System

Abstract

An object of a system according to an embodiment is to unify management of an operation process of a restaurant and realize efficient operation.SOLUTION: A system includes a reservation management part, an order management part, a cooking instruction part, an accounting management part, an inventory management part, and a cooking part. The reservation management unit receives a reservation from a customer. The order management unit receives an order from a customer. The cooking instruction unit issues an instruction to the robot chef based on the order received by the order management unit. The transaction management unit manages customer transactions. The inventory management unit manages an inventory of food ingredients. The cooking part performs cooking instructed by the cooking instruction part.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, the processes of reservations, ordering, cooking, accounting, and inventory management at restaurants were all dispersed, making efficient operation difficult.

[0005] The system according to the embodiment aims to centrally manage the restaurant operation process and achieve efficient operation. [Means for solving the problem]

[0006] The system according to the embodiment comprises a reservation management unit, an order management unit, a cooking instruction unit, an accounting management unit, an inventory management unit, and a cooking unit. The reservation management unit accepts customer reservations. The order management unit accepts customer orders. The cooking instruction unit issues instructions to the robot chef based on the orders accepted by the order management unit. The accounting management unit manages customer accounting. The inventory management unit manages ingredient inventory. The cooking unit performs cooking as instructed by the cooking instruction unit. [Effects of the Invention]

[0007] The system according to the embodiment can centrally manage the restaurant operation process and achieve efficient operation. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 cafe restaurant system according to the embodiment of the present invention is a system that handles everything from customer reservations to ordering, cooking, accounting, and inventory management in an integrated manner, enabling the cafe restaurant system to provide customers with consistently high-quality food while achieving efficient operation.

[0029] A cafe restaurant system according to an embodiment includes a reservation management unit, an order management unit, a cooking instruction unit, an accounting management unit, an inventory management unit, and a cooking unit. The reservation management unit accepts customer reservations. For example, when a customer makes a reservation using a smartphone or tablet, the reservation management unit receives the information and confirms the reservation. The reservation management unit can also accept reservations via telephone or website. For example, for telephone reservations, an operator enters the reservation information into the system, and the reservation management unit manages the information. For reservations via websites, the reservation information is automatically updated in the system through form input. The order management unit accepts customer orders. For example, when a customer visits the restaurant, the order management unit accepts the order and issues instructions to the robot chef. The order management unit can also accept orders via a tablet on the table or a smartphone app. For example, when a customer selects a menu item on the tablet and confirms the order, the information is sent to the order management unit. The cooking instruction unit issues instructions to the robot chef based on the order accepted by the order management unit. For example, if a customer orders "spaghetti bolognese," the cooking instruction unit sends that information to the robot chef, who then begins cooking. The cooking instruction unit can also communicate the customer's special requests (such as allergies or spiciness adjustments) to the robot chef. The accounting management unit manages the customer's bill. For example, after a customer finishes their meal, the accounting management unit automatically calculates the bill and presents the customer with a payment method. Customers can pay cashlessly using their smartphones or tablets. The accounting management unit also accepts cash payments and can manage payments at the register. For example, if a customer pays cash, the accounting management unit calculates the amount and issues a receipt. The inventory management unit manages ingredient stock. For example, the inventory management unit monitors ingredient usage in real time and automatically places orders when inventory is low. The inventory management unit also analyzes ingredient usage and makes suggestions to minimize waste. For example, the inventory management unit analyzes ingredient consumption patterns and suggests the appropriate time to place orders. The cooking unit performs cooking as instructed by the cooking instruction unit. For example, the robot chef uses high-precision cooking techniques to prepare dishes according to customer orders.The robot chef can always provide food of the same quality without relying on the skills of the staff. The cooking department can also monitor the status of ingredients during cooking in real time and select the optimal cooking method. For example, the robot chef monitors the freshness and temperature of ingredients and adjusts cooking time and heat. This allows the cafe restaurant system according to the embodiment to consistently handle everything from customer reservations to ordering, cooking, accounting, and inventory management. For example, customers can easily make reservations using their smartphones and smoothly place their orders when they arrive. Having the robot chef handle cooking also makes it possible to consistently provide high-quality food. Furthermore, the inventory management department can efficiently manage ingredient inventory, reducing food waste and enabling sustainable operations.

[0030] The reservation management unit can analyze a customer's past order history and make individually customized menu suggestions. The reservation management unit can, for example, use generative AI to analyze a customer's past order history and make individually customized menu suggestions. For example, a new vegetarian menu can be suggested to a customer who has previously preferred vegetarian menus. The reservation management unit can also make menu suggestions taking into account a customer's allergy information and food preferences. For example, a nut-free menu can be suggested to a customer with a nut allergy. This makes it possible to make customized menu suggestions based on a customer's past order history.

[0031] The reservation management unit can predict the congestion level of the store in real time and suggest the optimal time to visit. For example, the reservation management unit can use AI to predict the congestion level of the store in real time and suggest the optimal time to visit. For example, it can predict peak congestion times based on past data and suggest to customers to avoid those times. The reservation management unit can also predict congestion levels using real-time people flow data. For example, sensors inside the store can detect the flow of people and predict the congestion level based on that information. This makes it possible to avoid congestion by suggesting the optimal time to visit the store to customers.

[0032] The reservation management unit can accept reservations and orders by voice using a voice recognition function. The reservation management unit accepts reservations and orders by voice, for example, using voice recognition technology. For example, a customer can complete a reservation by simply speaking into a smartphone. The reservation management unit can also accept customer orders using voice recognition technology. For example, when a customer places an order by speaking into a tablet on a table, that information is sent to the order management unit. This makes it possible to make reservations and orders by voice, improving convenience.

[0033] The reservation management unit can register customer allergy information and food preferences in advance and automatically take them into consideration when ordering. The reservation management unit, for example, registers customer allergy information and food preferences in advance and automatically takes them into consideration when ordering. For example, it can suggest nut-free menu items to customers with nut allergies. The reservation management unit can also make menu suggestions based on a customer's food preferences. For example, it can suggest a new vegetarian menu item to a customer who has previously preferred vegetarian menu items. This makes it possible to order while taking into consideration a customer's allergy information and food preferences.

[0034] The cooking instruction unit can automatically generate new recipes according to customer preferences. The cooking instruction unit automatically generates new recipes according to customer preferences, for example, using generation AI. For example, if a customer likes spicy food, it generates a new spicy recipe. The cooking instruction unit can also generate recipes taking into account a customer's allergy information and food preferences. For example, it generates a recipe that does not include nuts for a customer with a nut allergy. In this way, customer satisfaction is improved by automatically generating new recipes according to customer preferences.

[0035] The cooking unit can monitor the freshness and quality of ingredients in real time and select the optimal cooking method. For example, the cooking unit monitors the freshness of ingredients in real time while the robot chef is cooking and selects the optimal cooking method. For example, when using very fresh vegetables, it will cook them in a short time. The cooking unit can also monitor the quality of ingredients and adjust the cooking method. For example, when meat is of high quality, it will be cooked at a low temperature. In this way, by monitoring the freshness and quality of ingredients in real time and selecting the optimal cooking method, it is possible to always provide high-quality food.

[0036] The cooking department can use 3D printing technology to create dishes with complex shapes. For example, the cooking department can introduce 3D printing technology into a robot chef to create dishes with complex shapes. For example, it can create three-dimensional desserts. The cooking department can also use 3D printing technology to create customized dishes according to customer requests. For example, if a customer requests a cake with a specific shape, it can create a cake that matches that shape. In this way, using 3D printing technology, it is possible to create dishes with complex shapes.

[0037] The cooking unit can monitor the aroma and sounds of cooking in real time and maintain an optimal cooking environment. For example, the cooking unit can monitor the aroma of cooking in real time while the robot chef is cooking and maintain an optimal cooking environment. For example, if the aroma becomes strong, the heat can be adjusted. The cooking unit can also monitor the sounds of cooking and adjust the cooking environment. For example, if the sound of frying changes, the oil temperature can be adjusted. In this way, by monitoring the aroma and sounds of cooking in real time, an optimal cooking environment can be maintained.

[0038] The accounting management department can analyze a customer's past payment history and suggest the optimal payment method. For example, the accounting management department can use generative AI to analyze a customer's past payment history and suggest the optimal payment method. For example, it can suggest credit card payment to a customer who has used credit cards in the past. The accounting management department can also recommend specific payment methods based on a customer's payment history. For example, it can prioritize suggesting payment methods that a customer uses frequently. This improves payment convenience by suggesting the optimal payment method based on the customer's past payment history.

[0039] The accounting management department can reflect exchange rates in real time and propose the most appropriate currency for payment to foreign customers. The accounting management department, for example, reflects exchange rates in real time and proposes the most appropriate currency for payment to foreign customers. For example, for a customer who wishes to pay in dollars, it proposes payment in dollars based on the latest exchange rate. The accounting management department can also propose payment in an appropriate currency based on the customer's nationality and place of residence. For example, it proposes payment in euros for a customer from Europe. This improves payment convenience by proposing the most appropriate currency for payment to foreign customers.

[0040] The accounting management department can realize cashless payments using facial recognition technology. The accounting management department can realize cashless payments using, for example, facial recognition technology. For example, a customer can simply stand at the cash register, where facial recognition is performed and payment is completed. The accounting management department can also use facial recognition technology to verify the identity of a customer. For example, a customer can access membership information and use special offers through facial recognition. As a result, cashless payments become possible using facial recognition technology, improving convenience.

[0041] The accounting department can work with loyalty programs to automatically award points. For example, points are automatically added as soon as a customer makes a payment. The accounting department can also provide benefits based on a customer's loyalty program status. For example, a special discount is offered to gold members. By working with loyalty programs, points can be automatically awarded to customers.

[0042] The inventory management department can predict the optimal timing for ordering based on past data. For example, the inventory management department can use generative AI to predict the optimal timing for ordering based on past data. For example, it can adjust the timing for ordering based on past sales data. The inventory management department can also analyze ingredient consumption patterns and suggest the appropriate timing for ordering. For example, it can place orders at times when a particular ingredient is frequently used. This makes it possible to predict the optimal timing for ordering based on past data, thereby improving the efficiency of inventory management.

[0043] The inventory management unit can monitor the freshness of ingredients in real time and suggest the optimal order of use. The inventory management unit, for example, can monitor the freshness of ingredients in real time and suggest the optimal order of use. For example, it can prioritize the use of ingredients whose freshness has decreased. The inventory management unit can also monitor the expiration dates of ingredients and adjust the order of use. For example, it can prioritize the use of ingredients that are close to their expiration date. In this way, food waste can be reduced by monitoring the freshness of ingredients in real time and suggesting the optimal order of use.

[0044] The inventory management department can use IoT technology to track the location and status of ingredients in real time. The inventory management department can use IoT technology to track the location and status of ingredients in real time, for example, by monitoring the temperature and humidity of ingredients. The inventory management department can also use GPS technology to track the location of ingredients. For example, the location of ingredients during delivery can be confirmed in real time. In this way, the location and status of ingredients can be tracked in real time by using IoT technology.

[0045] The inventory management department can work with other stores to share surplus ingredients. The inventory management department, for example, works with other stores to share surplus ingredients. For example, ingredients are shared with nearby stores. The inventory management department can also exchange ingredients with other stores through a surplus ingredient sharing platform. For example, information about surplus ingredients can be shared through an online platform and provided to stores in need. In this way, food waste can be reduced by working with other stores to share surplus ingredients.

[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 cafe restaurant system can also be equipped with a health management unit that manages customer health data. For example, if a customer registers their health data in advance, the health management unit can suggest health-conscious menus based on that data. The health management unit can also adjust the nutritional balance of meals according to the customer's health condition. For example, it can suggest low-carb menus to customers with diabetes. This makes it possible to suggest meals according to the customer's health condition, and can be used as part of health management.

[0048] The reservation management unit can also propose optimal reservation plans taking into account the customer's travel plans. For example, if the customer is traveling, it can suggest restaurants close to tourist attractions at the customer's destination. The reservation management unit can also adjust reservation times to suit the customer's travel schedule. For example, it can suggest an early dinner to coincide with the flight's departure time. This allows for flexible reservation proposals that fit the customer's travel plans.

[0049] The reservation management department can also suggest special events taking into account the customer's hobbies and interests. For example, if the customer is a music lover, it can suggest a live music event. If the customer is interested in art, it can suggest an art exhibition. In this way, customer satisfaction can be improved by suggesting events that match the customer's hobbies and interests.

[0050] The reservation management department can also analyze a customer's past visit history and propose special benefits for repeat customers. For example, customers who have visited multiple times in the past can be offered a discount coupon that can be used on their next visit. The reservation management department can also propose special membership programs based on the frequency of a customer's visit. For example, a special membership card could be issued to customers who visit frequently. This makes it possible to improve customer loyalty by proposing special benefits for repeat customers.

[0051] The kitchen can also provide information about the origin of ingredients. For example, by informing customers about the origin of the vegetables and meat used, the safety and quality of the ingredients can be emphasized. The kitchen can also provide information about the producers of the ingredients. For example, the kitchen can tell customers that the vegetables used are grown by local farmers. By providing information about the origin of ingredients, customers can feel at ease.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The reservation management department accepts a customer's reservation. For example, when a customer makes a reservation using a smartphone or tablet, the reservation management department receives the information and confirms the reservation. The reservation management department can also accept reservations made by phone or via a website. For example, when making a reservation by phone, an operator enters the reservation information into the system, and the reservation management department manages that information. When making a reservation via a website, the reservation information is automatically reflected in the system via form input. Step 2: The order management unit accepts the customer's order. For example, when a customer arrives at the restaurant, the order management unit accepts the order and issues instructions to the robot chef. The order management unit can also accept orders via a tablet on the table or a smartphone app. For example, when a customer selects a menu item on the tablet and confirms their order, that information is sent to the order management unit. Step 3: The cooking instruction unit issues instructions to the robot chef based on the order received by the order management unit. For example, if a customer orders "spaghetti bolognese," the cooking instruction unit sends that information to the robot chef, who then begins cooking. The cooking instruction unit can also communicate any special requests from customers (such as allergies or spiciness adjustments) to the robot chef. Step 4: The accounting management department manages the customer's bill. For example, after a customer finishes their meal, the accounting management department automatically calculates the bill and presents the customer with a payment method. Customers can make cashless payments using their smartphones or tablets. The accounting management department also handles cash payments and can manage payments at the cash register. For example, if a customer pays in cash, the accounting management department calculates the amount and issues a receipt. Step 5: The inventory management department manages the inventory of ingredients. For example, the inventory management department monitors ingredient usage in real time and automatically places orders when inventory is low. The inventory management department also analyzes ingredient usage and makes suggestions to minimize waste. For example, the inventory management department analyzes ingredient consumption patterns and suggests the appropriate timing for ordering. Step 6: The cooking unit carries out the cooking instructions given by the cooking instruction unit. For example, a robot chef uses highly precise cooking techniques to prepare food according to the customer's order. The robot chef can always provide food of the same quality without relying on the skills of the staff. The cooking unit can also monitor the condition of ingredients during cooking in real time and select the optimal cooking method. For example, the robot chef can monitor the freshness and temperature of ingredients and adjust cooking time and heat.

[0054] (Example 2) The cafe restaurant system according to the embodiment of the present invention is a system that handles everything from customer reservations to ordering, cooking, accounting, and inventory management in an integrated manner, enabling the cafe restaurant system to provide customers with consistently high-quality food while achieving efficient operation.

[0055] A cafe restaurant system according to an embodiment includes a reservation management unit, an order management unit, a cooking instruction unit, an accounting management unit, an inventory management unit, and a cooking unit. The reservation management unit accepts customer reservations. For example, when a customer makes a reservation using a smartphone or tablet, the reservation management unit receives the information and confirms the reservation. The reservation management unit can also accept reservations via telephone or website. For example, for telephone reservations, an operator enters the reservation information into the system, and the reservation management unit manages the information. For reservations via websites, the reservation information is automatically updated in the system through form input. The order management unit accepts customer orders. For example, when a customer visits the restaurant, the order management unit accepts the order and issues instructions to the robot chef. The order management unit can also accept orders via a tablet on the table or a smartphone app. For example, when a customer selects a menu item on the tablet and confirms the order, the information is sent to the order management unit. The cooking instruction unit issues instructions to the robot chef based on the order accepted by the order management unit. For example, if a customer orders "spaghetti bolognese," the cooking instruction unit sends that information to the robot chef, who then begins cooking. The cooking instruction unit can also communicate the customer's special requests (such as allergies or spiciness adjustments) to the robot chef. The accounting management unit manages the customer's bill. For example, after a customer finishes their meal, the accounting management unit automatically calculates the bill and presents the customer with a payment method. Customers can pay cashlessly using their smartphones or tablets. The accounting management unit also accepts cash payments and can manage payments at the register. For example, if a customer pays cash, the accounting management unit calculates the amount and issues a receipt. The inventory management unit manages ingredient stock. For example, the inventory management unit monitors ingredient usage in real time and automatically places orders when inventory is low. The inventory management unit also analyzes ingredient usage and makes suggestions to minimize waste. For example, the inventory management unit analyzes ingredient consumption patterns and suggests the appropriate time to place orders. The cooking unit performs cooking as instructed by the cooking instruction unit. For example, the robot chef uses high-precision cooking techniques to prepare dishes according to customer orders.The robot chef can always provide food of the same quality without relying on the skills of the staff. The cooking department can also monitor the status of ingredients during cooking in real time and select the optimal cooking method. For example, the robot chef monitors the freshness and temperature of ingredients and adjusts cooking time and heat. This allows the cafe restaurant system according to the embodiment to consistently handle everything from customer reservations to ordering, cooking, accounting, and inventory management. For example, customers can easily make reservations using their smartphones and smoothly place their orders when they arrive. Having the robot chef handle cooking also makes it possible to consistently provide high-quality food. Furthermore, the inventory management department can efficiently manage ingredient inventory, reducing food waste and enabling sustainable operations.

[0056] The reservation management unit can analyze a customer's past order history and make individually customized menu suggestions. The reservation management unit can, for example, use generative AI to analyze a customer's past order history and make individually customized menu suggestions. For example, a new vegetarian menu can be suggested to a customer who has previously preferred vegetarian menus. The reservation management unit can also make menu suggestions taking into account a customer's allergy information and food preferences. For example, a nut-free menu can be suggested to a customer with a nut allergy. This makes it possible to make customized menu suggestions based on a customer's past order history.

[0057] The reservation management unit can predict the congestion level of the store in real time and suggest the optimal time to visit. For example, the reservation management unit can use AI to predict the congestion level of the store in real time and suggest the optimal time to visit. For example, it can predict peak congestion times based on past data and suggest to customers to avoid those times. The reservation management unit can also predict congestion levels using real-time people flow data. For example, sensors inside the store can detect the flow of people and predict the congestion level based on that information. This makes it possible to avoid congestion by suggesting the optimal time to visit the store to customers.

[0058] The reservation management unit can analyze the customer's emotions and suggest benefits or services that will elicit positive emotions. The reservation management unit can, for example, use an emotion estimation function to analyze the customer's emotions and suggest benefits or services that will elicit positive emotions. For example, if the customer is feeling stressed, the reservation management unit can suggest benefits that will help the customer relax. The reservation management unit can also provide special services according to the customer's emotions. For example, if the customer is happy, the reservation management unit can offer a free dessert. In this way, customer satisfaction can be improved by suggesting benefits and services that are according to the customer's emotions.

[0059] The reservation management unit can accept reservations and orders by voice using a voice recognition function. The reservation management unit accepts reservations and orders by voice, for example, using voice recognition technology. For example, a customer can complete a reservation by simply speaking into a smartphone. The reservation management unit can also accept customer orders using voice recognition technology. For example, when a customer places an order by speaking into a tablet on a table, that information is sent to the order management unit. This makes it possible to make reservations and orders by voice, improving convenience.

[0060] The reservation management unit can register customer allergy information and food preferences in advance and automatically take them into consideration when ordering. The reservation management unit, for example, registers customer allergy information and food preferences in advance and automatically takes them into consideration when ordering. For example, it can suggest nut-free menu items to customers with nut allergies. The reservation management unit can also make menu suggestions based on a customer's food preferences. For example, it can suggest a new vegetarian menu item to a customer who has previously preferred vegetarian menu items. This makes it possible to order while taking into consideration a customer's allergy information and food preferences.

[0061] The reservation management unit can monitor customer emotions in real time and suggest the optimal customer service style. The reservation management unit can monitor customer emotions in real time using, for example, an emotion estimation function and suggest the optimal customer service style. For example, if a customer is nervous, the reservation management unit can suggest a customer service style that will help them relax. The reservation management unit can also provide special services according to the customer's emotions. For example, if a customer is happy, the reservation management unit can offer a free dessert. In this way, customer satisfaction can be improved by suggesting the optimal customer service style according to the customer's emotions.

[0062] The cooking instruction unit can automatically generate new recipes according to customer preferences. The cooking instruction unit automatically generates new recipes according to customer preferences, for example, using generation AI. For example, if a customer likes spicy food, it generates a new spicy recipe. The cooking instruction unit can also generate recipes taking into account a customer's allergy information and food preferences. For example, it generates a recipe that does not include nuts for a customer with a nut allergy. In this way, customer satisfaction is improved by automatically generating new recipes according to customer preferences.

[0063] The cooking unit can monitor the freshness and quality of ingredients in real time and select the optimal cooking method. For example, the cooking unit monitors the freshness of ingredients in real time while the robot chef is cooking and selects the optimal cooking method. For example, when using very fresh vegetables, it will cook them in a short time. The cooking unit can also monitor the quality of ingredients and adjust the cooking method. For example, when meat is of high quality, it will be cooked at a low temperature. In this way, by monitoring the freshness and quality of ingredients in real time and selecting the optimal cooking method, it is possible to always provide high-quality food.

[0064] The cooking unit can suggest cooking methods or presentations according to the customer's emotions. For example, the cooking unit uses an emotion estimation function to suggest cooking methods according to the customer's emotions. For example, if the customer is relaxed, the cooking unit suggests a simple cooking method. The cooking unit can also suggest presentations according to the customer's emotions. For example, if the customer is happy, the cooking unit suggests a gorgeous presentation. In this way, by suggesting cooking methods and presentations according to the customer's emotions, customer satisfaction is improved.

[0065] The cooking department can use 3D printing technology to create dishes with complex shapes. For example, the cooking department can introduce 3D printing technology into a robot chef to create dishes with complex shapes. For example, it can create three-dimensional desserts. The cooking department can also use 3D printing technology to create customized dishes according to customer requests. For example, if a customer requests a cake with a specific shape, it can create a cake that matches that shape. In this way, using 3D printing technology, it is possible to create dishes with complex shapes.

[0066] The cooking unit can monitor the aroma and sounds of cooking in real time and maintain an optimal cooking environment. For example, the cooking unit can monitor the aroma of cooking in real time while the robot chef is cooking and maintain an optimal cooking environment. For example, if the aroma becomes strong, the heat can be adjusted. The cooking unit can also monitor the sounds of cooking and adjust the cooking environment. For example, if the sound of frying changes, the oil temperature can be adjusted. In this way, by monitoring the aroma and sounds of cooking in real time, an optimal cooking environment can be maintained.

[0067] The cooking department can propose a food presentation that matches the customer's emotions. For example, the cooking department uses an emotion estimation function to propose a food presentation that matches the customer's emotions. For example, if the customer is relaxed, the cooking department proposes a simple presentation. The cooking department can also propose food presentation that matches the customer's emotions. For example, if the customer is happy, the cooking department proposes a gorgeous presentation. In this way, by proposing food presentation that matches the customer's emotions, customer satisfaction is improved.

[0068] The accounting management department can analyze a customer's past payment history and suggest the optimal payment method. For example, the accounting management department can use generative AI to analyze a customer's past payment history and suggest the optimal payment method. For example, it can suggest credit card payment to a customer who has used credit cards in the past. The accounting management department can also recommend specific payment methods based on a customer's payment history. For example, it can prioritize suggesting payment methods that a customer uses frequently. This improves payment convenience by suggesting the optimal payment method based on the customer's past payment history.

[0069] The accounting management department can reflect exchange rates in real time and propose the most appropriate currency for payment to foreign customers. The accounting management department, for example, reflects exchange rates in real time and proposes the most appropriate currency for payment to foreign customers. For example, for a customer who wishes to pay in dollars, it proposes payment in dollars based on the latest exchange rate. The accounting management department can also propose payment in an appropriate currency based on the customer's nationality and place of residence. For example, it proposes payment in euros for a customer from Europe. This improves payment convenience by proposing the most appropriate currency for payment to foreign customers.

[0070] The accounting management department can analyze the customer's emotions and suggest benefits or services that will elicit positive emotions. For example, the accounting management department can analyze the customer's emotions using an emotion estimation function and suggest benefits or services that will elicit positive emotions. For example, if the customer is feeling stressed, the accounting management department can suggest benefits that will help the customer relax. The accounting management department can also provide special services according to the customer's emotions. For example, if the customer is happy, the accounting management department can offer a free dessert. In this way, customer satisfaction can be improved by suggesting benefits and services that are according to the customer's emotions.

[0071] The accounting management department can realize cashless payments using facial recognition technology. The accounting management department can realize cashless payments using, for example, facial recognition technology. For example, a customer can simply stand at the cash register, where facial recognition is performed and payment is completed. The accounting management department can also use facial recognition technology to verify the identity of a customer. For example, a customer can access membership information and use special offers through facial recognition. As a result, cashless payments become possible using facial recognition technology, improving convenience.

[0072] The accounting department can work with loyalty programs to automatically award points. For example, points are automatically added as soon as a customer makes a payment. The accounting department can also provide benefits based on a customer's loyalty program status. For example, a special discount is offered to gold members. By working with loyalty programs, points can be automatically awarded to customers.

[0073] The accounting management department can monitor customer emotions in real time and propose the optimal customer service style. The accounting management department can, for example, use an emotion estimation function to monitor customer emotions in real time and propose the optimal customer service style. For example, if a customer is nervous, it can propose a customer service style that will help them relax. The accounting management department can also provide special services according to the customer's emotions. For example, if a customer is happy, it can offer a free dessert. In this way, customer satisfaction can be improved by proposing the optimal customer service style according to the customer's emotions.

[0074] The inventory management department can predict the optimal timing for ordering based on past data. For example, the inventory management department can use generative AI to predict the optimal timing for ordering based on past data. For example, it can adjust the timing for ordering based on past sales data. The inventory management department can also analyze ingredient consumption patterns and suggest the appropriate timing for ordering. For example, it can place orders at times when a particular ingredient is frequently used. This makes it possible to predict the optimal timing for ordering based on past data, thereby improving the efficiency of inventory management.

[0075] The inventory management unit can monitor the freshness of ingredients in real time and suggest the optimal order of use. The inventory management unit, for example, can monitor the freshness of ingredients in real time and suggest the optimal order of use. For example, it can prioritize the use of ingredients whose freshness has decreased. The inventory management unit can also monitor the expiration dates of ingredients and adjust the order of use. For example, it can prioritize the use of ingredients that are close to their expiration date. In this way, food waste can be reduced by monitoring the freshness of ingredients in real time and suggesting the optimal order of use.

[0076] The inventory management unit can reduce food waste by suggesting menu items according to the customer's emotions. The inventory management unit can reduce food waste by suggesting menu items according to the customer's emotions, for example, by using an emotion estimation function. For example, if the customer is relaxed, a simple menu item is suggested. The inventory management unit can also suggest special menu items based on the customer's emotions. For example, if the customer is happy, a special dessert is suggested. In this way, food waste can be reduced by suggesting menu items according to the customer's emotions.

[0077] The inventory management department can use IoT technology to track the location and status of ingredients in real time. The inventory management department can use IoT technology to track the location and status of ingredients in real time, for example, by monitoring the temperature and humidity of ingredients. The inventory management department can also use GPS technology to track the location of ingredients. For example, the location of ingredients during delivery can be confirmed in real time. In this way, the location and status of ingredients can be tracked in real time by using IoT technology.

[0078] The inventory management department can work with other stores to share surplus ingredients. The inventory management department, for example, works with other stores to share surplus ingredients. For example, ingredients are shared with nearby stores. The inventory management department can also exchange ingredients with other stores through a surplus ingredient sharing platform. For example, information about surplus ingredients can be shared through an online platform and provided to stores in need. In this way, food waste can be reduced by working with other stores to share surplus ingredients.

[0079] The inventory management unit can improve inventory turnover by implementing promotions according to the customer's emotions. The inventory management unit can improve inventory turnover by implementing promotions according to the customer's emotions, for example, by using an emotion estimation function. For example, if the customer is relaxed, the inventory management unit can promote a menu that will help them relax. The inventory management unit can also implement special promotions based on the customer's emotions. For example, if the customer is happy, the inventory management unit can offer a special discount. In this way, by implementing promotions according to the customer's emotions, the inventory turnover can be improved.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The cafe restaurant system can also be equipped with a health management unit that manages customer health data. For example, if a customer registers their health data in advance, the health management unit can suggest health-conscious menus based on that data. The health management unit can also adjust the nutritional balance of meals according to the customer's health condition. For example, it can suggest low-carb menus to customers with diabetes. This makes it possible to suggest meals according to the customer's health condition, and can be used as part of health management.

[0082] The reservation management department can further estimate the customer's emotions and suggest special events or campaigns based on the estimated emotions. For example, if a customer is feeling stressed, it can suggest a relaxing yoga event. Or, if a customer is happy, it can suggest a special dinner show. In this way, by suggesting events and campaigns according to the customer's emotions, it is possible to improve customer satisfaction.

[0083] The reservation management unit can also propose optimal reservation plans taking into account the customer's travel plans. For example, if the customer is traveling, it can suggest restaurants close to tourist attractions at the customer's destination. The reservation management unit can also adjust reservation times to suit the customer's travel schedule. For example, it can suggest an early dinner to coincide with the flight's departure time. This allows for flexible reservation proposals that fit the customer's travel plans.

[0084] The reservation management unit can further estimate the customer's emotions and suggest a special menu based on the estimated emotions. For example, if the customer is tired, it can suggest a menu that will replenish energy. Also, if the customer is relaxed, it can suggest herbal tea that has a relaxing effect. In this way, by suggesting a special menu that corresponds to the customer's emotions, it is possible to improve customer satisfaction.

[0085] The reservation management department can also suggest special events taking into account the customer's hobbies and interests. For example, if the customer is a music lover, it can suggest a live music event. If the customer is interested in art, it can suggest an art exhibition. In this way, customer satisfaction can be improved by suggesting events that match the customer's hobbies and interests.

[0086] The reservation management unit can further estimate the customer's emotions and provide special services based on the estimated emotions. For example, if the customer is nervous, it can suggest a relaxing massage service. Also, if the customer is happy, it can suggest a special commemorative photo service. In this way, by providing special services according to the customer's emotions, it is possible to improve customer satisfaction.

[0087] The reservation management department can also analyze a customer's past visit history and propose special benefits for repeat customers. For example, customers who have visited multiple times in the past can be offered a discount coupon that can be used on their next visit. The reservation management department can also propose special membership programs based on the frequency of a customer's visit. For example, a special membership card could be issued to customers who visit frequently. This makes it possible to improve customer loyalty by proposing special benefits for repeat customers.

[0088] The cooking instruction unit can further estimate the customer's emotions and adjust the cooking method based on the estimated emotions. For example, if the customer is relaxed, it can suggest a slow cooking method. If the customer is in a hurry, it can also suggest a quick cooking method. In this way, by suggesting a cooking method that suits the customer's emotions, it is possible to improve customer satisfaction.

[0089] The kitchen can also provide information about the origin of ingredients. For example, by informing customers about the origin of the vegetables and meat used, the safety and quality of the ingredients can be emphasized. The kitchen can also provide information about the producers of the ingredients. For example, the kitchen can tell customers that the vegetables used are grown by local farmers. By providing information about the origin of ingredients, customers can feel at ease.

[0090] The cooking department can also estimate the customer's emotions and adjust the presentation of food based on the estimated emotions. For example, if the customer is relaxed, it can suggest a simple and subdued presentation. On the other hand, if the customer is happy, it can suggest a gorgeous and colorful presentation. In this way, by suggesting presentations that match the customer's emotions, it is possible to improve customer satisfaction.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reservation management department accepts a customer's reservation. For example, when a customer makes a reservation using a smartphone or tablet, the reservation management department receives the information and confirms the reservation. The reservation management department can also accept reservations made by phone or via a website. For example, when making a reservation by phone, an operator enters the reservation information into the system, and the reservation management department manages that information. When making a reservation via a website, the reservation information is automatically reflected in the system via form input. Step 2: The order management unit accepts the customer's order. For example, when a customer arrives at the restaurant, the order management unit accepts the order and issues instructions to the robot chef. The order management unit can also accept orders via a tablet on the table or a smartphone app. For example, when a customer selects a menu item on the tablet and confirms their order, that information is sent to the order management unit. Step 3: The cooking instruction unit issues instructions to the robot chef based on the order received by the order management unit. For example, if a customer orders "spaghetti bolognese," the cooking instruction unit sends that information to the robot chef, who then begins cooking. The cooking instruction unit can also communicate any special requests from customers (such as allergies or spiciness adjustments) to the robot chef. Step 4: The accounting management department manages the customer's bill. For example, after a customer finishes their meal, the accounting management department automatically calculates the bill and presents the customer with a payment method. Customers can make cashless payments using their smartphones or tablets. The accounting management department also handles cash payments and can manage payments at the cash register. For example, if a customer pays in cash, the accounting management department calculates the amount and issues a receipt. Step 5: The inventory management department manages the inventory of ingredients. For example, the inventory management department monitors ingredient usage in real time and automatically places orders when inventory is low. The inventory management department also analyzes ingredient usage and makes suggestions to minimize waste. For example, the inventory management department analyzes ingredient consumption patterns and suggests the appropriate timing for ordering. Step 6: The cooking unit carries out the cooking instructions given by the cooking instruction unit. For example, a robot chef uses highly precise cooking techniques to prepare food according to the customer's order. The robot chef can always provide food of the same quality without relying on the skills of the staff. The cooking unit can also monitor the condition of ingredients during cooking in real time and select the optimal cooking method. For example, the robot chef can monitor the freshness and temperature of ingredients and adjust cooking time and heat.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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. a reservation management department that accepts customer reservations; an order management department that accepts customer orders; a cooking instruction unit that issues instructions to a robot chef based on the orders received by the order management unit; An accounting management department that manages customer accounting; The inventory control department manages the inventory of ingredients, a cooking unit that performs cooking instructed by the cooking instruction unit. A system characterized by:

2. The reservation management unit Analyze the customer's past order history and provide personalized menu suggestions 2. The system of claim 1.

3. The reservation management unit Accept reservations and orders by voice using voice recognition functionality 2. The system of claim 1.

4. The cooking instruction unit Automatically generate new recipes based on the customer's preferences 2. The system of claim 1.

5. The accounting management department Analyze the customer's past payment history and suggest the most suitable payment method 2. The system of claim 1.

6. The inventory management unit Predicting the optimal order timing based on past data 2. The system of claim 1.

7. The reservation management unit Analyzing the customer's emotions and offering them special offers or services that will elicit positive emotions 2. The system of claim 1.

8. The cooking section includes: Propose cooking methods or presentations that correspond to the customer's feelings 2. The system of claim 1.

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