System

Abstract

An object of a system according to an embodiment is to enable store visitors to efficiently sit side by side.SOLUTION: A system according to an embodiment includes a dedicated application, a AI, a tablet, and a point system. The dedicated application has a function of allowing a customer to stand in line and select a seat. The AI is capable of detecting vacancy information through an in-store camera. The tablet has a function of displaying an option of whether or not movement is possible in a case where there is a preceding customer at the guide target seat. The point system has a function in which a customer uses points and a preceding customer acquires the points.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, customers who want to sit next to each other at restaurants and other establishments are forced to wait, which has the problem of reducing the turnover rate at the restaurant.

[0005] The system according to the embodiment aims to enable customers to line up and be seated efficiently. [Means for solving the problem]

[0006] The system according to the embodiment comprises a dedicated app, AI, a tablet, and a points system. The dedicated app has a function that allows customers to line up and choose where to sit. The AI ​​has a function that detects vacant seats using cameras inside the store. The tablet has a function that displays options for whether or not to move if there is a previous customer at the seat to which the customer is being guided. The points system has a function that allows customers to use points and the previous customer to earn points. [Effects of the Invention]

[0007] The system according to the embodiment can enable customers to line up and be seated efficiently. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The seat reservation system according to the embodiment of the present invention is a system for efficiently reserving seats when customers want to sit side by side at counter seats in a restaurant, etc. This allows customers to sit side by side efficiently, improving the restaurant's turnover rate and reducing customer waiting times.

[0029] The seat reservation system according to the embodiment includes a dedicated app, AI, a tablet, and a point system. The dedicated app allows customers to select "seat next to each other" when they open the app and select "seat next to each other," allowing the system to automatically adjust the seating arrangement. The AI ​​detects available seats through in-store cameras. For example, the AI ​​analyzes camera footage to identify the current availability and the seats where customers are likely to finish eating. The AI ​​performs analysis based on the camera footage and prompts containing analysis instructions. The tablet displays a prompt for customers to move if there is a customer in the seat they are being guided to. For example, the tablet displays a prompt asking "Do you want to move?", allowing the customer to tap this option to choose whether or not to move. The point system allows customers to use points and the customer to earn points. For example, if a customer selects "seat next to each other" and allows the customer to move, the customer uses 100 points, and the customer who moves earns 100 points. As a result, the seat reservation system according to the embodiment allows customers to line up and sit efficiently, improving the store's turnover rate and reducing waiting times for customers.

[0030] The dedicated app can learn a customer's seating preferences based on their past visit history and automatically suggest the most suitable seat. For example, the dedicated app stores a customer's past visit history and learns their seating preferences based on that data. For example, if a customer has preferred a window seat in the past, the app will prioritize a window seat the next time they visit. The dedicated app also adds a function to automatically suggest the most suitable seat based on a customer's past visit history when the customer uses the app. For example, if a customer has preferred a quiet seat in the past, the app will prioritize a quiet seat. The dedicated app also learns a customer's seating preferences and automatically suggests the most suitable seat when the customer visits. For example, if a customer has preferred a specific seat in the past, the app will prioritize that seat if it is available. This improves customer satisfaction by suggesting the most suitable seat based on the customer's preferences.

[0031] The dedicated app can use real-time location information of customers to suggest the optimal time to visit the store, taking into account the level of congestion in the store. For example, the dedicated app can add a function to acquire real-time location information of customers and suggest the optimal time to visit, taking into account the level of congestion in the store. For example, it can calculate the travel time from the customer's current location and suggest a time period when the store is less crowded. The dedicated app can also add a function to analyze the level of congestion in the store in real time based on the customer's location information and suggest the optimal time to visit. For example, it can notify the customer of a time period when the store is less crowded, taking into account the travel time from the customer's current location. The dedicated app can also add a function to analyze the level of congestion in the store in real time based on the customer's location information and suggest the optimal time to visit. For example, it can calculate the travel time from the customer's current location and notify the customer of a time period when the store is less crowded. This allows customers to know the optimal time to visit, thereby reducing waiting times.

[0032] The dedicated app can integrate seat reservation functions with other restaurants and cafes, allowing for centralized management of seat reservations at multiple stores. The dedicated app can, for example, integrate seat reservation functions with other restaurants and cafes, allowing for centralized management of seat reservations at multiple stores. For example, it is possible to make seat reservations at multiple stores using the same app. The dedicated app can also add seat reservation functions with other restaurants and cafes, allowing for centralized management of seat reservations at multiple stores. For example, it is possible to make seat reservations at multiple stores using the same app. The dedicated app can also integrate seat reservation functions with other restaurants and cafes, allowing for centralized management of seat reservations at multiple stores. For example, it is possible to make seat reservations at multiple stores using the same app. This allows for centralized management of seat reservations at multiple stores, improving convenience for customers.

[0033] The dedicated app adds a communication function between customers and provides a chat function that allows them to directly negotiate seating adjustments. The dedicated app, for example, adds a communication function between customers and provides a chat function that allows them to directly negotiate seating adjustments. For example, customers can make seating adjustments through chat with each other. The dedicated app also adds a communication function between customers and provides a chat function that allows them to directly negotiate seating adjustments. For example, customers can make seating adjustments through chat with each other. The dedicated app also adds a communication function between customers and provides a chat function that allows them to directly negotiate seating adjustments. For example, customers can make seating adjustments through chat with each other. This allows customers to negotiate directly with each other, making seating adjustments smoother.

[0034] AI can learn the eating pace of customers and improve the accuracy of predicting when they will finish eating. For example, AI can learn the eating pace of customers and improve the accuracy of predicting when they will finish eating. For example, it can predict when they will finish eating based on past data. AI can also learn the eating pace of customers and improve the accuracy of predicting when they will finish eating. For example, it can predict when they will finish eating based on past data. AI can also learn the eating pace of customers and improve the accuracy of predicting when they will finish eating. For example, it can predict when they will finish eating based on past data. In this way, by learning the eating pace of customers, it can more accurately predict when they will finish eating.

[0035] AI can analyze in-restaurant voice data and estimate the progress of a meal from the content of the conversation. For example, AI can add a function to analyze in-restaurant voice data and estimate the progress of a meal from the content of the conversation. For example, it can estimate the progress of a meal from the content of the conversation. Furthermore, AI can add a function to analyze in-restaurant voice data and estimate the progress of a meal from the content of the conversation. For example, it can estimate the progress of a meal from the content of the conversation. Furthermore, AI can add a function to analyze in-restaurant voice data and estimate the progress of a meal from the content of the conversation. For example, it can estimate the progress of a meal from the content of the conversation. This makes it possible to more accurately grasp the progress of a meal by analyzing in-restaurant voice data.

[0036] AI can analyze the temperature and lighting conditions in a store and suggest comfortable seating arrangements. For example, AI can add a function that analyzes the temperature and lighting conditions in a store and suggest comfortable seating arrangements. For example, it can suggest comfortable seats based on the temperature and lighting conditions. Also, AI can add a function that analyzes the temperature and lighting conditions in a store and suggest comfortable seating arrangements. For example, it can suggest comfortable seats based on the temperature and lighting conditions. Also, AI can add a function that analyzes the temperature and lighting conditions in a store and suggest comfortable seating arrangements. For example, it can suggest comfortable seats based on the temperature and lighting conditions. In this way, by analyzing the in-store environment, it can suggest comfortable seats for customers.

[0037] AI can analyze the congestion situation in a store in real time and notify customers of the optimal time to visit. For example, AI can add a function to analyze the congestion situation in a store in real time and notify customers of the optimal time to visit. For example, it can notify them of a less crowded time period. AI can also add a function to analyze the congestion situation in a store in real time and notify customers of the optimal time to visit. For example, it can notify them of a less crowded time period. AI can also add a function to analyze the congestion situation in a store in real time and notify customers of the optimal time to visit. For example, it can notify them of a less crowded time period. This allows customers to know the optimal time to visit, thereby reducing waiting times.

[0038] The tablet can visually display the advantages and disadvantages of moving, making it easier for previous customers to decide to move. The tablet, for example, adds a function to visually display the advantages and disadvantages of moving. For example, the points gained by moving and the comfort of the new seat are shown in graphs and icons. The tablet can also add a function to visually display the advantages and disadvantages of moving. For example, the points gained by moving and the comfort of the new seat are shown in graphs and icons. The tablet can also add a function to visually display the advantages and disadvantages of moving. For example, the points gained by moving and the comfort of the new seat are shown in graphs and icons. In this way, by visually displaying the advantages and disadvantages of moving, it is easier for previous customers to decide to move.

[0039] The tablet can predict the comfort level of the seat after the user has been moved and display it to the previous customer. The tablet, for example, adds a function to predict the comfort level of the seat after the user has been moved and display it to the previous customer. For example, it predicts the temperature and lighting conditions of the seat after the user has been moved and displays the comfort level. The tablet also adds a function to predict the comfort level of the seat after the user has been moved and display it to the previous customer. For example, it predicts the temperature and lighting conditions of the seat after the user has been moved and displays the comfort level. The tablet also adds a function to predict the comfort level of the seat after the user has been moved and display it to the previous customer. For example, it predicts the temperature and lighting conditions of the seat after the user has been moved and displays the comfort level. In this way, by predicting the comfort level of the seat after the user has been moved, it becomes easier for the previous customer to choose to move.

[0040] The tablet can add a communication function with other customers and provide a chat function that allows them to directly negotiate seating arrangements. The tablet can add a communication function with other customers and provide a chat function that allows them to directly negotiate seating arrangements. For example, customers can make seating arrangements through chat with each other. The tablet can also add a communication function with other customers and provide a chat function that allows them to directly negotiate seating arrangements. For example, customers can make seating arrangements through chat with each other. The tablet can also add a communication function with other customers and provide a chat function that allows them to directly negotiate seating arrangements. For example, customers can make seating arrangements through chat with each other. This allows customers to negotiate directly with each other, making seating arrangements smoother.

[0041] The tablet can display the in-store menu and service information, and provide incentives to previous customers when they choose to move on. The tablet, for example, displays the in-store menu and service information, and provides incentives to previous customers when they choose to move on. For example, it displays information about special offers and discounts that can be obtained by moving on. The tablet can also display the in-store menu and service information, and provide incentives to previous customers when they choose to move on. For example, it displays information about special offers and discounts that can be obtained by moving on. The tablet can also display the in-store menu and service information, and provide incentives to previous customers when they choose to move on. For example, it displays information about special offers and discounts that can be obtained by moving on. This provides incentives to previous customers when they choose to move on, and encourages them to move on.

[0042] A point system can provide benefits and discounts based on a customer's past usage history. The point system, for example, adds a function to provide benefits and discounts based on a customer's past usage history. For example, a benefit is provided to a customer who has used a lot of points in the past. The point system also adds a function to provide benefits and discounts based on a customer's past usage history. For example, a benefit is provided to a customer who has used a lot of points in the past. The point system also adds a function to provide benefits and discounts based on a customer's past usage history. For example, a benefit is provided to a customer who has used a lot of points in the past. In this way, by providing benefits and discounts based on a customer's past usage history, the repeat customer rate increases.

[0043] The point system can add a function that allows customers to transfer points to each other, thereby promoting flexible point usage. The point system can add a function that allows customers to transfer points to each other, thereby promoting flexible point usage. For example, points can be transferred to friends or family. The point system can also add a function that allows customers to transfer points to each other, thereby promoting flexible point usage. For example, points can be transferred to friends or family. The point system can also add a function that allows customers to transfer points to each other, thereby promoting flexible point usage. For example, points can be transferred to friends or family. This allows customers to transfer points to each other, thereby making point usage more flexible and improving customer satisfaction.

[0044] The point system can add collaboration with other stores and services to expand the range of use of points. The point system can add collaboration with other stores and services to expand the range of use of points. For example, it can make it possible to use points at affiliated stores. The point system can also add collaboration with other stores and services to expand the range of use of points. For example, it can make it possible to use points at affiliated stores. The point system can also add collaboration with other stores and services to expand the range of use of points. For example, it can make it possible to use points at affiliated stores. This expands the range of use of points by collaborating with other stores and services, improving convenience for customers.

[0045] The point system can provide customized benefits based on the behavioral data of customers. The point system, for example, adds a function to provide customized benefits based on the behavioral data of customers. For example, benefits are provided based on past usage history. The point system also adds a function to provide customized benefits based on the behavioral data of customers. For example, benefits are provided based on past usage history. The point system also adds a function to provide customized benefits based on the behavioral data of customers. For example, benefits are provided based on past usage history. In this way, by providing customized benefits based on the behavioral data of customers, customer satisfaction is improved.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The seat reservation system can monitor the health of customers and suggest seating arrangements that take their health into consideration. For example, it can measure their heart rate and blood pressure to suggest seats where they can relax. It can also suggest seats that offer menus that avoid certain ingredients based on allergy information. It can also suggest quiet seats or seats with good air quality depending on customers' health conditions. This improves customer satisfaction by allowing for seating arrangements that take their health into consideration.

[0048] The seat reservation system can suggest seating arrangements based on specific themes, based on the hobbies and interests of customers. For example, a seat decorated with movie posters can be suggested for a customer who loves movies. A seat playing the music of a customer's favorite artist can also be suggested for a customer who loves reading. Furthermore, a quiet seat suitable for reading can be suggested for a customer who loves reading. In this way, customer satisfaction can be improved by arranging seats based on the customer's hobbies and interests.

[0049] The seat reservation system can suggest the most suitable menu based on the customer's past order history. For example, it can suggest a menu that suits the customer's preferences based on dishes previously ordered. It can also suggest new menu items based on past order history. Furthermore, it can also suggest menu items that take into consideration the customer's health condition based on past order history. In this way, customer satisfaction can be improved by suggesting menu items based on the customer's preferences.

[0050] The seat reservation system can suggest specific events and campaigns based on a customer's past visit history. For example, if a customer has participated in a specific event in the past, a similar event can be suggested. It can also suggest campaigns that customers may be interested in based on their past visit history. It can also suggest events that customers may want to participate in based on their past visit history. This improves customer satisfaction by suggesting events and campaigns based on the customer's interests.

[0051] The seat reservation system can suggest the most suitable parking lot based on the customer's real-time location information. For example, it can suggest the parking lot closest to the customer's current location. It can also analyze parking availability in real time and suggest available parking lots. It can also suggest the most economical parking lot based on parking fee information. This allows customers to park smoothly, improving customer satisfaction.

[0052] The seat reservation system can suggest the optimal route based on the customer's real-time location information. For example, it can suggest the quickest route from the customer's current location. It can also analyze traffic conditions in real time and suggest routes that avoid traffic jams. It can also suggest the most convenient route based on information about public transportation. This allows customers to arrive smoothly, improving customer satisfaction.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The dedicated app has a function that allows customers to choose whether to sit side by side. For example, when a customer opens the dedicated app and selects "Sit side by side," the system automatically adjusts the seats. Step 2: The AI ​​has the ability to detect vacant seats through in-store cameras. For example, the AI ​​analyzes camera footage to identify the current seat availability and seats where customers are likely to finish eating. The generating AI performs its analysis based on the camera footage and prompts containing instructions for analyzing it. Step 3: If there is a passenger in the seat to be guided, the tablet has the function of displaying the option of whether or not to move. For example, the tablet will display the option "Do you want to move?", and the passenger can tap this option to choose whether or not to move. Step 4: The point system allows customers to use points and previous customers to earn points. For example, if a customer selects "sit next to each other" and asks the previous customer to move to their seat, the customer uses 100 points and the previous customer who moved earns 100 points.

[0055] (Example 2) The seat reservation system according to the embodiment of the present invention is a system for efficiently reserving seats when customers want to sit side by side at counter seats in a restaurant, etc. This allows customers to sit side by side efficiently, improving the restaurant's turnover rate and reducing customer waiting times.

[0056] The seat reservation system according to the embodiment includes a dedicated app, AI, a tablet, and a point system. The dedicated app allows customers to select "seat next to each other" when they open the app and select "seat next to each other," allowing the system to automatically adjust the seating arrangement. The AI ​​detects available seats through in-store cameras. For example, the AI ​​analyzes camera footage to identify the current availability and the seats where customers are likely to finish eating. The AI ​​performs analysis based on the camera footage and prompts containing analysis instructions. The tablet displays a prompt for customers to move if there is a customer in the seat they are being guided to. For example, the tablet displays a prompt asking "Do you want to move?", allowing the customer to tap this option to choose whether or not to move. The point system allows customers to use points and the customer to earn points. For example, if a customer selects "seat next to each other" and allows the customer to move, the customer uses 100 points, and the customer who moves earns 100 points. As a result, the seat reservation system according to the embodiment allows customers to line up and sit efficiently, improving the store's turnover rate and reducing waiting times for customers.

[0057] The dedicated app can learn a customer's seating preferences based on their past visit history and automatically suggest the most suitable seat. For example, the dedicated app stores a customer's past visit history and learns their seating preferences based on that data. For example, if a customer has preferred a window seat in the past, the app will prioritize a window seat the next time they visit. The dedicated app also adds a function to automatically suggest the most suitable seat based on a customer's past visit history when the customer uses the app. For example, if a customer has preferred a quiet seat in the past, the app will prioritize a quiet seat. The dedicated app also learns a customer's seating preferences and automatically suggests the most suitable seat when the customer visits. For example, if a customer has preferred a specific seat in the past, the app will prioritize that seat if it is available. This improves customer satisfaction by suggesting the most suitable seat based on the customer's preferences.

[0058] The dedicated app can use real-time location information of customers to suggest the optimal time to visit the store, taking into account the level of congestion in the store. For example, the dedicated app can add a function to acquire real-time location information of customers and suggest the optimal time to visit, taking into account the level of congestion in the store. For example, it can calculate the travel time from the customer's current location and suggest a time period when the store is less crowded. The dedicated app can also add a function to analyze the level of congestion in the store in real time based on the customer's location information and suggest the optimal time to visit. For example, it can notify the customer of a time period when the store is less crowded, taking into account the travel time from the customer's current location. The dedicated app can also add a function to analyze the level of congestion in the store in real time based on the customer's location information and suggest the optimal time to visit. For example, it can calculate the travel time from the customer's current location and notify the customer of a time period when the store is less crowded. This allows customers to know the optimal time to visit, thereby reducing waiting times.

[0059] The dedicated app can use an emotion estimation function to analyze the emotional state of customers and suggest seating arrangements that reduce stress. The dedicated app is equipped with an emotion estimation function, for example, and analyzes the emotional state of customers in real time. For example, it uses a camera or microphone to analyze the customer's facial expressions and voice and suggests seating arrangements that reduce stress. The dedicated app also uses the emotion estimation function to analyze the emotional state of customers and suggest seating arrangements that reduce stress. For example, if a customer wants to relax, it suggests a quiet seat. The dedicated app also adds an emotion estimation function to analyze the emotional state of customers in real time and suggests seating arrangements that reduce stress. For example, if a customer wants to relax, it suggests a quiet seat. In this way, the dedicated app reduces stress for customers by suggesting the optimal seat based on their emotional state.

[0060] The dedicated app can integrate seat reservation functions with other restaurants and cafes, allowing for centralized management of seat reservations at multiple stores. The dedicated app can, for example, integrate seat reservation functions with other restaurants and cafes, allowing for centralized management of seat reservations at multiple stores. For example, it is possible to make seat reservations at multiple stores using the same app. The dedicated app can also add seat reservation functions with other restaurants and cafes, allowing for centralized management of seat reservations at multiple stores. For example, it is possible to make seat reservations at multiple stores using the same app. The dedicated app can also integrate seat reservation functions with other restaurants and cafes, allowing for centralized management of seat reservations at multiple stores. For example, it is possible to make seat reservations at multiple stores using the same app. This allows for centralized management of seat reservations at multiple stores, improving convenience for customers.

[0061] The dedicated app adds a communication function between customers and provides a chat function that allows them to directly negotiate seating adjustments. The dedicated app, for example, adds a communication function between customers and provides a chat function that allows them to directly negotiate seating adjustments. For example, customers can make seating adjustments through chat with each other. The dedicated app also adds a communication function between customers and provides a chat function that allows them to directly negotiate seating adjustments. For example, customers can make seating adjustments through chat with each other. The dedicated app also adds a communication function between customers and provides a chat function that allows them to directly negotiate seating adjustments. For example, customers can make seating adjustments through chat with each other. This allows customers to negotiate directly with each other, making seating adjustments smoother.

[0062] The dedicated app can use the emotion estimation function to analyze the emotions of customers when they use the app in real time, and provide an interface that elicits positive emotions. The dedicated app, for example, uses the emotion estimation function to analyze the emotions of customers when they use the app in real time, and provide an interface that elicits positive emotions. For example, it can provide an interface that helps customers relax. The dedicated app can also use the emotion estimation function to analyze the emotions of customers when they use the app in real time, and provide an interface that elicits positive emotions. For example, it can provide an interface that helps customers relax. The dedicated app can also use the emotion estimation function to analyze the emotions of customers when they use the app in real time, and provide an interface that elicits positive emotions. For example, it can provide an interface that helps customers relax. This elicits positive emotions when customers use the app, and improves the user experience.

[0063] AI can learn the eating pace of customers and improve the accuracy of predicting when they will finish eating. For example, AI can learn the eating pace of customers and improve the accuracy of predicting when they will finish eating. For example, it can predict when they will finish eating based on past data. AI can also learn the eating pace of customers and improve the accuracy of predicting when they will finish eating. For example, it can predict when they will finish eating based on past data. AI can also learn the eating pace of customers and improve the accuracy of predicting when they will finish eating. For example, it can predict when they will finish eating based on past data. In this way, by learning the eating pace of customers, it can more accurately predict when they will finish eating.

[0064] AI can analyze in-restaurant voice data and estimate the progress of a meal from the content of the conversation. For example, AI can add a function to analyze in-restaurant voice data and estimate the progress of a meal from the content of the conversation. For example, it can estimate the progress of a meal from the content of the conversation. Furthermore, AI can add a function to analyze in-restaurant voice data and estimate the progress of a meal from the content of the conversation. For example, it can estimate the progress of a meal from the content of the conversation. Furthermore, AI can add a function to analyze in-restaurant voice data and estimate the progress of a meal from the content of the conversation. For example, it can estimate the progress of a meal from the content of the conversation. This makes it possible to more accurately grasp the progress of a meal by analyzing in-restaurant voice data.

[0065] AI can analyze the temperature and lighting conditions in a store and suggest comfortable seating arrangements. For example, AI can add a function that analyzes the temperature and lighting conditions in a store and suggest comfortable seating arrangements. For example, it can suggest comfortable seats based on the temperature and lighting conditions. Also, AI can add a function that analyzes the temperature and lighting conditions in a store and suggest comfortable seating arrangements. For example, it can suggest comfortable seats based on the temperature and lighting conditions. Also, AI can add a function that analyzes the temperature and lighting conditions in a store and suggest comfortable seating arrangements. For example, it can suggest comfortable seats based on the temperature and lighting conditions. In this way, by analyzing the in-store environment, it can suggest comfortable seats for customers.

[0066] AI can analyze the congestion situation in a store in real time and notify customers of the optimal time to visit. For example, AI can add a function to analyze the congestion situation in a store in real time and notify customers of the optimal time to visit. For example, it can notify them of a less crowded time period. AI can also add a function to analyze the congestion situation in a store in real time and notify customers of the optimal time to visit. For example, it can notify them of a less crowded time period. AI can also add a function to analyze the congestion situation in a store in real time and notify customers of the optimal time to visit. For example, it can notify them of a less crowded time period. This allows customers to know the optimal time to visit, thereby reducing waiting times.

[0067] AI can use the emotion estimation function to analyze the emotional state of customers and suggest seating arrangements that will reduce stress. For example, AI can use the emotion estimation function to analyze the emotional state of customers and suggest seating arrangements that will reduce stress. For example, it can suggest seats that will allow customers to relax. AI can also use the emotion estimation function to analyze the emotional state of customers and suggest seating arrangements that will reduce stress. For example, it can suggest seats that will allow customers to relax. AI can also use the emotion estimation function to analyze the emotional state of customers and suggest seating arrangements that will reduce stress. For example, it can suggest seats that will allow customers to relax. In this way, by suggesting the optimal seat based on the emotional state of customers, stress for customers is reduced.

[0068] The tablet can visually display the advantages and disadvantages of moving, making it easier for previous customers to decide to move. The tablet, for example, adds a function to visually display the advantages and disadvantages of moving. For example, the points gained by moving and the comfort of the new seat are shown in graphs and icons. The tablet can also add a function to visually display the advantages and disadvantages of moving. For example, the points gained by moving and the comfort of the new seat are shown in graphs and icons. The tablet can also add a function to visually display the advantages and disadvantages of moving. For example, the points gained by moving and the comfort of the new seat are shown in graphs and icons. In this way, by visually displaying the advantages and disadvantages of moving, it is easier for previous customers to decide to move.

[0069] The tablet can predict the comfort level of the seat after the user has been moved and display it to the previous customer. The tablet, for example, adds a function to predict the comfort level of the seat after the user has been moved and display it to the previous customer. For example, it predicts the temperature and lighting conditions of the seat after the user has been moved and displays the comfort level. The tablet also adds a function to predict the comfort level of the seat after the user has been moved and display it to the previous customer. For example, it predicts the temperature and lighting conditions of the seat after the user has been moved and displays the comfort level. The tablet also adds a function to predict the comfort level of the seat after the user has been moved and display it to the previous customer. For example, it predicts the temperature and lighting conditions of the seat after the user has been moved and displays the comfort level. In this way, by predicting the comfort level of the seat after the user has been moved, it becomes easier for the previous customer to choose to move.

[0070] The tablet uses the emotion estimation function to analyze the emotional state of the preceding customer and make travel suggestions at the optimal timing. The tablet, for example, uses the emotion estimation function to analyze the emotional state of the preceding customer in real time and make travel suggestions at the optimal timing. For example, it makes travel suggestions when the preceding customer is relaxed. The tablet also uses the emotion estimation function to analyze the emotional state of the preceding customer in real time and make travel suggestions at the optimal timing. For example, it makes travel suggestions when the preceding customer is relaxed. The tablet also uses the emotion estimation function to analyze the emotional state of the preceding customer in real time and make travel suggestions at the optimal timing. For example, it makes travel suggestions when the preceding customer is relaxed. In this way, by making travel suggestions at the optimal timing based on the emotional state of the preceding customer, it becomes easier for the preceding customer to choose a travel option.

[0071] The tablet can add a communication function with other customers and provide a chat function that allows them to directly negotiate seating arrangements. The tablet can add a communication function with other customers and provide a chat function that allows them to directly negotiate seating arrangements. For example, customers can make seating arrangements through chat with each other. The tablet can also add a communication function with other customers and provide a chat function that allows them to directly negotiate seating arrangements. For example, customers can make seating arrangements through chat with each other. The tablet can also add a communication function with other customers and provide a chat function that allows them to directly negotiate seating arrangements. For example, customers can make seating arrangements through chat with each other. This allows customers to negotiate directly with each other, making seating arrangements smoother.

[0072] The tablet can display the in-store menu and service information, and provide incentives to previous customers when they choose to move on. The tablet, for example, displays the in-store menu and service information, and provides incentives to previous customers when they choose to move on. For example, it displays information about special offers and discounts that can be obtained by moving on. The tablet can also display the in-store menu and service information, and provide incentives to previous customers when they choose to move on. For example, it displays information about special offers and discounts that can be obtained by moving on. The tablet can also display the in-store menu and service information, and provide incentives to previous customers when they choose to move on. For example, it displays information about special offers and discounts that can be obtained by moving on. This provides incentives to previous customers when they choose to move on, and encourages them to move on.

[0073] The tablet uses the emotion estimation function to analyze the emotional state of the preceding customer and make travel suggestions at the optimal timing. The tablet, for example, uses the emotion estimation function to analyze the emotional state of the preceding customer in real time and make travel suggestions at the optimal timing. For example, it makes travel suggestions when the preceding customer is relaxed. The tablet also uses the emotion estimation function to analyze the emotional state of the preceding customer in real time and make travel suggestions at the optimal timing. For example, it makes travel suggestions when the preceding customer is relaxed. The tablet also uses the emotion estimation function to analyze the emotional state of the preceding customer in real time and make travel suggestions at the optimal timing. For example, it makes travel suggestions when the preceding customer is relaxed. In this way, by making travel suggestions at the optimal timing based on the emotional state of the preceding customer, it becomes easier for the preceding customer to choose a travel option.

[0074] A point system can provide benefits and discounts based on a customer's past usage history. The point system, for example, adds a function to provide benefits and discounts based on a customer's past usage history. For example, a benefit is provided to a customer who has used a lot of points in the past. The point system also adds a function to provide benefits and discounts based on a customer's past usage history. For example, a benefit is provided to a customer who has used a lot of points in the past. The point system also adds a function to provide benefits and discounts based on a customer's past usage history. For example, a benefit is provided to a customer who has used a lot of points in the past. In this way, by providing benefits and discounts based on a customer's past usage history, the repeat customer rate increases.

[0075] The point system can add a function that allows customers to transfer points to each other, thereby promoting flexible point usage. The point system can add a function that allows customers to transfer points to each other, thereby promoting flexible point usage. For example, points can be transferred to friends or family. The point system can also add a function that allows customers to transfer points to each other, thereby promoting flexible point usage. For example, points can be transferred to friends or family. The point system can also add a function that allows customers to transfer points to each other, thereby promoting flexible point usage. For example, points can be transferred to friends or family. This allows customers to transfer points to each other, thereby making point usage more flexible and improving customer satisfaction.

[0076] The point system can add collaboration with other stores and services to expand the range of use of points. The point system can add collaboration with other stores and services to expand the range of use of points. For example, it can make it possible to use points at affiliated stores. The point system can also add collaboration with other stores and services to expand the range of use of points. For example, it can make it possible to use points at affiliated stores. The point system can also add collaboration with other stores and services to expand the range of use of points. For example, it can make it possible to use points at affiliated stores. This expands the range of use of points by collaborating with other stores and services, improving convenience for customers.

[0077] The point system can provide customized benefits based on the behavioral data of customers. The point system, for example, adds a function to provide customized benefits based on the behavioral data of customers. For example, benefits are provided based on past usage history. The point system also adds a function to provide customized benefits based on the behavioral data of customers. For example, benefits are provided based on past usage history. The point system also adds a function to provide customized benefits based on the behavioral data of customers. For example, benefits are provided based on past usage history. In this way, by providing customized benefits based on the behavioral data of customers, customer satisfaction is improved.

[0078] The point system can use the emotion estimation function to analyze the emotional state of a customer and suggest the optimal timing for using points. The point system, for example, uses the emotion estimation function to analyze the emotional state of a customer and suggest the optimal timing for using points. For example, it suggests using points when the customer is relaxed. The point system also uses the emotion estimation function to analyze the emotional state of a customer and suggest the optimal timing for using points. For example, it suggests using points when the customer is relaxed. The point system also uses the emotion estimation function to analyze the emotional state of a customer and suggest the optimal timing for using points. For example, it suggests using points when the customer is relaxed. This makes it possible to effectively use points by suggesting the optimal timing for using points based on the emotional state of a customer.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The seat reservation system can monitor the health of customers and suggest seating arrangements that take their health into consideration. For example, it can measure their heart rate and blood pressure to suggest seats where they can relax. It can also suggest seats that offer menus that avoid certain ingredients based on allergy information. It can also suggest quiet seats or seats with good air quality depending on customers' health conditions. This improves customer satisfaction by allowing for seating arrangements that take their health into consideration.

[0081] The seat reservation system can suggest seating arrangements based on specific themes, based on the hobbies and interests of customers. For example, a seat decorated with movie posters can be suggested for a customer who loves movies. A seat playing the music of a customer's favorite artist can also be suggested for a customer who loves reading. Furthermore, a quiet seat suitable for reading can be suggested for a customer who loves reading. In this way, customer satisfaction can be improved by arranging seats based on the customer's hobbies and interests.

[0082] The seat reservation system can estimate the emotional state of customers and provide benefits according to their emotions. For example, if a customer is feeling stressed, it can provide a free relaxing drink. If the customer is happy, it can also provide a free dessert. Furthermore, if the customer is tired, it can suggest using a massage chair. In this way, customer satisfaction can be improved by providing benefits according to the customer's emotional state.

[0083] The seat reservation system can estimate the emotional state of a customer and provide music that matches that emotion. For example, if a customer wants to relax, relaxing music can be played. If a customer wants to cheer up, upbeat music can be played. Furthermore, if a customer wants to concentrate, music that helps improve concentration can be played. In this way, customer satisfaction can be improved by providing music that matches the customer's emotional state.

[0084] The seat reservation system can estimate the emotional state of a customer and provide lighting that matches that emotion. For example, if a customer wants to relax, it can provide soft, warm lighting. If a customer wants to feel energized, it can provide bright lighting. Furthermore, if a customer wants to concentrate, it can provide white lighting. In this way, lighting that matches the customer's emotional state can be provided, improving customer satisfaction.

[0085] The seat reservation system can suggest the most suitable menu based on the customer's past order history. For example, it can suggest a menu that suits the customer's preferences based on dishes previously ordered. It can also suggest new menu items based on past order history. Furthermore, it can also suggest menu items that take into consideration the customer's health condition based on past order history. In this way, customer satisfaction can be improved by suggesting menu items based on the customer's preferences.

[0086] The seat reservation system can suggest specific events and campaigns based on a customer's past visit history. For example, if a customer has participated in a specific event in the past, a similar event can be suggested. It can also suggest campaigns that customers may be interested in based on their past visit history. It can also suggest events that customers may want to participate in based on their past visit history. This improves customer satisfaction by suggesting events and campaigns based on the customer's interests.

[0087] The seat reservation system can suggest the most suitable parking lot based on the customer's real-time location information. For example, it can suggest the parking lot closest to the customer's current location. It can also analyze parking availability in real time and suggest available parking lots. It can also suggest the most economical parking lot based on parking fee information. This allows customers to park smoothly, improving customer satisfaction.

[0088] The seat reservation system can suggest the optimal route based on the customer's real-time location information. For example, it can suggest the quickest route from the customer's current location. It can also analyze traffic conditions in real time and suggest routes that avoid traffic jams. It can also suggest the most convenient route based on information about public transportation. This allows customers to arrive smoothly, improving customer satisfaction.

[0089] The seat reservation system can estimate the emotional state of a customer and provide services according to that emotion. For example, if a customer is feeling stressed, a relaxing massage service can be provided. If a customer is happy, a special dessert can be served. Furthermore, if a customer is tired, a refreshing drink can be served. In this way, customer satisfaction can be improved by providing services according to the customer's emotional state.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The dedicated app has a function that allows customers to choose whether to sit side by side. For example, when a customer opens the dedicated app and selects "Sit side by side," the system automatically adjusts the seats. Step 2: The AI ​​has the ability to detect vacant seats through in-store cameras. For example, the AI ​​analyzes camera footage to identify the current seat availability and seats where customers are likely to finish eating. The generating AI performs its analysis based on the camera footage and prompts containing instructions for analyzing it. Step 3: If there is a passenger in the seat to be guided, the tablet has the function of displaying the option of whether or not to move. For example, the tablet will display the option "Do you want to move?", and the passenger can tap this option to choose whether or not to move. Step 4: The point system allows customers to use points and previous customers to earn points. For example, if a customer selects "sit next to each other" and asks the previous customer to move to their seat, the customer uses 100 points and the previous customer who moved earns 100 points.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0098] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0102] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0103] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0112] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0142] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0143] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0144] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0145] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0146] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0147] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0148] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0150] 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.

[0151] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0152] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0153] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0156] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0157] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Equipped with a dedicated app, The dedicated app is It has the function of allowing customers to line up and choose where to sit, Equipped with AI, The AI ​​is It has the function of detecting vacant seats through cameras in the store, Equipped with a tablet, The tablet is It has a function that displays the option to move or not if there is a previous occupant in the seat to be guided, Equipped with a points system, The point system is Customers can use points and previous customers can earn points. A system characterized by:

2. The dedicated app is The system learns the customer's seating preferences based on their past visit history and automatically suggests the most suitable seat.

2. The system of claim 1.

3. The dedicated app is Using the real-time location information of the customer, the system suggests the best time to visit the store, taking into account the congestion situation inside the store.

2. The system of claim 1.

4. The dedicated app is Analyze the emotional state of the customer and suggest a seating arrangement that minimizes stress 2. The system of claim 1.

5. The dedicated app is Integrates seat reservation functions for other restaurants and cafes, allowing for centralized management of seat reservations at multiple establishments.

2. The system of claim 1.

6. The dedicated app is Add a communication function between customers and provide a chat function that allows direct negotiation of seat adjustments.

2. The system of claim 1.

7. The dedicated app is Analyze the emotions of the customers when they use the app in real time and provide an interface that elicits positive emotions.

2. The system of claim 1.

8. The AI ​​is Learn the eating pace of the customers and improve the accuracy of predicting when they will finish eating 2. The system of claim 1.

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