System

Abstract

An object of a system according to an embodiment is to quickly provide details of a menu in a restaurant and reduce stress of a customer.SOLUTION: A system includes a menu display part, a generation AI part, and a chat functional part. The menu display unit displays a menu on the tablet. The generation and AI unit provides a detailed description of the dish based on the menu displayed by the menu display unit. The chat functional unit performs question answering in real time based on the explanation provided by the generation AI 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, if a customer wants to know details about a menu item at a restaurant, they have to wait for a waiter, which can increase stress for the customer.

[0005] The system according to the embodiment aims to quickly provide menu details in restaurants and reduce stress for customers. [Means for solving the problem]

[0006] The system according to the embodiment includes a menu display unit, a generation AI unit, and a chat function unit. The menu display unit displays a menu on a tablet. The generation AI unit provides detailed explanations of dishes based on the menu displayed by the menu display unit. The chat function unit responds to questions in real time based on the explanations provided by the generation AI unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly provide menu details in restaurants, reducing customer stress. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The restaurant order support system according to the embodiment of the present invention displays a menu on a tablet, and a generation AI provides detailed explanations of dishes and answers questions in real time through a chat function. This allows users to quickly obtain detailed information without having to wait for a waiter.

[0029] A restaurant order support system according to an embodiment includes a menu display unit, a generation AI unit, and a chat function unit. The menu display unit displays a menu on a tablet. For example, the menu display unit can display a list of dishes or a menu with images. The menu display unit also allows a user to view the menu by operating the tablet. The generation AI unit provides detailed descriptions of dishes based on the menu displayed by the menu display unit. For example, the generation AI unit generates detailed descriptions including the ingredients, cooking methods, calorie information, and allergy information. The generation AI unit can also provide information about the history, cultural background, nutritional balance, and health benefits of the dish. The chat function unit responds to questions in real time based on the descriptions provided by the generation AI unit. For example, the chat function unit instantly generates and provides answers to questions entered by a user. The chat function unit can also learn the user's past question history and provide more personalized answers. This allows the restaurant order support system according to an embodiment to quickly obtain detailed information without having to wait for a waiter. For example, a user can obtain detailed information about dishes through a tablet, allowing them to choose their dishes with confidence. It also reduces the burden on store staff and improves the quality of service.

[0030] The generation AI unit can provide detailed descriptions including the ingredients, cooking method, calorie information, and allergy information for the dish. For example, the generation AI unit provides specific types and information about the ingredients of the dish. For example, it displays a list of main ingredients and seasonings. The generation AI unit also explains specific steps and techniques for cooking. For example, it provides detailed explanations of cooking methods such as baking, boiling, and steaming. The generation AI unit also provides specific calculation methods and standards for calorie information. For example, it displays the calorie content per serving and the content of major nutrients. The generation AI unit also explains the specific content and provision method of allergy information. For example, it displays a list of allergens contained and whether the dish is allergy-friendly. This allows the user to obtain detailed information about the dish. For example, the user can choose dishes that are suitable for their allergies. The calorie information can also be used to choose healthy meals.

[0031] The generative AI can provide detailed explanations, including the history and cultural background of a dish. For example, the generative AI provides historical background, such as the origin and development of a dish, and regional variations. For example, it explains the origin of paella and its variations across Spain. The generative AI also introduces cultural episodes and traditional events related to the dish. For example, it explains the history of sushi and its role in traditional Japanese festivals. The generative AI also explains the origin of a dish's name and how it is loved in a particular region. For example, it explains the origin of the name carbonara and its popularity in Italy. This allows users to understand the history and cultural background of a dish. For example, knowing the background of a dish can deepen users' interest in it. Furthermore, understanding the cultural value of a dish can increase their enjoyment of the meal.

[0032] The generation AI unit provides information about the nutritional balance and health benefits of dishes and can make meal suggestions based on the user's health condition. For example, the generation AI unit analyzes the nutritional components of each dish and suggests balanced meals. For example, it suggests healthy menus based on the vitamin and mineral content. The generation AI unit also takes into account the user's health condition and allergy information to suggest appropriate dishes. For example, it suggests low-carb dishes for a diabetic user. The generation AI unit also suggests dishes that contain ingredients with specific health benefits. For example, it introduces dishes that use ingredients that boost the immune system. This allows the user to receive meal suggestions based on their health condition. For example, the user can choose dishes that suit their health condition. They can also choose meals that are expected to have specific health benefits.

[0033] The generation AI unit can provide images and videos of dishes. For example, the generation AI unit can provide a photo of the finished dish or a video of the cooking process. For example, it can allow the user to check the appearance and size of the dish. The generation AI unit can also visually display the internal structure of the dish and the arrangement of ingredients. For example, it can display the contents of a sandwich or the layers of a cake. The generation AI unit can also visually display the cooking process of the dish. For example, it can display how to boil pasta or how to pour sauce. This allows the user to obtain visual information about the dish. For example, by checking the appearance and cooking process of the dish, the user can gain confidence in their choice of dish. It can also give a concrete image of the dish.

[0034] The generation AI unit can provide a recommendation function based on the user's preferences. For example, the generation AI unit suggests recommended dishes based on the user's past order history and preferences. For example, it suggests dishes that suit the user, such as "Here's something similar to the dish you previously ordered." The generation AI unit also provides dish pairing information. For example, it provides information on pairing wines and desserts that go well with dishes. This allows the user to be suggested dishes that suit their preferences. For example, this can reduce the anxiety of users when trying new dishes. Furthermore, the dish pairing information can enable them to enjoy a more satisfying meal.

[0035] The generation AI unit supports multiple languages, displaying menus and providing answers to questions based on the user's language. For example, the generation AI unit can display menus in languages ​​other than Japanese and provide answers to questions in those languages. For example, it supports multiple languages ​​such as English, Chinese, and Korean. The generation AI unit also uses an emotion estimation function to analyze the user's emotions in real time when choosing a dish and make suggestions to elicit positive emotions. For example, it can suggest recommended dishes if the user is unsure. This allows foreign tourists to choose dishes without stress. For example, users can check the menu in their own language and get answers to their questions. Furthermore, suggestions using the emotion estimation function can give them confidence in their dish selection.

[0036] The generation AI unit can provide information on the origin and producer of ingredients for a dish. For example, the generation AI unit provides information on the origin of ingredients for a dish. For example, in response to a question such as, "Where were these vegetables grown?", it provides origin information. The generation AI unit also provides producer information for ingredients for a dish. For example, in response to a question such as, "Which farm was this meat produced on?", it provides the producer's name and farm information. The generation AI unit also provides traceability information for ingredients for a dish. For example, in response to a question such as, "Which fishing port did this fish come from?", it provides information on the fishing port and fisherman. This allows users to know the origin and producer information of ingredients for a dish. For example, by checking the traceability of ingredients, users can choose dishes with peace of mind. Furthermore, knowing the origin and producer information allows them to gain a deeper understanding of the value of a dish.

[0037] The generative AI unit can provide scientific background and technical details about cooking methods for dishes. For example, the generative AI unit provides the scientific background about cooking methods for dishes. For example, in response to a question such as, "Why is this dish cooked at this temperature?", it explains the scientific reasons. The generative AI unit also provides detailed information about cooking techniques for dishes. For example, in response to a question such as, "How does this dish achieve its texture?", it explains technical details. The generative AI unit also provides experimental data and research results about cooking methods for dishes. For example, in response to a question such as, "What experimental results is this cooking method based on?", it provides specific data. This allows users to understand the scientific background and technical details about cooking methods for dishes. For example, by understanding the science behind cooking methods, users can develop a deeper interest. Furthermore, by learning the details of cooking techniques, they can gain a deeper understanding of the value of cooking.

[0038] The generation AI unit can provide quizzes and games about cooking ingredients and cooking methods. For example, the generation AI unit provides a quiz about cooking ingredients. For example, it may ask a question such as, "What spices are used in this dish?" to attract the user's interest. The generation AI unit also provides a game about cooking methods. For example, it may ask a game such as, "Choose the correct steps to make this dish." This deepens the user's understanding. The generation AI unit also provides trivia about cooking ingredients and cooking methods. For example, it may ask a trivia such as, "Which country do the ingredients in this dish come from?" to attract the user's interest. This allows the user to become interested in cooking through quizzes and games about cooking ingredients and cooking methods. For example, the user can deepen their knowledge of cooking while enjoying quizzes and games. Furthermore, learning the background of a dish through trivia increases the enjoyment of eating.

[0039] The generative AI unit can collect user feedback on ingredients and cooking methods for dishes and use it to improve the menu. For example, the generative AI unit collects user feedback on ingredients for dishes and uses it to improve the menu. For example, it collects feedback such as, "Please tell us your opinion on the ingredients for this dish." The generative AI unit also collects user feedback on cooking methods for dishes and uses it to improve the menu. For example, it collects feedback such as, "What do you think about the cooking method for this dish?" The generative AI unit also makes suggestions to improve ingredients and cooking methods for dishes based on user feedback. For example, it makes suggestions such as, "Based on user feedback, we have changed the ingredients for this dish." This makes it possible to improve the menu based on user feedback. For example, users can enjoy a menu that reflects their opinions. Feedback also deepens communication with the restaurant.

[0040] The chat function unit can learn the user's past question history and provide more personalized answers. The chat function unit, for example, learns the user's past question history and provides personalized answers. For example, a user who has previously asked about allergy information is given priority in providing information about allergies. The chat function unit also provides related information based on the user's past question history. For example, a user who has previously asked about a specific dish is provided with new information related to that dish. The chat function unit also analyzes the user's past question history and provides answers based on the user's preferences and interests. For example, a user who has previously asked about vegetarian dishes is provided with information about vegetarian dishes. This allows the user to obtain personalized answers based on the user's past question history. For example, the user can quickly obtain information that matches their interests. Furthermore, personalized answers can provide a more satisfying service.

[0041] The chat function unit can provide recipes and cooking videos related to a user's question, thereby supporting the user in recreating the dish at home. For example, the chat function unit provides recipes related to a user's question. For example, in response to a question such as "Please tell me the recipe for this dish," a detailed recipe is provided. The chat function unit also provides cooking videos related to a user's question. For example, in response to a question such as "Please tell me how to make this dish," a video showing the cooking process is provided. The chat function unit also provides advice to support the user in recreating the dish at home in response to a user's question. For example, in response to a question such as "Please tell me the key points to make this dish at home," specific advice is provided. This allows the user to receive support in recreating the dish at home. For example, the user can refer to recipes and cooking videos to recreate restaurant dishes at home. Furthermore, receiving specific advice increases the success rate of cooking.

[0042] The chat function unit may incorporate voice recognition to enable the user to input questions by voice. The chat function unit may incorporate voice recognition to enable the user to input questions by voice. For example, the user may ask, "What's in this dish?" by voice. The chat function unit may also use voice recognition to convert the user's question into text, and the generation AI may generate an answer to the question. For example, the question input by voice may be converted into text and the answer may be provided. The chat function unit may also use voice recognition to enable the user to input questions hands-free. For example, the user may input a question by voice while selecting a dish and receive an answer. This allows the user to input questions by voice. For example, the user may input a question without using their hands and receive a quick answer. Furthermore, the use of voice recognition enables more natural dialogue.

[0043] The chat function unit can provide reviews and ratings from other users in response to a user's question. For example, the chat function unit provides reviews and ratings from other users in response to a user's question. For example, in response to a question such as "Is this dish delicious?", reviews from other users are displayed. The chat function unit also provides ranking information based on the ratings of other users in response to a user's question. For example, in response to a question such as "How much does this dish rate?", an evaluation score is displayed. The chat function unit also provides recommendation information based on the reviews and ratings of other users in response to a user's question. For example, in response to a question such as "Should I order this dish?", recommendations based on the opinions of other users are displayed. This allows the user to refer to the reviews and ratings of other users. For example, the user can choose a dish based on the opinions of other users. Furthermore, by referring to the reviews and ratings, the user can feel more confident in their choice of dish.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The restaurant order support system may further include a health management unit that analyzes the user's meal history and supports health management. For example, the health management unit may suggest nutritionally balanced meals based on the user's past meal history. The health management unit may also provide meal plans according to the user's health goals (e.g., weight loss or muscle building). Furthermore, the health management unit may point out excesses or deficiencies of specific nutrients based on the user's meal history and provide advice for improvement. This allows the user to receive support in maintaining a healthy diet. For example, the user can select meals that match their health goals. Furthermore, a nutritionally balanced diet can improve their health.

[0046] The restaurant ordering support system may further include a preference learning unit that learns the user's food preferences and makes personalized menu suggestions. For example, the preference learning unit may predict and suggest dishes that the user will like based on the user's past order history and ratings. The preference learning unit may also analyze the user's eating habits and suggest new dishes. Furthermore, the preference learning unit may suggest specific dishes or ingredient combinations based on the user's food preferences. This allows the user to easily find dishes that suit their tastes. For example, the user may feel less anxious about trying new dishes. The preference learning unit may also allow the user to enjoy a more satisfying meal.

[0047] The restaurant order support system may further include a timing suggestion unit that suggests menus according to the timing of the user's meal. For example, when the user selects breakfast, lunch, or dinner, the timing suggestion unit may suggest dishes suitable for each time period. The timing suggestion unit may also suggest menus suited to meal times based on the user's schedule. Furthermore, the timing suggestion unit may suggest dishes suited to specific events or seasons. This allows the user to select the optimal dishes suited to the timing of the meal. For example, the user may select a light dish suitable for breakfast or a hearty dish suitable for dinner. The user may also enjoy dishes suited to specific events or seasons.

[0048] The restaurant order support system may further include a pace suggestion unit that suggests menu items according to the user's eating pace. For example, if the user is in a hurry, the pace suggestion unit may suggest dishes that can be served in a short time. Also, if the user wants to enjoy a meal at a leisurely pace, the pace suggestion unit may suggest dishes that can be enjoyed over time. Furthermore, the pace suggestion unit may adjust the timing of food serving according to the user's eating pace. This allows the user to select dishes that suit their eating pace. For example, if the user is in a hurry, the user can select dishes that can be served quickly. Also, if the user wants to enjoy a meal at a leisurely pace, the user can select dishes that can be enjoyed over time.

[0049] The restaurant order support system may further include a budget suggestion unit that suggests menus according to the user's meal budget. For example, the budget suggestion unit may suggest optimal dishes within the budget set by the user. The budget suggestion unit may also suggest a combination of dishes according to the budget based on the user's past ordering history. The budget suggestion unit may also suggest dishes that will provide maximum satisfaction within a specific budget. This allows the user to select dishes that fit their budget. For example, the user can enjoy a satisfying meal within their budget. The budget suggestion unit may also help the user find dishes with high cost performance.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The menu display unit displays a menu on the tablet. For example, the menu display unit displays a list of dishes or a menu with images, allowing the user to view the menu by operating the tablet. Step 2: The AI ​​generation unit provides detailed descriptions of dishes based on the menu displayed by the menu display unit. For example, the AI ​​generation unit generates information about the ingredients, cooking methods, calorie information, allergy information, the history and cultural background of the dish, nutritional balance, and health benefits of the dish. Step 3: The chat function unit responds to questions in real time based on the explanations provided by the generation AI unit. For example, the chat function unit instantly generates answers to questions entered by the user and provides them to the user. The chat function unit can also learn from the user's past question history and provide more personalized answers.

[0052] (Example 2) The restaurant order support system according to the embodiment of the present invention displays a menu on a tablet, and a generation AI provides detailed explanations of dishes and answers questions in real time through a chat function. This allows users to quickly obtain detailed information without having to wait for a waiter.

[0053] A restaurant order support system according to an embodiment includes a menu display unit, a generation AI unit, and a chat function unit. The menu display unit displays a menu on a tablet. For example, the menu display unit can display a list of dishes or a menu with images. The menu display unit also allows a user to view the menu by operating the tablet. The generation AI unit provides detailed descriptions of dishes based on the menu displayed by the menu display unit. For example, the generation AI unit generates detailed descriptions including the ingredients, cooking methods, calorie information, and allergy information. The generation AI unit can also provide information about the history, cultural background, nutritional balance, and health benefits of the dish. The chat function unit responds to questions in real time based on the descriptions provided by the generation AI unit. For example, the chat function unit instantly generates and provides answers to questions entered by a user. The chat function unit can also learn the user's past question history and provide more personalized answers. This allows the restaurant order support system according to an embodiment to quickly obtain detailed information without having to wait for a waiter. For example, a user can obtain detailed information about dishes through a tablet, allowing them to choose their dishes with confidence. It also reduces the burden on store staff and improves the quality of service.

[0054] The generation AI unit can provide detailed descriptions including the ingredients, cooking method, calorie information, and allergy information for the dish. For example, the generation AI unit provides specific types and information about the ingredients of the dish. For example, it displays a list of main ingredients and seasonings. The generation AI unit also explains specific steps and techniques for cooking. For example, it provides detailed explanations of cooking methods such as baking, boiling, and steaming. The generation AI unit also provides specific calculation methods and standards for calorie information. For example, it displays the calorie content per serving and the content of major nutrients. The generation AI unit also explains the specific content and provision method of allergy information. For example, it displays a list of allergens contained and whether the dish is allergy-friendly. This allows the user to obtain detailed information about the dish. For example, the user can choose dishes that are suitable for their allergies. The calorie information can also be used to choose healthy meals.

[0055] The generative AI can provide detailed explanations, including the history and cultural background of a dish. For example, the generative AI provides historical background, such as the origin and development of a dish, and regional variations. For example, it explains the origin of paella and its variations across Spain. The generative AI also introduces cultural episodes and traditional events related to the dish. For example, it explains the history of sushi and its role in traditional Japanese festivals. The generative AI also explains the origin of a dish's name and how it is loved in a particular region. For example, it explains the origin of the name carbonara and its popularity in Italy. This allows users to understand the history and cultural background of a dish. For example, knowing the background of a dish can deepen users' interest in it. Furthermore, understanding the cultural value of a dish can increase their enjoyment of the meal.

[0056] The generation AI unit provides information about the nutritional balance and health benefits of dishes and can make meal suggestions based on the user's health condition. For example, the generation AI unit analyzes the nutritional components of each dish and suggests balanced meals. For example, it suggests healthy menus based on the vitamin and mineral content. The generation AI unit also takes into account the user's health condition and allergy information to suggest appropriate dishes. For example, it suggests low-carb dishes for a diabetic user. The generation AI unit also suggests dishes that contain ingredients with specific health benefits. For example, it introduces dishes that use ingredients that boost the immune system. This allows the user to receive meal suggestions based on their health condition. For example, the user can choose dishes that suit their health condition. They can also choose meals that are expected to have specific health benefits.

[0057] The generation AI unit can use the emotion estimation function to suggest dishes that correspond to the user's emotional state. For example, the generation AI unit can use the emotion estimation function to suggest dishes that have a relaxing effect when the user is feeling stressed. For example, it can recommend herbal tea or dishes that use ingredients with a relaxing effect. The generation AI unit can also suggest dishes that are suitable for replenishing energy when the user is tired. For example, it can recommend high-protein dishes or smoothies that replenish energy. The generation AI unit can also suggest dishes suitable for parties when the user is in a good mood. For example, it can recommend appetizers and desserts that can be shared. This makes it possible to suggest dishes that correspond to the user's emotional state. For example, the user can choose dishes that match their emotional state. They can also enjoy meals that correspond to their emotional state.

[0058] The generation AI unit can provide images and videos of dishes. For example, the generation AI unit can provide a photo of the finished dish or a video of the cooking process. For example, it can allow the user to check the appearance and size of the dish. The generation AI unit can also visually display the internal structure of the dish and the arrangement of ingredients. For example, it can display the contents of a sandwich or the layers of a cake. The generation AI unit can also visually display the cooking process of the dish. For example, it can display how to boil pasta or how to pour sauce. This allows the user to obtain visual information about the dish. For example, by checking the appearance and cooking process of the dish, the user can gain confidence in their choice of dish. It can also give a concrete image of the dish.

[0059] The generation AI unit can provide a recommendation function based on the user's preferences. For example, the generation AI unit suggests recommended dishes based on the user's past order history and preferences. For example, it suggests dishes that suit the user, such as "Here's something similar to the dish you previously ordered." The generation AI unit also provides dish pairing information. For example, it provides information on pairing wines and desserts that go well with dishes. This allows the user to be suggested dishes that suit their preferences. For example, this can reduce the anxiety of users when trying new dishes. Furthermore, the dish pairing information can enable them to enjoy a more satisfying meal.

[0060] The generation AI unit supports multiple languages, displaying menus and providing answers to questions based on the user's language. For example, the generation AI unit can display menus in languages ​​other than Japanese and provide answers to questions in those languages. For example, it supports multiple languages ​​such as English, Chinese, and Korean. The generation AI unit also uses an emotion estimation function to analyze the user's emotions in real time when choosing a dish and make suggestions to elicit positive emotions. For example, it can suggest recommended dishes if the user is unsure. This allows foreign tourists to choose dishes without stress. For example, users can check the menu in their own language and get answers to their questions. Furthermore, suggestions using the emotion estimation function can give them confidence in their dish selection.

[0061] The generation AI unit can provide information on the origin and producer of ingredients for a dish. For example, the generation AI unit provides information on the origin of ingredients for a dish. For example, in response to a question such as, "Where were these vegetables grown?", it provides origin information. The generation AI unit also provides producer information for ingredients for a dish. For example, in response to a question such as, "Which farm was this meat produced on?", it provides the producer's name and farm information. The generation AI unit also provides traceability information for ingredients for a dish. For example, in response to a question such as, "Which fishing port did this fish come from?", it provides information on the fishing port and fisherman. This allows users to know the origin and producer information of ingredients for a dish. For example, by checking the traceability of ingredients, users can choose dishes with peace of mind. Furthermore, knowing the origin and producer information allows them to gain a deeper understanding of the value of a dish.

[0062] The generative AI unit can provide scientific background and technical details about cooking methods for dishes. For example, the generative AI unit provides the scientific background about cooking methods for dishes. For example, in response to a question such as, "Why is this dish cooked at this temperature?", it explains the scientific reasons. The generative AI unit also provides detailed information about cooking techniques for dishes. For example, in response to a question such as, "How does this dish achieve its texture?", it explains technical details. The generative AI unit also provides experimental data and research results about cooking methods for dishes. For example, in response to a question such as, "What experimental results is this cooking method based on?", it provides specific data. This allows users to understand the scientific background and technical details about cooking methods for dishes. For example, by understanding the science behind cooking methods, users can develop a deeper interest. Furthermore, by learning the details of cooking techniques, they can gain a deeper understanding of the value of cooking.

[0063] The generation AI unit can use the emotion estimation function to analyze the emotions a user has toward specific ingredients or cooking methods and provide information to elicit positive emotions. For example, the generation AI unit can use the emotion estimation function to analyze the emotions a user has toward specific ingredients and provide information to elicit positive emotions. For example, it can introduce positive stories about the user's favorite ingredients. The generation AI unit can also analyze the emotions a user has toward specific cooking methods and provide information to elicit positive emotions. For example, it can introduce success stories about cooking methods that the user is interested in. The generation AI unit can also analyze the emotions a user has toward specific ingredients or cooking methods and provide advice to elicit positive emotions. For example, it can provide information that gives a sense of security when the user is feeling anxious. This allows the user to have positive emotions toward specific ingredients or cooking methods. For example, the user can develop a deeper interest in their favorite ingredients or cooking methods. Furthermore, obtaining information that elicits positive emotions allows the user to feel more confident in their cooking choices.

[0064] The generation AI unit can provide quizzes and games about cooking ingredients and cooking methods. For example, the generation AI unit provides a quiz about cooking ingredients. For example, it may ask a question such as, "What spices are used in this dish?" to attract the user's interest. The generation AI unit also provides a game about cooking methods. For example, it may ask a game such as, "Choose the correct steps to make this dish." This deepens the user's understanding. The generation AI unit also provides trivia about cooking ingredients and cooking methods. For example, it may ask a trivia such as, "Which country do the ingredients in this dish come from?" to attract the user's interest. This allows the user to become interested in cooking through quizzes and games about cooking ingredients and cooking methods. For example, the user can deepen their knowledge of cooking while enjoying quizzes and games. Furthermore, learning the background of a dish through trivia increases the enjoyment of eating.

[0065] The generative AI unit can collect user feedback on ingredients and cooking methods for dishes and use it to improve the menu. For example, the generative AI unit collects user feedback on ingredients for dishes and uses it to improve the menu. For example, it collects feedback such as, "Please tell us your opinion on the ingredients for this dish." The generative AI unit also collects user feedback on cooking methods for dishes and uses it to improve the menu. For example, it collects feedback such as, "What do you think about the cooking method for this dish?" The generative AI unit also makes suggestions to improve ingredients and cooking methods for dishes based on user feedback. For example, it makes suggestions such as, "Based on user feedback, we have changed the ingredients for this dish." This makes it possible to improve the menu based on user feedback. For example, users can enjoy a menu that reflects their opinions. Feedback also deepens communication with the restaurant.

[0066] The generation AI unit can use the emotion estimation function to analyze the user's emotions in real time when viewing detailed information about a dish and provide information that elicits positive emotions. For example, the generation AI unit can use the emotion estimation function to analyze the user's emotions in real time when viewing detailed information about a dish and provide information that elicits positive emotions. For example, if the user is interested, it can provide information that will continue to pique their interest. The generation AI unit also analyzes the user's emotions when viewing detailed information about a dish and displays a message that elicits positive emotions. For example, it can display a message such as "This dish is very popular!". The generation AI unit also analyzes the user's emotions when viewing detailed information about a dish and provides an interface that elicits positive emotions. For example, it can adopt a design that allows the user to enjoy viewing information. This allows the user to have positive emotions when viewing detailed information about a dish. For example, by viewing information with interest, the user can gain confidence in their dish selection. Furthermore, obtaining information that elicits positive emotions increases the enjoyment of eating.

[0067] The chat function unit can learn the user's past question history and provide more personalized answers. The chat function unit, for example, learns the user's past question history and provides personalized answers. For example, a user who has previously asked about allergy information is given priority in providing information about allergies. The chat function unit also provides related information based on the user's past question history. For example, a user who has previously asked about a specific dish is provided with new information related to that dish. The chat function unit also analyzes the user's past question history and provides answers based on the user's preferences and interests. For example, a user who has previously asked about vegetarian dishes is provided with information about vegetarian dishes. This allows the user to obtain personalized answers based on the user's past question history. For example, the user can quickly obtain information that matches their interests. Furthermore, personalized answers can provide a more satisfying service.

[0068] The chat function unit can provide recipes and cooking videos related to a user's question, thereby supporting the user in recreating the dish at home. For example, the chat function unit provides recipes related to a user's question. For example, in response to a question such as "Please tell me the recipe for this dish," a detailed recipe is provided. The chat function unit also provides cooking videos related to a user's question. For example, in response to a question such as "Please tell me how to make this dish," a video showing the cooking process is provided. The chat function unit also provides advice to support the user in recreating the dish at home in response to a user's question. For example, in response to a question such as "Please tell me the key points to make this dish at home," specific advice is provided. This allows the user to receive support in recreating the dish at home. For example, the user can refer to recipes and cooking videos to recreate restaurant dishes at home. Furthermore, receiving specific advice increases the success rate of cooking.

[0069] The chat function unit can use the emotion estimation function to analyze the user's emotional response to the question and generate an answer to reduce the negative emotion. The chat function unit, for example, uses the emotion estimation function to analyze the user's emotional response to the question and generate an answer to reduce the negative emotion. For example, if the user is feeling anxious, an answer that gives a sense of security is provided. The chat function unit also uses the emotion estimation function to analyze the user's emotional response to the question and generate an answer to elicit positive emotion. For example, if the user is interested, an answer that maintains the user's interest is provided. The chat function unit also uses the emotion estimation function to analyze the user's emotional response to the question and provide an answer with an appropriate tone and expression. For example, if the user is angry, a calm and polite answer is provided. This provides an answer to reduce the user's negative emotion. For example, the user can receive an answer to their question with peace of mind, without feeling anxious or anger. Furthermore, receiving an answer that elicits positive emotion improves satisfaction with the service.

[0070] The chat function unit may incorporate voice recognition to enable the user to input questions by voice. The chat function unit may incorporate voice recognition to enable the user to input questions by voice. For example, the user may ask, "What's in this dish?" by voice. The chat function unit may also use voice recognition to convert the user's question into text, and the generation AI may generate an answer to the question. For example, the question input by voice may be converted into text and the answer may be provided. The chat function unit may also use voice recognition to enable the user to input questions hands-free. For example, the user may input a question by voice while selecting a dish and receive an answer. This allows the user to input questions by voice. For example, the user may input a question without using their hands and receive a quick answer. Furthermore, the use of voice recognition enables more natural dialogue.

[0071] The chat function unit can provide reviews and ratings from other users in response to a user's question. For example, the chat function unit provides reviews and ratings from other users in response to a user's question. For example, in response to a question such as "Is this dish delicious?", reviews from other users are displayed. The chat function unit also provides ranking information based on the ratings of other users in response to a user's question. For example, in response to a question such as "How much does this dish rate?", an evaluation score is displayed. The chat function unit also provides recommendation information based on the reviews and ratings of other users in response to a user's question. For example, in response to a question such as "Should I order this dish?", recommendations based on the opinions of other users are displayed. This allows the user to refer to the reviews and ratings of other users. For example, the user can choose a dish based on the opinions of other users. Furthermore, by referring to the reviews and ratings, the user can feel more confident in their choice of dish.

[0072] The chat function unit uses the emotion estimation function to adjust the tone and expression of a response according to the user's emotional state, thereby enabling a more empathetic response. The chat function unit, for example, uses the emotion estimation function to adjust the tone and expression of a response according to the user's emotional state. For example, if the user is feeling anxious, the chat function unit provides a response in a tone that gives a sense of security. The chat function unit also uses the emotion estimation function to provide an empathetic response according to the user's emotional state. For example, if the user is excited, the chat function unit provides a response that shares that excitement. The chat function unit also uses the emotion estimation function to provide a response in appropriate expression according to the user's emotional state. For example, if the user is angry, the chat function unit provides a response in a calm and polite manner. This allows the user to receive an empathetic response according to their emotional state. For example, by receiving a response that is in tune with their emotions, the user can feel a sense of security and satisfaction. Furthermore, the empathetic response improves the quality of service.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The restaurant order support system may further include a health management unit that analyzes the user's meal history and supports health management. For example, the health management unit may suggest nutritionally balanced meals based on the user's past meal history. The health management unit may also provide meal plans according to the user's health goals (e.g., weight loss or muscle building). Furthermore, the health management unit may point out excesses or deficiencies of specific nutrients based on the user's meal history and provide advice for improvement. This allows the user to receive support in maintaining a healthy diet. For example, the user can select meals that match their health goals. Furthermore, a nutritionally balanced diet can improve their health.

[0075] The restaurant ordering support system may further include a preference learning unit that learns the user's food preferences and makes personalized menu suggestions. For example, the preference learning unit may predict and suggest dishes that the user will like based on the user's past order history and ratings. The preference learning unit may also analyze the user's eating habits and suggest new dishes. Furthermore, the preference learning unit may suggest specific dishes or ingredient combinations based on the user's food preferences. This allows the user to easily find dishes that suit their tastes. For example, the user may feel less anxious about trying new dishes. The preference learning unit may also allow the user to enjoy a more satisfying meal.

[0076] The restaurant order support system may further include a timing suggestion unit that suggests menus according to the timing of the user's meal. For example, when the user selects breakfast, lunch, or dinner, the timing suggestion unit may suggest dishes suitable for each time period. The timing suggestion unit may also suggest menus suited to meal times based on the user's schedule. Furthermore, the timing suggestion unit may suggest dishes suited to specific events or seasons. This allows the user to select the optimal dishes suited to the timing of the meal. For example, the user may select a light dish suitable for breakfast or a hearty dish suitable for dinner. The user may also enjoy dishes suited to specific events or seasons.

[0077] The restaurant order support system may further include a pace suggestion unit that suggests menu items according to the user's eating pace. For example, if the user is in a hurry, the pace suggestion unit may suggest dishes that can be served in a short time. Also, if the user wants to enjoy a meal at a leisurely pace, the pace suggestion unit may suggest dishes that can be enjoyed over time. Furthermore, the pace suggestion unit may adjust the timing of food serving according to the user's eating pace. This allows the user to select dishes that suit their eating pace. For example, if the user is in a hurry, the user can select dishes that can be served quickly. Also, if the user wants to enjoy a meal at a leisurely pace, the user can select dishes that can be enjoyed over time.

[0078] The restaurant order support system may further include a budget suggestion unit that suggests menus according to the user's meal budget. For example, the budget suggestion unit may suggest optimal dishes within the budget set by the user. The budget suggestion unit may also suggest a combination of dishes according to the budget based on the user's past ordering history. The budget suggestion unit may also suggest dishes that will provide maximum satisfaction within a specific budget. This allows the user to select dishes that fit their budget. For example, the user can enjoy a satisfying meal within their budget. The budget suggestion unit may also help the user find dishes with high cost performance.

[0079] The restaurant order assistance system may further include a music providing unit that provides music according to the user's emotional state. For example, the music providing unit may provide relaxing music when the user wants to relax. Alternatively, the music providing unit may provide energetic music when the user wants to cheer up. Furthermore, the music providing unit may provide music that matches the user's emotion when the user is in a particular emotional state. This allows the user to enjoy music that matches their emotional state while eating. For example, the user can listen to relaxing music when they want to relax. Alternatively, the user can listen to energetic music when they want to cheer up.

[0080] The restaurant order assistance system may further include a lighting adjustment unit that adjusts lighting according to the user's emotional state. For example, the lighting adjustment unit may provide soft lighting when the user wants to relax. Alternatively, the lighting adjustment unit may provide bright lighting when the user wants to concentrate. Furthermore, the lighting adjustment unit may adjust the color and brightness of lighting according to the user's emotional state. This allows the user to enjoy lighting that suits their emotional state while eating. For example, the user can enjoy soft lighting when they want to relax, and bright lighting when they want to concentrate.

[0081] The restaurant order assistance system can further include an aroma providing unit that provides an aroma according to the user's emotional state. For example, the aroma providing unit can provide an aroma with a relaxing effect when the user wants to relax. Also, the aroma providing unit can provide an energetic aroma when the user wants to feel energized. Furthermore, when the user is in a particular emotional state, the aroma providing unit can provide an aroma that matches that emotion. This allows the user to enjoy an aroma that matches their emotional state while eating. For example, the user can enjoy an aroma with a relaxing effect when they want to relax. Also, the user can enjoy an energetic aroma when they want to feel energized.

[0082] The restaurant order assistance system may further include a seat suggestion unit that suggests a seat according to the user's emotional state. For example, the seat suggestion unit may suggest a seat in a quiet location when the user wants to relax. Furthermore, the seat suggestion unit may suggest a seat in a location where the user can easily interact with other users when the user is in a sociable mood. Furthermore, the seat suggestion unit may suggest an optimal seat according to the user's emotional state. This allows the user to select a seat that suits their emotional state. For example, when the user wants to relax, the user may choose a seat in a quiet location. Furthermore, when the user is in a sociable mood, the user may choose a seat in a location where the user can easily interact with other users.

[0083] The restaurant order support system can further include a dessert suggestion unit that suggests desserts according to the user's emotional state. For example, the dessert suggestion unit can suggest a dessert with a relaxing effect when the user wants to relax. Also, the dessert suggestion unit can suggest an energetic dessert when the user wants to cheer up. Furthermore, when the user is in a particular emotional state, the dessert suggestion unit can suggest a dessert that matches that emotion. This allows the user to enjoy a dessert that matches their emotional state at the end of a meal. For example, the user can enjoy a dessert with a relaxing effect when they want to relax. Also, the user can enjoy an energetic dessert when they want to cheer up.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The menu display unit displays a menu on the tablet. For example, the menu display unit displays a list of dishes or a menu with images, allowing the user to view the menu by operating the tablet. Step 2: The AI ​​generation unit provides detailed descriptions of dishes based on the menu displayed by the menu display unit. For example, the AI ​​generation unit generates information about the ingredients, cooking methods, calorie information, allergy information, the history and cultural background of the dish, nutritional balance, and health benefits of the dish. Step 3: The chat function unit responds to questions in real time based on the explanations provided by the generation AI unit. For example, the chat function unit instantly generates answers to questions entered by the user and provides them to the user. The chat function unit can also learn from the user's past question history and provide more personalized answers.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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]

[0153] 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 menu display unit that displays a menu on the tablet; a generation AI unit that provides detailed explanations of dishes based on the menu displayed by the menu display unit; a chat function unit that answers questions in real time based on the explanations provided by the generation AI unit; A system characterized by:

2. The generation AI unit Provide a detailed description of the dish, including ingredients, cooking instructions, calorie information, and allergy information 2. The system of claim 1.

3. The generation AI unit Provide a detailed description of the dish, including its history and cultural background 2. The system of claim 1.

4. The generation AI unit Provides information about the nutritional balance and health benefits of the dishes, and makes meal suggestions based on the user's health condition 2. The system of claim 1.

5. The generation AI unit Providing recipe suggestions based on the user's emotional state 2. The system of claim 1.

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