System

Abstract

An object of a system according to an embodiment is to grasp a deviation of nutrients in individual meals in real time and propose an appropriate meal schedule.SOLUTION: A system according to an embodiment includes a camera, a generation AI, a database, an analyzer, and a schedule determiner. The camera is mounted on the smart glasses. The generation AI analyzes the image of the foodstuff captured by the camera. The database is referred to by the generated AI. The analyzing unit analyzes nutrients based on the information of the foods analyzed by the generation AI. The schedule determination unit determines a meal schedule on the basis of the uningested nutrient specified by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to grasp the nutritional imbalances in individual meals in real time and propose appropriate meal schedules.

[0005] The system according to the embodiment aims to grasp the nutrient imbalance in each individual meal in real time and propose an appropriate meal schedule. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, a generation AI, a database, an analysis unit, and a schedule determination unit. The camera is mounted on smart glasses. The generation AI analyzes food images captured by the camera. The generation AI references the database. The analysis unit analyzes nutrients based on the food information analyzed by the generation AI. The schedule determination unit determines a meal schedule based on the nutrients that have not been ingested and that have been identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the nutrient imbalance in each individual meal in real time and propose an appropriate meal schedule. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The nutrition management system according to an embodiment of the present invention uses a camera mounted on smart glasses to recognize images of food being eaten, and a generation AI analyzes the nutrients being ingested, helping users achieve a balanced diet. This allows the nutrition management system to grasp the user's nutritional balance in real time and propose an appropriate meal schedule.

[0029] A nutrition management system according to an embodiment includes a camera, a generation AI, a database, an analysis unit, and a schedule determination unit. The camera is mounted on smart glasses and captures food in real time as the user places it in their mouth. For example, the camera captures high-resolution images of the food and sends them to the generation AI. The camera also uses a wide-angle lens to capture a wide range of food. Furthermore, the camera can capture clear images even in low-light environments. The generation AI analyzes the food images captured by the camera. For example, the generation AI identifies the type and shape of food using a text generation AI (e.g., LLM). The generation AI can also estimate the ingredients used from the quantity and appearance of food using a multimodal generation AI. The generation AI can also analyze the texture and temperature of food using image recognition technology. For example, the text generation AI has learned a large amount of image data and has advanced image recognition capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses texture analysis technology to analyze the texture of the food's surface and a temperature sensor to detect the food's temperature. The database, which is referenced by the generation AI, stores information about food. For example, the database includes nutritional information for processed foods and general recipe information. The database is also regularly updated, with new food information added. Furthermore, the database is stored on the cloud and can be accessed by the generation AI in real time. The analysis unit analyzes the nutrients the user is consuming based on the food information analyzed by the generation AI. For example, the analysis unit calculates the amount of vitamins and minerals contained in the food to understand the user's nutritional intake. The analysis unit can also refer to the user's past dietary history to identify nutrient deficiencies or excesses. Furthermore, the analysis unit monitors the user's physical condition and health status in real time and analyzes nutrients based on that data. The schedule determination unit determines a meal schedule based on the nutrients that the analysis unit identifies as being inadequate. For example, the schedule determination unit suggests a meal plan that includes foods rich in vitamin D.The schedule determination unit can also propose a customized meal schedule taking into account the user's lifestyle and food preferences. Furthermore, the schedule determination unit can also propose a meal schedule that incorporates seasonal ingredients taking into account the season and local ingredients. This allows the nutrition management system according to the embodiment to grasp the user's nutritional balance in real time and propose an appropriate meal schedule. For example, the output unit displays the proposed meal schedule to the user via a smartphone app or web application. If the user desires feedback in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0030] When analyzing food images, generative AI can detect the food's temperature using an infrared sensor and analyze the food's texture using image analysis technology. For example, when analyzing food images, generative AI can detect the food's temperature using an infrared sensor and identify the type of food based on the temperature information, such as hot dishes or cold desserts. To detect the food's texture, generative AI can also analyze the surface texture of the food using image analysis technology, recognizing, for example, the crispness of fried food or the smoothness of soup. Furthermore, by combining a temperature sensor with texture analysis, generative AI can more accurately grasp the state of food, for example, recognizing whether bread is freshly baked or the thawed state of frozen food. This enables more accurate food recognition by taking into account the food's temperature and texture.

[0031] When analyzing food images, the generative AI can detect the lighting conditions of the dining environment, adjust brightness and color temperature, and analyze background information to accurately extract the food's contours. For example, when analyzing food images, the generative AI can detect the lighting conditions of the user's dining environment and adjust brightness and color temperature to improve recognition accuracy. The generative AI also analyzes background information of the dining environment, taking into account, for example, the color of the tablecloth and tableware, to accurately extract the food's contours. The generative AI also incorporates algorithms that correct for variations in lighting and background in real time, enabling highly accurate food recognition even in scenes such as outdoor picnics and restaurant dining. This improves the accuracy of food recognition by taking the dining environment into account.

[0032] When estimating food quantities, generative AI takes into account the density and mass of the food, uses image analysis technology to calculate the food's volume, and combines this data with density data to estimate the food's quantity. For example, when analyzing food images, generative AI considers the food's density to estimate the quantity. For example, it distinguishes between light foods like bread and cake and heavy foods like meat and fish. To estimate the food's mass, generative AI also uses image analysis technology to calculate the food's volume and combines this data with density data to calculate the exact quantity. For example, it estimates the amount of soup based on the size and depth of the bowl. Generative AI also incorporates an algorithm that adjusts the food quantity in real time based on density and mass data, allowing it to accurately estimate the amount of salad ingredients, for example. By taking food density and mass into account, more accurate food quantity estimations become possible.

[0033] When guessing ingredients from the appearance of food, the generation AI can refer to the user's past eating history and reflect the user's eating habits. For example, the generation AI can refer to the user's past eating history to guess ingredients from the appearance of food. For example, it can prioritize recognizing ingredients that the user eats frequently. The generation AI can also learn the user's eating habits based on the eating history data and identify ingredients from the appearance of food. For example, it can take into account the user's preferred seasonings and cooking methods. The generation AI can also update the past eating history in real time and guess food ingredients based on the latest data. For example, if a new ingredient is tried, that information is immediately reflected. This makes it possible to guess ingredients that reflect the user's eating habits by referring to the user's past eating history.

[0034] Generative AI can analyze food's aroma and sound to infer ingredients from the amount and appearance of food. For example, to analyze food's aroma, generative AI uses sensors installed in smart glasses to collect aroma data and identify the ingredients of the food. For example, it can detect the aroma of spices to infer the ingredients of curry. Generative AI can also analyze food sounds by installing microphones in smart glasses to collect the sounds of chewing food and cooking to identify ingredients. For example, it can detect the crackling sound of fried food to infer the type of fried food. Generative AI can also develop algorithms that combine aroma and sound data with image analysis to more accurately infer food ingredients. For example, it can identify the type of fish based on the aroma and sound of grilled fish. This makes it possible to more accurately infer ingredients by using food's aroma and sound in analysis.

[0035] When inferring ingredients from the amount and appearance of food, generative AI can link cameras with other sensors to analyze the condition of food from multiple angles. For example, generative AI could install a temperature sensor in smart glasses to collect food temperature data and identify ingredients. For example, it could infer ingredients based on the temperature of hot soup or cold dessert. Generative AI could also link other sensors (such as a humidity sensor) to smart glasses and analyze food humidity data to identify ingredients. For example, it could infer cooking methods and ingredients based on foods with high humidity levels. Generative AI could also combine data from temperature and humidity sensors with image analysis to analyze the condition of food from multiple angles and make more accurate ingredient inferences. For example, it could identify ingredients based on the temperature and humidity of freshly baked bread. In this way, linking cameras with other sensors enables multifaceted analysis of food conditions and more accurate ingredient inferences.

[0036] When referencing the database, the generative AI can automatically incorporate the latest nutritional research data and reflect it in the analysis. For example, when referencing the database, the generative AI automatically incorporates the latest nutritional research data and updates the nutritional information of foods. For example, it reflects new vitamin and mineral research results. The generative AI also regularly adds the latest nutritional research data to the database and analyzes the nutritional value of foods based on that data. For example, it adds the nutritional value of new ingredients to the database. The generative AI also incorporates the latest nutritional research data in real time and builds a system that instantly updates the nutritional information of foods. For example, it updates the database every time new research results are published. This allows for more accurate nutrient analysis by incorporating the latest nutritional research data.

[0037] When referencing the database, the generation AI can obtain detailed information such as the food's manufacturer and production date, and reflect this in the nutrient analysis. For example, when referencing the database, the generation AI can obtain detailed information such as the food's manufacturer and production date, and reflect this in the nutrient analysis. For example, it will prioritize analyzing the nutritional value of foods with more recent production dates. The generation AI can also add detailed information such as the food's manufacturer and production date to the database, and perform nutrient analysis based on that data. For example, it can perform a detailed analysis of the nutritional value of foods from a specific manufacturer. The generation AI can also build a system that more accurately analyzes the nutritional composition information of foods based on information about the manufacturer and production date. For example, it can correct the nutritional value of foods with older production dates. In this way, by obtaining detailed information such as the food's manufacturer and production date, more accurate nutrient analysis is possible.

[0038] The generating AI can monitor the user's physical condition and health status in real time and analyze nutrients based on that data. For example, the generating AI can monitor the user's physical condition data (e.g., heart rate and blood pressure) in real time and analyze nutrients based on that data. For example, it can suggest nutritional supplements after exercise. The generating AI can also analyze health status (e.g., stress level) in real time and analyze nutrients based on that data. For example, it can suggest foods that have a relaxing effect when stress is high. The generating AI can also collect data on physical condition and health status in real time and build a system that analyzes nutrients based on that data. For example, it can suggest energy replenishment when sleep is lacking. This makes it possible to monitor the user's physical condition and health status in real time, enabling more accurate nutrient analysis.

[0039] Generative AI can compare nutrient analysis results with a user's long-term health data to predict health risks. For example, generative AI can compare nutrient analysis results with a user's long-term health data to build a system that predicts health risks. For example, it can predict future nutritional deficiencies based on past data. Generative AI can also analyze nutrient analysis results based on long-term health data (e.g., blood test results) to identify health risks. For example, it can predict bone density loss due to vitamin D deficiency. Generative AI can also integrate nutrient analysis results with long-term health data to develop a system that predicts health risks in real time. For example, it can predict diabetes risk and suggest appropriate nutritional intake. This makes it possible to predict health risks by comparing with long-term health data.

[0040] Generative AI can visualize nutrient analysis results and provide them to users. Generative AI, for example, builds a system that visualizes nutrient analysis results and provides them to users. For example, it displays nutrient intake status using graphs and charts. Generative AI also allows users to intuitively understand the visualized nutrient analysis results. For example, it uses color coding and icons to show nutrient balance. Generative AI also develops a system that visualizes nutrient analysis results in real time, allowing users to understand them immediately. For example, it displays nutrient intake status in dashboard format. This visualization allows users to intuitively understand the nutrient analysis results.

[0041] The generation AI can propose a customized meal schedule by taking into account the user's lifestyle and dietary preferences. For example, the generation AI can analyze the user's lifestyle data (e.g., work schedule and exercise habits) and propose a customized meal schedule based on that data. For example, it can provide a simple meal plan for busy days. The generation AI can also propose a customized meal schedule by taking into account the user's dietary preferences. For example, it can propose a menu that includes many of the user's favorite ingredients. The generation AI can also update lifestyle and dietary preference data in real time, building a system that proposes a customized meal schedule based on the latest information. For example, it can provide a meal plan that matches new hobbies or habits. This makes it possible to propose a customized meal schedule by taking into account the user's lifestyle and dietary preferences.

[0042] Generative AI can incorporate seasonal ingredients into meal schedules by taking into account the season and local ingredients. For example, generative AI can analyze seasonal and local data to propose meal schedules that incorporate seasonal ingredients. For example, in spring, it can provide menus that include plenty of fresh vegetables and fruits. Generative AI can also propose meal schedules that take into account local specialties and traditional dishes. For example, it can provide special menus to coincide with local festivals and events. Generative AI can also build a system that updates seasonal and local data in real time and proposes meal schedules that incorporate seasonal ingredients based on the latest information. For example, it can provide menus based on the availability of ingredients in each season. This makes it possible to propose meal schedules that incorporate seasonal ingredients by taking into account the season and local ingredients.

[0043] The generating AI can link meal schedules with other health management apps to provide comprehensive health management. For example, the generating AI can link meal schedules with other health management apps to build a system for comprehensive health management. For example, it can link with an exercise app to provide meal plans based on the amount of exercise. The generating AI can also adjust meal schedules based on data obtained from the health management app (e.g., sleep data). For example, it can suggest energy replenishment when sleep is lacking. The generating AI can also share data with other health management apps in real time to develop a system for comprehensive health management. For example, it can link with a stress management app to provide meal plans with a relaxing effect. This makes it possible to provide comprehensive health management by linking with other health management apps.

[0044] Generative AI can share meal schedule suggestions with family and friends, enabling them to work together to achieve a healthy diet. For example, generative AI could build a system that shares the suggested meal schedule with family and friends. For example, it could suggest menus that take into account the nutritional balance of the entire family. Generative AI could also develop an app that uses the shared meal schedule to enable family and friends to work together to achieve a healthy diet. For example, it could provide a function to create a joint shopping list. Generative AI could also build a system that updates the meal schedule sharing function in real time and shares the latest information with family and friends. For example, it could adjust the menu depending on the availability of ingredients. This makes it possible to work together to achieve a healthy diet by sharing with family and friends.

[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 nutrition management system may further include an exercise data acquisition unit that acquires the user's exercise data. For example, exercise data may be acquired from a smartwatch or fitness tracker to ascertain the user's exercise volume and calorie expenditure. The exercise data acquisition unit may also learn the user's exercise habits and suggest appropriate nutritional supplementation. For example, it may suggest taking protein after exercise or replenishing energy before exercise. Furthermore, the exercise data acquisition unit may provide a meal plan based on the user's exercise goals. For example, it may suggest a high-protein diet for a user who engages in strength training. This allows for more comprehensive health management by taking exercise data into consideration.

[0047] The nutrition management system may further include a sleep data acquisition unit that acquires the user's sleep data. For example, sleep data may be acquired from a smartwatch or sleep tracker to understand the user's sleep patterns and quality. The sleep data acquisition unit may also suggest appropriate nutritional supplementation based on the user's sleep state. For example, it may suggest energy replenishment when the user is sleep deprived and foods that promote good quality sleep. Furthermore, the sleep data acquisition unit may also provide a meal plan based on the user's sleep goals. For example, it may suggest herbal teas with relaxing effects or foods containing melatonin. This allows for more comprehensive health management by taking sleep data into consideration.

[0048] The nutrition management system can further include a stress monitoring unit that monitors the user's stress level. For example, it measures heart rate and electrodermal activity to grasp the user's stress level in real time. The stress monitoring unit can also suggest foods with a relaxing effect based on the user's stress state. For example, it can suggest chamomile tea or dark chocolate when stress is high. Furthermore, the stress monitoring unit can also provide a meal plan based on the user's stress management goals. For example, it can suggest a meal plan aimed at reducing stress. This allows for more comprehensive health management by taking stress data into consideration.

[0049] The nutrition management system can also be equipped with an allergy management unit that manages a user's allergy information. For example, a user can register foods to which they are allergic in a database, and avoid allergic foods when suggesting meals. The allergy management unit can also monitor a user's allergic reactions in real time and issue a warning if allergic symptoms occur. Furthermore, the allergy management unit can suggest alternative foods based on the user's allergy information. For example, it can suggest soy milk or almond milk to a user with a dairy allergy. This makes it possible to suggest safer and more appropriate meals by taking allergy information into consideration.

[0050] The nutrition management system can further include a dietary history management unit that manages the user's dietary history in detail. For example, it can record detailed data on the foods and nutrients the user has consumed in the past to understand long-term nutritional balance. The dietary history management unit can also identify nutrient deficiencies or excesses based on the user's dietary history and make appropriate dietary suggestions. For example, if the user has had a vitamin C deficiency in the past, it can suggest foods that are high in vitamin C. Furthermore, the dietary history management unit can update the user's dietary history in real time, building a system that makes dietary suggestions based on the latest data. This allows for more accurate nutritional management by managing the dietary history in detail.

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

[0052] Step 1: A camera is installed in the smart glasses and captures the food the user puts in their mouth in real time. For example, the camera can capture high-resolution images of the food and send them to the generative AI. The camera also uses a wide-angle lens to capture a wide range of food. Furthermore, the camera can capture clear images even in low-light environments. Step 2: The generative AI analyzes the food image captured by the camera. For example, the generative AI may use text generation AI (e.g., LLM) to identify the type and shape of the food. The generative AI may also use multimodal generation AI to infer the ingredients used from the amount and appearance of the food. The generative AI may also use image recognition technology to analyze the texture and temperature of the food. For example, the text generation AI has learned large amounts of image data and has advanced image recognition capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generative AI may use texture analysis technology to analyze the texture of the food's surface and a temperature sensor to detect the food's temperature. Step 3: The database is referenced by the Generative AI and stores information about food. For example, the database contains nutritional information for processed foods and general recipe information. The database is also regularly updated with new food information. Furthermore, the database is stored on the cloud and can be accessed by the Generative AI in real time. Step 4: The analysis unit analyzes the nutrients the user is consuming based on the food information analyzed by the generation AI. For example, the analysis unit calculates the amount of vitamins and minerals contained in the food and understands the user's nutritional intake. The analysis unit can also refer to the user's past dietary history to identify nutrient deficiencies or excesses. Furthermore, the analysis unit monitors the user's physical condition and health in real time and analyzes nutrients based on that data. Step 5: The schedule determination unit determines a meal schedule based on the missing nutrients identified by the analysis unit. For example, the schedule determination unit proposes a meal plan that includes foods rich in vitamin D. The schedule determination unit can also propose a customized meal schedule taking into account the user's lifestyle and food preferences. Furthermore, the schedule determination unit can also propose a meal schedule that incorporates seasonal ingredients taking into account the season and local ingredients.

[0053] (Example 2) The nutrition management system according to an embodiment of the present invention uses a camera mounted on smart glasses to recognize images of food being eaten, and a generation AI analyzes the nutrients being ingested, helping users achieve a balanced diet. This allows the nutrition management system to grasp the user's nutritional balance in real time and propose an appropriate meal schedule.

[0054] A nutrition management system according to an embodiment includes a camera, a generation AI, a database, an analysis unit, and a schedule determination unit. The camera is mounted on smart glasses and captures food in real time as the user places it in their mouth. For example, the camera captures high-resolution images of the food and sends them to the generation AI. The camera also uses a wide-angle lens to capture a wide range of food. Furthermore, the camera can capture clear images even in low-light environments. The generation AI analyzes the food images captured by the camera. For example, the generation AI identifies the type and shape of food using a text generation AI (e.g., LLM). The generation AI can also estimate the ingredients used from the quantity and appearance of food using a multimodal generation AI. The generation AI can also analyze the texture and temperature of food using image recognition technology. For example, the text generation AI has learned a large amount of image data and has advanced image recognition capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses texture analysis technology to analyze the texture of the food's surface and a temperature sensor to detect the food's temperature. The database, which is referenced by the generation AI, stores information about food. For example, the database includes nutritional information for processed foods and general recipe information. The database is also regularly updated, with new food information added. Furthermore, the database is stored on the cloud and can be accessed by the generation AI in real time. The analysis unit analyzes the nutrients the user is consuming based on the food information analyzed by the generation AI. For example, the analysis unit calculates the amount of vitamins and minerals contained in the food to understand the user's nutritional intake. The analysis unit can also refer to the user's past dietary history to identify nutrient deficiencies or excesses. Furthermore, the analysis unit monitors the user's physical condition and health status in real time and analyzes nutrients based on that data. The schedule determination unit determines a meal schedule based on the nutrients that the analysis unit identifies as being inadequate. For example, the schedule determination unit suggests a meal plan that includes foods rich in vitamin D.The schedule determination unit can also propose a customized meal schedule taking into account the user's lifestyle and food preferences. Furthermore, the schedule determination unit can also propose a meal schedule that incorporates seasonal ingredients taking into account the season and local ingredients. This allows the nutrition management system according to the embodiment to grasp the user's nutritional balance in real time and propose an appropriate meal schedule. For example, the output unit displays the proposed meal schedule to the user via a smartphone app or web application. If the user desires feedback in paper form, the results are printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the user.

[0055] When analyzing food images, generative AI can detect the food's temperature using an infrared sensor and analyze the food's texture using image analysis technology. For example, when analyzing food images, generative AI can detect the food's temperature using an infrared sensor and identify the type of food based on the temperature information, such as hot dishes or cold desserts. To detect the food's texture, generative AI can also analyze the surface texture of the food using image analysis technology, recognizing, for example, the crispness of fried food or the smoothness of soup. Furthermore, by combining a temperature sensor with texture analysis, generative AI can more accurately grasp the state of food, for example, recognizing whether bread is freshly baked or the thawed state of frozen food. This enables more accurate food recognition by taking into account the food's temperature and texture.

[0056] When analyzing food images, the generative AI can detect the lighting conditions of the dining environment, adjust brightness and color temperature, and analyze background information to accurately extract the food's contours. For example, when analyzing food images, the generative AI can detect the lighting conditions of the user's dining environment and adjust brightness and color temperature to improve recognition accuracy. The generative AI also analyzes background information of the dining environment, taking into account, for example, the color of the tablecloth and tableware, to accurately extract the food's contours. The generative AI also incorporates algorithms that correct for variations in lighting and background in real time, enabling highly accurate food recognition even in scenes such as outdoor picnics and restaurant dining. This improves the accuracy of food recognition by taking the dining environment into account.

[0057] The generative AI uses its emotion estimation function to analyze the user's facial expressions and voice to estimate the emotions the user felt when choosing food, and can learn the user's preferences based on the emotional data. For example, the generative AI analyzes the user's facial expressions and voice to estimate the emotions they felt when choosing food. For example, it can detect smiles or excited voices to identify positive emotions. The generative AI also learns the user's preferences for food choices in the past based on the emotion estimation data, suggesting desserts for a user with a sweet tooth, for example. The generative AI also monitors the user's emotional responses in real time and makes suggestions that elicit positive emotions when choosing food. For example, it can introduce new dishes when the user appears to be enjoying themselves. This allows the system to analyze the user's emotions and learn their preferences, enabling more appropriate food suggestions.

[0058] When estimating food quantities, generative AI takes into account the density and mass of the food, uses image analysis technology to calculate the food's volume, and combines this data with density data to estimate the food's quantity. For example, when analyzing food images, generative AI considers the food's density to estimate the quantity. For example, it distinguishes between light foods like bread and cake and heavy foods like meat and fish. To estimate the food's mass, generative AI also uses image analysis technology to calculate the food's volume and combines this data with density data to calculate the exact quantity. For example, it estimates the amount of soup based on the size and depth of the bowl. Generative AI also incorporates an algorithm that adjusts the food quantity in real time based on density and mass data, allowing it to accurately estimate the amount of salad ingredients, for example. By taking food density and mass into account, more accurate food quantity estimations become possible.

[0059] When guessing ingredients from the appearance of food, the generation AI can refer to the user's past eating history and reflect the user's eating habits. For example, the generation AI can refer to the user's past eating history to guess ingredients from the appearance of food. For example, it can prioritize recognizing ingredients that the user eats frequently. The generation AI can also learn the user's eating habits based on the eating history data and identify ingredients from the appearance of food. For example, it can take into account the user's preferred seasonings and cooking methods. The generation AI can also update the past eating history in real time and guess food ingredients based on the latest data. For example, if a new ingredient is tried, that information is immediately reflected. This makes it possible to guess ingredients that reflect the user's eating habits by referring to the user's past eating history.

[0060] The generative AI uses its emotion estimation function to analyze the emotions of users when selecting food and can infer the ingredients used in food from its appearance based on the emotional data. For example, the generative AI uses its emotion estimation function to analyze the emotions of users when selecting food and can infer ingredients based on that emotional data. For example, if a user appears to be enjoying themselves, it can identify their favorite ingredients. The generative AI also develops an algorithm to infer ingredients from the appearance of food based on the user's emotional response. For example, if a user has strong positive emotions, it can prioritize recognizing the user's favorite ingredients. The generative AI also collects emotion estimation data in real time and builds a system that infers ingredients based on the user's emotions. For example, if a user is relaxed, it can identify their favorite herbs and spices. This makes it possible to analyze a user's emotions and infer ingredients based on their emotions.

[0061] Generative AI can analyze food's aroma and sound to infer ingredients from the amount and appearance of food. For example, to analyze food's aroma, generative AI uses sensors installed in smart glasses to collect aroma data and identify the ingredients of the food. For example, it can detect the aroma of spices to infer the ingredients of curry. Generative AI can also analyze food sounds by installing microphones in smart glasses to collect the sounds of chewing food and cooking to identify ingredients. For example, it can detect the crackling sound of fried food to infer the type of fried food. Generative AI can also develop algorithms that combine aroma and sound data with image analysis to more accurately infer food ingredients. For example, it can identify the type of fish based on the aroma and sound of grilled fish. This makes it possible to more accurately infer ingredients by using food's aroma and sound in analysis.

[0062] When inferring ingredients from the amount and appearance of food, generative AI can link cameras with other sensors to analyze the condition of food from multiple angles. For example, generative AI could install a temperature sensor in smart glasses to collect food temperature data and identify ingredients. For example, it could infer ingredients based on the temperature of hot soup or cold dessert. Generative AI could also link other sensors (such as a humidity sensor) to smart glasses and analyze food humidity data to identify ingredients. For example, it could infer cooking methods and ingredients based on foods with high humidity levels. Generative AI could also combine data from temperature and humidity sensors with image analysis to analyze the condition of food from multiple angles and make more accurate ingredient inferences. For example, it could identify ingredients based on the temperature and humidity of freshly baked bread. In this way, linking cameras with other sensors enables multifaceted analysis of food conditions and more accurate ingredient inferences.

[0063] When referencing the database, the generative AI can automatically incorporate the latest nutritional research data and reflect it in the analysis. For example, when referencing the database, the generative AI automatically incorporates the latest nutritional research data and updates the nutritional information of foods. For example, it reflects new vitamin and mineral research results. The generative AI also regularly adds the latest nutritional research data to the database and analyzes the nutritional value of foods based on that data. For example, it adds the nutritional value of new ingredients to the database. The generative AI also incorporates the latest nutritional research data in real time and builds a system that instantly updates the nutritional information of foods. For example, it updates the database every time new research results are published. This allows for more accurate nutrient analysis by incorporating the latest nutritional research data.

[0064] When referencing the database, the generation AI can obtain detailed information such as the food's manufacturer and production date, and reflect this in the nutrient analysis. For example, when referencing the database, the generation AI can obtain detailed information such as the food's manufacturer and production date, and reflect this in the nutrient analysis. For example, it will prioritize analyzing the nutritional value of foods with more recent production dates. The generation AI can also add detailed information such as the food's manufacturer and production date to the database, and perform nutrient analysis based on that data. For example, it can perform a detailed analysis of the nutritional value of foods from a specific manufacturer. The generation AI can also build a system that more accurately analyzes the nutritional composition information of foods based on information about the manufacturer and production date. For example, it can correct the nutritional value of foods with older production dates. In this way, by obtaining detailed information such as the food's manufacturer and production date, more accurate nutrient analysis is possible.

[0065] The generative AI can use its emotion estimation function to analyze the emotions a user has when choosing processed foods and perform database lookups based on the emotional data. For example, the generative AI can use its emotion estimation function to analyze the emotions a user has when choosing processed foods and perform database lookups based on that emotional data. For example, if the user appears to be having fun, it can identify their favorite processed foods. The generative AI can also develop an algorithm that looks up a database of processed foods based on the user's emotional response. For example, if the user has strong positive emotions, it can prioritize displaying the user's favorite processed foods. The generative AI can also collect emotion estimation data in real time and build a system that looks up a database based on the user's emotions. For example, it can identify a user's favorite snack when the user is relaxed. This makes it possible to analyze the user's emotions and perform database lookups based on emotions.

[0066] The generating AI can monitor the user's physical condition and health status in real time and analyze nutrients based on that data. For example, the generating AI can monitor the user's physical condition data (e.g., heart rate and blood pressure) in real time and analyze nutrients based on that data. For example, it can suggest nutritional supplements after exercise. The generating AI can also analyze health status (e.g., stress level) in real time and analyze nutrients based on that data. For example, it can suggest foods that have a relaxing effect when stress is high. The generating AI can also collect data on physical condition and health status in real time and build a system that analyzes nutrients based on that data. For example, it can suggest energy replenishment when sleep is lacking. This makes it possible to monitor the user's physical condition and health status in real time, enabling more accurate nutrient analysis.

[0067] Generative AI can compare nutrient analysis results with a user's long-term health data to predict health risks. For example, generative AI can compare nutrient analysis results with a user's long-term health data to build a system that predicts health risks. For example, it can predict future nutritional deficiencies based on past data. Generative AI can also analyze nutrient analysis results based on long-term health data (e.g., blood test results) to identify health risks. For example, it can predict bone density loss due to vitamin D deficiency. Generative AI can also integrate nutrient analysis results with long-term health data to develop a system that predicts health risks in real time. For example, it can predict diabetes risk and suggest appropriate nutritional intake. This makes it possible to predict health risks by comparing with long-term health data.

[0068] Generative AI can visualize nutrient analysis results and provide them to users. Generative AI, for example, builds a system that visualizes nutrient analysis results and provides them to users. For example, it displays nutrient intake status using graphs and charts. Generative AI also allows users to intuitively understand the visualized nutrient analysis results. For example, it uses color coding and icons to show nutrient balance. Generative AI also develops a system that visualizes nutrient analysis results in real time, allowing users to understand them immediately. For example, it displays nutrient intake status in dashboard format. This visualization allows users to intuitively understand the nutrient analysis results.

[0069] The generation AI can propose a customized meal schedule by taking into account the user's lifestyle and dietary preferences. For example, the generation AI can analyze the user's lifestyle data (e.g., work schedule and exercise habits) and propose a customized meal schedule based on that data. For example, it can provide a simple meal plan for busy days. The generation AI can also propose a customized meal schedule by taking into account the user's dietary preferences. For example, it can propose a menu that includes many of the user's favorite ingredients. The generation AI can also update lifestyle and dietary preference data in real time, building a system that proposes a customized meal schedule based on the latest information. For example, it can provide a meal plan that matches new hobbies or habits. This makes it possible to propose a customized meal schedule by taking into account the user's lifestyle and dietary preferences.

[0070] Generative AI can incorporate seasonal ingredients into meal schedules by taking into account the season and local ingredients. For example, generative AI can analyze seasonal and local data to propose meal schedules that incorporate seasonal ingredients. For example, in spring, it can provide menus that include plenty of fresh vegetables and fruits. Generative AI can also propose meal schedules that take into account local specialties and traditional dishes. For example, it can provide special menus to coincide with local festivals and events. Generative AI can also build a system that updates seasonal and local data in real time and proposes meal schedules that incorporate seasonal ingredients based on the latest information. For example, it can provide menus based on the availability of ingredients in each season. This makes it possible to propose meal schedules that incorporate seasonal ingredients by taking into account the season and local ingredients.

[0071] The generation AI can use the emotion estimation function to analyze the user's emotions regarding the meal schedule and suggest a schedule based on the emotion data. For example, the generation AI can use the emotion estimation function to analyze the user's emotions regarding the meal schedule and suggest a schedule based on that emotion data. For example, when the user appears to be enjoying themselves, the generation AI can suggest their favorite menu items. The generation AI can also develop an algorithm to suggest a meal schedule based on the user's emotional response. For example, when positive emotions are strong, the generation AI can suggest a menu item that contains many of the user's favorite ingredients. The generation AI can also collect emotion estimation data in real time and build a system that suggests a meal schedule based on the user's emotions. For example, when the user is relaxing, the generation AI can suggest a menu item that contains ingredients that have a relaxing effect. This makes it possible to suggest a meal schedule based on emotions by analyzing the user's emotions.

[0072] The generating AI can link meal schedules with other health management apps to provide comprehensive health management. For example, the generating AI can link meal schedules with other health management apps to build a system for comprehensive health management. For example, it can link with an exercise app to provide meal plans based on the amount of exercise. The generating AI can also adjust meal schedules based on data obtained from the health management app (e.g., sleep data). For example, it can suggest energy replenishment when sleep is lacking. The generating AI can also share data with other health management apps in real time to develop a system for comprehensive health management. For example, it can link with a stress management app to provide meal plans with a relaxing effect. This makes it possible to provide comprehensive health management by linking with other health management apps.

[0073] Generative AI can share meal schedule suggestions with family and friends, enabling them to work together to achieve a healthy diet. For example, generative AI could build a system that shares the suggested meal schedule with family and friends. For example, it could suggest menus that take into account the nutritional balance of the entire family. Generative AI could also develop an app that uses the shared meal schedule to enable family and friends to work together to achieve a healthy diet. For example, it could provide a function to create a joint shopping list. Generative AI could also build a system that updates the meal schedule sharing function in real time and shares the latest information with family and friends. For example, it could adjust the menu depending on the availability of ingredients. This makes it possible to work together to achieve a healthy diet by sharing with family and friends.

[0074] The generative AI can use its emotion estimation function to monitor a user's emotions regarding their meal schedule in real time and make suggestions that elicit positive emotions. For example, the generative AI can use its emotion estimation function to monitor a user's emotions regarding their meal schedule in real time and make suggestions that elicit positive emotions. For example, when the user smiles, it can suggest their favorite menu items. The generative AI can also analyze the user's emotional responses and identify meal schedules that elicit positive emotions based on past data. For example, it can re-suggest menu items that the user enjoyed in the past. The generative AI can also provide an interface based on the emotion estimation data to help the user elicit positive emotions regarding their meal schedule. For example, it can present encouraging messages or success stories. This makes it possible to make suggestions that elicit positive emotions by monitoring the user's emotions in real time.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The nutrition management system may further include an exercise data acquisition unit that acquires the user's exercise data. For example, exercise data may be acquired from a smartwatch or fitness tracker to ascertain the user's exercise volume and calorie expenditure. The exercise data acquisition unit may also learn the user's exercise habits and suggest appropriate nutritional supplementation. For example, it may suggest taking protein after exercise or replenishing energy before exercise. Furthermore, the exercise data acquisition unit may provide a meal plan based on the user's exercise goals. For example, it may suggest a high-protein diet for a user who engages in strength training. This allows for more comprehensive health management by taking exercise data into consideration.

[0077] The nutrition management system may further include a sleep data acquisition unit that acquires the user's sleep data. For example, sleep data may be acquired from a smartwatch or sleep tracker to understand the user's sleep patterns and quality. The sleep data acquisition unit may also suggest appropriate nutritional supplementation based on the user's sleep state. For example, it may suggest energy replenishment when the user is sleep deprived and foods that promote good quality sleep. Furthermore, the sleep data acquisition unit may also provide a meal plan based on the user's sleep goals. For example, it may suggest herbal teas with relaxing effects or foods containing melatonin. This allows for more comprehensive health management by taking sleep data into consideration.

[0078] The nutrition management system can further include a stress monitoring unit that monitors the user's stress level. For example, it measures heart rate and electrodermal activity to grasp the user's stress level in real time. The stress monitoring unit can also suggest foods with a relaxing effect based on the user's stress state. For example, it can suggest chamomile tea or dark chocolate when stress is high. Furthermore, the stress monitoring unit can also provide a meal plan based on the user's stress management goals. For example, it can suggest a meal plan aimed at reducing stress. This allows for more comprehensive health management by taking stress data into consideration.

[0079] The nutrition management system can also be equipped with an allergy management unit that manages a user's allergy information. For example, a user can register foods to which they are allergic in a database, and avoid allergic foods when suggesting meals. The allergy management unit can also monitor a user's allergic reactions in real time and issue a warning if allergic symptoms occur. Furthermore, the allergy management unit can suggest alternative foods based on the user's allergy information. For example, it can suggest soy milk or almond milk to a user with a dairy allergy. This makes it possible to suggest safer and more appropriate meals by taking allergy information into consideration.

[0080] The nutrition management system can further include a dietary history management unit that manages the user's dietary history in detail. For example, it can record detailed data on the foods and nutrients the user has consumed in the past to understand long-term nutritional balance. The dietary history management unit can also identify nutrient deficiencies or excesses based on the user's dietary history and make appropriate dietary suggestions. For example, if the user has had a vitamin C deficiency in the past, it can suggest foods that are high in vitamin C. Furthermore, the dietary history management unit can update the user's dietary history in real time, building a system that makes dietary suggestions based on the latest data. This allows for more accurate nutritional management by managing the dietary history in detail.

[0081] The generative AI can use its emotion estimation function to analyze the user's emotions while eating and estimate their meal satisfaction. For example, it can analyze the user's facial expressions and voice while eating to evaluate their satisfaction. The generative AI can also learn the user's preferred ingredients and cooking methods based on meal satisfaction data and reflect this in its next meal suggestions. For example, if a user expresses high satisfaction with a particular dish, it will re-suggest that dish. Furthermore, the generative AI can monitor the user's satisfaction in real time and make suggestions for improvement if satisfaction decreases during the meal. This makes it possible to analyze the user's emotions and suggest meals that will provide greater satisfaction.

[0082] The generation AI can use its emotion estimation function to analyze the user's emotions before a meal and estimate their level of expectation for the meal. For example, it can analyze the user's facial expressions and voice before a meal to evaluate their expectation level. The generation AI can also learn the ingredients and dishes the user is looking forward to based on the meal expectation data and reflect this in its next meal suggestions. For example, if the user expresses high expectations for a particular dish, it will suggest that dish. Furthermore, the generation AI can monitor the user's expectation level in real time and make suggestions for improvement if expectations drop before a meal. This makes it possible to analyze the user's emotions and make meal suggestions that are more likely to generate expectations.

[0083] The generation AI can use its emotion estimation function to analyze the user's emotions after a meal and evaluate their satisfaction with the meal. For example, it can analyze the user's facial expressions and voice after a meal to evaluate their satisfaction. The generation AI can also learn the user's preferred ingredients and cooking methods based on the meal satisfaction data and reflect this in its next meal suggestions. For example, if a user expresses high satisfaction with a particular dish, it will re-suggest that dish. Furthermore, the generation AI can monitor the user's satisfaction in real time and make suggestions for improvement if satisfaction decreases after a meal. This makes it possible to analyze the user's emotions and recommend meals that will provide greater satisfaction.

[0084] The generative AI can use its emotion estimation function to analyze the user's emotions while eating and monitor meal satisfaction in real time. For example, it can analyze the user's facial expressions and voice while eating to evaluate satisfaction. The generative AI can also learn the user's preferred ingredients and cooking methods based on meal satisfaction data and reflect this in its next meal suggestions. For example, if a user expresses high satisfaction with a particular dish, it will re-suggest that dish. Furthermore, the generative AI can monitor the user's satisfaction in real time and make suggestions for improvement if satisfaction decreases during a meal. This makes it possible to analyze the user's emotions and make more satisfying meal suggestions.

[0085] The generation AI can use its emotion estimation function to analyze the user's emotions before a meal and monitor their expectations for the meal in real time. For example, it can analyze the user's facial expressions and voice before a meal to evaluate their expectations. The generation AI can also learn the ingredients and dishes the user is looking forward to based on the meal expectation data and reflect this in its next meal suggestions. For example, if the user expresses high expectations for a particular dish, it will suggest that dish. Furthermore, the generation AI can monitor the user's expectations in real time and make suggestions for improvement if their expectations drop before a meal. This makes it possible to analyze the user's emotions and make meal suggestions that are more likely to generate high expectations.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: A camera is installed in the smart glasses and captures the food the user puts in their mouth in real time. For example, the camera can capture high-resolution images of the food and send them to the generative AI. The camera also uses a wide-angle lens to capture a wide range of food. Furthermore, the camera can capture clear images even in low-light environments. Step 2: The generative AI analyzes the food image captured by the camera. For example, the generative AI may use text generation AI (e.g., LLM) to identify the type and shape of the food. The generative AI may also use multimodal generation AI to infer the ingredients used from the amount and appearance of the food. The generative AI may also use image recognition technology to analyze the texture and temperature of the food. For example, the text generation AI has learned large amounts of image data and has advanced image recognition capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generative AI may use texture analysis technology to analyze the texture of the food's surface and a temperature sensor to detect the food's temperature. Step 3: The database is referenced by the Generative AI and stores information about food. For example, the database contains nutritional information for processed foods and general recipe information. The database is also regularly updated with new food information. Furthermore, the database is stored on the cloud and can be accessed by the Generative AI in real time. Step 4: The analysis unit analyzes the nutrients the user is consuming based on the food information analyzed by the generation AI. For example, the analysis unit calculates the amount of vitamins and minerals contained in the food and understands the user's nutritional intake. The analysis unit can also refer to the user's past dietary history to identify nutrient deficiencies or excesses. Furthermore, the analysis unit monitors the user's physical condition and health in real time and analyzes nutrients based on that data. Step 5: The schedule determination unit determines a meal schedule based on the missing nutrients identified by the analysis unit. For example, the schedule determination unit proposes a meal plan that includes foods rich in vitamin D. The schedule determination unit can also propose a customized meal schedule taking into account the user's lifestyle and food preferences. Furthermore, the schedule determination unit can also propose a meal schedule that incorporates seasonal ingredients taking into account the season and local ingredients.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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]

[0155] 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. The camera mounted on the smart glasses A generation AI that analyzes the image of the food captured by the camera; A database referenced by the generation AI; an analysis unit that analyzes nutrients based on the food information analyzed by the generating AI; a schedule determination unit that determines a meal schedule based on the nutrients that are not being ingested, which are identified by the analysis unit; A system characterized by:

2. The generated AI is Analyzes the user's facial expressions and voice, estimates the emotions the user had when selecting food, and learns the user's preferences based on the emotional data.

2. The system of claim 1.

3. The generated AI is When estimating the amount of the food, the density and mass of the food are taken into consideration, and the volume of the food is calculated using image analysis technology, and the volume of the food is estimated in combination with the density data.

2. The system of claim 1.

4. The generated AI is When referencing the database, the latest nutritional research data is automatically retrieved and reflected in the analysis.

2. The system of claim 1.

5. The generated AI is Monitor the user's physical condition and health status in real time and analyze the nutrients based on the data.

2. The system of claim 1.

6. The generated AI is Taking into consideration the user's lifestyle and dietary preferences, the meal schedule is customized and proposed.

2. The system of claim 1.

7. The generated AI is The user's feelings about the meal schedule are analyzed, and a schedule is proposed based on the emotion data.

2. The system of claim 1.

8. The generated AI is The system monitors the user's feelings about the meal schedule in real time and makes suggestions to elicit positive emotions.

2. The system of claim 1.

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