System

Abstract

An object of a system according to an embodiment is to allow a user to accurately grasp the content of his / her own meal and optimize the nutrition balance and the calorie intake.SOLUTION: According to an embodiment, a system includes a meal photo analyzer, a nutritional value recognizer, a feedback provider, and a recipe suggester. The meal photograph analysis unit analyzes a photograph of a meal taken by a user. The nutrition value recognition unit recognizes a nutrition value of each food item from the picture of the meal analyzed by the meal picture analysis unit. The feedback providing unit provides feedback on the nutrition balance and the calorie intake based on the nutrition value recognized by the nutrition value recognizing unit. The recipe suggester suggests an individualized recipe based on the feedback provided by the feedback provider.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult for users to accurately understand their own dietary habits and optimize their nutritional balance and calorie intake.

[0005] The system according to the embodiment aims to enable a user to accurately understand the contents of his / her diet and to optimize nutritional balance and calorie intake. [Means for solving the problem]

[0006] The system according to the embodiment includes a meal photo analysis unit, a nutritional value recognition unit, a feedback provision unit, and a recipe suggestion unit. The meal photo analysis unit analyzes meal photos taken by a user. The nutritional value recognition unit recognizes the nutritional value of each food item from the meal photos analyzed by the meal photo analysis unit. The feedback provision unit provides feedback on nutritional balance and calorie intake based on the nutritional value recognized by the nutritional value recognition unit. The recipe suggestion unit suggests individual recipes based on the feedback provided by the feedback provision unit. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to accurately understand the contents of his / her diet and optimize the nutritional balance and calorie intake. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The nutrition management system according to an embodiment of the present invention analyzes the user's dietary details in detail, and the AI ​​generation system provides advice to optimize nutritional balance and calorie intake. This allows the nutrition management system to grasp the user's dietary details in detail and optimize nutritional balance and calorie intake.

[0029] A nutrition management system according to an embodiment includes a meal photo analysis unit, a nutritional value recognition unit, a feedback provision unit, and a recipe suggestion unit. The meal photo analysis unit analyzes meal photos taken by a user. For example, the meal photo analysis unit uses a generation AI to recognize the types and quantities of food in the photos. The meal photo analysis unit can also adjust the analysis accuracy depending on the resolution and format of the photos. For example, it analyzes photos taken with a smartphone or photos with a certain resolution or higher. The nutritional value recognition unit recognizes the nutritional value of each food item from the meal photos analyzed by the meal photo analysis unit. For example, the nutritional value recognition unit uses a generation AI to calculate nutrients such as calories, protein, fat, carbohydrates, vitamins, and minerals. The nutritional value recognition unit can also calculate detailed nutritional value based on the type and quantity of food. For example, the generation AI references a food nutritional value database to accurately recognize the nutritional value of each food item. The feedback provision unit provides feedback on nutritional balance and calorie intake based on the nutritional value recognized by the nutritional value recognition unit. For example, the feedback provision unit uses a generation AI to generate specific advice based on the user's diet history and health goals. The feedback providing unit can also provide advice in text format or visual feedback using graphs and charts. For example, the feedback providing unit can provide specific advice such as, "Today's meal is low in protein. It would be a good idea to add fish or beans to your next meal." The recipe suggestion unit can suggest individual recipes based on the feedback provided by the feedback providing unit. For example, the recipe suggestion unit can use a generation AI to generate recipes to optimize the user's nutritional balance and calorie intake. The recipe suggestion unit can also suggest optimal recipes taking into account the user's preferences and allergy information. For example, the recipe suggestion unit can provide recipes tailored to the user's needs, such as "low-calorie, high-protein recipes." This allows the nutrition management system according to the embodiment to analyze the user's diet in detail and provide advice to optimize the nutritional balance and calorie intake. For example, the user can understand their diet in detail and receive specific advice to maintain a healthy diet.It also helps you improve your daily diet through personalized recipes and meal plans.

[0030] The food photo analysis unit can recognize food cooking methods and calculate nutritional values ​​based on that. For example, when analyzing a food photo, the generative AI recognizes the food cooking method. For example, since the calorie and fat content of grilled meat and fried meat differ, the cooking method is taken into account when calculating nutritional value. The food photo analysis unit can also accurately recognize cooking methods such as grilling, boiling, and frying using cooking method classification criteria and recognition algorithms. This allows for more accurate calculation of nutritional value by taking the food cooking method into account.

[0031] The meal photo analysis unit can evaluate the freshness and quality of food and calculate nutritional value based on that. For example, when analyzing a meal photo, the generation AI evaluates the freshness of the food. For example, because the vitamin content of fresh and old vegetables differs, the nutritional value is calculated taking freshness into account. The meal photo analysis unit can also use criteria such as changes in color and shape, and shelf life to evaluate food quality. For example, the generation AI evaluates the appearance, smell, and texture of food to determine quality. This allows the nutritional value to be calculated taking into account the freshness and quality of the food.

[0032] The meal photo analysis unit can recognize the origin and production method of food and calculate nutritional value based on that. For example, when analyzing a meal photo, the generation AI recognizes the origin of the food and calculates nutritional value based on that. For example, since the nutritional value of locally grown vegetables and imported vegetables differs, the origin is taken into consideration. The meal photo analysis unit can also recognize the production method of food (organic, pesticide-free, etc.) and calculate nutritional value based on that. For example, the generation AI can recognize the origin and production method using food label information or barcode scanning. This makes it possible to calculate nutritional value taking into account the origin and production method of food.

[0033] The meal photo analysis unit can integrate the analysis results of meal photos with other health data to provide a comprehensive health assessment. For example, the meal photo analysis unit can integrate the analysis results of meal photos with the user's exercise data to provide a comprehensive health assessment. For example, on days when the user exercises a lot, it can provide advice on increasing calorie intake. The meal photo analysis unit can also integrate with the user's health data, such as sleep data and heart rate data, to provide a comprehensive health assessment. For example, the generative AI can continuously analyze the user's health data and support health management. This allows the analysis results of meal photos to be integrated with other health data to provide a comprehensive health assessment.

[0034] The feedback providing unit can provide personalized nutritional advice by taking into account the user's genetic information and blood test results. For example, when providing feedback, the generation AI can provide personalized nutritional advice by taking into account the user's genetic information. For example, a user with a specific gene mutation can be recommended to take a specific vitamin. The feedback providing unit can also provide personalized nutritional advice by taking into account the user's blood test results. For example, the generation AI can generate nutritional advice based on the user's blood sugar and cholesterol levels. This makes it possible to provide personalized nutritional advice by taking into account the user's genetic information and blood test results.

[0035] The feedback providing unit can take seasonal and climate fluctuations into account and include suggestions to optimize nutritional balance for each season. For example, the feedback providing unit can include suggestions to optimize nutritional balance for each season and take seasonal fluctuations into account in the feedback content. For example, advice to increase vitamin D intake in winter can be provided. The feedback providing unit can also make suggestions to optimize nutritional balance and take climate fluctuations into account. For example, the generation AI generates nutritional advice based on data such as temperature, humidity, and precipitation. This makes it possible to provide suggestions to optimize nutritional balance and take seasonal and climate fluctuations into account.

[0036] The feedback providing unit can provide appropriate advice by taking into account the user's cultural background and religious restrictions. For example, when providing feedback, the generation AI can provide appropriate advice by taking into account the user's cultural background. For example, the generation AI can recommend foods that are important in a particular culture. The feedback providing unit can also provide appropriate advice by taking into account the user's religious restrictions. For example, the generation AI can generate nutritional advice by taking into account the user's dietary restrictions and religious events. This allows the generation AI to provide appropriate advice by taking into account the user's cultural background and religious restrictions.

[0037] The feedback providing unit can link the dietary content of the user's family and housemates to make suggestions to optimize the nutritional balance of the entire household. For example, the feedback providing unit can link the feedback content with the dietary content of the user's family and housemates to make suggestions to optimize the nutritional balance of the entire household. For example, it can advise all family members to eat a balanced diet. The feedback providing unit can also take into account the dietary history of the user's family and housemates to make suggestions to optimize the nutritional balance of the entire household. For example, the generation AI can analyze the dietary data of all family members and propose an optimal meal plan. This can link the dietary content of the user's family and housemates to make suggestions to optimize the nutritional balance of the entire household.

[0038] The recipe suggestion unit can analyze the user's past eating history and suggest recipes with a wide variety to prevent the user from getting bored. For example, when suggesting a recipe, the recipe suggestion unit uses a generation AI to analyze the user's past eating history and suggest recipes with a wide variety to prevent the user from getting bored. For example, it provides recipes using the same ingredients but with different cooking methods. The recipe suggestion unit can also generate recipes with a wide variety based on the user's eating history. For example, the generation AI takes into consideration the user's preferences and allergy information to suggest recipes that will prevent the user from getting bored. This makes it possible to analyze the user's past eating history and suggest recipes with a wide variety to prevent the user from getting bored.

[0039] The recipe suggestion unit can provide a feasible recipe by taking into account the user's cooking skills and kitchen equipment. For example, when proposing a recipe, the generation AI takes into account the user's cooking skills and provides a feasible recipe. For example, it may propose simple recipes for beginners and complex recipes for advanced users. The recipe suggestion unit can also provide a feasible recipe by taking into account the user's kitchen equipment. For example, the generation AI generates a recipe based on the user's kitchen equipment (oven, mixer, refrigerator, etc.). This makes it possible to provide a feasible recipe by taking into account the user's cooking skills and kitchen equipment.

[0040] The recipe suggestion unit can provide recipes that utilize local ingredients, taking into account the availability of ingredients in the user's area. For example, when suggesting a recipe, the generation AI can provide recipes that utilize local ingredients, taking into account the availability of ingredients in the user's area. For example, the generation AI can suggest recipes that use fresh vegetables available at a local market. The recipe suggestion unit can also generate recipes based on the availability of ingredients in the user's area. For example, the generation AI can suggest recipes taking into account seasonal availability and distribution conditions. This allows the recipe suggestion unit to provide recipes that utilize local ingredients, taking into account the availability of ingredients in the user's area.

[0041] The recipe suggestion unit can provide recipes that provide optimal post-exercise nutrition by taking into account the user's exercise plan and fitness goals. For example, when proposing a recipe, the generation AI can provide recipes that provide optimal post-exercise nutrition by taking into account the user's exercise plan. For example, the generation AI can provide recipes that are high in protein after strength training. The recipe suggestion unit can also provide recipes that provide optimal post-exercise nutrition by taking into account the user's fitness goals. For example, the generation AI generates recipes based on the user's exercise schedule and fitness goals. This allows the recipes to be provided that provide optimal post-exercise nutrition by taking into account the user's exercise plan and fitness goals.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The nutrition management system can further include a cultural adaptation component that takes into account a user's cultural background and religious restrictions regarding diet. For example, it can provide appropriate nutritional advice by respecting foods and eating habits that are important in a particular culture. It can also take into account religious dietary restrictions to ensure that a user can enjoy their meal with peace of mind. For example, it can support a user's religious diet by recommending halal or kosher foods. This allows it to provide personalized nutritional advice that takes into account a user's cultural background and religious restrictions.

[0044] The nutrition management system can further include an environmental assessment unit that evaluates the environmental impact of the user's meals. For example, it can recommend environmentally friendly ingredients by taking into account the production process and transportation distance of the ingredients. It can also calculate the carbon footprint of the ingredients selected by the user and provide advice on reducing the environmental impact. For example, it can suggest using locally produced ingredients to reduce the environmental impact of transportation. This makes it possible to provide nutrition advice that takes into account the impact of the user's meal choices on the environment.

[0045] The nutrition management system can also include an economic evaluation unit that takes into account the economic aspects of the user's diet. For example, it evaluates the price and cost performance of ingredients and makes suggestions to optimize nutritional balance within a budget. It can also provide advice on achieving a healthy diet while keeping costs down by utilizing sale information and coupons. For example, it can reduce the user's financial burden by recommending inexpensive, nutritious ingredients. This allows it to provide nutrition advice that takes into account the user's financial situation.

[0046] The nutrition management system can further include a social evaluation unit that takes into account the social aspects of the user's meals. For example, it can take into account mealtimes the user shares with friends and family and suggest recipes that everyone can enjoy. It can also provide advice that promotes communication through meals and strengthens social connections. For example, suggesting recipes suitable for parties or picnics can increase the user's social satisfaction. This makes it possible to provide nutrition advice that takes into account the user's social aspects.

[0047] The nutrition management system can further include an allergy management unit that takes into account allergy information related to the user's diet. For example, if a user is allergic to a particular food, the system can advise the user to avoid that food. It can also suggest alternative foods based on the allergy information. For example, if a user has a dairy allergy, it can suggest recipes that do not contain dairy products. This allows the system to provide safe nutrition advice that takes into account the user's allergy information.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The food photo analysis unit analyzes food photos taken by the user. For example, the food photo analysis unit uses generative AI to recognize the types and quantities of food in the photos. The food photo analysis unit can also adjust the analysis accuracy depending on the resolution and format of the photos. For example, it analyzes photos taken with a smartphone or photos with a certain resolution or higher. Step 2: The nutritional value recognition unit recognizes the nutritional value of each food from the meal photos analyzed by the meal photo analysis unit. For example, the nutritional value recognition unit uses the generation AI to calculate nutrients such as calories, protein, fat, carbohydrates, vitamins, and minerals. The nutritional value recognition unit can also calculate detailed nutritional values ​​based on the type and amount of food. For example, the generation AI references a food nutritional value database to accurately recognize the nutritional value of each food. Step 3: The feedback providing unit provides feedback on nutritional balance and calorie intake based on the nutritional value recognized by the nutritional value recognition unit. For example, the feedback providing unit uses a generation AI to generate specific advice based on the user's dietary history and health goals. The feedback providing unit can also provide text advice or visual feedback using graphs and charts. For example, it may provide specific advice such as, "Today's meal is lacking in protein. It would be a good idea to add fish or beans to your next meal." Step 4: The recipe suggestion unit suggests individual recipes based on the feedback provided by the feedback provision unit. For example, the recipe suggestion unit uses a generation AI to generate recipes that optimize the user's nutritional balance and calorie intake. The recipe suggestion unit can also suggest optimal recipes taking into account the user's preferences and allergy information. For example, it can provide recipes tailored to the user's needs, such as "low-calorie, high-protein recipes."

[0050] (Example 2) The nutrition management system according to an embodiment of the present invention analyzes the user's dietary details in detail, and the AI ​​generation system provides advice to optimize nutritional balance and calorie intake. This allows the nutrition management system to grasp the user's dietary details in detail and optimize nutritional balance and calorie intake.

[0051] A nutrition management system according to an embodiment includes a meal photo analysis unit, a nutritional value recognition unit, a feedback provision unit, and a recipe suggestion unit. The meal photo analysis unit analyzes meal photos taken by a user. For example, the meal photo analysis unit uses a generation AI to recognize the types and quantities of food in the photos. The meal photo analysis unit can also adjust the analysis accuracy depending on the resolution and format of the photos. For example, it analyzes photos taken with a smartphone or photos with a certain resolution or higher. The nutritional value recognition unit recognizes the nutritional value of each food item from the meal photos analyzed by the meal photo analysis unit. For example, the nutritional value recognition unit uses a generation AI to calculate nutrients such as calories, protein, fat, carbohydrates, vitamins, and minerals. The nutritional value recognition unit can also calculate detailed nutritional value based on the type and quantity of food. For example, the generation AI references a food nutritional value database to accurately recognize the nutritional value of each food item. The feedback provision unit provides feedback on nutritional balance and calorie intake based on the nutritional value recognized by the nutritional value recognition unit. For example, the feedback provision unit uses a generation AI to generate specific advice based on the user's diet history and health goals. The feedback providing unit can also provide advice in text format or visual feedback using graphs and charts. For example, the feedback providing unit can provide specific advice such as, "Today's meal is low in protein. It would be a good idea to add fish or beans to your next meal." The recipe suggestion unit can suggest individual recipes based on the feedback provided by the feedback providing unit. For example, the recipe suggestion unit can use a generation AI to generate recipes to optimize the user's nutritional balance and calorie intake. The recipe suggestion unit can also suggest optimal recipes taking into account the user's preferences and allergy information. For example, the recipe suggestion unit can provide recipes tailored to the user's needs, such as "low-calorie, high-protein recipes." This allows the nutrition management system according to the embodiment to analyze the user's diet in detail and provide advice to optimize the nutritional balance and calorie intake. For example, the user can understand their diet in detail and receive specific advice to maintain a healthy diet.It also helps you improve your daily diet through personalized recipes and meal plans.

[0052] The food photo analysis unit can recognize food cooking methods and calculate nutritional values ​​based on that. For example, when analyzing a food photo, the generative AI recognizes the food cooking method. For example, since the calorie and fat content of grilled meat and fried meat differ, the cooking method is taken into account when calculating nutritional value. The food photo analysis unit can also accurately recognize cooking methods such as grilling, boiling, and frying using cooking method classification criteria and recognition algorithms. This allows for more accurate calculation of nutritional value by taking the food cooking method into account.

[0053] The meal photo analysis unit can evaluate the freshness and quality of food and calculate nutritional value based on that. For example, when analyzing a meal photo, the generation AI evaluates the freshness of the food. For example, because the vitamin content of fresh and old vegetables differs, the nutritional value is calculated taking freshness into account. The meal photo analysis unit can also use criteria such as changes in color and shape, and shelf life to evaluate food quality. For example, the generation AI evaluates the appearance, smell, and texture of food to determine quality. This allows the nutritional value to be calculated taking into account the freshness and quality of the food.

[0054] The meal photo analysis unit can analyze the user's emotions and prioritize analysis of meals that evoke strong positive emotions. For example, when analyzing meal photos, the generation AI analyzes the user's emotions and prioritizes analysis of meals that evoke strong positive emotions. For example, if the user is enjoying a meal, the meal will be analyzed in detail. The meal photo analysis unit can also use an emotion estimation function to estimate emotions from the user's facial expressions, voice, text, etc. For example, the generation AI can determine positive emotions based on the user's smile or expression of joy. This allows it to prioritize analysis of meals that evoke strong positive emotions.

[0055] The meal photo analysis unit can recognize the origin and production method of food and calculate nutritional value based on that. For example, when analyzing a meal photo, the generation AI recognizes the origin of the food and calculates nutritional value based on that. For example, since the nutritional value of locally grown vegetables and imported vegetables differs, the origin is taken into consideration. The meal photo analysis unit can also recognize the production method of food (organic, pesticide-free, etc.) and calculate nutritional value based on that. For example, the generation AI can recognize the origin and production method using food label information or barcode scanning. This makes it possible to calculate nutritional value taking into account the origin and production method of food.

[0056] The meal photo analysis unit can integrate the analysis results of meal photos with other health data to provide a comprehensive health assessment. For example, the meal photo analysis unit can integrate the analysis results of meal photos with the user's exercise data to provide a comprehensive health assessment. For example, on days when the user exercises a lot, it can provide advice on increasing calorie intake. The meal photo analysis unit can also integrate with the user's health data, such as sleep data and heart rate data, to provide a comprehensive health assessment. For example, the generative AI can continuously analyze the user's health data and support health management. This allows the analysis results of meal photos to be integrated with other health data to provide a comprehensive health assessment.

[0057] The meal photo analysis unit can provide feedback on the user's emotions in real time and make suggestions that will elicit positive emotions. For example, when taking a meal photo, the generation AI can provide feedback on the user's emotions in real time and make suggestions that will elicit positive emotions. For example, it can encourage the user to smile and enjoy their meal. The meal photo analysis unit can also use an emotion estimation function to analyze emotions in real time from the user's facial expressions, voice, text, etc. For example, the generation AI can provide feedback based on the user's emotion score. This allows the generation AI to provide feedback on the user's emotions in real time and make suggestions that will elicit positive emotions.

[0058] The feedback providing unit can provide personalized nutritional advice by taking into account the user's genetic information and blood test results. For example, when providing feedback, the generation AI can provide personalized nutritional advice by taking into account the user's genetic information. For example, a user with a specific gene mutation can be recommended to take a specific vitamin. The feedback providing unit can also provide personalized nutritional advice by taking into account the user's blood test results. For example, the generation AI can generate nutritional advice based on the user's blood sugar and cholesterol levels. This makes it possible to provide personalized nutritional advice by taking into account the user's genetic information and blood test results.

[0059] The feedback providing unit can take seasonal and climate fluctuations into account and include suggestions to optimize nutritional balance for each season. For example, the feedback providing unit can include suggestions to optimize nutritional balance for each season and take seasonal fluctuations into account in the feedback content. For example, advice to increase vitamin D intake in winter can be provided. The feedback providing unit can also make suggestions to optimize nutritional balance and take climate fluctuations into account. For example, the generation AI generates nutritional advice based on data such as temperature, humidity, and precipitation. This makes it possible to provide suggestions to optimize nutritional balance and take seasonal and climate fluctuations into account.

[0060] The feedback providing unit can analyze the user's emotional state and provide advice that elicits positive emotions. For example, when providing feedback, the generation AI analyzes the user's emotional state and provides advice that elicits positive emotions. For example, if the user is feeling stressed, the feedback providing unit can recommend foods that have a relaxing effect. The feedback providing unit can also use an emotion estimation function to analyze emotions from the user's facial expressions, voice, text, etc. For example, the generation AI provides advice based on the user's emotion score. This allows the generation AI to analyze the user's emotional state and provide advice that elicits positive emotions.

[0061] The feedback providing unit can provide appropriate advice by taking into account the user's cultural background and religious restrictions. For example, when providing feedback, the generation AI can provide appropriate advice by taking into account the user's cultural background. For example, the generation AI can recommend foods that are important in a particular culture. The feedback providing unit can also provide appropriate advice by taking into account the user's religious restrictions. For example, the generation AI can generate nutritional advice by taking into account the user's dietary restrictions and religious events. This allows the generation AI to provide appropriate advice by taking into account the user's cultural background and religious restrictions.

[0062] The feedback providing unit can link the dietary content of the user's family and housemates to make suggestions to optimize the nutritional balance of the entire household. For example, the feedback providing unit can link the feedback content with the dietary content of the user's family and housemates to make suggestions to optimize the nutritional balance of the entire household. For example, it can advise all family members to eat a balanced diet. The feedback providing unit can also take into account the dietary history of the user's family and housemates to make suggestions to optimize the nutritional balance of the entire household. For example, the generation AI can analyze the dietary data of all family members and propose an optimal meal plan. This can link the dietary content of the user's family and housemates to make suggestions to optimize the nutritional balance of the entire household.

[0063] The feedback providing unit can monitor the user's emotional state in real time and continuously provide feedback according to the emotion. The feedback providing unit can, for example, use an emotion estimation function to monitor the user's emotional state in real time and continuously provide feedback according to the emotion. For example, if the user is feeling stressed, the feedback providing unit can recommend foods that have a relaxing effect. The feedback providing unit can also continuously monitor the user's emotional state and provide feedback. For example, the generation AI provides feedback based on the user's emotion score and updates advice according to changes in emotion. This makes it possible to monitor the user's emotional state in real time and continuously provide feedback according to the emotion.

[0064] The recipe suggestion unit can analyze the user's past eating history and suggest recipes with a wide variety to prevent the user from getting bored. For example, when suggesting a recipe, the recipe suggestion unit uses a generation AI to analyze the user's past eating history and suggest recipes with a wide variety to prevent the user from getting bored. For example, it provides recipes using the same ingredients but with different cooking methods. The recipe suggestion unit can also generate recipes with a wide variety based on the user's eating history. For example, the generation AI takes into consideration the user's preferences and allergy information to suggest recipes that will prevent the user from getting bored. This makes it possible to analyze the user's past eating history and suggest recipes with a wide variety to prevent the user from getting bored.

[0065] The recipe suggestion unit can provide a feasible recipe by taking into account the user's cooking skills and kitchen equipment. For example, when proposing a recipe, the generation AI takes into account the user's cooking skills and provides a feasible recipe. For example, it may propose simple recipes for beginners and complex recipes for advanced users. The recipe suggestion unit can also provide a feasible recipe by taking into account the user's kitchen equipment. For example, the generation AI generates a recipe based on the user's kitchen equipment (oven, mixer, refrigerator, etc.). This makes it possible to provide a feasible recipe by taking into account the user's cooking skills and kitchen equipment.

[0066] The recipe suggestion unit can analyze the user's emotional state and suggest dishes that elicit positive emotions. For example, when suggesting a recipe, the recipe suggestion unit uses the generation AI to analyze the user's emotional state and suggest dishes that elicit positive emotions. For example, if the user is feeling stressed, the recipe suggestion unit can suggest dishes that have a relaxing effect. The recipe suggestion unit can also use the emotion estimation function to generate recipes based on the user's emotional state. For example, the generation AI can suggest dishes that elicit positive emotions based on the user's emotion score. This makes it possible to analyze the user's emotional state and suggest dishes that elicit positive emotions.

[0067] The recipe suggestion unit can provide recipes that utilize local ingredients, taking into account the availability of ingredients in the user's area. For example, when suggesting a recipe, the generation AI can provide recipes that utilize local ingredients, taking into account the availability of ingredients in the user's area. For example, the generation AI can suggest recipes that use fresh vegetables available at a local market. The recipe suggestion unit can also generate recipes based on the availability of ingredients in the user's area. For example, the generation AI can suggest recipes taking into account seasonal availability and distribution conditions. This allows the recipe suggestion unit to provide recipes that utilize local ingredients, taking into account the availability of ingredients in the user's area.

[0068] The recipe suggestion unit can provide recipes that provide optimal post-exercise nutrition by taking into account the user's exercise plan and fitness goals. For example, when proposing a recipe, the generation AI can provide recipes that provide optimal post-exercise nutrition by taking into account the user's exercise plan. For example, the generation AI can provide recipes that are high in protein after strength training. The recipe suggestion unit can also provide recipes that provide optimal post-exercise nutrition by taking into account the user's fitness goals. For example, the generation AI generates recipes based on the user's exercise schedule and fitness goals. This allows the recipes to be provided that provide optimal post-exercise nutrition by taking into account the user's exercise plan and fitness goals.

[0069] The recipe suggestion unit can monitor the user's emotional state in real time and continuously suggest recipes that correspond to the emotion. The recipe suggestion unit can, for example, use an emotion estimation function to monitor the user's emotional state in real time and continuously suggest recipes that correspond to the emotion. For example, if the user is feeling stressed, it can suggest dishes that have a relaxing effect. The recipe suggestion unit can also continuously monitor the user's emotional state and suggest recipes. For example, the generation AI updates recipes based on the user's emotion score and makes suggestions according to changes in emotion. This makes it possible to monitor the user's emotional state in real time and continuously suggest recipes that correspond to the emotion.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The nutrition management system can further include a cultural adaptation component that takes into account a user's cultural background and religious restrictions regarding diet. For example, it can provide appropriate nutritional advice by respecting foods and eating habits that are important in a particular culture. It can also take into account religious dietary restrictions to ensure that a user can enjoy their meal with peace of mind. For example, it can support a user's religious diet by recommending halal or kosher foods. This allows it to provide personalized nutritional advice that takes into account a user's cultural background and religious restrictions.

[0072] The nutrition management system can further include an environmental assessment unit that evaluates the environmental impact of the user's meals. For example, it can recommend environmentally friendly ingredients by taking into account the production process and transportation distance of the ingredients. It can also calculate the carbon footprint of the ingredients selected by the user and provide advice on reducing the environmental impact. For example, it can suggest using locally produced ingredients to reduce the environmental impact of transportation. This makes it possible to provide nutrition advice that takes into account the impact of the user's meal choices on the environment.

[0073] The nutrition management system can also include an economic evaluation unit that takes into account the economic aspects of the user's diet. For example, it evaluates the price and cost performance of ingredients and makes suggestions to optimize nutritional balance within a budget. It can also provide advice on achieving a healthy diet while keeping costs down by utilizing sale information and coupons. For example, it can reduce the user's financial burden by recommending inexpensive, nutritious ingredients. This allows it to provide nutrition advice that takes into account the user's financial situation.

[0074] The nutrition management system can further include a social evaluation unit that takes into account the social aspects of the user's meals. For example, it can take into account mealtimes the user shares with friends and family and suggest recipes that everyone can enjoy. It can also provide advice that promotes communication through meals and strengthens social connections. For example, suggesting recipes suitable for parties or picnics can increase the user's social satisfaction. This makes it possible to provide nutrition advice that takes into account the user's social aspects.

[0075] The nutrition management system can further include a psychological evaluation unit that takes into account the psychological aspects of the user's diet. For example, it can recommend foods that have a relaxing effect or foods that boost the user's mood based on the user's stress level and mood. It can also provide advice to support the user's psychological health through diet. For example, it can suggest herbal tea to reduce stress or chocolate to boost the user's mood. This allows it to provide nutrition advice that takes into account the user's psychological aspects.

[0076] The nutrition management system may further include an emotion monitoring unit that monitors the user's emotional state regarding food in real time and provides feedback according to the emotion. For example, if the user is feeling stressed, foods with a relaxing effect may be recommended. The system may also continuously monitor the user's emotional state and provide feedback. For example, if the user is feeling happy, foods that will help maintain that emotion may be suggested. This allows the system to monitor the user's emotional state in real time and provide feedback according to the emotion.

[0077] The nutrition management system can further include an environment suggestion unit that analyzes the user's emotional state and suggests a dining environment that will elicit positive emotions. For example, it can suggest a dining location or atmosphere that will help the user relax. It can also use the emotion estimation function to suggest a dining environment that matches the user's emotional state. For example, suggesting a dining environment that uses quiet music and comfortable lighting will elicit positive emotions from the user. This makes it possible to suggest a dining environment that takes the user's emotional state into consideration.

[0078] The nutrition management system may further include a timing suggestion unit that analyzes the user's emotional state and suggests meal times according to the emotion. For example, if the user is feeling stressed, it may suggest that the user eat foods with a relaxing effect at an appropriate time. The emotion estimation function may also be used to suggest meal times according to the user's emotional state. For example, if the user is tired, it may suggest foods that will replenish energy at an appropriate time. This allows meal times to be suggested taking the user's emotional state into consideration.

[0079] The nutrition management system may further include a content suggestion unit that analyzes the user's emotional state and suggests meal contents according to the emotion. For example, if the user is sad, foods that will lift the user's mood may be suggested. The emotion estimation function may also be used to suggest meal contents according to the user's emotional state. For example, if the user is happy, foods that will help maintain that emotion may be suggested. In this way, meal contents can be suggested taking the user's emotional state into consideration.

[0080] The nutrition management system can further include an allergy management unit that takes into account allergy information related to the user's diet. For example, if a user is allergic to a particular food, the system can advise the user to avoid that food. It can also suggest alternative foods based on the allergy information. For example, if a user has a dairy allergy, it can suggest recipes that do not contain dairy products. This allows the system to provide safe nutrition advice that takes into account the user's allergy information.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The food photo analysis unit analyzes food photos taken by the user. For example, the food photo analysis unit uses generative AI to recognize the types and quantities of food in the photos. The food photo analysis unit can also adjust the analysis accuracy depending on the resolution and format of the photos. For example, it analyzes photos taken with a smartphone or photos with a certain resolution or higher. Step 2: The nutritional value recognition unit recognizes the nutritional value of each food from the meal photos analyzed by the meal photo analysis unit. For example, the nutritional value recognition unit uses the generation AI to calculate nutrients such as calories, protein, fat, carbohydrates, vitamins, and minerals. The nutritional value recognition unit can also calculate detailed nutritional values ​​based on the type and amount of food. For example, the generation AI references a food nutritional value database to accurately recognize the nutritional value of each food. Step 3: The feedback providing unit provides feedback on nutritional balance and calorie intake based on the nutritional value recognized by the nutritional value recognition unit. For example, the feedback providing unit uses a generation AI to generate specific advice based on the user's dietary history and health goals. The feedback providing unit can also provide text advice or visual feedback using graphs and charts. For example, it may provide specific advice such as, "Today's meal is lacking in protein. It would be a good idea to add fish or beans to your next meal." Step 4: The recipe suggestion unit suggests individual recipes based on the feedback provided by the feedback provision unit. For example, the recipe suggestion unit uses a generation AI to generate recipes that optimize the user's nutritional balance and calorie intake. The recipe suggestion unit can also suggest optimal recipes taking into account the user's preferences and allergy information. For example, it can provide recipes tailored to the user's needs, such as "low-calorie, high-protein recipes."

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[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 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.

[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 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).

[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] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] 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]

[0150] 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 photo analysis unit that analyzes meal photos taken by a user; a nutritional value recognition unit that recognizes the nutritional value of each food from the meal photograph analyzed by the meal photograph analysis unit; a feedback providing unit that provides feedback on nutritional balance and calorie intake based on the nutritional value recognized by the nutritional value recognition unit; a recipe suggestion unit that suggests individual recipes based on the feedback provided by the feedback providing unit. A system characterized by:

2. The meal photo analysis unit Recognize how food is prepared and calculate its nutritional value accordingly 2. The system of claim 1.

3. The meal photo analysis unit Identify the origin and production method of food and calculate the nutritional value based on that 2. The system of claim 1.

4. The feedback providing unit: Providing personalized nutrition advice based on the user's genetic information and blood test results 2. The system of claim 1.

5. The recipe suggestion unit Analyze the user's past meal history and suggest a wide variety of recipes to prevent boredom.

2. The system of claim 1.

6. The meal photo analysis unit Analyzing the emotions of the user and prioritizing meals associated with strong positive emotions 2. The system of claim 1.

7. The feedback providing unit: Analyze the user's emotional state and provide advice that elicits positive emotions 2. The system of claim 1.

8. The recipe suggestion unit Analyze the user's emotional state and suggest dishes that elicit positive emotions 2. The system of claim 1.

Images