System

Abstract

An object of a system according to an embodiment is to improve the efficiency of tasks such as order reception, reservation management, and menu suggestion in a restaurant.SOLUTION: A system according to an embodiment includes an order reception unit, a generation AI, a reservation optimizing unit, and a menu suggestion unit. The order reception unit receives an order from a customer. The generation AI analyzes the order received by the order reception unit and transmits the order to the kitchen. The reservation optimization unit optimizes the reservation of the customer. The generating AI manages the reservation information optimized by the reservation optimizing unit. The menu proposal unit analyzes the preference of the customer and the past order history and proposes a menu. The generating AI provides the menu suggested by the menu suggester.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] Conventional technologies do not efficiently handle tasks such as order taking, reservation management, and menu suggestions at restaurants, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of operations such as order reception, reservation management, and menu suggestions at restaurants. [Means for solving the problem]

[0006] The system according to the embodiment includes an order receiving unit, a generation AI, a reservation optimization unit, and a menu suggestion unit. The order receiving unit accepts customer orders. The generation AI analyzes the orders accepted by the order receiving unit and transmits them to the kitchen. The reservation optimization unit optimizes customer reservations. The generation AI manages the reservation information optimized by the reservation optimization unit. The menu suggestion unit proposes menus by analyzing customer preferences and past order history. The generation AI provides the menu proposed by the menu suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of operations such as order reception, reservation management, and menu suggestions at restaurants. [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 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) An automated system according to an embodiment of the present invention is a system that uses generative AI to automate tasks such as order taking, reservation optimization, and menu suggestions in restaurants and other eateries, thereby improving customer satisfaction and streamlining operations. The automated system analyzes customer preferences and past order histories to provide optimal service, reducing human error and variations and ensuring consistency in quality.

[0029] An automation system according to an embodiment includes an order receiving unit, a reservation optimization unit, and a menu suggestion unit. The order receiving unit accepts customer orders. For example, when a customer enters an order using a smartphone or tablet, a generation AI analyzes the order and transmits it to the kitchen. The generation AI analyzes the order details using, for example, a text generation AI (e.g., LLM) and issues appropriate instructions. The reservation optimization unit optimizes customer reservations. For example, when a customer makes a reservation online, the generation AI analyzes the reservation information and suggests the optimal seat and time. The generation AI analyzes the reservation information using, for example, a multimodal generation AI and makes the optimal suggestion. The menu suggestion unit analyzes customer preferences and past order history to suggest a menu. For example, the generation AI suggests a menu that suits the customer based on the customer's previous orders and allergy information. The generation AI uses, for example, keyword extraction technology to analyze the customer's preferences and past order history and suggest the optimal menu. As a result, the automation system according to an embodiment automates customer order acceptance, reservation optimization, and menu suggestions, thereby improving customer satisfaction and improving business efficiency. For example, customers can enjoy a smooth ordering experience, and employees can focus on productive work with the support of generative AI.

[0030] The order reception unit can analyze customers' voice orders in real time, understand the order details using natural language processing, and transmit them to the kitchen. For example, when a customer enters an order by voice using a smartphone or tablet, the order reception unit has the generation AI analyze the voice data in real time and understand the order details using natural language processing technology. For example, if a customer says, "I'd like a cheeseburger and a Coke, please," the generation AI converts the order into text data and transmits it to the kitchen. This allows the system to analyze customers' voice orders in real time, understand the order details using natural language processing, and transmit them to the kitchen, thereby reducing ordering errors and delays.

[0031] The order reception unit can use facial recognition technology to automatically retrieve the order history of regular customers and make suggestions based on past orders. For example, when a customer enters the store, the order reception unit uses facial recognition technology to identify the regular customer, and the generation AI automatically retrieves that customer's past order history. For example, based on dishes that the regular customer has ordered in the past, the generation AI may suggest, "Would you like to order the same menu as last time?" This makes it possible to improve customer satisfaction by making suggestions based on the order history of regular customers.

[0032] The order reception unit can automatically consider the customer's allergy information and dietary restrictions to suggest an appropriate menu. For example, when a customer places an order using a smartphone or tablet, the order reception unit's generation AI automatically considers the customer's allergy information and dietary restrictions to suggest an appropriate menu. For example, if a customer enters "nut allergy," the generation AI will suggest a menu that does not contain nuts. This makes it possible to improve customer safety and satisfaction by suggesting menus that take into account the customer's allergy information and dietary restrictions.

[0033] The order reception unit checks inventory status in real time as it receives an order, reducing the risk of running out of stock. For example, when a customer places an order using a smartphone or tablet, the order reception unit uses the generation AI to analyze the order details and check inventory status in real time. For example, when a customer orders a "steak," the generation AI checks the inventory and, if it is out of stock, notifies the customer that "steak is out of stock." This allows inventory status to be checked in real time, reducing the risk of running out of stock and improving customer satisfaction.

[0034] The reservation optimization unit can analyze past reservation data, predict peak time periods, and promote the distribution of reservations. For example, the reservation optimization unit uses generative AI to analyze past reservation data and identify peak time periods. For example, it can predict from past data that Friday nights will be the busiest, and make suggestions to distribute reservations during that time period. This makes it possible to predict peak time periods and promote the distribution of reservations, thereby avoiding congestion and improving customer satisfaction.

[0035] The reservation optimization unit can use customer location information to suggest the optimal arrival time and minimize waiting times. For example, when a customer makes a reservation online, the generation AI obtains the customer's location information and suggests the optimal arrival time. For example, the reservation optimization unit predicts the customer's arrival time based on the distance from their home to the restaurant, minimizing waiting times. This makes it possible to improve customer satisfaction by using the customer's location information to suggest the optimal arrival time and minimize waiting times.

[0036] The reservation optimization unit can automatically consider special requests from customers when making a reservation and suggest special services. For example, when a customer makes a reservation online, the generation AI automatically considers special requests and suggests special services. For example, if a customer enters "birthday," the generation AI suggests "We will prepare a birthday cake." This makes it possible to improve customer satisfaction by considering special requests from customers and suggesting special services.

[0037] The reservation optimization unit can manage reservation cancellations and changes in real time and instantly update available seat information. For example, when a customer cancels or changes a reservation online, the generation AI manages that information in real time and instantly updates available seat information. For example, when a customer cancels a reservation, the generation AI reflects that information and notifies other customers of available seats. This makes it possible to manage reservation cancellations and changes in real time and instantly update available seat information, thereby improving customer satisfaction.

[0038] The menu suggestion unit can analyze a customer's dietary history and health data to suggest a nutritionally balanced menu. For example, when a customer uses a smartphone or tablet to select a menu, the generation AI analyzes the customer's dietary history and health data to suggest a nutritionally balanced menu. For example, if a customer requests a "low-calorie meal," the generation AI will suggest a menu that meets that request. This makes it possible to support the customer's health by analyzing the customer's dietary history and health data and suggesting a nutritionally balanced menu.

[0039] The menu suggestion unit automatically suggests menus according to the season and weather, thereby improving customer satisfaction. For example, when a customer uses a smartphone or tablet to select a menu, the menu suggestion unit uses the generation AI to suggest a menu taking the season and weather into consideration. For example, on a hot summer day, it might suggest "cold soup." This makes it possible to improve customer satisfaction by suggesting menus according to the season and weather.

[0040] The menu suggestion unit can propose a personalized menu taking into account the customer's cultural background and food preferences. For example, when a customer uses a smartphone or tablet to select a menu, the generation AI proposes a personalized menu taking into account the customer's cultural background and food preferences. For example, if a customer prefers "Japanese food," the generation AI will propose "sushi and tempura." This makes it possible to improve customer satisfaction by proposing a personalized menu taking into account the customer's cultural background and food preferences.

[0041] When proposing a menu, the menu suggestion unit can take into account the cooking time and difficulty of the dish and make suggestions that fit the customer's schedule. For example, when a customer uses a smartphone or tablet to select a menu, the menu suggestion unit's generation AI takes into account the cooking time and difficulty of the dish and makes suggestions that fit the customer's schedule. For example, if a customer requests a "dish that can be cooked in a short time," the generation AI will suggest "salad or soup." This makes it possible to improve customer satisfaction by proposing a menu that fits the customer's schedule, taking into account the cooking time and difficulty of the dish.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The order reception unit not only accepts customer orders, but also predicts the cooking time for the food based on the order details and notifies the customer when it will be served. For example, if a customer orders a steak, the AI ​​predicts the cooking time and notifies them that the steak will be ready in approximately 20 minutes. This allows customers to understand the waiting time and reduces stress after ordering. The order reception unit can also display calorie information and nutritional information for the food ordered by the customer. For example, if a customer orders a salad, the AI ​​displays a message saying, "This salad is 200 calories and rich in vitamin C." Furthermore, the order reception unit can automatically check the allergy information of the food ordered by the customer and display a warning if the food contains an allergic ingredient. For example, if a customer orders a dessert with nuts, the AI ​​displays a message saying, "This dessert contains nuts." This ensures customer safety and improves customer satisfaction.

[0044] The order-taking unit not only analyzes customers' voice orders in real time, but can also learn the customer's pronunciation and accent characteristics to improve the accuracy of voice recognition. For example, when a customer orders "pizza," the generation AI learns their pronunciation and accent, improving recognition accuracy for future orders. The order-taking unit can also automatically detect the language used by the customer when placing a voice order and respond in the appropriate language. For example, when a customer places an order in English, the generation AI responds in English and confirms the order details. The order-taking unit can also learn specific phrases and expressions used by customers when placing voice orders to enable more natural conversations. For example, if a customer says, "I'd like the usual, please," the generation AI can suggest, "Would you like to order the same menu item as last time?" based on their past order history. This improves the customer's voice ordering experience.

[0045] The order reception unit not only automatically takes into account the customer's allergy information and dietary restrictions, but can also suggest menus that suit the customer's health condition. For example, if a customer inputs "high blood pressure," the generation AI will suggest low-salt menus. Similarly, if a customer inputs "diabetes," the generation AI will suggest low-carb menus. Furthermore, the order reception unit can also suggest nutritionally balanced menus that suit the customer's health condition. For example, if a customer inputs "vitamin deficiency," the generation AI will suggest vitamin-rich menus. In this way, by suggesting menus that suit the customer's health condition, it is possible to support the customer's health and increase satisfaction.

[0046] The order receiving unit not only checks inventory status in real time when an order is received, but can also automatically request replenishment if stock is low. For example, when a customer orders "steak," the generation AI checks the inventory, and if stock is low, it notifies the customer, "Your steak is low. Would you like to request a replenishment?" The order receiving unit can also suggest alternative menu items to the customer based on inventory status. For example, when a customer orders "steak," if the generation AI is out of stock, it will suggest, "Steak is out of stock. We recommend chicken instead." Furthermore, the order receiving unit can promote specific menu items based on inventory status. For example, it could suggest a menu item with high stock as "Today's Recommendation." This can streamline inventory management and improve customer satisfaction.

[0047] The reservation optimization unit not only analyzes past reservation data to predict peak hours, but can also provide incentives to promote reservations during specific time periods. For example, if there are few reservations during the weekday daytime, the generation AI can suggest an incentive such as "a 10% discount if you make a reservation during the weekday daytime." The reservation optimization unit can also promote reservations in conjunction with specific events or campaigns. For example, it could suggest a "couples-only dinner" for Valentine's Day. Furthermore, the reservation optimization unit can promote reservations during specific time periods based on a customer's past reservation history. For example, if a customer has made a reservation on a Friday night in the past, it could suggest, "You'll receive a special benefit if you make a reservation on the next Friday night." This helps distribute reservations and improves customer satisfaction.

[0048] The reservation optimization unit not only uses the customer's location information to suggest the optimal arrival time, but can also adjust the arrival time taking traffic conditions into account. For example, when a customer makes a reservation, the generation AI checks the traffic situation and suggests, "Considering the current traffic conditions, the optimal arrival time would be 30 minutes later." The reservation optimization unit can also suggest the optimal route based on the customer's location information. For example, it could suggest the shortest route from the customer's home to the restaurant. Furthermore, the reservation optimization unit can start preparing food to coincide with the customer's arrival time based on the customer's location information. For example, it could start preparing food 30 minutes before the customer's arrival so that the food is ready when they arrive. This minimizes customer waiting time and improves satisfaction.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The order reception unit accepts a customer order. For example, when a customer enters an order using a smartphone or tablet, the generation AI analyzes the order and transmits it to the kitchen. The generation AI analyzes the order using, for example, a text generation AI (e.g., LLM) and issues appropriate instructions. Step 2: The reservation optimization unit optimizes customer reservations. For example, when a customer makes a reservation online, the generation AI analyzes the reservation information and suggests the optimal seat and time. The generation AI analyzes the reservation information using, for example, multimodal generation AI and makes the optimal suggestion. Step 3: The menu suggestion unit analyzes the customer's preferences and past order history to suggest a menu. For example, the generation AI suggests a menu that suits the customer based on the dishes they have ordered in the past and allergy information. For example, the generation AI uses keyword extraction technology to analyze the customer's preferences and past order history and suggest the optimal menu.

[0051] (Example 2) An automated system according to an embodiment of the present invention is a system that uses generative AI to automate tasks such as order taking, reservation optimization, and menu suggestions in restaurants and other eateries, thereby improving customer satisfaction and streamlining operations. The automated system analyzes customer preferences and past order histories to provide optimal service, reducing human error and variations and ensuring consistency in quality.

[0052] An automation system according to an embodiment includes an order receiving unit, a reservation optimization unit, and a menu suggestion unit. The order receiving unit accepts customer orders. For example, when a customer enters an order using a smartphone or tablet, a generation AI analyzes the order and transmits it to the kitchen. The generation AI analyzes the order details using, for example, a text generation AI (e.g., LLM) and issues appropriate instructions. The reservation optimization unit optimizes customer reservations. For example, when a customer makes a reservation online, the generation AI analyzes the reservation information and suggests the optimal seat and time. The generation AI analyzes the reservation information using, for example, a multimodal generation AI and makes the optimal suggestion. The menu suggestion unit analyzes customer preferences and past order history to suggest a menu. For example, the generation AI suggests a menu that suits the customer based on the customer's previous orders and allergy information. The generation AI uses, for example, keyword extraction technology to analyze the customer's preferences and past order history and suggest the optimal menu. As a result, the automation system according to an embodiment automates customer order acceptance, reservation optimization, and menu suggestions, thereby improving customer satisfaction and improving business efficiency. For example, customers can enjoy a smooth ordering experience, and employees can focus on productive work with the support of generative AI.

[0053] The order reception unit can analyze customers' voice orders in real time, understand the order details using natural language processing, and transmit them to the kitchen. For example, when a customer enters an order by voice using a smartphone or tablet, the order reception unit has the generation AI analyze the voice data in real time and understand the order details using natural language processing technology. For example, if a customer says, "I'd like a cheeseburger and a Coke, please," the generation AI converts the order into text data and transmits it to the kitchen. This allows the system to analyze customers' voice orders in real time, understand the order details using natural language processing, and transmit them to the kitchen, thereby reducing ordering errors and delays.

[0054] The order reception unit can use facial recognition technology to automatically retrieve the order history of regular customers and make suggestions based on past orders. For example, when a customer enters the store, the order reception unit uses facial recognition technology to identify the regular customer, and the generation AI automatically retrieves that customer's past order history. For example, based on dishes that the regular customer has ordered in the past, the generation AI may suggest, "Would you like to order the same menu as last time?" This makes it possible to improve customer satisfaction by making suggestions based on the order history of regular customers.

[0055] The order reception unit can use the emotion estimation function to infer emotions from a customer's facial expressions and tone of voice, providing a less stressful ordering experience. For example, when a customer places an order using a smartphone or tablet, the order reception unit uses a camera or microphone to analyze the customer's facial expressions and tone of voice and infer their emotions. For example, if a customer is feeling stressed, the generation AI suggests, "Please relax and order." This makes it possible to improve customer satisfaction by inferring the customer's emotions and providing a less stressful ordering experience.

[0056] The order reception unit can automatically consider the customer's allergy information and dietary restrictions to suggest an appropriate menu. For example, when a customer places an order using a smartphone or tablet, the order reception unit's generation AI automatically considers the customer's allergy information and dietary restrictions to suggest an appropriate menu. For example, if a customer enters "nut allergy," the generation AI will suggest a menu that does not contain nuts. This makes it possible to improve customer safety and satisfaction by suggesting menus that take into account the customer's allergy information and dietary restrictions.

[0057] The order reception unit checks inventory status in real time as it receives an order, reducing the risk of running out of stock. For example, when a customer places an order using a smartphone or tablet, the order reception unit uses the generation AI to analyze the order details and check inventory status in real time. For example, when a customer orders a "steak," the generation AI checks the inventory and, if it is out of stock, notifies the customer that "steak is out of stock." This allows inventory status to be checked in real time, reducing the risk of running out of stock and improving customer satisfaction.

[0058] The order reception unit uses the emotion estimation function to detect in real time any anxieties or questions customers may have when placing an order and provide appropriate support. For example, when a customer places an order using a smartphone or tablet, the order reception unit uses the camera and microphone to analyze the customer's facial expressions and tone of voice and estimate their emotions. For example, if a customer is feeling anxious, the generation AI will provide support by asking, "Is there anything I can help you with?" This makes it possible to detect in real time any anxieties or questions customers may have when placing an order and provide appropriate support, thereby improving customer satisfaction.

[0059] The reservation optimization unit can analyze past reservation data, predict peak time periods, and promote the distribution of reservations. For example, the reservation optimization unit uses generative AI to analyze past reservation data and identify peak time periods. For example, it can predict from past data that Friday nights will be the busiest, and make suggestions to distribute reservations during that time period. This makes it possible to predict peak time periods and promote the distribution of reservations, thereby avoiding congestion and improving customer satisfaction.

[0060] The reservation optimization unit can use customer location information to suggest the optimal arrival time and minimize waiting times. For example, when a customer makes a reservation online, the generation AI obtains the customer's location information and suggests the optimal arrival time. For example, the reservation optimization unit predicts the customer's arrival time based on the distance from their home to the restaurant, minimizing waiting times. This makes it possible to improve customer satisfaction by using the customer's location information to suggest the optimal arrival time and minimize waiting times.

[0061] The reservation optimization unit uses the emotion estimation function to estimate the customer's expectations when making a reservation and can suggest seats and services that meet those expectations. For example, when a customer makes an online reservation, the reservation optimization unit uses a camera and microphone to analyze the customer's facial expressions and tone of voice to estimate their emotions. For example, if a customer is expecting a special event, the generation AI might suggest, "We will prepare a special seat for you." This makes it possible to estimate the customer's expectations and suggest seats and services that meet those expectations, thereby improving customer satisfaction.

[0062] The reservation optimization unit can automatically consider special requests from customers when making a reservation and suggest special services. For example, when a customer makes a reservation online, the generation AI automatically considers special requests and suggests special services. For example, if a customer enters "birthday," the generation AI suggests "We will prepare a birthday cake." This makes it possible to improve customer satisfaction by considering special requests from customers and suggesting special services.

[0063] The reservation optimization unit can manage reservation cancellations and changes in real time and instantly update available seat information. For example, when a customer cancels or changes a reservation online, the generation AI manages that information in real time and instantly updates available seat information. For example, when a customer cancels a reservation, the generation AI reflects that information and notifies other customers of available seats. This makes it possible to manage reservation cancellations and changes in real time and instantly update available seat information, thereby improving customer satisfaction.

[0064] The reservation optimization unit uses the emotion estimation function to detect customer anxieties and questions in real time when making a reservation and can provide appropriate support. For example, when a customer makes a reservation online, the reservation optimization unit's generation AI uses a camera and microphone to analyze the customer's facial expressions and tone of voice to estimate their emotions. For example, if the customer is feeling anxious, the generation AI will provide support by asking, "Is there anything I can help you with?" This makes it possible to detect customer anxieties and questions in real time and provide appropriate support, thereby improving customer satisfaction.

[0065] The menu suggestion unit can analyze a customer's dietary history and health data to suggest a nutritionally balanced menu. For example, when a customer uses a smartphone or tablet to select a menu, the generation AI analyzes the customer's dietary history and health data to suggest a nutritionally balanced menu. For example, if a customer requests a "low-calorie meal," the generation AI will suggest a menu that meets that request. This makes it possible to support the customer's health by analyzing the customer's dietary history and health data and suggesting a nutritionally balanced menu.

[0066] The menu suggestion unit automatically suggests menus according to the season and weather, thereby improving customer satisfaction. For example, when a customer uses a smartphone or tablet to select a menu, the menu suggestion unit uses the generation AI to suggest a menu taking the season and weather into consideration. For example, on a hot summer day, it might suggest "cold soup." This makes it possible to improve customer satisfaction by suggesting menus according to the season and weather.

[0067] The menu suggestion unit can use the emotion estimation function to estimate the customer's current mood and physical condition and suggest a menu that corresponds to that. For example, when a customer uses a smartphone or tablet to select a menu, the generation AI uses a camera or microphone to analyze the customer's facial expression and tone of voice and estimate their emotion. For example, if the customer is tired, the generation AI will suggest a "relaxing herbal tea." This makes it possible to improve customer satisfaction by suggesting a menu that matches the customer's mood and physical condition.

[0068] The menu suggestion unit can propose a personalized menu taking into account the customer's cultural background and food preferences. For example, when a customer uses a smartphone or tablet to select a menu, the generation AI proposes a personalized menu taking into account the customer's cultural background and food preferences. For example, if a customer prefers "Japanese food," the generation AI will propose "sushi and tempura." This makes it possible to improve customer satisfaction by proposing a personalized menu taking into account the customer's cultural background and food preferences.

[0069] When proposing a menu, the menu suggestion unit can take into account the cooking time and difficulty of the dish and make suggestions that fit the customer's schedule. For example, when a customer uses a smartphone or tablet to select a menu, the menu suggestion unit's generation AI takes into account the cooking time and difficulty of the dish and makes suggestions that fit the customer's schedule. For example, if a customer requests a "dish that can be cooked in a short time," the generation AI will suggest "salad or soup." This makes it possible to improve customer satisfaction by proposing a menu that fits the customer's schedule, taking into account the cooking time and difficulty of the dish.

[0070] The menu suggestion unit uses the emotion estimation function to detect in real time any anxieties or questions customers may have when selecting a menu and can provide appropriate support. For example, when a customer uses a smartphone or tablet to select a menu, the menu suggestion unit's generation AI uses a camera or microphone to analyze the customer's facial expressions and tone of voice to estimate their emotions. For example, if a customer is feeling anxious, the generation AI will provide support by asking, "Is there anything I can help you with?" This makes it possible to detect in real time any anxieties or questions customers may have when selecting a menu and provide appropriate support, thereby improving customer satisfaction.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The order reception unit not only accepts customer orders, but also predicts the cooking time for the food based on the order details and notifies the customer when it will be served. For example, if a customer orders a steak, the AI ​​predicts the cooking time and notifies them that the steak will be ready in approximately 20 minutes. This allows customers to understand the waiting time and reduces stress after ordering. The order reception unit can also display calorie information and nutritional information for the food ordered by the customer. For example, if a customer orders a salad, the AI ​​displays a message saying, "This salad is 200 calories and rich in vitamin C." Furthermore, the order reception unit can automatically check the allergy information of the food ordered by the customer and display a warning if the food contains an allergic ingredient. For example, if a customer orders a dessert with nuts, the AI ​​displays a message saying, "This dessert contains nuts." This ensures customer safety and improves customer satisfaction.

[0073] The order-taking unit not only analyzes customers' voice orders in real time, but can also learn the customer's pronunciation and accent characteristics to improve the accuracy of voice recognition. For example, when a customer orders "pizza," the generation AI learns their pronunciation and accent, improving recognition accuracy for future orders. The order-taking unit can also automatically detect the language used by the customer when placing a voice order and respond in the appropriate language. For example, when a customer places an order in English, the generation AI responds in English and confirms the order details. The order-taking unit can also learn specific phrases and expressions used by customers when placing voice orders to enable more natural conversations. For example, if a customer says, "I'd like the usual, please," the generation AI can suggest, "Would you like to order the same menu item as last time?" based on their past order history. This improves the customer's voice ordering experience.

[0074] The order reception unit not only uses facial recognition technology to automatically retrieve the order history of regular customers, but can also infer their current mood from their facial expressions and suggest menu items accordingly. For example, if a customer looks tired, the generation AI might suggest, "Would you like some relaxing herbal tea?" The order reception unit can also use facial recognition technology to infer a customer's age and gender and suggest menu items accordingly. For example, it might suggest a "popular smoothie" to a younger customer and an "easy-to-digest" dish to an older customer. Furthermore, the order reception unit can use facial recognition technology to detect stress or anxiety from a customer's facial expression and provide a relaxing environment. For example, if a customer seems stressed, the generation AI might suggest, "Would you like some relaxing music playing?" This can improve customer satisfaction.

[0075] The order reception unit can not only use the emotion estimation function to infer a customer's emotions from their facial expressions and tone of voice, but can also provide special services according to the customer's emotions. For example, if a customer is happy, the generation AI can suggest, "We'll serve you today's special dessert." If the customer is sad, the generation AI can suggest, "We'll serve you a relaxing herbal tea." Furthermore, the order reception unit can use the emotion estimation function to monitor a customer's stress level and provide a relaxing environment if stress levels are high. For example, if a customer is feeling stressed, the generation AI can suggest, "Would you like some relaxing music played?" This makes it possible to improve customer satisfaction by providing special services according to the customer's emotions.

[0076] The order reception unit not only automatically takes into account the customer's allergy information and dietary restrictions, but can also suggest menus that suit the customer's health condition. For example, if a customer inputs "high blood pressure," the generation AI will suggest low-salt menus. Similarly, if a customer inputs "diabetes," the generation AI will suggest low-carb menus. Furthermore, the order reception unit can also suggest nutritionally balanced menus that suit the customer's health condition. For example, if a customer inputs "vitamin deficiency," the generation AI will suggest vitamin-rich menus. In this way, by suggesting menus that suit the customer's health condition, it is possible to support the customer's health and increase satisfaction.

[0077] The order receiving unit not only checks inventory status in real time when an order is received, but can also automatically request replenishment if stock is low. For example, when a customer orders "steak," the generation AI checks the inventory, and if stock is low, it notifies the customer, "Your steak is low. Would you like to request a replenishment?" The order receiving unit can also suggest alternative menu items to the customer based on inventory status. For example, when a customer orders "steak," if the generation AI is out of stock, it will suggest, "Steak is out of stock. We recommend chicken instead." Furthermore, the order receiving unit can promote specific menu items based on inventory status. For example, it could suggest a menu item with high stock as "Today's Recommendation." This can streamline inventory management and improve customer satisfaction.

[0078] The order reception unit can not only use the emotion estimation function to detect in real time any anxieties or questions customers may have when placing an order, but can also provide customized support based on the customer's emotions. For example, if a customer is feeling anxious, the generation AI can suggest, "Is there something I can help you with? Please let us know if there is anything specific we can help you with." If the customer has a question, the generation AI can suggest, "If you have any questions, we will provide detailed explanations." Furthermore, the order reception unit can use the emotion estimation function to provide special services based on the customer's emotions. For example, if a customer is feeling stressed, the generation AI can suggest, "We will serve you a relaxing herbal tea." This allows for customized support based on the customer's emotions, thereby improving customer satisfaction.

[0079] The reservation optimization unit not only analyzes past reservation data to predict peak hours, but can also provide incentives to promote reservations during specific time periods. For example, if there are few reservations during the weekday daytime, the generation AI can suggest an incentive such as "a 10% discount if you make a reservation during the weekday daytime." The reservation optimization unit can also promote reservations in conjunction with specific events or campaigns. For example, it could suggest a "couples-only dinner" for Valentine's Day. Furthermore, the reservation optimization unit can promote reservations during specific time periods based on a customer's past reservation history. For example, if a customer has made a reservation on a Friday night in the past, it could suggest, "You'll receive a special benefit if you make a reservation on the next Friday night." This helps distribute reservations and improves customer satisfaction.

[0080] The reservation optimization unit not only uses the customer's location information to suggest the optimal arrival time, but can also adjust the arrival time taking traffic conditions into account. For example, when a customer makes a reservation, the generation AI checks the traffic situation and suggests, "Considering the current traffic conditions, the optimal arrival time would be 30 minutes later." The reservation optimization unit can also suggest the optimal route based on the customer's location information. For example, it could suggest the shortest route from the customer's home to the restaurant. Furthermore, the reservation optimization unit can start preparing food to coincide with the customer's arrival time based on the customer's location information. For example, it could start preparing food 30 minutes before the customer's arrival so that the food is ready when they arrive. This minimizes customer waiting time and improves satisfaction.

[0081] The reservation optimization unit not only uses the emotion estimation function to estimate customer expectations when making a reservation, but can also provide special services that meet customer expectations. For example, if a customer is expecting a special event, the generation AI can suggest, "We'll serve you a special dessert." If the customer feels like relaxing, the generation AI can suggest, "We'll provide you with a seat where you can relax." Furthermore, the reservation optimization unit can use the emotion estimation function to suggest customized menus that meet customer expectations. For example, if a customer is health-conscious, the generation AI can suggest, "We'll provide you with a low-calorie menu." This allows for the provision of special services that meet customer expectations, thereby improving customer satisfaction.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The order reception unit accepts a customer order. For example, when a customer enters an order using a smartphone or tablet, the generation AI analyzes the order and transmits it to the kitchen. The generation AI analyzes the order using, for example, a text generation AI (e.g., LLM) and issues appropriate instructions. Step 2: The reservation optimization unit optimizes customer reservations. For example, when a customer makes a reservation online, the generation AI analyzes the reservation information and suggests the optimal seat and time. The generation AI analyzes the reservation information using, for example, multimodal generation AI and makes the optimal suggestion. Step 3: The menu suggestion unit analyzes the customer's preferences and past order history to suggest a menu. For example, the generation AI suggests a menu that suits the customer based on the dishes they have ordered in the past and allergy information. For example, the generation AI uses keyword extraction technology to analyze the customer's preferences and past order history and suggest the optimal menu.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 system equipped with a generative AI, an order reception unit that receives orders from customers; A generation AI that analyzes the order received by the order receiving unit and transmits it to the kitchen; a reservation optimization unit that optimizes customer reservations; A generation AI that manages reservation information optimized by the reservation optimization unit; A menu suggestion department that analyzes customer preferences and past order history to suggest menu items; a generation AI that provides the menu suggested by the menu suggestion unit; A system characterized by:

2. The order receiving unit The customer's voice order is analyzed in real time, and natural language processing is used to understand the order and transmit it to the kitchen.

2. The system of claim 1.

3. The order receiving unit Automatically consider customer allergies and dietary restrictions to suggest appropriate menu items 2. The system of claim 1.

4. The reservation optimization unit Analyze past booking data to predict peak times and spread bookings out 2. The system of claim 1.

5. The menu suggestion unit Analyzes customer's dietary history and health data to suggest nutritionally balanced menus 2. The system of claim 1.

6. The order receiving unit Using emotion estimation functionality, the system can infer emotions from customers' facial expressions and tone of voice, providing a stress-free ordering experience.

2. The system of claim 1.

7. The reservation optimization unit Using emotion estimation functionality, we estimate customer expectations at the time of reservation and suggest seats and services that meet those expectations.

2. The system of claim 1.

8. The menu suggestion unit Using emotion estimation functionality, the system estimates the customer's current mood and physical condition and suggests menu items accordingly.

2. The system of claim 1.

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