System

Abstract

An object of a system according to an embodiment is to expand the role of a robot in a restaurant and improve customer experience.SOLUTION: A system includes a customer service part, an order reception part, and a confirmation part. The customer service unit automatically greets a customer when the customer enters the store, and guides the customer to a seat. The order reception unit receives an order from the customer guided by the customer service unit. The confirmation unit confirms the order details received by the order reception unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the role of robots in restaurants was passive, limiting their ability to improve customer experience.

[0005] The system according to the embodiment aims to expand the role of robots in restaurants and improve the customer experience. [Means for solving the problem]

[0006] The system according to the embodiment includes a customer service unit, an order reception unit, and a confirmation unit. The customer service unit automatically greets customers when they enter the store and guides them to their seats. The order reception unit receives orders from customers guided by the customer service unit. The confirmation unit confirms the order details received by the order reception unit. [Effects of the Invention]

[0007] The system according to the embodiment can expand the role of robots in restaurants and improve the customer experience. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The restaurant robot system according to the embodiment of the present invention is a system that uses a humanoid robot equipped with generative AI to actively serve customers. As a result, the restaurant robot system can improve the quality of service provided to customers, increase repeat customers, and acquire new customers.

[0029] A restaurant robot system according to an embodiment includes a customer service unit, an order receiving unit, and a confirmation unit. The customer service unit automatically greets customers when they enter a restaurant and guides them to their seats. For example, the customer service unit greets customers by saying "Welcome" when they enter the restaurant, detects available seats, and guides them to their seats. The customer service unit can also explain the menu and recommend dishes to customers. For example, the customer service unit may speak to customers by saying, "Today's recommendation is XX. How do you like it?" The order receiving unit accepts orders from customers guided by the customer service unit. For example, if a customer says, "I'd like to order XX," the order receiving unit responds, "Yes, XX. Understood," and records the order details. The confirmation unit confirms the order details accepted by the order receiving unit. For example, the confirmation unit confirms the order details with the customer, asking, "Are you sure you want to order XX?" This allows the restaurant robot system according to an embodiment to improve the quality of service provided to customers and is expected to increase repeat customers.

[0030] The customer service department can provide individually customized greetings and suggestions based on the customer's past visit history. The customer service department can provide individually customized greetings based on the customer's past visit history, for example. For example, the customer service department can provide a greeting that reflects the customer's name and past orders, such as, "Welcome back, Mr. / Ms. XX. Last time you had XX, but what would you like this time?" The customer service department can also provide individually customized suggestions based on the customer's past visit history. For example, the customer service department can provide a suggestion tailored to the customer's preferences, such as, "Mr. / Ms. XX, last time you had a fish dish, but how about some fresh salmon sashimi today?" This improves the quality of service provided to customers, and is expected to increase the number of repeat customers.

[0031] The customer service department can register customer preferences and allergy information in advance and make menu suggestions based on that information. For example, the customer service department may register customer preferences and allergy information in advance and make menu suggestions based on that information. For example, the customer service department may make suggestions based on the customer's preferences, such as, "Mr. / Ms. XX, you had a fish dish last time, but how about some fresh salmon sashimi today?" The customer service department can also make safe menu suggestions based on the customer's allergy information. For example, the customer service department may make suggestions to allow the customer to enjoy their meal with peace of mind, such as, "Mr. / Ms. XX, based on your allergy information, we have prepared an allergy-friendly menu for you today." This makes it possible to make suggestions based on the customer's preferences and allergy information, improving customer satisfaction.

[0032] The customer service department can analyze the congestion status of the store and guide customers to the most suitable seat. For example, the customer service department can analyze the congestion status of the store in real time and guide customers to the most suitable seat. For example, the customer service department can provide comfortable seats to customers by saying, "We currently have a window seat available, so we will show you there." The customer service department can also inform customers of waiting times depending on the congestion status. For example, the customer service department can inform customers of waiting times by saying, "You will need to wait a little while now, but we will show you as soon as a seat becomes available." This improves the quality of service provided to customers and is expected to increase repeat customers.

[0033] The order receiving unit can make reorder suggestions or introduce new menu items based on the customer's order history. The order receiving unit makes reorder suggestions based on the customer's order history, for example. For example, the order receiving unit makes suggestions tailored to the customer's preferences, such as, "How about the XX you had last time?" The order receiving unit can also introduce new menu items. For example, the order receiving unit introduces new menu items by saying, "Today we have a new menu item, XX, available. Please try it." This makes it possible to make suggestions based on the customer's order history, improving customer satisfaction.

[0034] The order receiving unit can support multiple languages, allowing for smooth service to foreign customers. The order receiving unit can support multiple languages, allowing for smooth service to foreign customers. For example, the order receiving unit provides menus in major languages ​​such as English, Chinese, and Spanish. The order receiving unit can also confirm order details with foreign customers in multiple languages. For example, the order receiving unit can confirm in English by asking, "Would you like to order XX?" This allows for smooth service to foreign customers, improving customer satisfaction.

[0035] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0036] The customer service department can monitor the health status of customers and make health-conscious menu suggestions. For example, if a customer is not feeling well, the customer service department can make health-conscious suggestions such as, "We have prepared a soup that is easy to digest today." The customer service department can also suggest low-calorie or high-protein menus based on the customer's health goals. For example, the customer service department can make suggestions that match the customer's health goals, such as, "For those on a diet, we recommend a low-calorie salad." This allows the customer to receive health-conscious service and improves customer satisfaction.

[0037] The customer service department can customize the content of the conversation based on the customer's hobbies and interests. For example, if the customer is a sports fan, the customer service department can have a conversation about sports, such as, "Have you seen the latest game?". If the customer likes traveling, the customer service department can also have a conversation about travel, such as, "Have you traveled anywhere recently?". Furthermore, if the customer likes music, the customer service department can have a conversation about music, such as, "What's your favorite song these days?". This makes it possible to have a conversation based on the customer's hobbies and interests, thereby improving customer satisfaction.

[0038] The customer service department can suggest specific menus and services based on the customer's age and gender. For example, the customer service department can suggest a children's menu to a customer with children by saying, "We have a children's menu available." The customer service department can also suggest a menu for elderly customers by saying, "We have a menu that is easy to digest." Furthermore, the customer service department can suggest a beauty-conscious menu to a female customer by saying, "We have a beauty-friendly menu available." This allows services to be provided based on the customer's age and gender, improving customer satisfaction.

[0039] The customer service department can suggest specific menus and services based on the time a customer visits the restaurant. For example, the customer service department can suggest a breakfast menu to a customer who visits in the morning by saying, "We have a breakfast menu available." The customer service department can also suggest a lunch menu to a customer who visits during lunch time by saying, "What would you like to order from today's lunch menu?" The customer service department can also suggest a dinner menu to a customer who visits during dinner time by saying, "We have a dinner menu available today." This allows services to be provided based on the customer's time of visit, improving customer satisfaction.

[0040] The customer service department can monitor the pace at which customers eat and serve the next dish at the appropriate time. For example, the customer service department can smoothly serve the next dish when the customer has finished eating, saying, "We'll bring you the next dish." If a customer is enjoying their meal slowly, the customer service department can provide service that matches the customer's pace, saying, "We'll bring you the next dish at a pace that matches your eating pace." Furthermore, if a customer is in a hurry, the customer service department can provide prompt service, saying, "If you're in a hurry, we'll bring you the next dish right away." This provides service that matches the customer's eating pace, improving customer satisfaction.

[0041] The customer service department can provide special offers and discounts based on the frequency of a customer's visits. For example, the customer service department can provide special offers to regular customers by saying, "Thank you for your continued patronage. We'll give you a special free dessert today." The customer service department can also provide discounts to first-time customers by saying, "Thank you for your first visit. We'll give you a discount coupon that you can use next time." Furthermore, the customer service department can provide special offers to customers who have visited the store more than a certain number of times by saying, "Since you've visited us more than x times, we're offering you a special service." In this way, special offers and discounts can be provided based on the frequency of a customer's visits, improving customer satisfaction.

[0042] The processing flow of the first embodiment will be briefly explained below.

[0043] Step 1: The customer service department automatically greets customers when they enter the restaurant and guides them to their seats. For example, the customer service department greets customers with "Welcome" when they enter the restaurant, detects vacant seats, and guides them to their seats. The customer service department can also explain the menu to customers and suggest recommended dishes. For example, it might address the customer by saying, "Today's recommendation is XX. How do you like it?" Step 2: The order reception unit receives orders from customers guided by the customer service unit. For example, if a customer says, "I'd like to order XX," the order reception unit responds, "Yes, XX. Understood," and records the order details. Step 3: The confirmation unit confirms the order details accepted by the order acceptance unit. For example, the confirmation unit may confirm the order details with the customer again, asking, "Are you sure you want to order XX?"

[0044] (Example 2) The restaurant robot system according to the embodiment of the present invention is a system that uses a humanoid robot equipped with generative AI to actively serve customers. As a result, the restaurant robot system can improve the quality of service provided to customers, increase repeat customers, and acquire new customers.

[0045] A restaurant robot system according to an embodiment includes a customer service unit, an order receiving unit, and a confirmation unit. The customer service unit automatically greets customers when they enter a restaurant and guides them to their seats. For example, the customer service unit greets customers by saying "Welcome" when they enter the restaurant, detects available seats, and guides them to their seats. The customer service unit can also explain the menu and recommend dishes to customers. For example, the customer service unit may speak to customers by saying, "Today's recommendation is XX. How do you like it?" The order receiving unit accepts orders from customers guided by the customer service unit. For example, if a customer says, "I'd like to order XX," the order receiving unit responds, "Yes, XX. Understood," and records the order details. The confirmation unit confirms the order details accepted by the order receiving unit. For example, the confirmation unit confirms the order details with the customer, asking, "Are you sure you want to order XX?" This allows the restaurant robot system according to an embodiment to improve the quality of service provided to customers and is expected to increase repeat customers.

[0046] The customer service department can provide individually customized greetings and suggestions based on the customer's past visit history. The customer service department can provide individually customized greetings based on the customer's past visit history, for example. For example, the customer service department can provide a greeting that reflects the customer's name and past orders, such as, "Welcome back, Mr. / Ms. XX. Last time you had XX, but what would you like this time?" The customer service department can also provide individually customized suggestions based on the customer's past visit history. For example, the customer service department can provide a suggestion tailored to the customer's preferences, such as, "Mr. / Ms. XX, last time you had a fish dish, but how about some fresh salmon sashimi today?" This improves the quality of service provided to customers, and is expected to increase the number of repeat customers.

[0047] The customer service unit can analyze a customer's facial expression and tone of voice and provide service according to their emotions. The customer service unit, for example, analyzes a customer's facial expression and provides service according to their emotions. For example, if a customer enters the store with a smile, the customer service unit will greet them according to their positive emotions, such as, "Welcome, you look very healthy today!" The customer service unit can also analyze a customer's tone of voice and provide service according to their emotions. For example, if a customer places an order in a tired voice, the customer service unit will respond according to the customer's condition, such as, "Thank you for your hard work. I'd like XX. Understood." This makes it possible to provide service according to the customer's emotions, thereby improving customer satisfaction.

[0048] The customer service unit can use the emotion estimation function to estimate the customer's emotions in real time and generate a conversation to elicit positive emotions. The customer service unit, for example, uses the emotion estimation function to estimate the customer's emotions in real time and generate a conversation to elicit positive emotions. For example, if a customer is nervous, the customer service unit may use a conversation that puts the customer at ease, such as, "Please relax and enjoy yourself. We have a special menu for you today." Furthermore, if a customer looks happy, the customer service unit may use a conversation that elicits positive emotions, such as, "You look like you're having a great time today. Do you have anything special planned?" This makes it possible to provide customer service that is in line with the customer's emotions, thereby improving customer satisfaction.

[0049] The customer service department can register customer preferences and allergy information in advance and make menu suggestions based on that information. For example, the customer service department may register customer preferences and allergy information in advance and make menu suggestions based on that information. For example, the customer service department may make suggestions based on the customer's preferences, such as, "Mr. / Ms. XX, you had a fish dish last time, but how about some fresh salmon sashimi today?" The customer service department can also make safe menu suggestions based on the customer's allergy information. For example, the customer service department may make suggestions to allow the customer to enjoy their meal with peace of mind, such as, "Mr. / Ms. XX, based on your allergy information, we have prepared an allergy-friendly menu for you today." This makes it possible to make suggestions based on the customer's preferences and allergy information, improving customer satisfaction.

[0050] The customer service department can analyze the congestion status of the store and guide customers to the most suitable seat. For example, the customer service department can analyze the congestion status of the store in real time and guide customers to the most suitable seat. For example, the customer service department can provide comfortable seats to customers by saying, "We currently have a window seat available, so we will show you there." The customer service department can also inform customers of waiting times depending on the congestion status. For example, the customer service department can inform customers of waiting times by saying, "You will need to wait a little while now, but we will show you as soon as a seat becomes available." This improves the quality of service provided to customers and is expected to increase repeat customers.

[0051] The customer service unit can use the emotion estimation function to adjust music and lighting to help customers relax. For example, the customer service unit can use the emotion estimation function to select music that will help customers relax and play it in the store. For example, if a customer is feeling nervous, the customer service unit can play classical music that has a relaxing effect. The customer service unit can also adjust lighting to help customers relax. For example, the customer service unit can change the lighting in the store to warm colors to create a relaxing atmosphere. This makes it possible to provide customer service that is tailored to the customer's emotions, thereby improving customer satisfaction.

[0052] The order receiving unit can make reorder suggestions or introduce new menu items based on the customer's order history. The order receiving unit makes reorder suggestions based on the customer's order history, for example. For example, the order receiving unit makes suggestions tailored to the customer's preferences, such as, "How about the XX you had last time?" The order receiving unit can also introduce new menu items. For example, the order receiving unit introduces new menu items by saying, "Today we have a new menu item, XX, available. Please try it." This makes it possible to make suggestions based on the customer's order history, improving customer satisfaction.

[0053] The order receiving unit can analyze the tone of a customer's voice and facial expression, and provide an appropriate response when confirming the order details. The order receiving unit, for example, analyzes the tone of a customer's voice, and provides an appropriate response when confirming the order details. For example, if a customer places an order in a tired voice, the order receiving unit will provide a response appropriate to the customer's condition, such as "Thank you for your hard work. I understand that you ordered XX." The order receiving unit can also analyze the customer's facial expression, and provide an appropriate response when confirming the order details. For example, if a customer looks anxious, the order receiving unit will confirm the order details in accordance with the customer's condition, such as "Is there a mistake in the order?" This makes it possible to provide a response appropriate to the customer's emotions, and improves customer satisfaction.

[0054] The order receiving unit can use the emotion estimation function to generate a conversation to reduce the stress felt by customers when ordering. The order receiving unit, for example, uses the emotion estimation function to generate a conversation to reduce the stress felt by customers when ordering. For example, if a customer is nervous, the order receiving unit may use a conversation to reassure them, such as, "Please relax and enjoy yourself. Would you like to order XX?" Furthermore, if a customer is unsure what to order, the order receiving unit may use a conversation to reduce the customer's stress, such as, "Are you unsure what to order? Let us introduce some recommended menu items." This reduces the customer's stress and improves the ordering experience.

[0055] The order receiving unit can support multiple languages, allowing for smooth service to foreign customers. The order receiving unit can support multiple languages, allowing for smooth service to foreign customers. For example, the order receiving unit provides menus in major languages ​​such as English, Chinese, and Spanish. The order receiving unit can also confirm order details with foreign customers in multiple languages. For example, the order receiving unit can confirm in English by asking, "Would you like to order XX?" This allows for smooth service to foreign customers, improving customer satisfaction.

[0056] The order receiving unit can use the emotion estimation function to monitor in real time the level of satisfaction felt by customers when placing an order, thereby improving the quality of service. The order receiving unit, for example, uses the emotion estimation function to monitor in real time the level of satisfaction felt by customers when placing an order. For example, if the customer looks satisfied, the order receiving unit provides positive feedback such as, "Thank you for your order. We will bring it to you right away." Furthermore, if the customer looks dissatisfied, the order receiving unit takes action to improve customer satisfaction such as, "Please let us know if there is anything you are dissatisfied with." In this way, customer satisfaction is monitored in real time, thereby improving the quality of service.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The customer service department can monitor the health status of customers and make health-conscious menu suggestions. For example, if a customer is not feeling well, the customer service department can make health-conscious suggestions such as, "We have prepared a soup that is easy to digest today." The customer service department can also suggest low-calorie or high-protein menus based on the customer's health goals. For example, the customer service department can make suggestions that match the customer's health goals, such as, "For those on a diet, we recommend a low-calorie salad." This allows the customer to receive health-conscious service and improves customer satisfaction.

[0059] The customer service department can customize the content of the conversation based on the customer's hobbies and interests. For example, if the customer is a sports fan, the customer service department can have a conversation about sports, such as, "Have you seen the latest game?". If the customer likes traveling, the customer service department can also have a conversation about travel, such as, "Have you traveled anywhere recently?". Furthermore, if the customer likes music, the customer service department can have a conversation about music, such as, "What's your favorite song these days?". This makes it possible to have a conversation based on the customer's hobbies and interests, thereby improving customer satisfaction.

[0060] The customer service department can estimate the customer's emotions and provide special benefits or services according to the specific emotions. For example, if the customer is happy, the customer service department can provide a special benefit such as, "Today, we will serve you a free dessert as a special treat." If the customer is sad, the customer service department can provide a relaxing service such as, "Thank you for your hard work. Today, we will serve you a special relaxing tea as a special treat." Furthermore, if the customer is angry, the customer service department can respond to resolve the customer's dissatisfaction by saying, "If there is anything you are dissatisfied with, please let us know. We will make a special effort to accommodate you." This allows the customer to receive special benefits or services according to their emotions, improving customer satisfaction.

[0061] The customer service unit can estimate the customer's emotions and provide music and images that correspond to those emotions. For example, if the customer wants to relax, the customer service unit can play music that has a relaxing effect. If the customer wants to have fun, the customer service unit can also provide entertaining images. Furthermore, if the customer wants to concentrate, the customer service unit can play music that helps the customer concentrate. This provides music and images that correspond to the customer's emotions, improving customer satisfaction.

[0062] The customer service department can estimate the customer's emotions and suggest drinks according to the emotions. For example, if a customer wants to relax, the customer service department can suggest a drink in the form of, "How about some herbal tea that has a relaxing effect?". If a customer wants to cheer up, the customer service department can suggest a drink in the form of, "How about a smoothie that will replenish your energy?". Furthermore, if a customer wants to concentrate, the customer service department can suggest a drink in the form of, "How about some coffee that will help you concentrate?". This makes it possible to suggest drinks according to the customer's emotions, thereby improving customer satisfaction.

[0063] The customer service department can suggest specific menus and services based on the customer's age and gender. For example, the customer service department can suggest a children's menu to a customer with children by saying, "We have a children's menu available." The customer service department can also suggest a menu for elderly customers by saying, "We have a menu that is easy to digest." Furthermore, the customer service department can suggest a beauty-conscious menu to a female customer by saying, "We have a beauty-friendly menu available." This allows services to be provided based on the customer's age and gender, improving customer satisfaction.

[0064] The customer service department can suggest specific menus and services based on the time a customer visits the restaurant. For example, the customer service department can suggest a breakfast menu to a customer who visits in the morning by saying, "We have a breakfast menu available." The customer service department can also suggest a lunch menu to a customer who visits during lunch time by saying, "What would you like to order from today's lunch menu?" The customer service department can also suggest a dinner menu to a customer who visits during dinner time by saying, "We have a dinner menu available today." This allows services to be provided based on the customer's time of visit, improving customer satisfaction.

[0065] The customer service department can monitor the pace at which customers eat and serve the next dish at the appropriate time. For example, the customer service department can smoothly serve the next dish when the customer has finished eating, saying, "We'll bring you the next dish." If a customer is enjoying their meal slowly, the customer service department can provide service that matches the customer's pace, saying, "We'll bring you the next dish at a pace that matches your eating pace." Furthermore, if a customer is in a hurry, the customer service department can provide prompt service, saying, "If you're in a hurry, we'll bring you the next dish right away." This provides service that matches the customer's eating pace, improving customer satisfaction.

[0066] The customer service department can estimate the customer's emotions and suggest a dessert according to the emotion. For example, if the customer is happy, the customer service department can offer a special treat by saying, "Today we'll give you a special dessert for free." If the customer is sad, the customer service department can offer a relaxing dessert by saying, "Thank you for your hard work. Today we've prepared a special, relaxing dessert for you." Furthermore, if the customer is angry, the customer service department can respond to resolve the customer's dissatisfaction by saying, "If there's anything you're dissatisfied with, please let us know. We'll make a special effort to accommodate you." This makes it possible to suggest desserts according to the customer's emotions, thereby improving customer satisfaction.

[0067] The customer service department can provide special offers and discounts based on the frequency of a customer's visits. For example, the customer service department can provide special offers to regular customers by saying, "Thank you for your continued patronage. We'll give you a special free dessert today." The customer service department can also provide discounts to first-time customers by saying, "Thank you for your first visit. We'll give you a discount coupon that you can use next time." Furthermore, the customer service department can provide special offers to customers who have visited the store more than a certain number of times by saying, "Since you've visited us more than x times, we're offering you a special service." In this way, special offers and discounts can be provided based on the frequency of a customer's visits, improving customer satisfaction.

[0068] The processing flow of the second embodiment will be briefly explained below.

[0069] Step 1: The customer service department automatically greets customers when they enter the restaurant and guides them to their seats. For example, the customer service department greets customers with "Welcome" when they enter the restaurant, detects vacant seats, and guides them to their seats. The customer service department can also explain the menu to customers and suggest recommended dishes. For example, it might address the customer by saying, "Today's recommendation is XX. How do you like it?" Step 2: The order reception unit receives orders from customers guided by the customer service unit. For example, if a customer says, "I'd like to order XX," the order reception unit responds, "Yes, XX. Understood," and records the order details. Step 3: The confirmation unit confirms the order details accepted by the order acceptance unit. For example, the confirmation unit may confirm the order details with the customer again, asking, "Are you sure you want to order XX?"

[0070] 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.

[0071] 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.

[0072] 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.

[0073] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0074] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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).

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0089] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0104] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0124] 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."

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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]

[0137] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Equipped with a humanoid robot equipped with generative AI, The robot The customer service department automatically greets customers when they enter the store and guides them to their seats. an order receiving unit that receives orders from customers guided by the customer service unit; a confirmation unit that confirms the order content accepted by the order acceptance unit. A system characterized by:

2. The customer service department includes: Analyze the customer's facial expressions and tone of voice and provide customer service according to their emotions.

2. The system of claim 1.

3. The customer service department includes: The customer's preferences and allergy information are registered in advance, and menu suggestions are made based on that information.

2. The system of claim 1.

4. The order receiving unit Based on the customer's order history, suggest reorders and introduce new menu items.

2. The system of claim 1.

5. The order receiving unit Analyze the customer's tone of voice and facial expressions to respond appropriately when confirming the order details 2. The system of claim 1.

6. The order receiving unit Using emotion estimation function, a conversation is generated to reduce the stress felt by the customer when placing an order.

2. The system of claim 1.

7. The order receiving unit Support multiple languages ​​to ensure smooth response to foreign customers 2. The system of claim 1.

8. The order receiving unit Using emotion estimation function, the satisfaction level felt by the customer when placing an order is monitored in real time to improve the quality of service.

2. The system of claim 1.

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