System

Abstract

An object of a system according to an embodiment is to maintain a balanced eating habit at the time of dining out.SOLUTION: A system includes a physical information storage part, an image reading part, a menu analysis part, a recommended menu extraction part, and a voice transmission part. The body information storage unit stores body information and meal data of the user. The image reading unit reads the eating-out menu. The menu analyzer analyzes the menu read by the image reader. The recommended menu extraction unit extracts a recommended menu from the menu analyzed by the menu analysis unit. The voice transmission unit transmits the recommended menu extracted by the recommended menu extraction unit by voice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have made it difficult to maintain a balanced diet when eating out.

[0005] The system according to the embodiment aims to maintain a balanced diet when eating out. [Means for solving the problem]

[0006] The system according to the embodiment includes a physical information storage unit, an image reading unit, a menu analysis unit, a recommended menu extraction unit, and an audio transmission unit. The physical information storage unit stores the user's physical information and dietary data. The image reading unit reads restaurant menus. The menu analysis unit analyzes the menus read by the image reading unit. The recommended menu extraction unit extracts recommended menus from the menus analyzed by the menu analysis unit. The audio transmission unit transmits the recommended menus extracted by the recommended menu extraction unit by audio. [Effects of the Invention]

[0007] The system according to the embodiment allows for maintaining a balanced diet when eating out. [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) A health maintenance system according to an embodiment of the present invention is a system in which a generation AI extracts recommended menus to help maintain health and achieve a balanced diet even when eating out. As a result, the health maintenance system can help maintain health by achieving a balanced diet even when eating out.

[0029] A health maintenance system according to an embodiment includes a physical information storage unit, an image reading unit, a menu analysis unit, a recommended menu extraction unit, and an audio transmission unit. The physical information storage unit stores a user's physical information and dietary data. For example, it collects the user's height, weight, age, gender, allergy information, etc., and stores them in a database. Dietary data can also be collected by the user recording their daily dietary habits. For example, data is accumulated by inputting dietary habits using a smartphone app. The image reading unit reads restaurant menus. For example, it may take an image of the menu using image reading glasses and save it as digital data. Alternatively, the menu can be photographed using a smartphone camera. For example, it may take an image of the menu and analyze it using image recognition technology. The menu analysis unit analyzes the menu read by the image reading unit. For example, it may analyze the dish names, ingredients, calorie information, etc., listed on the menu. It may also use OCR technology to extract text information and store it in a database. The recommended menu extraction unit extracts recommended menus from the menu analyzed by the menu analysis unit. For example, it may extract multiple meals that the user currently needs to eat from the menu based on the user's physical information and dietary data. For example, if a user has not eaten many vegetables recently, the generation AI will prioritize extracting menu items that contain a lot of vegetables. The audio transmission unit will audibly announce the recommended menu items extracted by the recommended menu extraction unit. For example, it will announce the following in the form of, "Based on your current health condition, we recommend the following menu items: 1st place: Caesar salad, 2nd place: Grilled chicken, 3rd place: Minestrone soup." This allows the health maintenance system according to the embodiment to help users maintain a balanced diet even when eating out, helping them maintain their health. For example, by selecting healthy menu items when eating out, users can maintain a balanced nutritional profile and stay healthy. Furthermore, the generation AI will always provide recommended menu items based on the latest physical information and dietary data, allowing it to suggest the optimal diet for each individual user.

[0030] The physical information accumulation unit also collects the user's daily activity and sleep data, enabling more precise dietary recommendations. The physical information accumulation unit collects daily activity data from, for example, the user's smartwatch or fitness tracker, and the generation AI analyzes the data to reflect it in dietary recommendations. For example, it calculates the necessary nutrients based on the number of steps taken and calories burned. The physical information accumulation unit also collects the user's sleep data from a smartphone app or wearable device, and the generation AI analyzes the data to reflect it in dietary recommendations. For example, it adjusts the necessary nutrients based on the quality and duration of sleep. The physical information accumulation unit also collects the user's daily stress level from a smartwatch or stress monitoring device, and the generation AI analyzes the data to reflect it in dietary recommendations. For example, if stress is high, it recommends foods with a relaxing effect. This enables precise dietary recommendations that take the user's activity and sleep data into account.

[0031] The physical information storage unit uses voice recognition technology to input meal data, reducing the burden on the user. For example, the physical information storage unit develops a smartphone app that allows users to input meal details by voice, and the generation AI analyzes the voice data and stores the meal data. For example, the user may input "I had toast and coffee for breakfast." The physical information storage unit also uses voice recognition technology to build a system that automatically records data simply by the user speaking the details of their meal. For example, the user speaks the details of their meal into a smart speaker. When the user inputs the details of their meal by voice, the generation AI analyzes them in real time and automatically classifies and stores the meal data. For example, if the user says "I had salad and chicken for lunch," the generation AI analyzes it and stores it in a database. This reduces the burden on the user and makes it easier to input meal data by using voice recognition technology.

[0032] The image reading unit can take pictures of not only menu images but also actual dishes, and analyze them based on their actual appearance and quantity. The image reading unit, for example, uses a camera built into glasses to take pictures of not only menu images but also actual dishes, and the generation AI analyzes the images to accumulate dietary data. For example, it calculates nutritional value based on the appearance and quantity of the dishes. The image reading unit can also use a camera built into glasses to take pictures of actual dishes, and the generation AI analyzes the images to build a system that evaluates the balance of a meal. For example, it analyzes the color and shape of the dish to determine the nutritional balance. The image reading unit can also use a camera built into glasses to take pictures of actual dishes, and the generation AI analyzes the images to calculate the calories of a meal. For example, it calculates calories based on the amount of food and ingredients. This enables accurate analysis based on the actual dish.

[0033] The recommended menu extraction unit can propose seasonal menus by taking into account ingredients according to the season and weather. For example, the recommended menu extraction unit builds a seasonal ingredient database, and the generation AI proposes seasonal menus based on that data. For example, in spring, it recommends menus using fresh vegetables and fruits. The recommended menu extraction unit also collects weather data in real time, and the generation AI proposes menus appropriate for the weather based on that data. For example, on cold days, it recommends hot soups and stews. The recommended menu extraction unit also considers the nutritional value of ingredients according to the season and weather, and the generation AI proposes healthy menus based on that data. For example, in summer, it recommends menus that allow for hydration. This makes it possible to propose menus that are appropriate for the season.

[0034] The recommended menu extraction unit can prioritize recommending particularly preferred menus based on the user's past meal history. The recommended menu extraction unit, for example, analyzes the user's past meal history and builds a system that prioritizes recommending particularly preferred menus. For example, it prioritizes displaying menus that the user frequently orders. The recommended menu extraction unit also analyzes patterns of menus preferred by the generation AI based on the user's meal history data and suggests recommended menus based on those patterns. For example, it analyzes trends in specific ingredients and dishes. The recommended menu extraction unit also proposes recommended menus based on the user's past meal history, taking into account the nutritional value and calories of menus preferred by the generation AI. For example, it prioritizes displaying low-calorie menus that the user prefers. This makes it possible to recommend menus based on the user's preferences.

[0035] The voice transmission unit can include nutritional information and health benefits of the dishes in the voice guidance, allowing the user to understand the reason for their selection. For example, the voice transmission unit may include nutritional information of the dishes in the voice guidance, building a system that allows the user to understand the reason for their selection. For example, the voice transmission unit may provide a prompt saying, "This dish is rich in vitamin C." The voice transmission unit may also develop a system that includes health benefits of the dishes in the voice guidance, allowing the user to understand the reason for their selection. For example, the voice transmission unit may provide a prompt saying, "This dish will boost your immune system." The voice transmission unit may also develop a system that includes nutritional information and health benefits of the dishes in the voice guidance, allowing the user to understand the reason for their selection. For example, the voice transmission unit may provide a prompt saying, "This dish is low in calories and perfect for dieting." This makes it easier for the user to understand the reason for their selection.

[0036] The voice transmission unit can provide information that enhances the enjoyment of a meal by including the history and cultural background of the dish in the voice guidance. The voice transmission unit, for example, builds a system that includes the history and cultural background of the dish in the voice guidance to allow the user to enjoy a meal. For example, it may provide guidance such as, "This dish is a traditional Italian dish." The voice transmission unit also develops a system that includes the cultural background of the dish in the voice guidance to allow the user to enjoy a meal. For example, it may provide guidance such as, "This dish is often eaten at celebratory occasions." The voice transmission unit also builds a system that includes the history and cultural background of the dish in the voice guidance to allow the user to enjoy a meal. For example, it may provide guidance such as, "This dish is a recipe that has been passed down since ancient Roman times." This makes it possible to provide information that enhances the enjoyment of a meal.

[0037] The recommended menu extraction unit can customize menus according to the user's budget and time. For example, the recommended menu extraction unit constructs a system in which a user's budget information is input and a generation AI recommends menus that can be selected within that budget based on that data. For example, menus in a price range according to the budget are displayed. The recommended menu extraction unit also develops a system in which a user's time information is input and a generation AI recommends menus that can be prepared in a short time based on that data. For example, menus with short cooking times are displayed preferentially. The recommended menu extraction unit also constructs a system in which a generation AI customizes and suggests the optimal menu according to the user's budget and time. For example, a nutritionally balanced menu within the budget is recommended. This makes it possible to customize menus according to the user's budget and time.

[0038] The voice transmission unit provides voice guidance in multiple languages, and can also accommodate foreign users. For example, the voice transmission unit builds a system that provides voice guidance in multiple languages, and can also accommodate foreign users. For example, voice guidance is provided in English and Chinese. The voice transmission unit also develops a system that makes voice guidance multilingual, and can also accommodate foreign users. For example, voice guidance is provided in a language selected by the user. The voice transmission unit also builds a system that provides voice guidance in multiple languages, and can also accommodate foreign users. For example, menu contents are announced in English and French. This makes it possible to accommodate foreign users.

[0039] The recommended menu extraction unit can propose locally-focused menus that incorporate local specialties and ingredients. For example, the recommended menu extraction unit builds a database of local specialties for each region, and the generation AI proposes locally-focused menus based on that data. For example, it recommends menus that use fresh local vegetables and fruits. The recommended menu extraction unit also builds a system that prioritizes recommending menus that use local ingredients. For example, it recommends dishes that use fish caught at local fishing ports. The recommended menu extraction unit also develops a system that revitalizes the local economy by recommending menus that use local specialties and ingredients. For example, it recommends dishes that use local agricultural products. This makes it possible to propose locally-focused menus.

[0040] The voice transmission unit can customize the voice guidance to a user's preferred voice or character. For example, the voice transmission unit builds a voice guidance system that allows the user to select their preferred voice or character. For example, guidance is provided in the voice of an anime character. The voice transmission unit also develops a system that customizes the voice guidance to the user's preferences. For example, guidance is provided in the voice of a celebrity that the user likes. The voice transmission unit also builds a voice guidance system customized to the user's preferred voice or character. For example, recommended menus are provided in a voice selected by the user. This makes it possible to provide voice guidance according to the user's preferences.

[0041] The recommended menu extraction unit can analyze a user's order history and suggest the optimal menu in advance for the next time they eat out. The recommended menu extraction unit, for example, analyzes a user's past order history and builds a system that suggests the optimal menu in advance for the next time they eat out. For example, it prioritizes suggesting menus that the user prefers. The recommended menu extraction unit also analyzes patterns of menus preferred by the generation AI based on the user's order history data and suggests the optimal menu for the next time they eat out based on those patterns. For example, it analyzes trends in specific dishes and ingredients. The recommended menu extraction unit also develops a system in which the generation AI suggests the optimal menu for the next time they eat out based on the user's order history. For example, it prioritizes suggesting low-calorie menus that the user prefers. This makes it possible to suggest the optimal menu for the next time they eat out.

[0042] The recommended menu extraction unit can suggest dish customization options (e.g., low-salt, gluten-free) at the time of ordering. The recommended menu extraction unit will, for example, build a system that suggests dish customization options when a user places an order. For example, it will display options such as "low-salt" and "gluten-free." The recommended menu extraction unit will also develop a system in which a generation AI will suggest the optimal customization option at the time of ordering based on the user's health information. For example, if the user has high blood pressure, it will suggest "low-salt." The recommended menu extraction unit will also build a system in which a generation AI will suggest the optimal customization option at the time of ordering based on the user's preferences and allergy information. For example, if the user has a gluten allergy, it will suggest "gluten-free." This makes it possible to suggest customization options according to the user's health condition.

[0043] The recommended menu extraction unit can link the user's order with a smartphone app and automatically record the order details. The recommended menu extraction unit, for example, links the user's order details with the smartphone app and builds a system that automatically records them. For example, the order history is saved in the app. The recommended menu extraction unit also develops a system that links with the smartphone app and automatically records the user's order details. For example, the order details are saved in the cloud. The recommended menu extraction unit also links with the smartphone app and builds a system that automatically records the user's order details. For example, the order history is saved in the app and used as a reference the next time the user orders. This makes it possible to automatically record order details.

[0044] The recommended menu extraction unit can add a function to refer to other users' reviews and ratings when ordering. The recommended menu extraction unit builds a system that adds a function to refer to other users' reviews and ratings when a user places an order. For example, it displays the number of stars and comments in reviews. The recommended menu extraction unit also develops a system in which a generation AI suggests the optimal menu when ordering based on other users' reviews and ratings. For example, it displays highly rated menus preferentially. The recommended menu extraction unit also builds a system that adds a function to refer to other users' reviews and ratings when a user places an order. For example, it displays the number of stars and comments in reviews to make it easier for users to make a selection. This makes it possible to refer to other users' reviews and ratings.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The health maintenance system can further include a preference learning unit that learns the user's food preferences. The preference learning unit collects data on the user's past menu and ingredient selections, and the generation AI analyzes that data to learn the user's preferences. For example, it analyzes the user's frequently selected ingredients and cooking trends and reflects them in the next recommended menu. The preference learning unit also collects feedback provided by the user after meals, and the generation AI uses that data to more precisely learn the user's preferences. For example, it analyzes feedback such as "This dish was delicious" or "I don't like this ingredient." Furthermore, the preference learning unit can suggest special menus according to seasons or events based on the user's preference data. For example, it can recommend menus for special occasions such as Christmas or birthdays. This enables more personalized meal suggestions based on the user's preferences.

[0047] The health maintenance system can further include an environmental monitoring unit that monitors the user's eating environment. The environmental monitoring unit collects environmental data about the place where the user eats, and the generation AI analyzes that data and reflects it in meal recommendations. For example, it monitors the temperature, humidity, and lighting brightness of the eating area to suggest the optimal eating environment. The environment monitoring unit also collects the sound environment when the user eats, and the generation AI makes meal recommendations based on that data. For example, it recommends a menu that allows for relaxation in a quiet environment. Furthermore, the environment monitoring unit monitors the seating arrangement and table height when the user eats, and the generation AI makes meal recommendations based on that data. For example, it suggests seating arrangements that allow the user to eat in a comfortable posture. This makes it possible to suggest more comfortable meals based on the user's eating environment.

[0048] The health maintenance system can further include a timing management unit that manages the user's meal timings. The timing management unit collects the user's daily schedule and activity data, and the generation AI analyzes that data to suggest optimal meal timings. For example, meal timings are adjusted to match the user's work and exercise schedules. The timing management unit also allows the generation AI to suggest optimal meal timings based on the user's sleep data. For example, the generation AI adjusts the timing of breakfast and dinner taking into account the quality and duration of sleep. The timing management unit also allows the generation AI to suggest optimal meal timings based on the user's digestion data. For example, it suggests eating meals at times when digestion is best. This makes it possible to suggest optimal meal timings based on the user's schedule and physical condition.

[0049] The health maintenance system can further include a quality evaluation unit that evaluates the quality of the user's meals. The quality evaluation unit analyzes the nutritional value and calories of the meals consumed by the user, and the generation AI evaluates the quality of the meals based on that data. For example, it evaluates whether the meals are balanced and suggests supplementary menus if necessary nutrients are lacking. The quality evaluation unit also evaluates the taste and appearance of the meals consumed by the user, and the generation AI evaluates the quality of the meals based on that data. For example, it evaluates the deliciousness and good appearance and suggests menus that will increase the user's satisfaction. Furthermore, the quality evaluation unit evaluates the safety of the meals consumed by the user, and the generation AI evaluates the quality of the meals based on that data. For example, it evaluates the freshness of the ingredients and the safety of the cooking method and suggests safe meals. This allows for a comprehensive evaluation of the quality of the user's meals and suggests healthier and more satisfying meals.

[0050] The health maintenance system can further include a cost management unit that manages the user's meal costs. The cost management unit collects the expenses incurred when the user eats out, and the generation AI analyzes that data to manage costs. For example, it suggests cost-effective menus so that the user can enjoy meals within their budget. The cost management unit also collects coupon and discount information that the user can use when eating out, and the generation AI manages costs based on that data. For example, it prioritizes suggesting menus that offer discounts. The cost management unit also records the expenses incurred when the user eats out, and the generation AI manages costs based on that data. For example, it tallys up monthly eating out expenses and provides advice to prevent going over budget. This allows the user to effectively manage their eating out expenses and suggest economical meals.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The physical information storage unit stores the user's physical information and dietary data. For example, it collects the user's height, weight, age, gender, allergy information, etc., and stores them in a database. Dietary data can also be collected by having the user record their daily dietary information. For example, data can be accumulated by entering dietary information using a smartphone app. Step 2: The image reader reads the restaurant menu. For example, image reading glasses are used to take a picture of the menu and save it as digital data. Alternatively, the menu can be photographed using a smartphone camera. For example, an image of the menu can be taken and analyzed using image recognition technology. Step 3: The menu analysis unit analyzes the menu read by the image reader. For example, it analyzes the names of dishes, ingredients, calorie information, etc. listed on the menu. It can also extract text information using OCR technology and store it in a database. Step 4: The recommended menu extraction unit extracts recommended menus from the menu analyzed by the menu analysis unit. For example, based on the user's physical information and dietary data, it extracts multiple meals from the menu that the user currently needs to eat. For example, if the user has not been eating many vegetables recently, the generation AI will prioritize extracting menus that contain a lot of vegetables. Step 5: The voice transmission unit audibly announces the recommended menu items extracted by the recommended menu extraction unit, for example, "Based on your current health condition, we recommend the following menu items: 1st place: Caesar salad, 2nd place: grilled chicken, 3rd place: minestrone soup."

[0053] (Example 2) A health maintenance system according to an embodiment of the present invention is a system in which a generation AI extracts recommended menus to help maintain health and achieve a balanced diet even when eating out. As a result, the health maintenance system can help maintain health by achieving a balanced diet even when eating out.

[0054] A health maintenance system according to an embodiment includes a physical information storage unit, an image reading unit, a menu analysis unit, a recommended menu extraction unit, and an audio transmission unit. The physical information storage unit stores a user's physical information and dietary data. For example, it collects the user's height, weight, age, gender, allergy information, etc., and stores them in a database. Dietary data can also be collected by the user recording their daily dietary habits. For example, data is accumulated by inputting dietary habits using a smartphone app. The image reading unit reads restaurant menus. For example, it may take an image of the menu using image reading glasses and save it as digital data. Alternatively, the menu can be photographed using a smartphone camera. For example, it may take an image of the menu and analyze it using image recognition technology. The menu analysis unit analyzes the menu read by the image reading unit. For example, it may analyze the dish names, ingredients, calorie information, etc., listed on the menu. It may also use OCR technology to extract text information and store it in a database. The recommended menu extraction unit extracts recommended menus from the menu analyzed by the menu analysis unit. For example, it may extract multiple meals that the user currently needs to eat from the menu based on the user's physical information and dietary data. For example, if a user has not eaten many vegetables recently, the generation AI will prioritize extracting menu items that contain a lot of vegetables. The audio transmission unit will audibly announce the recommended menu items extracted by the recommended menu extraction unit. For example, it will announce the following in the form of, "Based on your current health condition, we recommend the following menu items: 1st place: Caesar salad, 2nd place: Grilled chicken, 3rd place: Minestrone soup." This allows the health maintenance system according to the embodiment to help users maintain a balanced diet even when eating out, helping them maintain their health. For example, by selecting healthy menu items when eating out, users can maintain a balanced nutritional profile and stay healthy. Furthermore, the generation AI will always provide recommended menu items based on the latest physical information and dietary data, allowing it to suggest the optimal diet for each individual user.

[0055] The physical information accumulation unit also collects the user's daily activity and sleep data, enabling more precise dietary recommendations. The physical information accumulation unit collects daily activity data from, for example, the user's smartwatch or fitness tracker, and the generation AI analyzes the data to reflect it in dietary recommendations. For example, it calculates the necessary nutrients based on the number of steps taken and calories burned. The physical information accumulation unit also collects the user's sleep data from a smartphone app or wearable device, and the generation AI analyzes the data to reflect it in dietary recommendations. For example, it adjusts the necessary nutrients based on the quality and duration of sleep. The physical information accumulation unit also collects the user's daily stress level from a smartwatch or stress monitoring device, and the generation AI analyzes the data to reflect it in dietary recommendations. For example, if stress is high, it recommends foods with a relaxing effect. This enables precise dietary recommendations that take the user's activity and sleep data into account.

[0056] The physical information storage unit uses voice recognition technology to input meal data, reducing the burden on the user. For example, the physical information storage unit develops a smartphone app that allows users to input meal details by voice, and the generation AI analyzes the voice data and stores the meal data. For example, the user may input "I had toast and coffee for breakfast." The physical information storage unit also uses voice recognition technology to build a system that automatically records data simply by the user speaking the details of their meal. For example, the user speaks the details of their meal into a smart speaker. When the user inputs the details of their meal by voice, the generation AI analyzes them in real time and automatically classifies and stores the meal data. For example, if the user says "I had salad and chicken for lunch," the generation AI analyzes it and stores it in a database. This reduces the burden on the user and makes it easier to input meal data by using voice recognition technology.

[0057] The physical information accumulation unit uses an emotion estimation function to record the user's emotions regarding food and can make meal recommendations based on those emotions. For example, the physical information accumulation unit analyzes the user's emotions while eating using facial expression recognition technology and makes meal recommendations based on that data. For example, a camera captures the user's facial expressions while eating, and the generation AI analyzes the emotions. When inputting the meal contents, the physical information accumulation unit also allows the user to input the emotions they felt regarding the meal via voice, and the generation AI analyzes the emotional data and reflects it in the meal recommendations. For example, saying, "This salad was delicious." The physical information accumulation unit also records the user's emotions regarding food using a smartphone app, and the generation AI makes emotion-based meal recommendations based on that data. For example, the user selects their emotion in the app after eating. This makes it possible to make meal recommendations based on the user's emotions.

[0058] The image reading unit can take pictures of not only menu images but also actual dishes, and analyze them based on their actual appearance and quantity. The image reading unit, for example, uses a camera built into glasses to take pictures of not only menu images but also actual dishes, and the generation AI analyzes the images to accumulate dietary data. For example, it calculates nutritional value based on the appearance and quantity of the dishes. The image reading unit can also use a camera built into glasses to take pictures of actual dishes, and the generation AI analyzes the images to build a system that evaluates the balance of a meal. For example, it analyzes the color and shape of the dish to determine the nutritional balance. The image reading unit can also use a camera built into glasses to take pictures of actual dishes, and the generation AI analyzes the images to calculate the calories of a meal. For example, it calculates calories based on the amount of food and ingredients. This enables accurate analysis based on the actual dish.

[0059] The image reader uses an emotion estimation function to analyze the user's emotions when viewing a menu and prioritize recommending their preferred menu items. The image reader, for example, uses a camera built into the glasses to capture the user's facial expression when viewing a menu, and the generation AI analyzes the facial expression to estimate the emotion. For example, it analyzes expressions such as smiling and surprised. The image reader also uses a sensor built into the glasses to measure the user's biological reactions (e.g., heart rate and electrodermal activity) when viewing a menu, and the generation AI analyzes the data to estimate the emotion. The image reader also uses a microphone built into the glasses to record the user's voice reaction when viewing a menu, and the generation AI analyzes the voice to estimate the emotion. For example, it analyzes the tone of voice and the choice of words. This makes it possible to recommend menu items based on the user's preferences.

[0060] The recommended menu extraction unit can propose seasonal menus by taking into account ingredients according to the season and weather. For example, the recommended menu extraction unit builds a seasonal ingredient database, and the generation AI proposes seasonal menus based on that data. For example, in spring, it recommends menus using fresh vegetables and fruits. The recommended menu extraction unit also collects weather data in real time, and the generation AI proposes menus appropriate for the weather based on that data. For example, on cold days, it recommends hot soups and stews. The recommended menu extraction unit also considers the nutritional value of ingredients according to the season and weather, and the generation AI proposes healthy menus based on that data. For example, in summer, it recommends menus that allow for hydration. This makes it possible to propose menus that are appropriate for the season.

[0061] The recommended menu extraction unit can prioritize recommending particularly preferred menus based on the user's past meal history. The recommended menu extraction unit, for example, analyzes the user's past meal history and builds a system that prioritizes recommending particularly preferred menus. For example, it prioritizes displaying menus that the user frequently orders. The recommended menu extraction unit also analyzes patterns of menus preferred by the generation AI based on the user's meal history data and suggests recommended menus based on those patterns. For example, it analyzes trends in specific ingredients and dishes. The recommended menu extraction unit also proposes recommended menus based on the user's past meal history, taking into account the nutritional value and calories of menus preferred by the generation AI. For example, it prioritizes displaying low-calorie menus that the user prefers. This makes it possible to recommend menus based on the user's preferences.

[0062] The recommended menu extraction unit can use the emotion estimation function to recommend a menu that matches the user's current mood. For example, the recommended menu extraction unit analyzes the user's current mood using facial expression recognition technology and recommends a menu that matches the mood based on the data. For example, when the user wants to relax, it recommends light meals or desserts. The recommended menu extraction unit also analyzes the user's current mood using voice recognition technology and recommends a menu that matches the mood based on the data. For example, when the user is feeling low, it recommends a nutritious menu. The recommended menu extraction unit also analyzes the user's current mood using a biosensor and recommends a menu that matches the mood based on the data. For example, when the user is under a lot of stress, it recommends a menu that has a relaxing effect. This makes it possible to recommend menus based on the user's mood.

[0063] The voice transmission unit can include nutritional information and health benefits of the dishes in the voice guidance, allowing the user to understand the reason for their selection. For example, the voice transmission unit may include nutritional information of the dishes in the voice guidance, building a system that allows the user to understand the reason for their selection. For example, the voice transmission unit may provide a prompt saying, "This dish is rich in vitamin C." The voice transmission unit may also develop a system that includes health benefits of the dishes in the voice guidance, allowing the user to understand the reason for their selection. For example, the voice transmission unit may provide a prompt saying, "This dish will boost your immune system." The voice transmission unit may also develop a system that includes nutritional information and health benefits of the dishes in the voice guidance, allowing the user to understand the reason for their selection. For example, the voice transmission unit may provide a prompt saying, "This dish is low in calories and perfect for dieting." This makes it easier for the user to understand the reason for their selection.

[0064] The voice transmission unit can use its emotion estimation function to analyze the user's reactions in real time and dynamically adjust the contents of the recommended menu. For example, the voice transmission unit will capture the user's facial expression with a camera during voice guidance, and the generation AI will analyze the expression to infer the user's emotion and dynamically adjust the contents of the recommended menu. For example, the menu will be changed if the user expresses surprise. The voice transmission unit will also record the user's vocal responses during voice guidance, and the generation AI will analyze the voice to infer the user's emotion and dynamically adjust the contents of the recommended menu. For example, a menu item will be highlighted if the user shows interest. The voice transmission unit will also use sensors to measure the user's biological reactions (e.g., heart rate and electrodermal activity) during voice guidance, and the generation AI will analyze the data to infer the user's emotion and dynamically adjust the contents of the recommended menu. This will enable dynamic menu adjustment based on the user's reactions.

[0065] The voice transmission unit can provide information that enhances the enjoyment of a meal by including the history and cultural background of the dish in the voice guidance. The voice transmission unit, for example, builds a system that includes the history and cultural background of the dish in the voice guidance to allow the user to enjoy a meal. For example, it may provide guidance such as, "This dish is a traditional Italian dish." The voice transmission unit also develops a system that includes the cultural background of the dish in the voice guidance to allow the user to enjoy a meal. For example, it may provide guidance such as, "This dish is often eaten at celebratory occasions." The voice transmission unit also builds a system that includes the history and cultural background of the dish in the voice guidance to allow the user to enjoy a meal. For example, it may provide guidance such as, "This dish is a recipe that has been passed down since ancient Roman times." This makes it possible to provide information that enhances the enjoyment of a meal.

[0066] The voice transmission unit uses an emotion estimation function to measure the user's level of concentration when listening to voice guidance and can provide information at the optimal timing. For example, the voice transmission unit will capture the user's facial expression with a camera while voice guidance is being listened to, and a generation AI will analyze that expression to estimate the user's level of concentration, building a system that provides information at the optimal timing. For example, important information will be conveyed when the user is concentrating. The voice transmission unit will also record the user's vocal responses during voice guidance, and a generation AI will analyze that voice to estimate the user's level of concentration, developing a system that provides information at the optimal timing. For example, detailed information will be provided when the user shows interest. The voice transmission unit will also use sensors to measure the user's biological responses (e.g., heart rate and electrodermal activity) during voice guidance, and a generation AI will analyze that data to estimate the user's level of concentration, building a system that provides information at the optimal timing. This will enable the provision of information based on the user's level of concentration.

[0067] The recommended menu extraction unit can customize menus according to the user's budget and time. For example, the recommended menu extraction unit constructs a system in which a user's budget information is input and a generation AI recommends menus that can be selected within that budget based on that data. For example, menus in a price range according to the budget are displayed. The recommended menu extraction unit also develops a system in which a user's time information is input and a generation AI recommends menus that can be prepared in a short time based on that data. For example, menus with short cooking times are displayed preferentially. The recommended menu extraction unit also constructs a system in which a generation AI customizes and suggests the optimal menu according to the user's budget and time. For example, a nutritionally balanced menu within the budget is recommended. This makes it possible to customize menus according to the user's budget and time.

[0068] The recommended menu extraction unit can use the emotion estimation function to analyze the user's emotions toward specific ingredients and recommend menus that include many of the user's favorite ingredients. For example, the recommended menu extraction unit can analyze the user's emotions toward specific ingredients using facial expression recognition technology and recommend menus that include many of the user's favorite ingredients based on the data. For example, it can analyze smiling expressions. The recommended menu extraction unit can also analyze the user's emotions toward specific ingredients using voice recognition technology and recommend menus that include many of the user's favorite ingredients based on the data. For example, it can analyze positive language usage. The recommended menu extraction unit can also analyze the user's emotions toward specific ingredients using a biosensor and recommend menus that include many of the user's favorite ingredients based on the data. For example, it can analyze heart rate and electrodermal activity. This makes it possible to recommend menus based on the user's preferences.

[0069] The voice transmission unit provides voice guidance in multiple languages, and can also accommodate foreign users. For example, the voice transmission unit builds a system that provides voice guidance in multiple languages, and can also accommodate foreign users. For example, voice guidance is provided in English and Chinese. The voice transmission unit also develops a system that makes voice guidance multilingual, and can also accommodate foreign users. For example, voice guidance is provided in a language selected by the user. The voice transmission unit also builds a system that provides voice guidance in multiple languages, and can also accommodate foreign users. For example, menu contents are announced in English and French. This makes it possible to accommodate foreign users.

[0070] The recommended menu extraction unit can propose locally-focused menus that incorporate local specialties and ingredients. For example, the recommended menu extraction unit builds a database of local specialties for each region, and the generation AI proposes locally-focused menus based on that data. For example, it recommends menus that use fresh local vegetables and fruits. The recommended menu extraction unit also builds a system that prioritizes recommending menus that use local ingredients. For example, it recommends dishes that use fish caught at local fishing ports. The recommended menu extraction unit also develops a system that revitalizes the local economy by recommending menus that use local specialties and ingredients. For example, it recommends dishes that use local agricultural products. This makes it possible to propose locally-focused menus.

[0071] The voice transmission unit can customize the voice guidance to a user's preferred voice or character. For example, the voice transmission unit builds a voice guidance system that allows the user to select their preferred voice or character. For example, guidance is provided in the voice of an anime character. The voice transmission unit also develops a system that customizes the voice guidance to the user's preferences. For example, guidance is provided in the voice of a celebrity that the user likes. The voice transmission unit also builds a voice guidance system customized to the user's preferred voice or character. For example, recommended menus are provided in a voice selected by the user. This makes it possible to provide voice guidance according to the user's preferences.

[0072] The recommended menu extraction unit can analyze a user's order history and suggest the optimal menu in advance for the next time they eat out. The recommended menu extraction unit, for example, analyzes a user's past order history and builds a system that suggests the optimal menu in advance for the next time they eat out. For example, it prioritizes suggesting menus that the user prefers. The recommended menu extraction unit also analyzes patterns of menus preferred by the generation AI based on the user's order history data and suggests the optimal menu for the next time they eat out based on those patterns. For example, it analyzes trends in specific dishes and ingredients. The recommended menu extraction unit also develops a system in which the generation AI suggests the optimal menu for the next time they eat out based on the user's order history. For example, it prioritizes suggesting low-calorie menus that the user prefers. This makes it possible to suggest the optimal menu for the next time they eat out.

[0073] The recommended menu extraction unit can suggest dish customization options (e.g., low-salt, gluten-free) at the time of ordering. The recommended menu extraction unit will, for example, build a system that suggests dish customization options when a user places an order. For example, it will display options such as "low-salt" and "gluten-free." The recommended menu extraction unit will also develop a system in which a generation AI will suggest the optimal customization option at the time of ordering based on the user's health information. For example, if the user has high blood pressure, it will suggest "low-salt." The recommended menu extraction unit will also build a system in which a generation AI will suggest the optimal customization option at the time of ordering based on the user's preferences and allergy information. For example, if the user has a gluten allergy, it will suggest "gluten-free." This makes it possible to suggest customization options according to the user's health condition.

[0074] The recommended menu extraction unit uses an emotion estimation function to analyze the level of satisfaction a user feels when ordering and can reflect this in the next recommendation. For example, the recommended menu extraction unit uses facial expression recognition technology to analyze the level of satisfaction a user feels when ordering and builds a system that reflects this data in the next recommendation. For example, it analyzes smiling expressions. The recommended menu extraction unit also uses voice recognition technology to analyze the level of satisfaction a user feels when ordering and develops a system that reflects this data in the next recommendation. For example, it analyzes positive language usage. The recommended menu extraction unit also uses a biosensor to analyze the level of satisfaction a user feels when ordering and builds a system that reflects this data in the next recommendation. For example, it analyzes heart rate and electrodermal activity. This makes it possible to make the next recommendation based on the user's satisfaction.

[0075] The recommended menu extraction unit can link the user's order with a smartphone app and automatically record the order details. The recommended menu extraction unit, for example, links the user's order details with the smartphone app and builds a system that automatically records them. For example, the order history is saved in the app. The recommended menu extraction unit also develops a system that links with the smartphone app and automatically records the user's order details. For example, the order details are saved in the cloud. The recommended menu extraction unit also links with the smartphone app and builds a system that automatically records the user's order details. For example, the order history is saved in the app and used as a reference the next time the user orders. This makes it possible to automatically record order details.

[0076] The recommended menu extraction unit can add a function to refer to other users' reviews and ratings when ordering. The recommended menu extraction unit builds a system that adds a function to refer to other users' reviews and ratings when a user places an order. For example, it displays the number of stars and comments in reviews. The recommended menu extraction unit also develops a system in which a generation AI suggests the optimal menu when ordering based on other users' reviews and ratings. For example, it displays highly rated menus preferentially. The recommended menu extraction unit also builds a system that adds a function to refer to other users' reviews and ratings when a user places an order. For example, it displays the number of stars and comments in reviews to make it easier for users to make a selection. This makes it possible to refer to other users' reviews and ratings.

[0077] The recommended menu extraction unit uses an emotion estimation function to analyze the anxieties and questions that users feel when ordering and can provide appropriate support. For example, the recommended menu extraction unit uses facial expression recognition technology to analyze the anxieties and questions that users feel when ordering and builds a system that provides appropriate support based on that data. For example, it analyzes anxious facial expressions. The recommended menu extraction unit also uses voice recognition technology to analyze the anxieties and questions that users feel when ordering and develops a system that provides appropriate support based on that data. For example, it analyzes the language used to express doubts. The recommended menu extraction unit also uses biosensors to analyze the anxieties and questions that users feel when ordering and builds a system that provides appropriate support based on that data. For example, it analyzes heart rate and electrodermal activity. This makes it possible to provide appropriate support for users' anxieties and questions.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The health maintenance system can further include a preference learning unit that learns the user's food preferences. The preference learning unit collects data on the user's past menu and ingredient selections, and the generation AI analyzes that data to learn the user's preferences. For example, it analyzes the user's frequently selected ingredients and cooking trends and reflects them in the next recommended menu. The preference learning unit also collects feedback provided by the user after meals, and the generation AI uses that data to more precisely learn the user's preferences. For example, it analyzes feedback such as "This dish was delicious" or "I don't like this ingredient." Furthermore, the preference learning unit can suggest special menus according to seasons or events based on the user's preference data. For example, it can recommend menus for special occasions such as Christmas or birthdays. This enables more personalized meal suggestions based on the user's preferences.

[0080] The health maintenance system can further include an environmental monitoring unit that monitors the user's eating environment. The environmental monitoring unit collects environmental data about the place where the user eats, and the generation AI analyzes that data and reflects it in meal recommendations. For example, it monitors the temperature, humidity, and lighting brightness of the eating area to suggest the optimal eating environment. The environment monitoring unit also collects the sound environment when the user eats, and the generation AI makes meal recommendations based on that data. For example, it recommends a menu that allows for relaxation in a quiet environment. Furthermore, the environment monitoring unit monitors the seating arrangement and table height when the user eats, and the generation AI makes meal recommendations based on that data. For example, it suggests seating arrangements that allow the user to eat in a comfortable posture. This makes it possible to suggest more comfortable meals based on the user's eating environment.

[0081] The health maintenance system can further include a timing management unit that manages the user's meal timings. The timing management unit collects the user's daily schedule and activity data, and the generation AI analyzes that data to suggest optimal meal timings. For example, meal timings are adjusted to match the user's work and exercise schedules. The timing management unit also allows the generation AI to suggest optimal meal timings based on the user's sleep data. For example, the generation AI adjusts the timing of breakfast and dinner taking into account the quality and duration of sleep. The timing management unit also allows the generation AI to suggest optimal meal timings based on the user's digestion data. For example, it suggests eating meals at times when digestion is best. This makes it possible to suggest optimal meal timings based on the user's schedule and physical condition.

[0082] The health maintenance system can further include a quality evaluation unit that evaluates the quality of the user's meals. The quality evaluation unit analyzes the nutritional value and calories of the meals consumed by the user, and the generation AI evaluates the quality of the meals based on that data. For example, it evaluates whether the meals are balanced and suggests supplementary menus if necessary nutrients are lacking. The quality evaluation unit also evaluates the taste and appearance of the meals consumed by the user, and the generation AI evaluates the quality of the meals based on that data. For example, it evaluates the deliciousness and good appearance and suggests menus that will increase the user's satisfaction. Furthermore, the quality evaluation unit evaluates the safety of the meals consumed by the user, and the generation AI evaluates the quality of the meals based on that data. For example, it evaluates the freshness of the ingredients and the safety of the cooking method and suggests safe meals. This allows for a comprehensive evaluation of the quality of the user's meals and suggests healthier and more satisfying meals.

[0083] The health maintenance system can further include a cost management unit that manages the user's meal costs. The cost management unit collects the expenses incurred when the user eats out, and the generation AI analyzes that data to manage costs. For example, it suggests cost-effective menus so that the user can enjoy meals within their budget. The cost management unit also collects coupon and discount information that the user can use when eating out, and the generation AI manages costs based on that data. For example, it prioritizes suggesting menus that offer discounts. The cost management unit also records the expenses incurred when the user eats out, and the generation AI manages costs based on that data. For example, it tallys up monthly eating out expenses and provides advice to prevent going over budget. This allows the user to effectively manage their eating out expenses and suggest economical meals.

[0084] The health maintenance system can further include a satisfaction evaluation unit that evaluates the user's satisfaction with the meal. The satisfaction evaluation unit analyzes the user's emotions while eating, and the generation AI evaluates the user's satisfaction with the meal based on that data. For example, it analyzes facial expressions and voices while eating to evaluate the user's level of satisfaction. The satisfaction evaluation unit also collects feedback provided by the user after eating, and the generation AI evaluates the user's satisfaction based on that data. For example, it analyzes feedback such as "This dish was delicious" or "I don't like this ingredient." Furthermore, the satisfaction evaluation unit can reflect this in its next meal suggestions based on the user's satisfaction data. For example, it may prioritize menus with high satisfaction ratings. This enables more personalized meal suggestions based on the user's satisfaction.

[0085] The health maintenance system can further include a stress evaluation unit that evaluates the user's stress level during meals. The stress evaluation unit analyzes the user's emotions while eating, and the generation AI evaluates the stress level based on that data. For example, it analyzes facial expressions and voice while eating to evaluate how relaxed the user is. The stress evaluation unit also collects feedback provided by the user after eating, and the generation AI evaluates the stress level based on that data. For example, it analyzes feedback such as "This dish helped me relax" or "This ingredient made me feel stressed." Furthermore, the stress evaluation unit can reflect this in its next meal suggestions based on the user's stress level data. For example, when stress is high, it will prioritize suggestions of menus that have a relaxing effect. This makes it possible to suggest more relaxing meals based on the user's stress level.

[0086] The health maintenance system can further include a motivation evaluation unit that evaluates the user's motivation for eating. The motivation evaluation unit analyzes the user's emotions when eating, and the generation AI evaluates the user's motivation based on that data. For example, it analyzes facial expressions and voice while eating to evaluate the user's motivation for eating. The motivation evaluation unit also collects feedback provided by the user after eating, and the generation AI evaluates the user's motivation based on that data. For example, it analyzes feedback such as "I was looking forward to eating this dish" or "I didn't really want to eat this ingredient." Furthermore, the motivation evaluation unit can reflect this in suggestions for the next meal based on the user's motivation data. For example, it may prioritize suggestions for menus that highly motivated the user. This makes it possible to make suggestions that will encourage the user to eat more enthusiastically based on their motivation.

[0087] The health maintenance system can further include a happiness evaluation unit that evaluates the user's happiness level during a meal. The happiness evaluation unit analyzes the user's emotions while eating, and the generation AI evaluates the happiness level based on that data. For example, it analyzes facial expressions and voice while eating to evaluate how happy the user feels. The happiness evaluation unit also collects feedback provided by the user after eating, and the generation AI evaluates the happiness level based on that data. For example, it analyzes feedback such as "This dish made me feel happy" or "I didn't like this ingredient very much." Furthermore, the happiness evaluation unit can reflect this in suggestions for the next meal based on the user's happiness data. For example, it may prioritize suggestions for menus that result in a high happiness level. This makes it possible to make suggestions that will help the user feel happier while eating based on their happiness level.

[0088] The health maintenance system can further include an energy evaluation unit that evaluates the energy level of the user's meal. The energy evaluation unit analyzes the user's emotions while eating, and the generation AI evaluates the energy level based on that data. For example, it analyzes facial expressions and voice while eating to evaluate how energetic the user is. The energy evaluation unit also collects feedback provided by the user after eating, and the generation AI evaluates the energy level based on that data. For example, it analyzes feedback such as "This dish gave me energy" or "This ingredient didn't give me much energy." Furthermore, the energy evaluation unit can reflect this in its next meal suggestions based on the user's energy level data. For example, it may prioritize menus with high energy levels. This makes it possible to suggest more energizing meals based on the user's energy level.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The physical information storage unit stores the user's physical information and dietary data. For example, it collects the user's height, weight, age, gender, allergy information, etc., and stores them in a database. Dietary data can also be collected by having the user record their daily dietary information. For example, data can be accumulated by entering dietary information using a smartphone app. Step 2: The image reader reads the restaurant menu. For example, image reading glasses are used to take a picture of the menu and save it as digital data. Alternatively, the menu can be photographed using a smartphone camera. For example, an image of the menu can be taken and analyzed using image recognition technology. Step 3: The menu analysis unit analyzes the menu read by the image reader. For example, it analyzes the names of dishes, ingredients, calorie information, etc. listed on the menu. It can also extract text information using OCR technology and store it in a database. Step 4: The recommended menu extraction unit extracts recommended menus from the menu analyzed by the menu analysis unit. For example, based on the user's physical information and dietary data, it extracts multiple meals from the menu that the user currently needs to eat. For example, if the user has not been eating many vegetables recently, the generation AI will prioritize extracting menus that contain a lot of vegetables. Step 5: The voice transmission unit audibly announces the recommended menu items extracted by the recommended menu extraction unit, for example, "Based on your current health condition, we recommend the following menu items: 1st place: Caesar salad, 2nd place: grilled chicken, 3rd place: minestrone soup."

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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]

[0158] 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 physical information storage unit that stores physical information and dietary data of a user; an image reading unit that reads restaurant menus; a menu analysis unit that analyzes the menu read by the image reading unit; a recommended menu extraction unit that extracts recommended menus from the menu analyzed by the menu analysis unit; a voice transmission unit that transmits the recommended menu extracted by the recommended menu extraction unit by voice. A system characterized by:

2. The physical information storage unit The system also collects the user's daily activity and sleep data to provide more precise dietary recommendations.

2. The system of claim 1.

3. The physical information accumulation unit Meal data is input using voice recognition technology, reducing the burden on the user.

2. The system of claim 1.

4. The physical information accumulation unit Recording the user's feelings about food and making food recommendations based on the feelings.

2. The system of claim 1.

5. The image reading unit In addition to the menu image, the actual food is photographed and analyzed based on its actual appearance and quantity.

2. The system of claim 1.

6. The image reading unit Analyzing the emotions of the user when viewing the menu and preferentially recommending the preferred menu 2. The system of claim 1.

7. The recommended menu extraction unit Considering ingredients that are appropriate for the season and weather, we propose seasonal menus.

2. The system of claim 1.

8. The recommended menu extraction unit Based on the user's past meal history, the menu that is particularly preferred is preferentially recommended.

2. The system of claim 1.

Images