System

Abstract

An object of a system according to an embodiment is to identify the content of a meal, grasp nutrients, and provide advice in accordance with a health goal.SOLUTION: A system according to an embodiment includes a meal identification unit, a nutrient grasping unit, and an advice providing unit. The meal identification unit analyzes an image or text information of a meal and identifies the content of the meal. The nutrient grasping unit calculates nutrients based on the content of the meal identified by the meal identification unit. The advice providing unit provides advice by comparing the nutrient information grasped by the nutrient grasping unit with the health goal of the user.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] Previous technologies had the problem of not being able to accurately identify the contents of meals, understand the nutrients, and provide advice tailored to health goals.

[0005] The system according to the embodiment aims to identify the contents of meals, understand nutrients, and provide advice tailored to health goals. [Means for solving the problem]

[0006] The system according to the embodiment includes a meal identification unit, a nutrient identification unit, and an advice providing unit. The meal identification unit analyzes images and text information of meals to identify the contents of the meals. The nutrient identification unit calculates nutrients based on the contents of the meals identified by the meal identification unit. The advice providing unit compares the nutrient information identified by the nutrient identification unit with the user's health goals and provides advice. [Effects of the Invention]

[0007] The system according to the embodiment can identify the contents of a meal, understand nutrients, and provide advice tailored to health goals. [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) A health promotion system according to an embodiment of the present invention uses a generative AI to identify meals and understand their nutrients, thereby supporting the promotion of target weight and ideal health conditions. This allows the health promotion system to analyze the user's diet and provide advice tailored to the user's health goals.

[0029] A health promotion system according to an embodiment includes a meal identification unit, a nutrient identification unit, and an advice provision unit. The meal identification unit analyzes images and text information of a meal to identify the meal's contents. For example, a user takes a photo of a meal and inputs the photo into the generation AI. The generation AI analyzes the photo and identifies the ingredients and type of dish. For example, the generation AI may identify the meal as "This is a salad, containing lettuce, tomato, cucumber, and dressing." The nutrient identification unit calculates the nutritional content of the meal based on the identified meal's contents. For example, the nutrient identification unit identifies the nutritional content of the entire meal based on information such as "Lettuce contains vitamin A and dietary fiber, and tomatoes contain vitamin C and lycopene." The generation AI receives inputs containing prompts containing instructions on what the user wants the generation AI to do, and the generation AI calculates the nutrients based on the prompts. The advice provision unit compares the identified nutrient information with the user's health goals and provides appropriate advice. For example, the advice provided may be specific, such as, "Today's meal is lacking in vitamin C, so it would be a good idea to include oranges and bell peppers at your next meal." This enables the health promotion system according to an embodiment to manage dietary needs in line with the user's health goals.

[0030] The meal identification unit can also identify the origin and production method of ingredients and provide this to the user. For example, the generation AI in the meal identification unit analyzes an image of a meal and identifies the origin information of the ingredients. For example, it identifies that tomatoes are from Italy and that lettuce is supplied from a local farm. The generation AI in the meal identification unit also identifies the production method of the ingredients (organic, pesticide-free, etc.). For example, it identifies organic vegetables and ingredients grown without pesticides and provides this to the user. This allows the user to obtain information about the origin and production method of the ingredients.

[0031] The meal classification unit can also evaluate the freshness and storage conditions of ingredients and notify the user. For example, the generation AI in the meal classification unit analyzes images of meals and evaluates the freshness of ingredients. For example, it determines the freshness from the color and texture of vegetables and notifies the user. The generation AI in the meal classification unit also evaluates the storage conditions of ingredients. For example, it analyzes the refrigerated or frozen storage conditions and notifies the user of the storage conditions. This allows the user to obtain information about the freshness and storage conditions of ingredients.

[0032] The meal identification unit can also identify cooking methods for meals and provide them to the user. For example, the generation AI analyzes images of meals and identifies cooking methods. For example, it identifies cooking methods such as grilled fish, simmered dishes, and steamed vegetables and notifies the user. The meal identification unit also analyzes text information and identifies cooking methods. For example, it analyzes recipe steps and provides cooking methods to the user. This allows the user to obtain information about cooking methods for meals.

[0033] The meal identification unit can add a function to share the meal identification results with other users and obtain feedback within the community. For example, the meal identification unit builds a platform for sharing the contents of meals identified by the generation AI with other users. For example, photos of meals and nutritional information can be shared and feedback can be obtained. The meal identification unit also allows the generation AI to collect feedback within the community and provide it to the user. For example, the generation AI can suggest improvements to meals based on comments and ratings from other users. This allows the user to obtain feedback from other users.

[0034] The nutrient grasping unit can estimate fluctuations in nutritional value from the shape and color of ingredients and notify the user. For example, the generation AI of the nutrient grasping unit analyzes images of meals and estimates fluctuations in nutritional value from the shape and color of ingredients. For example, it evaluates nutritional value based on the intensity of the color of vegetables and the ripeness of fruits. The generation AI of the nutrient grasping unit also notifies the user of fluctuations in nutritional value based on the shape and color of ingredients. For example, it notifies the user that brightly colored vegetables are rich in vitamins. This allows the user to know fluctuations in nutritional value based on the shape and color of ingredients.

[0035] The nutrient grasping unit can also analyze cooking recipes and cooking procedures when analyzing text information and provide them to the user. For example, the generation AI of the nutrient grasping unit analyzes text information and identifies cooking recipes and cooking procedures. For example, it analyzes the ingredients and procedures of a recipe and provides them to the user. The generation AI of the nutrient grasping unit also calculates nutrients based on the cooking recipes and cooking procedures. For example, it evaluates fluctuations in nutrients based on the cooking procedures and notifies the user. This allows the user to obtain information about cooking recipes and cooking procedures.

[0036] The nutrient grasping unit can also evaluate the plating and presentation of the meal in the image analysis and notify the user. For example, the generation AI analyzes an image of the meal and evaluates the plating and presentation. For example, it evaluates the placement of ingredients and color balance and notifies the user. The nutrient grasping unit also evaluates the meal based on the presentation of the meal by the generation AI. For example, it evaluates the beauty and balance of the appearance and notifies the user. This allows the user to obtain information about the plating and presentation of the meal.

[0037] The nutrient grasping unit can link the results of analyzing the text information with other health applications to support comprehensive health management. In the nutrient grasping unit, for example, the generation AI analyzes the text information and links the results with other health applications. For example, nutritional information and dietary history can be shared to support comprehensive health management. In addition, the generation AI in the nutrient grasping unit links with other health applications to comprehensively evaluate the user's health condition. For example, it links with fitness apps and sleep trackers to monitor the user's health condition. This allows the user to link with other health applications to perform comprehensive health management.

[0038] The nutrient grasping unit can also take into account nutrient fluctuations during the cooking process of ingredients and notify the user. For example, the generation AI in the nutrient grasping unit analyzes the cooking process of ingredients and evaluates nutrient fluctuations. For example, it takes into account the loss of vitamin C due to heating vegetables and the fluctuations in protein due to cooking meat. The generation AI in the nutrient grasping unit also notifies the user of nutrient fluctuations based on data from the cooking process. For example, it evaluates nutrient fluctuations based on cooking time and temperature and notifies the user. This allows the user to know the nutrient fluctuations during the cooking process of ingredients.

[0039] The nutrient identification unit can also evaluate the interactions between ingredients and provide the results to the user. For example, the generation AI in the nutrient identification unit analyzes the interactions between ingredients and evaluates the combination of ingredients that helps the absorption of specific vitamins. For example, it takes into account the interaction between vitamin C and iron and notifies the user. The generation AI in the nutrient identification unit also suggests combinations of ingredients that optimize nutrient absorption based on the interactions between ingredients. For example, it takes into account the interaction between vitamin D and calcium and notifies the user. This allows the user to know the absorption of nutrients based on the interactions between ingredients.

[0040] The nutrient grasping unit can add a function to share the nutrient grasping results with other users and obtain feedback within the community. For example, the nutrient grasping unit builds a platform for sharing nutrient information grasped by the generation AI with other users. For example, nutritional information about meals is shared and feedback is obtained. The nutrient grasping unit also allows the generation AI to collect feedback within the community and provide it to the user. For example, the generation AI suggests improvements to meals based on comments and ratings from other users. This allows the user to obtain feedback from other users.

[0041] The nutrient grasping unit can also take into consideration information about the seasonality and in-seasonity of ingredients and notify the user. For example, the generation AI of the nutrient grasping unit analyzes information about the seasonality and in-seasonity of ingredients and notifies the user. For example, it identifies vegetables and fruits that are in season in spring and provides them to the user. The generation AI of the nutrient grasping unit also evaluates fluctuations in nutrients based on the seasonality and in-seasonity information. For example, it notifies the user that seasonal ingredients are high in nutritional value. This allows the user to know information about the seasonality and in-seasonity of ingredients.

[0042] The nutrient grasping unit can perform customized nutrient calculations, taking into account the user's individual metabolic rate and allergy information. For example, the generating AI in the nutrient grasping unit analyzes the user's metabolic rate and performs individual nutrient calculations. For example, it adjusts calorie intake taking into account the basal metabolic rate and amount of exercise. The generating AI in the nutrient grasping unit also performs customized nutrient calculations based on the user's allergy information. For example, it excludes specific ingredients and suggests alternative ingredients. This makes it possible to perform nutrient calculations that take into account the user's individual metabolic rate and allergy information.

[0043] The nutrient grasping unit can evaluate nutrient variations, taking into account the storage method and cooking time of ingredients. For example, the generation AI of the nutrient grasping unit analyzes the storage method of ingredients and evaluates nutrient variations. For example, it takes into account variations in nutritional value due to freezing or refrigerating storage. The generation AI of the nutrient grasping unit also evaluates nutrient variations based on cooking time. For example, it evaluates nutrient variations based on heating time and cooking process time and notifies the user. This allows the user to know the nutrient variations based on the storage method and cooking time of ingredients.

[0044] The nutrient grasping unit can link the nutrient calculation results with other health applications to support comprehensive health management. In the nutrient grasping unit, for example, the generation AI links the nutrient calculation results with other health applications. For example, nutritional information and dietary history can be shared to support comprehensive health management. In addition, the generation AI links with other health applications to comprehensively evaluate the user's health status. For example, it links with fitness apps and sleep trackers to monitor the user's health status. This allows the user to link with other health applications to perform comprehensive health management.

[0045] The nutrient identification unit can also take into account the origin and production method of ingredients and notify the user. For example, the generation AI in the nutrient identification unit identifies the origin information of ingredients and reflects this in the nutrient calculation. For example, it takes into account that tomatoes are from Italy and that lettuce is supplied from a local farm. The generation AI in the nutrient identification unit also identifies the production method of ingredients (organic, pesticide-free, etc.) and reflects this in the nutrient calculation. For example, it takes into account organic vegetables and ingredients grown without pesticides and notifies the user. This allows the user to obtain information about the origin and production method of ingredients.

[0046] The nutrient grasping unit can perform customized nutrient calculations, taking into account the user's individual metabolic rate and allergy information. For example, the generating AI in the nutrient grasping unit analyzes the user's metabolic rate and performs individual nutrient calculations. For example, it adjusts calorie intake taking into account the basal metabolic rate and amount of exercise. The generating AI in the nutrient grasping unit also performs customized nutrient calculations based on the user's allergy information. For example, it excludes specific ingredients and suggests alternative ingredients. This makes it possible to perform nutrient calculations that take into account the user's individual metabolic rate and allergy information.

[0047] The nutrient grasping unit can evaluate nutrient variations, taking into account the storage method and cooking time of ingredients. For example, the generation AI of the nutrient grasping unit analyzes the storage method of ingredients and evaluates nutrient variations. For example, it takes into account variations in nutritional value due to freezing or refrigerating storage. The generation AI of the nutrient grasping unit also evaluates nutrient variations based on cooking time. For example, it evaluates nutrient variations based on heating time and cooking process time and notifies the user. This allows the user to know the nutrient variations based on the storage method and cooking time of ingredients.

[0048] The nutrient grasping unit can link the nutrient calculation results with other health applications to support comprehensive health management. In the nutrient grasping unit, for example, the generation AI links the nutrient calculation results with other health applications. For example, nutritional information and dietary history can be shared to support comprehensive health management. In addition, the generation AI links with other health applications to comprehensively evaluate the user's health status. For example, it links with fitness apps and sleep trackers to monitor the user's health status. This allows the user to link with other health applications to perform comprehensive health management.

[0049] The nutrient identification unit can also take into account the origin and production method of ingredients and notify the user. For example, the generation AI in the nutrient identification unit identifies the origin information of ingredients and reflects this in the nutrient calculation. For example, it takes into account that tomatoes are from Italy and that lettuce is supplied from a local farm. The generation AI in the nutrient identification unit also identifies the production method of ingredients (organic, pesticide-free, etc.) and reflects this in the nutrient calculation. For example, it takes into account organic vegetables and ingredients grown without pesticides and notifies the user. This allows the user to obtain information about the origin and production method of ingredients.

[0050] The nutrient grasping unit can perform customized nutrient calculations, taking into account the user's individual metabolic rate and allergy information. For example, the generating AI in the nutrient grasping unit analyzes the user's metabolic rate and performs individual nutrient calculations. For example, it adjusts calorie intake taking into account the basal metabolic rate and amount of exercise. The generating AI in the nutrient grasping unit also performs customized nutrient calculations based on the user's allergy information. For example, it excludes specific ingredients and suggests alternative ingredients. This makes it possible to perform nutrient calculations that take into account the user's individual metabolic rate and allergy information.

[0051] The nutrient grasping unit can evaluate nutrient variations, taking into account the storage method and cooking time of ingredients. For example, the generation AI of the nutrient grasping unit analyzes the storage method of ingredients and evaluates nutrient variations. For example, it takes into account variations in nutritional value due to freezing or refrigerating storage. The generation AI of the nutrient grasping unit also evaluates nutrient variations based on cooking time. For example, it evaluates nutrient variations based on heating time and cooking process time and notifies the user. This allows the user to know the nutrient variations based on the storage method and cooking time of ingredients.

[0052] The nutrient grasping unit can link the nutrient calculation results with other health applications to support comprehensive health management. In the nutrient grasping unit, for example, the generation AI links the nutrient calculation results with other health applications. For example, nutritional information and dietary history can be shared to support comprehensive health management. In addition, the generation AI links with other health applications to comprehensively evaluate the user's health status. For example, it links with fitness apps and sleep trackers to monitor the user's health status. This allows the user to link with other health applications to perform comprehensive health management.

[0053] The nutrient identification unit can also take into account the origin and production method of ingredients and notify the user. For example, the generation AI in the nutrient identification unit identifies the origin information of ingredients and reflects this in the nutrient calculation. For example, it takes into account that tomatoes are from Italy and that lettuce is supplied from a local farm. The generation AI in the nutrient identification unit also identifies the production method of ingredients (organic, pesticide-free, etc.) and reflects this in the nutrient calculation. For example, it takes into account organic vegetables and ingredients grown without pesticides and notifies the user. This allows the user to obtain information about the origin and production method of ingredients.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The health promotion system can also collect the user's exercise data and provide advice that takes into account the balance between diet and exercise. For example, if the user goes running, the system can adjust the calorie intake based on the calories burned. It can also suggest necessary nutrients based on the type and intensity of exercise. This allows the user to optimize the balance between diet and exercise and make it easier to achieve their health goals.

[0056] The health promotion system can also obtain the user's sleep data and provide advice that takes into account the relationship between diet and sleep. For example, if the user is not getting enough sleep, the generating AI will take that impact into account and suggest foods suitable for replenishing energy. It can also provide dietary advice to improve sleep quality. This allows users to optimize the balance between diet and sleep and improve their overall health.

[0057] The health promotion system can also monitor the user's stress level and suggest ingredients and meal plans that will help reduce stress. For example, if the user's stress level is high, the AI ​​generator can suggest ingredients and herbal teas that have a relaxing effect. It can also provide meal plans that include nutrients that are effective in reducing stress. This allows users to manage their stress through diet.

[0058] The health promotion system can also monitor the user's water intake and support proper hydration. For example, if the user is not drinking enough water, the generative AI will notify them of the importance of hydration and suggest appropriate drinks and water-rich foods. It can also provide advice on adjusting water intake depending on exercise and weather conditions. This allows users to maintain proper hydration and optimize their health.

[0059] The health promotion system can also analyze the user's dietary history and propose meal plans aimed at long-term health goals. For example, it can identify nutritional imbalances based on past dietary data and suggest areas for improvement. It can also provide meal plans tailored to the season or events. This allows users to manage their health from a long-term perspective.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The meal identification unit analyzes the image and text information of the meal to identify the contents of the meal. For example, a user takes a photo of the meal and inputs it into the generation AI. The generation AI analyzes the photo and identifies the ingredients and type of dish. For example, it may identify it as "This is a salad, and it contains lettuce, tomato, cucumber, and dressing." Step 2: The nutrient identification unit calculates the nutrients based on the identified food contents. For example, the nutritional information of the entire meal is identified based on information such as lettuce containing vitamin A and dietary fiber, and tomatoes containing vitamin C and lycopene. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI calculates the nutrients based on that prompt. Step 3: The advice provider compares the captured nutritional information with the user's health goals and provides appropriate advice. For example, it may provide specific advice such as, "Today's meal is lacking in vitamin C, so it would be good to eat oranges or bell peppers at your next meal."

[0062] (Example 2) A health promotion system according to an embodiment of the present invention uses a generative AI to identify meals and understand their nutrients, thereby supporting the promotion of target weight and ideal health conditions. This allows the health promotion system to analyze the user's diet and provide advice tailored to the user's health goals.

[0063] A health promotion system according to an embodiment includes a meal identification unit, a nutrient identification unit, and an advice provision unit. The meal identification unit analyzes images and text information of a meal to identify the meal's contents. For example, a user takes a photo of a meal and inputs the photo into the generation AI. The generation AI analyzes the photo and identifies the ingredients and type of dish. For example, the generation AI may identify the meal as "This is a salad, containing lettuce, tomato, cucumber, and dressing." The nutrient identification unit calculates the nutritional content of the meal based on the identified meal's contents. For example, the nutrient identification unit identifies the nutritional content of the entire meal based on information such as "Lettuce contains vitamin A and dietary fiber, and tomatoes contain vitamin C and lycopene." The generation AI receives inputs containing prompts containing instructions on what the user wants the generation AI to do, and the generation AI calculates the nutrients based on the prompts. The advice provision unit compares the identified nutrient information with the user's health goals and provides appropriate advice. For example, the advice provided may be specific, such as, "Today's meal is lacking in vitamin C, so it would be a good idea to include oranges and bell peppers at your next meal." This enables the health promotion system according to an embodiment to manage dietary needs in line with the user's health goals.

[0064] The meal identification unit can also identify the origin and production method of ingredients and provide this to the user. For example, the generation AI in the meal identification unit analyzes an image of a meal and identifies the origin information of the ingredients. For example, it identifies that tomatoes are from Italy and that lettuce is supplied from a local farm. The generation AI in the meal identification unit also identifies the production method of the ingredients (organic, pesticide-free, etc.). For example, it identifies organic vegetables and ingredients grown without pesticides and provides this to the user. This allows the user to obtain information about the origin and production method of the ingredients.

[0065] The meal classification unit can also evaluate the freshness and storage conditions of ingredients and notify the user. For example, the generation AI in the meal classification unit analyzes images of meals and evaluates the freshness of ingredients. For example, it determines the freshness from the color and texture of vegetables and notifies the user. The generation AI in the meal classification unit also evaluates the storage conditions of ingredients. For example, it analyzes the refrigerated or frozen storage conditions and notifies the user of the storage conditions. This allows the user to obtain information about the freshness and storage conditions of ingredients.

[0066] The food classification unit uses the emotion estimation function to analyze the emotions expressed by the user when photographing a meal, and can suggest ingredients and dishes that will elicit positive emotions. For example, the generation AI in the food classification unit analyzes the user's facial expressions and estimates the emotion expressed when photographing a meal. For example, it detects smiling or surprised expressions and evaluates positive emotions. The food classification unit also uses the generation AI to suggest ingredients and dishes that will elicit positive emotions based on the user's emotions. For example, if the user photographs a meal while smiling, the generation AI will suggest colorful salads and fruits to maintain that emotion. This makes it possible to suggest ingredients and dishes that will elicit positive emotions in the user.

[0067] The meal identification unit can also identify cooking methods for meals and provide them to the user. For example, the generation AI analyzes images of meals and identifies cooking methods. For example, it identifies cooking methods such as grilled fish, simmered dishes, and steamed vegetables and notifies the user. The meal identification unit also analyzes text information and identifies cooking methods. For example, it analyzes recipe steps and provides cooking methods to the user. This allows the user to obtain information about cooking methods for meals.

[0068] The meal identification unit can add a function to share the meal identification results with other users and obtain feedback within the community. For example, the meal identification unit builds a platform for sharing the contents of meals identified by the generation AI with other users. For example, photos of meals and nutritional information can be shared and feedback can be obtained. The meal identification unit also allows the generation AI to collect feedback within the community and provide it to the user. For example, the generation AI can suggest improvements to meals based on comments and ratings from other users. This allows the user to obtain feedback from other users.

[0069] The food identification unit uses the emotion estimation function to analyze the emotions a user has when choosing a meal and can suggest food choices that will elicit positive emotions. For example, the generation AI in the food identification unit analyzes the user's facial expressions and estimates the emotion they have when choosing a meal. For example, it detects expressions of smiles or surprise and evaluates positive emotions. The food identification unit also suggests food choices that will elicit positive emotions based on the user's emotions. For example, if a user chooses a meal with a smile, the generation AI will suggest balanced meals or favorite dishes to maintain that emotion. This makes it possible to suggest food choices that will elicit positive emotions in the user.

[0070] The nutrient grasping unit can estimate fluctuations in nutritional value from the shape and color of ingredients and notify the user. For example, the generation AI of the nutrient grasping unit analyzes images of meals and estimates fluctuations in nutritional value from the shape and color of ingredients. For example, it evaluates nutritional value based on the intensity of the color of vegetables and the ripeness of fruits. The generation AI of the nutrient grasping unit also notifies the user of fluctuations in nutritional value based on the shape and color of ingredients. For example, it notifies the user that brightly colored vegetables are rich in vitamins. This allows the user to know fluctuations in nutritional value based on the shape and color of ingredients.

[0071] The nutrient grasping unit can also analyze cooking recipes and cooking procedures when analyzing text information and provide them to the user. For example, the generation AI of the nutrient grasping unit analyzes text information and identifies cooking recipes and cooking procedures. For example, it analyzes the ingredients and procedures of a recipe and provides them to the user. The generation AI of the nutrient grasping unit also calculates nutrients based on the cooking recipes and cooking procedures. For example, it evaluates fluctuations in nutrients based on the cooking procedures and notifies the user. This allows the user to obtain information about cooking recipes and cooking procedures.

[0072] The nutrient grasping unit can use the emotion estimation function to analyze the emotions in the text information entered by the user and provide advice to elicit positive emotions. In the nutrient grasping unit, for example, the generation AI analyzes the text information entered by the user and estimates the emotions. For example, it distinguishes between positive and negative expressions and evaluates the emotions. In addition, the nutrient grasping unit allows the generation AI to provide advice to elicit positive emotions based on the user's emotions. For example, if the user expresses negative emotions, the generation AI suggests ingredients and dishes to improve those emotions. This makes it possible to provide advice to elicit positive emotions from the user.

[0073] The nutrient grasping unit can also evaluate the plating and presentation of the meal in the image analysis and notify the user. For example, the generation AI analyzes an image of the meal and evaluates the plating and presentation. For example, it evaluates the placement of ingredients and color balance and notifies the user. The nutrient grasping unit also evaluates the meal based on the presentation of the meal by the generation AI. For example, it evaluates the beauty and balance of the appearance and notifies the user. This allows the user to obtain information about the plating and presentation of the meal.

[0074] The nutrient grasping unit can link the results of analyzing the text information with other health applications to support comprehensive health management. In the nutrient grasping unit, for example, the generation AI analyzes the text information and links the results with other health applications. For example, nutritional information and dietary history can be shared to support comprehensive health management. In addition, the generation AI in the nutrient grasping unit links with other health applications to comprehensively evaluate the user's health condition. For example, it links with fitness apps and sleep trackers to monitor the user's health condition. This allows the user to link with other health applications to perform comprehensive health management.

[0075] The nutrient grasping unit uses the emotion estimation function to analyze the emotions a user has when taking a photo of a meal and can suggest a shooting method that will bring out positive emotions. For example, the generation AI in the nutrient grasping unit analyzes the user's facial expressions and estimates the emotion they are feeling when taking a photo of a meal. For example, it detects expressions of smile or surprise and evaluates positive emotions. The generation AI in the nutrient grasping unit also suggests a shooting method that will bring out positive emotions based on the user's emotions. For example, if a user takes a photo while smiling, the generation AI will suggest the optimal camera angle and lighting to maintain that emotion. This makes it possible to suggest a shooting method that will bring out positive emotions in the user.

[0076] The nutrient grasping unit can also take into account nutrient fluctuations during the cooking process of ingredients and notify the user. For example, the generation AI in the nutrient grasping unit analyzes the cooking process of ingredients and evaluates nutrient fluctuations. For example, it takes into account the loss of vitamin C due to heating vegetables and the fluctuations in protein due to cooking meat. The generation AI in the nutrient grasping unit also notifies the user of nutrient fluctuations based on data from the cooking process. For example, it evaluates nutrient fluctuations based on cooking time and temperature and notifies the user. This allows the user to know the nutrient fluctuations during the cooking process of ingredients.

[0077] The nutrient identification unit can also evaluate the interactions between ingredients and provide the results to the user. For example, the generation AI in the nutrient identification unit analyzes the interactions between ingredients and evaluates the combination of ingredients that helps the absorption of specific vitamins. For example, it takes into account the interaction between vitamin C and iron and notifies the user. The generation AI in the nutrient identification unit also suggests combinations of ingredients that optimize nutrient absorption based on the interactions between ingredients. For example, it takes into account the interaction between vitamin D and calcium and notifies the user. This allows the user to know the absorption of nutrients based on the interactions between ingredients.

[0078] The nutrient grasping unit uses the emotion estimation function to analyze the user's emotions regarding the nutrients they consume and can suggest a nutrient balance that will elicit positive emotions. For example, the generation AI in the nutrient grasping unit analyzes the user's facial expressions and estimates their emotions regarding the nutrients they consume. For example, it detects smiling or surprised expressions and evaluates positive emotions. The generation AI in the nutrient grasping unit also suggests a nutrient balance that will elicit positive emotions based on the user's emotions. For example, if the user expresses positive emotions, it will suggest a balanced diet and favorite ingredients to maintain those emotions. This makes it possible to suggest a nutrient balance that will elicit positive emotions from the user.

[0079] The nutrient grasping unit can add a function to share the nutrient grasping results with other users and obtain feedback within the community. For example, the nutrient grasping unit builds a platform for sharing nutrient information grasped by the generation AI with other users. For example, nutritional information about meals is shared and feedback is obtained. The nutrient grasping unit also allows the generation AI to collect feedback within the community and provide it to the user. For example, the generation AI suggests improvements to meals based on comments and ratings from other users. This allows the user to obtain feedback from other users.

[0080] The nutrient grasping unit can also take into consideration information about the seasonality and in-seasonity of ingredients and notify the user. For example, the generation AI of the nutrient grasping unit analyzes information about the seasonality and in-seasonity of ingredients and notifies the user. For example, it identifies vegetables and fruits that are in season in spring and provides them to the user. The generation AI of the nutrient grasping unit also evaluates fluctuations in nutrients based on the seasonality and in-seasonity information. For example, it notifies the user that seasonal ingredients are high in nutritional value. This allows the user to know information about the seasonality and in-seasonity of ingredients.

[0081] The nutrient grasping unit uses the emotion estimation function to analyze the user's emotions toward the nutrients they consume and can suggest food ingredient selections that will elicit positive emotions. For example, the generation AI in the nutrient grasping unit analyzes the user's facial expressions and estimates their emotions toward the nutrients they consume. For example, it detects smiling or surprised expressions and evaluates positive emotions. The generation AI in the nutrient grasping unit also suggests food ingredient selections that will elicit positive emotions based on the user's emotions. For example, if the user expresses positive emotions, it suggests balanced ingredients or favorite ingredients to maintain those emotions. This makes it possible to suggest food ingredient selections that will elicit positive emotions in the user.

[0082] The nutrient grasping unit can perform customized nutrient calculations, taking into account the user's individual metabolic rate and allergy information. For example, the generating AI in the nutrient grasping unit analyzes the user's metabolic rate and performs individual nutrient calculations. For example, it adjusts calorie intake taking into account the basal metabolic rate and amount of exercise. The generating AI in the nutrient grasping unit also performs customized nutrient calculations based on the user's allergy information. For example, it excludes specific ingredients and suggests alternative ingredients. This makes it possible to perform nutrient calculations that take into account the user's individual metabolic rate and allergy information.

[0083] The nutrient grasping unit can evaluate nutrient variations, taking into account the storage method and cooking time of ingredients. For example, the generation AI of the nutrient grasping unit analyzes the storage method of ingredients and evaluates nutrient variations. For example, it takes into account variations in nutritional value due to freezing or refrigerating storage. The generation AI of the nutrient grasping unit also evaluates nutrient variations based on cooking time. For example, it evaluates nutrient variations based on heating time and cooking process time and notifies the user. This allows the user to know the nutrient variations based on the storage method and cooking time of ingredients.

[0084] The nutrient grasping unit uses the emotion estimation function to analyze the user's emotions regarding the nutrients they consume and can suggest a nutrient balance that will elicit positive emotions. For example, the generation AI in the nutrient grasping unit analyzes the user's facial expressions and estimates their emotions regarding the nutrients they consume. For example, it detects smiling or surprised expressions and evaluates positive emotions. The generation AI in the nutrient grasping unit also suggests a nutrient balance that will elicit positive emotions based on the user's emotions. For example, if the user expresses positive emotions, it will suggest a balanced diet and favorite ingredients to maintain those emotions. This makes it possible to suggest a nutrient balance that will elicit positive emotions from the user.

[0085] The nutrient grasping unit can link the nutrient calculation results with other health applications to support comprehensive health management. In the nutrient grasping unit, for example, the generation AI links the nutrient calculation results with other health applications. For example, nutritional information and dietary history can be shared to support comprehensive health management. In addition, the generation AI links with other health applications to comprehensively evaluate the user's health status. For example, it links with fitness apps and sleep trackers to monitor the user's health status. This allows the user to link with other health applications to perform comprehensive health management.

[0086] The nutrient identification unit can also take into account the origin and production method of ingredients and notify the user. For example, the generation AI in the nutrient identification unit identifies the origin information of ingredients and reflects this in the nutrient calculation. For example, it takes into account that tomatoes are from Italy and that lettuce is supplied from a local farm. The generation AI in the nutrient identification unit also identifies the production method of ingredients (organic, pesticide-free, etc.) and reflects this in the nutrient calculation. For example, it takes into account organic vegetables and ingredients grown without pesticides and notifies the user. This allows the user to obtain information about the origin and production method of ingredients.

[0087] The nutrient grasping unit uses the emotion estimation function to analyze the user's emotions regarding the nutrients they consume and can suggest a nutrient balance that will elicit positive emotions. For example, the generation AI in the nutrient grasping unit analyzes the user's facial expressions and estimates their emotions regarding the nutrients they consume. For example, it detects smiling or surprised expressions and evaluates positive emotions. The generation AI in the nutrient grasping unit also suggests a nutrient balance that will elicit positive emotions based on the user's emotions. For example, if the user expresses positive emotions, it will suggest a balanced diet and favorite ingredients to maintain those emotions. This makes it possible to suggest a nutrient balance that will elicit positive emotions from the user.

[0088] The nutrient grasping unit can perform customized nutrient calculations, taking into account the user's individual metabolic rate and allergy information. For example, the generating AI in the nutrient grasping unit analyzes the user's metabolic rate and performs individual nutrient calculations. For example, it adjusts calorie intake taking into account the basal metabolic rate and amount of exercise. The generating AI in the nutrient grasping unit also performs customized nutrient calculations based on the user's allergy information. For example, it excludes specific ingredients and suggests alternative ingredients. This makes it possible to perform nutrient calculations that take into account the user's individual metabolic rate and allergy information.

[0089] The nutrient grasping unit can evaluate nutrient variations, taking into account the storage method and cooking time of ingredients. For example, the generation AI of the nutrient grasping unit analyzes the storage method of ingredients and evaluates nutrient variations. For example, it takes into account variations in nutritional value due to freezing or refrigerating storage. The generation AI of the nutrient grasping unit also evaluates nutrient variations based on cooking time. For example, it evaluates nutrient variations based on heating time and cooking process time and notifies the user. This allows the user to know the nutrient variations based on the storage method and cooking time of ingredients.

[0090] The nutrient grasping unit uses the emotion estimation function to analyze the user's emotions regarding the nutrients they consume and can suggest a nutrient balance that will elicit positive emotions. For example, the generation AI in the nutrient grasping unit analyzes the user's facial expressions and estimates their emotions regarding the nutrients they consume. For example, it detects smiling or surprised expressions and evaluates positive emotions. The generation AI in the nutrient grasping unit also suggests a nutrient balance that will elicit positive emotions based on the user's emotions. For example, if the user expresses positive emotions, it will suggest a balanced diet and favorite ingredients to maintain those emotions. This makes it possible to suggest a nutrient balance that will elicit positive emotions from the user.

[0091] The nutrient grasping unit can link the nutrient calculation results with other health applications to support comprehensive health management. In the nutrient grasping unit, for example, the generation AI links the nutrient calculation results with other health applications. For example, nutritional information and dietary history can be shared to support comprehensive health management. In addition, the generation AI links with other health applications to comprehensively evaluate the user's health status. For example, it links with fitness apps and sleep trackers to monitor the user's health status. This allows the user to link with other health applications to perform comprehensive health management.

[0092] The nutrient identification unit can also take into account the origin and production method of ingredients and notify the user. For example, the generation AI in the nutrient identification unit identifies the origin information of ingredients and reflects this in the nutrient calculation. For example, it takes into account that tomatoes are from Italy and that lettuce is supplied from a local farm. The generation AI in the nutrient identification unit also identifies the production method of ingredients (organic, pesticide-free, etc.) and reflects this in the nutrient calculation. For example, it takes into account organic vegetables and ingredients grown without pesticides and notifies the user. This allows the user to obtain information about the origin and production method of ingredients.

[0093] The nutrient grasping unit uses the emotion estimation function to analyze the user's emotions regarding the nutrients they consume and can suggest a nutrient balance that will elicit positive emotions. For example, the generation AI in the nutrient grasping unit analyzes the user's facial expressions and estimates their emotions regarding the nutrients they consume. For example, it detects smiling or surprised expressions and evaluates positive emotions. The generation AI in the nutrient grasping unit also suggests a nutrient balance that will elicit positive emotions based on the user's emotions. For example, if the user expresses positive emotions, it will suggest a balanced diet and favorite ingredients to maintain those emotions. This makes it possible to suggest a nutrient balance that will elicit positive emotions from the user.

[0094] The nutrient grasping unit can perform customized nutrient calculations, taking into account the user's individual metabolic rate and allergy information. For example, the generating AI in the nutrient grasping unit analyzes the user's metabolic rate and performs individual nutrient calculations. For example, it adjusts calorie intake taking into account the basal metabolic rate and amount of exercise. The generating AI in the nutrient grasping unit also performs customized nutrient calculations based on the user's allergy information. For example, it excludes specific ingredients and suggests alternative ingredients. This makes it possible to perform nutrient calculations that take into account the user's individual metabolic rate and allergy information.

[0095] The nutrient grasping unit can evaluate nutrient variations, taking into account the storage method and cooking time of ingredients. For example, the generation AI of the nutrient grasping unit analyzes the storage method of ingredients and evaluates nutrient variations. For example, it takes into account variations in nutritional value due to freezing or refrigerating storage. The generation AI of the nutrient grasping unit also evaluates nutrient variations based on cooking time. For example, it evaluates nutrient variations based on heating time and cooking process time and notifies the user. This allows the user to know the nutrient variations based on the storage method and cooking time of ingredients.

[0096] The nutrient grasping unit uses the emotion estimation function to analyze the user's emotions regarding the nutrients they consume and can suggest a nutrient balance that will elicit positive emotions. For example, the generation AI in the nutrient grasping unit analyzes the user's facial expressions and estimates their emotions regarding the nutrients they consume. For example, it detects smiling or surprised expressions and evaluates positive emotions. The generation AI in the nutrient grasping unit also suggests a nutrient balance that will elicit positive emotions based on the user's emotions. For example, if the user expresses positive emotions, it will suggest a balanced diet and favorite ingredients to maintain those emotions. This makes it possible to suggest a nutrient balance that will elicit positive emotions from the user.

[0097] The nutrient grasping unit can link the nutrient calculation results with other health applications to support comprehensive health management. In the nutrient grasping unit, for example, the generation AI links the nutrient calculation results with other health applications. For example, nutritional information and dietary history can be shared to support comprehensive health management. In addition, the generation AI links with other health applications to comprehensively evaluate the user's health status. For example, it links with fitness apps and sleep trackers to monitor the user's health status. This allows the user to link with other health applications to perform comprehensive health management.

[0098] The nutrient identification unit can also take into account the origin and production method of ingredients and notify the user. For example, the generation AI in the nutrient identification unit identifies the origin information of ingredients and reflects this in the nutrient calculation. For example, it takes into account that tomatoes are from Italy and that lettuce is supplied from a local farm. The generation AI in the nutrient identification unit also identifies the production method of ingredients (organic, pesticide-free, etc.) and reflects this in the nutrient calculation. For example, it takes into account organic vegetables and ingredients grown without pesticides and notifies the user. This allows the user to obtain information about the origin and production method of ingredients.

[0099] The nutrient grasping unit uses the emotion estimation function to analyze the user's emotions regarding the nutrients they consume and can suggest a nutrient balance that will elicit positive emotions. For example, the generation AI in the nutrient grasping unit analyzes the user's facial expressions and estimates their emotions regarding the nutrients they consume. For example, it detects smiling or surprised expressions and evaluates positive emotions. The generation AI in the nutrient grasping unit also suggests a nutrient balance that will elicit positive emotions based on the user's emotions. For example, if the user expresses positive emotions, it will suggest a balanced diet and favorite ingredients to maintain those emotions. This makes it possible to suggest a nutrient balance that will elicit positive emotions from the user.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The health promotion system can also collect the user's exercise data and provide advice that takes into account the balance between diet and exercise. For example, if the user goes running, the system can adjust the calorie intake based on the calories burned. It can also suggest necessary nutrients based on the type and intensity of exercise. This allows the user to optimize the balance between diet and exercise and make it easier to achieve their health goals.

[0102] The health promotion system can also obtain the user's sleep data and provide advice that takes into account the relationship between diet and sleep. For example, if the user is not getting enough sleep, the generating AI will take that impact into account and suggest foods suitable for replenishing energy. It can also provide dietary advice to improve sleep quality. This allows users to optimize the balance between diet and sleep and improve their overall health.

[0103] The health promotion system can also monitor the user's stress level and suggest ingredients and meal plans that will help reduce stress. For example, if the user's stress level is high, the AI ​​generator can suggest ingredients and herbal teas that have a relaxing effect. It can also provide meal plans that include nutrients that are effective in reducing stress. This allows users to manage their stress through diet.

[0104] The health promotion system can also monitor the user's water intake and support proper hydration. For example, if the user is not drinking enough water, the generative AI will notify them of the importance of hydration and suggest appropriate drinks and water-rich foods. It can also provide advice on adjusting water intake depending on exercise and weather conditions. This allows users to maintain proper hydration and optimize their health.

[0105] The health promotion system can also analyze the user's dietary history and propose meal plans aimed at long-term health goals. For example, it can identify nutritional imbalances based on past dietary data and suggest areas for improvement. It can also provide meal plans tailored to the season or events. This allows users to manage their health from a long-term perspective.

[0106] The health promotion system uses its emotion estimation function to analyze the emotions felt when a user eats a meal and can suggest a dining environment that will elicit positive emotions. For example, it can suggest lighting and music settings so that the user can eat in a relaxing environment. It can also provide advice on table settings and tableware selection that will elicit positive emotions while eating. This allows users to enjoy their meals more and contributes to promoting their health.

[0107] The health promotion system uses its emotion estimation function to analyze the emotions a user feels when eating a meal and can suggest meal timings that will elicit positive emotions. For example, it can advise the user to avoid eating during times when they are feeling stressed. It can also suggest eating at times when the user is relaxed, which will improve digestion and absorption. This allows the user to eat more effectively and contributes to the promotion of health.

[0108] The health promotion system uses its emotion estimation function to analyze the emotions felt when a user eats a meal and can suggest dining companions who will bring out positive emotions. For example, it can suggest friends or family members with whom the user can relax when eating. It can also suggest hosting dinner parties or events to provide a dining environment where the user can feel positive emotions. This allows users to enjoy their meals more and contributes to promoting their health.

[0109] The health promotion system uses the emotion estimation function to analyze the emotions a user feels when eating a meal and can suggest meal menus that elicit positive emotions. For example, it can suggest menus that elicit positive emotions based on the user's favorite ingredients and dishes. Furthermore, by selecting ingredients and dishes that help the user relax, meal satisfaction can be increased. This allows users to enjoy their meals more and contributes to promoting their health.

[0110] The health promotion system uses its emotion estimation function to analyze the emotions a user feels when eating a meal and can suggest a meal order that will elicit positive emotions. For example, it can suggest an order from appetizer to main dish to dessert to help the user relax. In addition, by devising a meal order that will help the user feel positive emotions, it can increase meal satisfaction. This allows the user to enjoy their meal more and contributes to promoting their health.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The meal identification unit analyzes the image and text information of the meal to identify the contents of the meal. For example, a user takes a photo of the meal and inputs it into the generation AI. The generation AI analyzes the photo and identifies the ingredients and type of dish. For example, it may identify it as "This is a salad, and it contains lettuce, tomato, cucumber, and dressing." Step 2: The nutrient identification unit calculates the nutrients based on the identified food contents. For example, the nutritional information of the entire meal is identified based on information such as lettuce containing vitamin A and dietary fiber, and tomatoes containing vitamin C and lycopene. The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI calculates the nutrients based on that prompt. Step 3: The advice provider compares the captured nutritional information with the user's health goals and provides appropriate advice. For example, it may provide specific advice such as, "Today's meal is lacking in vitamin C, so it would be good to eat oranges or bell peppers at your next meal."

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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, in order to avoid confusion and to 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.

[0179] 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]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a meal identification unit that analyzes images and text information of meals and identifies the contents of the meals; a nutrient grasping unit that calculates nutrients based on the contents of the meal identified by the meal identifying unit; an advice providing unit that compares the nutrient information grasped by the nutrient grasping unit with the user's health goal and provides advice to the user; A system characterized by:

2. The meal identifying unit is The system analyzes the emotions the user feels when taking a photo of a meal and suggests ingredients and dishes that will elicit positive emotions.

2. The system of claim 1.

3. The meal identifying unit is Cooking instructions for the meal are also identified and provided to the user.

2. The system of claim 1.

4. The nutrient grasping unit Estimates nutritional value fluctuations based on the shape and color of food ingredients and notifies users 2. The system of claim 1.

5. The nutrient grasping unit The fluctuation of the nutrients during the cooking process of the ingredients is also taken into consideration, and the user is notified.

2. The system of claim 1.

6. The nutrient grasping unit Customized nutrition calculations are performed, taking into account the user's individual metabolic rate and allergy information.

2. The system of claim 1.

7. The nutrient grasping unit Customized nutrition calculations are performed, taking into account the user's individual metabolic rate and allergy information.

2. The system of claim 1.

8. The nutrient grasping unit Customized nutrition calculations are performed, taking into account the user's individual metabolic rate and allergy information.

2. The system of claim 1.

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