save customers' order history and refer to it when they place future orders. This allows the reception desk to enhance customer convenience and contribute to acquiring repeat customers.

[0071] The processing unit handles orders and payments received by the reception department. For example, the processing unit verifies order details and checks inventory status. Specifically, once order details are entered into the system, the processing unit automatically accesses the inventory database to check if the ordered items are in stock. The processing unit also verifies payment information and authorizes the payment. Payment information is verified in cooperation with credit card companies and electronic money service providers, and the order is only confirmed after approval is received. Furthermore, the processing unit transmits the order details to the kitchen and begins cooking. For example, the processing unit verifies the order details, confirms the order if the items are in stock, and automatically processes the payment. Once the order is confirmed, the order details are displayed on a display or printer in the kitchen, allowing cooking staff to respond quickly. This streamlines the process from order to cooking start, enabling the processing unit to provide customers with faster service. In addition, the processing unit manages order priorities and can efficiently process multiple orders even when they come in simultaneously. For example, by starting cooking in the optimal order based on the order details and cooking time, the overall waiting time can be reduced. The processing unit can also monitor the progress of orders in real time and make adjustments as needed. This allows the processing unit to provide high-quality service to customers and improve their satisfaction.

[0072] The proposal department utilizes customer booking history and data to suggest the optimal booking schedule. For example, the proposal department analyzes past booking data to suggest the best booking time. Specifically, the proposal department retrieves customers' past booking history from the database and analyzes usage frequency and time-of-day trends. The proposal department also considers the customer's preferred time and congestion levels to suggest the optimal booking schedule. For example, if a customer wants to book during a specific time slot, the proposal department checks the congestion level for that time slot in real time and suggests the best booking time. Furthermore, the proposal department suggests the best booking plan based on the customer's preferences and past usage history. For example, the proposal department analyzes past booking data and suggests the time slot most convenient for the customer. This allows the proposal department to provide personalized booking suggestions to customers and improve customer convenience. In addition, the proposal department can use AI to analyze booking data and make more sophisticated suggestions. For example, it can use machine learning algorithms to predict customer booking patterns and suggest the best booking time. The proposal department can also collect customer feedback and continuously improve the accuracy of its suggestions. This allows the proposal department to always provide customers with the best booking suggestions and improve customer satisfaction.

[0073] The support department assists employees with their work. For example, the support department supports order confirmation and food delivery. Specifically, after an order is confirmed, the support department verifies the order details and communicates them to the cooking staff. When the food is ready, they notify the serving staff to ensure prompt service. The support department also supports inventory management and customer service. For example, if inventory is low, the support department automatically issues an alert to prompt replenishment. The support department also responds quickly to customer inquiries and complaints to resolve problems. Furthermore, the support department supports data entry and report creation. For example, by automatically collecting sales data and customer data and creating reports, it enables management to quickly understand the situation. This allows the support department to reduce the burden on employees and improve operational efficiency. In addition, the support department can utilize AI to support its operations. For example, it can provide a chatbot that automatically responds to customer inquiries using natural language processing technology. It can also use machine learning algorithms to manage inventory and forecast sales, and propose optimal inventory replenishment and sales strategies. This allows the support department to efficiently assist employees with their work and contribute to optimizing store operations.

[0074] The analytics department analyzes customer preferences and order history to propose the most suitable menu. For example, the analytics department uses data mining techniques to analyze customer preferences. Specifically, it retrieves past order history and attribute information from a database and uses data mining techniques to extract customer preferences and trends. The analytics department also analyzes customer order history using machine learning algorithms. For example, it predicts menu items that customers will like based on the menu items they have ordered in the past and how often they have ordered them. Furthermore, the analytics department proposes the most suitable menu considering customer attribute information. For example, it proposes the most suitable menu for a customer based on attribute information such as age, gender, and preferences. This allows the analytics department to provide personalized menu suggestions to customers and improve customer satisfaction. In addition, the analytics department can use AI to analyze data and make more sophisticated menu suggestions. For example, it uses deep learning techniques to learn customer order patterns and predict menu items that customers will like with high accuracy. The analytics department can also collect customer feedback and continuously improve the accuracy of its suggestions. This allows the analytics department to always provide the most suitable menu suggestions to customers and improve customer satisfaction. Furthermore, the analytics department can analyze sales and inventory data from across the entire store and propose optimal menu configurations and pricing. This allows the analytics department to contribute to more efficient store operations and increased sales.

[0075] The reception desk can accept orders and payments via smartphone. For example, the reception desk can provide a dedicated smartphone application, allowing customers to place orders and make payments through the app. For instance, when a customer selects dishes using the smartphone app and confirms their order, the reception desk can receive the order and automatically process the payment using AI. The reception desk can also offer a payment method using QR codes. For example, a customer can scan a QR code with their smartphone to complete the payment. Furthermore, the reception desk can also accept orders via voice input using a smartphone. For example, when a customer speaks their order into their smartphone, the AI ​​analyzes the voice and accepts the order. This improves customer convenience by allowing orders and payments to be made via smartphone. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk can input order data obtained through the smartphone app into a generating AI, and have the generating AI perform order analysis and payment processing.

[0076] The processing unit can confirm orders received by the reception unit and automatically process payments. For example, the processing unit can verify order details and check inventory status. For example, the processing unit can verify order details, confirm the order if inventory is available, and automatically process the payment. The processing unit can also verify payment information and approve payments. For example, the processing unit can verify credit card information and approve payments. Furthermore, the processing unit can transmit order details to the kitchen and begin cooking. For example, the processing unit can transmit order details to the kitchen and begin cooking. This reduces order errors and waiting times by confirming orders and processing payments automatically. Some or all of the above processes in the processing unit may be performed using AI, for example, or not using AI. For example, the processing unit can input order data received by the reception unit into a generating AI and have the generating AI perform order details verification and payment processing.

[0077] The suggestion department can utilize customer reservation history and data to propose the optimal reservation schedule. For example, the suggestion department can analyze past reservation data and propose the optimal reservation time. For example, the suggestion department can analyze past reservation data and propose the time slot that is most convenient for the customer. The suggestion department can also propose the optimal reservation schedule considering the customer's preferred time and congestion level. For example, the suggestion department can propose the optimal reservation time considering the customer's preferred time and congestion level. Furthermore, the suggestion department can propose the optimal reservation plan based on the customer's preferences and past usage history. For example, the suggestion department can propose the optimal reservation plan based on the customer's preferences and past usage history. By proposing the optimal reservation schedule, waiting times are reduced and customer convenience is improved. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not. For example, the suggestion department can input customer reservation history and data into a generating AI and have the generating AI produce a suggestion for the optimal reservation schedule.

[0078] The support department can assist employees with their work, reducing errors and waiting times. For example, the support department can assist with order confirmation and food delivery. The support department can also assist with inventory management and customer service. Furthermore, the support department can assist with data entry and report creation. By assisting employees with their work, this improves operational efficiency and reduces their workload. Some or all of the processes described above in the support department may be performed using AI, or not. For example, the support department can input order confirmation and food delivery into a generating AI and have the generating AI perform the work support.

[0079] The analysis department can analyze customer preferences and order history and propose the optimal menu. For example, the analysis department can analyze customer preferences using data mining techniques and propose the optimal menu. The analysis department can also analyze customer order history using machine learning algorithms and propose the optimal menu. Furthermore, the analysis department can propose the optimal menu considering customer attribute information. For example, the analysis department considers customer attribute information and proposes the optimal menu. As a result, customer satisfaction improves by analyzing customer preferences and order history. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input customer preferences and order history into a generating AI and have the generating AI propose the optimal menu.

[0080] The reception desk can estimate the customer's emotions and adjust the timing of order taking based on the estimated emotions. For example, if the customer is stressed, the AI ​​can quickly take the order to minimize waiting time. The reception desk can also take the order at a slower pace and provide detailed explanations if the customer is relaxed. The reception desk can also prioritize voice input and quickly take the order if the customer is in a hurry. This improves customer satisfaction by adjusting the timing of order taking according to the customer's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input customer emotion data into a generating AI and have the generating AI perform emotion estimation and order acceptance timing adjustments.

[0081] The reception desk can analyze a customer's past order history and select the optimal reception method. For example, the reception desk can automatically display menus that the customer has frequently ordered in the past as suggestions. The reception desk can also prioritize suggesting ordering methods (voice, text, etc.) that the customer has used in the past. The reception desk can also predict and suggest menus that the customer will use at a specific time of day based on their past order history. This allows the reception desk to select the optimal reception method by analyzing the customer's past order history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the customer's past order history data into a generating AI and have the generating AI select the optimal reception method.

[0082] The reception desk can filter orders based on the customer's current situation and areas of interest. For example, if a customer is health-conscious, the reception desk will prioritize displaying low-calorie or organic menu items. The reception desk can also suggest allergen-free menu items if a customer has allergies. The reception desk can also prioritize displaying menu items related to a particular dish if a customer is interested in that dish. This allows for more appropriate order taking by filtering based on the customer's current situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the customer's current situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0083] The reception desk can estimate a customer's emotions and prioritize orders based on those emotions. For example, if a customer is in a hurry, the AI ​​will prioritize that order. The reception desk can also prioritize orders from other customers who are in a hurry and postpone orders from relaxed customers if the customer is relaxed. The reception desk can also process orders quickly if a customer is stressed, thereby reducing their stress. This improves customer satisfaction by prioritizing orders according to customer emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk may input customer emotion data into a generating AI and have the generating AI perform emotion estimation and order prioritization.

[0084] The reception desk can prioritize accepting orders that are highly relevant by considering the customer's geographical location. For example, if the customer is nearby, the AI ​​will prioritize processing that order. The reception desk can also postpone orders if the customer is far away. The reception desk can also prioritize displaying menus related to a specific area if the customer is in that area. This allows for the priority acceptance of highly relevant orders by considering the customer's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the customer's geographical location data into a generating AI and have the generating AI prioritize accepting highly relevant orders.

[0085] The reception desk can analyze a customer's social media activity when taking an order and accept relevant orders. For example, if a customer mentions a particular dish on social media, the reception desk can prioritize suggesting that dish. The reception desk can also prioritize displaying the menu of a restaurant if a customer follows that restaurant on social media. The reception desk can also suggest menus related to an event if a customer participates in that event on social media. In this way, relevant orders can be accepted by analyzing a customer's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input customer social media activity data into a generating AI and have the generating AI take relevant orders.

[0086] The processing unit can estimate the customer's emotions and adjust the order processing method based on the estimated emotions. For example, if the customer is relaxed, the processing unit can process the order at a slow pace. The processing unit can also process the order quickly if the customer is in a hurry. The processing unit can also process the order quickly and reduce stress if the customer is stressed. This improves customer satisfaction by adjusting the order processing method according to the customer's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the processing unit may be performed using AI or not using AI. For example, the processing unit can input customer emotion data into a generating AI, which can then perform emotion estimation and adjust the order processing method.

[0087] The processing unit can adjust the level of detail in order processing based on the importance of the order. For example, in the case of an important order, the processing unit can have the AI ​​perform a detailed check and process it accurately. The processing unit can also have the AI ​​process general orders quickly. The processing unit can also have the AI ​​process special orders by following special procedures. This allows for efficient order processing by adjusting the level of detail in order processing based on the importance of the order. Some or all of the processing described above in the processing unit may be performed using AI, or not using AI. For example, the processing unit can input order importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in processing.

[0088] The processing unit can apply different processing algorithms depending on the order category when processing an order. For example, in the case of a drink order, the processing unit can apply an algorithm that allows the AI ​​to process it quickly. The processing unit can also apply an algorithm that allows the AI ​​to perform detailed verification in the case of a meal order. The processing unit can also apply an algorithm that allows the AI ​​to perform detailed verification in the case of a dessert order. In this way, efficient order processing becomes possible by applying different processing algorithms depending on the order category. Some or all of the above processing in the processing unit may be performed using AI, or not using AI. For example, the processing unit can input order category data into a generating AI and have the generating AI execute the application of different processing algorithms.

[0089] The processing unit can estimate a customer's emotions and determine the order processing priority based on the estimated emotions. For example, if a customer is in a hurry, the AI ​​will prioritize that order. The processing unit can also prioritize orders from other customers who are in a hurry and postpone orders from relaxed customers if the customer is relaxed. The processing unit can also process orders quickly if a customer is stressed, thereby reducing their stress. This improves customer satisfaction by prioritizing order processing according to customer emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input customer emotion data into a generating AI and have the generating AI perform emotion estimation and order processing priority determination.

[0090] The processing unit can determine the processing priority based on when the order was submitted. For example, the processing unit can prioritize orders submitted earlier. The processing unit can also prioritize orders with approaching deadlines. The processing unit can also prioritize orders submitted during specific time periods. This enables efficient order processing by determining the processing priority based on when the order was submitted. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input order submission time data into a generating AI and have the generating AI perform the processing priority determination.

[0091] The processing unit can adjust the order of processing based on the relevance of orders during order processing. For example, the processing unit can prioritize processing highly relevant orders based on the customer's past order history. The processing unit can also prioritize processing highly relevant orders based on the customer's current situation. The processing unit can also prioritize processing highly relevant orders based on the customer's areas of interest. This allows for efficient order processing by adjusting the order of processing based on the relevance of orders. Some or all of the above processing in the processing unit may be performed using AI, for example, or without AI. For example, the processing unit can input order relevance data into a generating AI and have the generating AI perform the processing order adjustment.

[0092] The suggestion unit can estimate the customer's emotions and adjust how it suggests booking schedules based on those emotions. For example, if the customer is relaxed, the suggestion unit can suggest booking schedules at a relaxed pace. If the customer is in a hurry, the suggestion unit can also suggest booking schedules quickly. If the customer is stressed, the suggestion unit can also suggest booking schedules designed to alleviate that stress. By adjusting how booking schedules are suggested according to the customer's emotions, customer satisfaction is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the proposal department can input customer emotion data into a generating AI and have the AI ​​perform emotion estimation and adjust the method of suggesting reservation schedules.

[0093] The suggestion unit can make optimal suggestions by referring to past reservation data when proposing a reservation schedule. For example, the suggestion unit can propose an optimal reservation schedule based on the customer's past reservation times. The suggestion unit can also propose a reservation schedule that avoids congestion based on the customer's past reservation history. The suggestion unit can also analyze the customer's past reservation history and propose the most efficient reservation schedule. In this way, the optimal reservation schedule can be proposed by referring to past reservation data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past reservation data into a generating AI and have the generating AI execute the proposal of an optimal reservation schedule.

[0094] The suggestion unit can make suggestions considering customer attribute information when proposing a reservation schedule. For example, the suggestion unit can propose the optimal reservation schedule based on the customer's age and gender. The suggestion unit can also propose the optimal reservation schedule based on the customer's occupation and lifestyle. The suggestion unit can also propose the optimal reservation schedule based on the customer's hobbies and interests. By considering customer attribute information, a more appropriate reservation schedule can be proposed. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input customer attribute information into a generating AI and have the generating AI execute the proposal of the optimal reservation schedule.

[0095] The suggestion system can estimate customer emotions and prioritize booking schedules based on those emotions. For example, if a customer is in a hurry, the AI ​​will prioritize suggesting that booking. The suggestion system can also prioritize bookings for other customers who are in a hurry and postpone bookings for other customers who are relaxed. The suggestion system can also quickly suggest bookings for customers who are stressed, thereby reducing their stress. This improves customer satisfaction by prioritizing booking schedules according to customer emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input customer emotion data into a generating AI and have the generating AI perform emotion estimation and priority determination of reservation schedules.

[0096] The suggestion unit can make optimal suggestions by considering the customer's geographical location when proposing a reservation schedule. For example, if the customer is nearby, the AI ​​will prioritize suggesting that reservation. The suggestion unit can also postpone suggesting a reservation if the customer is far away. The suggestion unit can also prioritize suggesting reservation schedules related to a specific area if the customer is in that area. This allows the suggestion unit to propose the optimal reservation schedule by considering the customer's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the customer's geographical location data into a generating AI and have the generating AI propose the optimal reservation schedule.

[0097] The suggestion unit can analyze the customer's social media activity when suggesting reservation schedules. For example, if the customer mentions a specific event on social media, the suggestion unit can suggest a reservation schedule related to that event. The suggestion unit can also prioritize suggesting reservation schedules for restaurants that the customer follows on social media. The suggestion unit can also suggest reservation schedules related to dishes that the customer is interested in on social media. In this way, relevant reservation schedules can be suggested by analyzing the customer's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the customer's social media activity data into a generating AI and have the generating AI perform the task of suggesting relevant reservation schedules.

[0098] The support department can estimate the customer's emotions and adjust its support methods based on the estimated emotions. For example, if the customer is relaxed, the AI ​​can provide support at a relaxed pace. The support department can also have the AI ​​provide support quickly if the customer is in a hurry. The support department can also have the AI ​​provide support quickly and alleviate stress if the customer is stressed. By adjusting the support methods according to the customer's emotions, customer satisfaction is improved. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support department may be performed using AI or not using AI. For example, the support department can input customer emotion data into a generating AI and have the AI ​​perform emotion estimation and adjust the methods of business support.

[0099] The support department can select the optimal support method by referring to an employee's past work history when providing business support. For example, the support department can use AI to suggest the optimal support method based on the employee's past work history. The support department can also prioritize support for specific tasks based on an employee's past work history. The support department can also analyze an employee's past work history and suggest efficient support methods. This allows the support department to select the optimal support method by referring to an employee's past work history. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input employee past work history data into a generating AI and have the generating AI select the optimal support method.

[0100] The support department can provide support while considering employee attribute information. For example, the support department can propose the most suitable support method based on the employee's age and experience. The support department can also prioritize support for specific tasks based on the employee's job responsibilities. The support department can also propose efficient support methods based on the employee's skills and abilities. This allows for more appropriate work support by considering employee attribute information. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input employee attribute information into a generating AI and have the generating AI propose the most suitable support method.

[0101] The support department can estimate customer emotions and prioritize business support based on those estimated emotions. For example, if a customer is in a hurry, the AI ​​will prioritize supporting that task. The support department can also prioritize the tasks of other urgent customers and postpone the tasks of relaxed customers if the customer is relaxed. The support department can also quickly support the task and alleviate the stress of a customer if they are stressed. This improves customer satisfaction by prioritizing business support according to customer emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processes described above in the support department may be performed using AI, for example, or without AI. For example, the support department can input customer emotion data into a generating AI and have the generating AI perform emotion estimation and priority determination of business support.

[0102] The support department can select the optimal support method by considering the geographical location of employees when providing support for their work. For example, if an employee is nearby, the AI ​​will prioritize supporting that task. The support department can also postpone tasks if the employee is far away. The support department can also prioritize supporting tasks related to a specific area if the employee is in that area. This allows the support department to select the optimal support method by considering the geographical location of employees. Some or all of the above processes in the support department may be performed using AI, or not. For example, the support department can input employee geographical location data into a generating AI and have the generating AI select the optimal support method.

[0103] The support department can analyze employees' social media activity to provide support when assisting with their work. For example, if an employee mentions a particular task on social media, the support department can prioritize supporting that task. The support department can also prioritize supporting tasks related to a particular skill if an employee indicates on social media that they possess that skill. The support department can also prioritize supporting tasks related to a particular project if an employee indicates on social media that they are participating in that project. In this way, by analyzing employees' social media activity, the support department can support relevant tasks. Some or all of the above processes in the support department may be performed using AI, for example, or not. For example, the support department can input employee social media activity data into a generating AI and have the generating AI perform support for relevant tasks.

[0104] The analysis unit can estimate the customer's emotions and adjust the menu suggestion method based on the estimated emotions. For example, if the customer is relaxed, the analysis unit can suggest menus at a relaxed pace. For example, if the customer is relaxed, the analysis unit can suggest menus at a relaxed pace. The analysis unit can also suggest menus quickly if the customer is in a hurry. For example, if the customer is in a hurry, the analysis unit can suggest menus quickly. The analysis unit can also suggest menus to alleviate stress if the customer is stressed. For example, if the analysis unit is stressed, the analysis unit can suggest menus to alleviate stress. By adjusting the menu suggestion method according to the customer's emotions, customer satisfaction is improved. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis department can input customer emotion data into a generating AI and have the AI ​​perform emotion estimation and adjust the menu suggestion method.

[0105] The analysis department can make optimal menu suggestions by referring to past order data. For example, the analysis department can suggest the optimal menu based on the menus the customer has ordered in the past. The analysis department can also suggest menus that avoid crowds based on the customer's past order history. The analysis department can also analyze the customer's past order history and suggest the most efficient menu. In this way, the optimal menu can be suggested by referring to past order data. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input past order data into a generation AI and have the generation AI perform the optimal menu suggestion.

[0106] The analysis department can make menu suggestions while considering customer attribute information. For example, the analysis department can suggest the optimal menu based on the customer's age and gender. The analysis department can also suggest the optimal menu based on the customer's occupation and lifestyle. The analysis department can also suggest the optimal menu based on the customer's hobbies and interests. By considering customer attribute information, more appropriate menu suggestions become possible. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input customer attribute information into a generating AI and have the generating AI suggest the optimal menu.

[0107] The analytics department can estimate customer emotions and prioritize menu suggestions based on those emotions. For example, if a customer is in a hurry, the AI ​​will prioritize suggesting that menu item. The analytics department can also prioritize menus for other customers who are in a hurry and postpone menus for relaxed customers if the customer is relaxed. The analytics department can also quickly suggest menus for customers who are stressed to alleviate their stress if the customer is stressed. This improves customer satisfaction by prioritizing menu suggestions according to customer emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input customer emotion data into a generating AI and have the generating AI perform emotion estimation and menu suggestion prioritization.

[0108] The analysis unit can make optimal menu suggestions by considering the customer's geographical location. For example, if the customer is nearby, the AI ​​will prioritize suggesting that menu item. The analysis unit can also postpone suggesting that menu item if the customer is far away. The analysis unit can also prioritize suggesting menu items related to a specific area if the customer is in that area. This allows the system to suggest the optimal menu by considering the customer's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the customer's geographical location data into a generating AI and have the generating AI suggest the optimal menu.

[0109] The analytics department can analyze a customer's social media activity when suggesting menus. For example, if a customer mentions a particular dish on social media, the analytics department will prioritize suggesting that dish. The analytics department can also prioritize suggesting menu items from a restaurant if the customer follows that restaurant on social media. The analytics department can also suggest menu items related to an event if the customer participates in that event on social media. In this way, relevant menu items can be suggested by analyzing the customer's social media activity. Some or all of the above processing in the analytics department may be performed using AI, or not. For example, the analytics department can input customer social media activity data into a generating AI and have the generating AI suggest relevant menu items.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception desk can monitor customers' health conditions and suggest the most suitable menu based on those conditions. For example, the reception desk can use data obtained from customers' smartwatches or fitness trackers to understand their current health status. If a customer has high blood pressure, it can suggest a low-sodium menu. If a customer has diabetes, it can prioritize displaying low-carbohydrate menus. Furthermore, if a customer is on a diet, it can suggest a low-calorie menu. This enables the suggestion of the most suitable menu according to the customer's health condition, leading to improved customer satisfaction.

[0112] The processing unit can provide real-time feedback on customer orders. For example, immediately after an order is received, the processing unit can send a confirmation message to the customer. It can also send a notification to the customer when the order has been relayed to the kitchen and cooking has begun. Furthermore, the processing unit can send a completion notification to the customer when the order is finished, allowing them to track the order's progress in real time. This allows customers to check the order's progress in real time, providing them with peace of mind.

[0113] The proposal department can analyze past customer feedback and suggest the optimal booking schedule. For example, based on feedback previously provided by customers, the proposal department can identify the times and services that customers were satisfied with. It can also suggest avoiding times and services that customers were dissatisfied with. Furthermore, the proposal department can use customer feedback to identify areas for service improvement and suggest better booking schedules. This allows for the suggestion of more satisfying booking schedules by leveraging past customer feedback.

[0114] The support department can assign tasks according to employees' skill levels. For example, the support department can evaluate employees' skill levels and assign appropriate tasks. New employees can be assigned simpler tasks, while experienced employees can be assigned more complex ones. The support department can also provide training programs to support employee skill development. Furthermore, the support department can monitor the progress of tasks according to employees' skill levels and provide support as needed. This enables optimal task assignment based on employee skill levels, improving work efficiency.

[0115] The analytics department can make cross-sell and up-sell suggestions based on customer purchase history. For example, the analytics department can analyze products a customer has previously purchased and suggest related products. If a customer orders pasta, it can suggest salads and desserts. Similarly, if a customer purchases items in a specific price range, it can suggest items in a higher price range. Furthermore, the analytics department can suggest specific promotions and discounts based on customer purchase history. This allows for cross-sell and up-sell suggestions utilizing customer purchase history, ultimately increasing sales.

[0116] The reception desk can estimate the customer's emotions and suggest a customized menu based on those emotions. For example, if the customer is stressed, the reception desk can suggest a relaxing herbal tea or a light snack. If the customer is happy, they can suggest a special dessert or a celebratory menu. Furthermore, if the customer is tired, they can suggest a menu suitable for replenishing energy. This allows for customized menu suggestions that respond to the customer's emotions, improving customer satisfaction.

[0117] The processing unit can estimate the customer's emotions and adjust the order processing speed based on those emotions. For example, if the customer is in a hurry, the processing unit will process the order quickly. If the customer is relaxed, it can process the order slowly and provide detailed explanations. Furthermore, if the customer is stressed, it can process the order quickly to alleviate their stress. This allows for adjusting the order processing speed according to the customer's emotions, thereby improving customer satisfaction.

[0118] The proposal department can estimate customer emotions and propose special events and promotions based on those estimates. For example, if a customer is happy, the proposal department can propose special events and promotions. If a customer is stressed, it can propose events and promotions that help them relax. Furthermore, if a customer is tired, it can propose events and promotions that help them refresh. This allows for the proposal of special events and promotions tailored to customer emotions, thereby improving customer satisfaction.

[0119] The support department can estimate the customer's emotions and provide customized support based on those estimates. For example, if a customer is stressed, the support department can provide prompt and courteous support. If the customer is relaxed, they can provide support at a slower pace and offer detailed explanations. Furthermore, if the customer is in a hurry, they can provide quick support and convey the necessary information concisely. This enables customized support tailored to the customer's emotions, leading to improved customer satisfaction.

[0120] The analytics department can estimate customer emotions and recommend menu items based on those emotions. For example, if a customer is relaxed, the analytics department can suggest menu items with relaxing effects. If a customer is stressed, it can suggest menu items with stress-reducing effects. Furthermore, if a customer is happy, it can suggest special desserts or celebratory menu items. This enables menu recommendations tailored to customer emotions, leading to improved customer satisfaction.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception area accepts customer orders and payments. This includes online orders, credit card payments, and electronic money payments. The reception area is equipped with a web interface for accepting online orders, a payment gateway for processing credit card payments, and a QR code scanner for accepting electronic money payments. Step 2: The processing unit processes the orders and payments received by the reception unit. For example, it verifies the order details, checks inventory status, verifies and approves payment information, and transmits the order details to the kitchen to begin cooking. Step 3: The proposal department uses customer booking history and data to suggest the optimal booking schedule. For example, they analyze past booking data and suggest the best booking time considering the customer's preferred time and congestion levels. Step 4: The support department assists employees with their work. For example, they help with order confirmation, food service, inventory management, customer service, data entry, and report creation. Step 5: The analysis department analyzes customer preferences and order history to propose the optimal menu. For example, they use data mining techniques and machine learning algorithms to analyze customer preferences and order history, and propose the optimal menu considering customer attribute information.

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, processing unit, proposal unit, support unit, and analysis unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and accepts customer orders and payments. The processing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and processes the orders and payments accepted by the reception unit. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes an optimal reservation schedule using the customer's reservation history and data. The support unit is implemented by, for example, the control unit 46A of the smart device 14 and supports operations as an assistant to employees. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes an optimal menu by analyzing customer preferences and order history. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, processing unit, proposal unit, support unit, and analysis unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives customer orders and payments. The processing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and processes the orders and payments received by the reception unit. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes an optimal reservation schedule using the customer's reservation history and data. The support unit is implemented, for example, by the control unit 46A of the smart glasses 214 and supports the work as an assistant to employees. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes an optimal menu by analyzing customer preferences and order history. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, processing unit, proposal unit, support unit, and analysis unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives customer orders and payments. The processing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and processes the orders and payments received by the reception unit. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes an optimal reservation schedule using the customer's reservation history and data. The support unit is implemented by, for example, the control unit 46A of the headset terminal 314 and supports the work as an assistant to employees. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes an optimal menu by analyzing customer preferences and order history. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the reception unit, processing unit, proposal unit, support unit, and analysis unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives customer orders and payments. The processing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and processes the orders and payments received by the reception unit. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes an optimal reservation schedule using the customer's reservation history and data. The support unit is implemented by, for example, the control unit 46A of the robot 414 and supports operations as an assistant to employees. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes an optimal menu by analyzing customer preferences and order history. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0176] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The reception area handles customer orders and payments, A processing unit that processes orders and payments received by the aforementioned reception unit, The proposal department uses customer reservation history and data to suggest the optimal reservation schedule, The support department assists employees with their work, It includes an analysis department that analyzes customer preferences and order history to suggest the optimal menu. A system characterized by the following features. (Note 2) The aforementioned reception unit is We accept orders and payments via smartphone. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned processing unit, The system confirms orders received by the aforementioned reception unit and automatically processes payments. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We utilize customer booking history and data to propose the optimal booking schedule. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit is They support employees in their work, helping to reduce errors and waiting times. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is We analyze customer preferences and order history to suggest the most suitable menu. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate customer emotions and adjust the timing of order acceptance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the customer's past order history and select the most suitable order processing method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving an order, filtering is performed based on the customer's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates customer emotions and determines the priority of orders to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When accepting orders, the system prioritizes orders that are highly relevant, taking into account the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When taking an order, the system analyzes the customer's social media activity and accepts related orders. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned processing unit, We estimate customer emotions and adjust order processing methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned processing unit, When processing an order, adjust the level of detail based on the importance of the order. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned processing unit, When processing an order, different processing algorithms are applied depending on the order category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned processing unit, The system estimates customer emotions and prioritizes order processing based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned processing unit, When processing an order, the order is prioritized based on when it was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned processing unit, When processing orders, the order of processing is adjusted based on the relevance of the orders. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, We estimate customer emotions and adjust how we suggest booking schedules based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When proposing a reservation schedule, we refer to past reservation data to make the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When proposing a reservation schedule, we take customer attribute information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, The system estimates customer emotions and prioritizes booking schedules based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When proposing a reservation schedule, we take the customer's geographical location into consideration to provide the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When proposing a reservation schedule, analyze the customer's social media activity to make suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit is We estimate customer emotions and adjust our business support methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit is When providing business support, the optimal support method is selected by referring to the employee's past work history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit is When providing business support, we take employee attribute information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit is Estimate customer emotions and prioritize business support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit is When providing business support, the most suitable support method is selected by considering the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit is During business support, we analyze employees' social media activity to provide support. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit is We estimate customer emotions and adjust the menu suggestion method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned analysis unit is When suggesting menu items, we refer to past order data to make the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned analysis unit is When suggesting menu items, take customer attribute information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned analysis unit is The system estimates customer emotions and prioritizes menu suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned analysis unit is When suggesting menu items, we take the customer's geographical location into consideration to provide the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned analysis unit is When suggesting menu items, analyze the customer's social media activity to make recommendations. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area handles customer orders and payments, A processing unit that processes orders and payments received by the aforementioned reception unit, The proposal department uses customer reservation history and data to suggest the optimal reservation schedule, The support department assists employees with their work, It includes an analysis department that analyzes customer preferences and order history to suggest the optimal menu. A system characterized by the following features.

2. The aforementioned reception unit is We accept orders and payments via smartphone. The system according to feature 1.

3. The aforementioned processing unit, The system confirms orders received by the aforementioned reception unit and automatically processes payments. The system according to feature 1.

4. The aforementioned proposal section is, We utilize customer booking history and data to propose the optimal booking schedule. The system according to feature 1.

5. The aforementioned support unit is They support employees in their work, helping to reduce errors and waiting times. The system according to feature 1.

6. The aforementioned analysis unit is We analyze customer preferences and order history to suggest the most suitable menu. The system according to feature 1.

7. The aforementioned reception unit is We estimate customer emotions and adjust the timing of order acceptance based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the customer's past order history and select the most suitable order processing method. The system according to feature 1.

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