System

Abstract

An object of the system according to the embodiment is to easily grasp the nutritional balance of meals and supplement appropriate nutrients.SOLUTION: A system according to an embodiment includes an imaging unit, an analysis unit, an identification unit, a generation unit, and a provision unit. The photographing unit photographs a picture of the meal. The analysis unit analyzes the photograph taken by the photographing unit. The specifying unit specifies a nutrient that is insufficient based on the result analyzed by the analyzing unit. The generation unit generates a recipe for supplementing the deficient nutrient specified by the specification unit. The providing unit provides the recipe generated by the generating unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to easily grasp the nutritional balance of meals and supplement with appropriate nutrients.

[0005] The system according to the embodiment aims to easily grasp the nutritional balance of meals and to supplement with appropriate nutrients. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, an analysis unit, an identification unit, a generation unit, and a provision unit. The photographing unit takes a photo of a meal. The analysis unit analyzes the photo taken by the photographing unit. The identification unit identifies a nutrient deficiency based on the analysis result by the analysis unit. The generation unit generates a recipe that supplements the nutrient deficiency identified by the identification unit. The provision unit provides the recipe generated by the generation unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily understand the nutritional balance of a meal and supplement with appropriate nutrients. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A nutrient recommendation system according to an embodiment of the present invention allows a user to take a photo of a meal, which is analyzed by an AI to identify any missing nutrients. The system then generates and provides the user with a recipe to supplement the missing nutrients. In this nutrient recommendation system, a user takes a photo of a meal, which is then analyzed by an AI to recognize the contents of the meal and identify which nutrients are missing. The AI ​​then generates and recommends a recipe to the user to supplement the missing nutrients. For example, in a nutrient recommendation system, a user takes a photo of a meal. The photo is input into an AI. The AI ​​uses image analysis technology to recognize the contents of the meal. For example, it identifies ingredients such as vegetables, meat, fish, and carbohydrates and calculates the nutrients for each. The AI ​​then analyzes the contents of the meal to identify which nutrients are missing. For example, if a nutrient such as vitamin C, iron, or calcium is missing, it identifies that nutrient. The AI ​​then generates a recipe to supplement the missing nutrients. For example, if a user is missing vitamin C, a recipe using ingredients rich in vitamin C is suggested. The recipe includes specific ingredient types and amounts, cooking methods, etc. Finally, the AI ​​suggests the generated recipe to the user. The user can use the suggested recipes as a reference to prepare a balanced meal. In this way, the nutrient suggestion system helps the user maintain a healthy diet. In this way, the nutrient suggestion system makes it easy for the user to achieve a balanced diet. For example, it can easily help people on a diet or who want to increase their intake of specific nutrients to achieve a balanced diet. In addition, recording the contents of meals can also be useful for long-term health management.

[0029] The nutrient recommendation system according to the embodiment includes a photographing unit, an analysis unit, an identification unit, a generation unit, and a provision unit. The photographing unit allows a user to photograph a meal. For example, the photograph can be taken using a smartphone camera. The photographing unit can also automatically adjust the type of camera, the photographing angle, and the lighting conditions to capture the optimal photograph. The analysis unit uses AI to analyze the photograph taken by the photographing unit. For example, the analysis unit can recognize the contents of the meal using image analysis technology. For example, the analysis unit can identify ingredients using object detection technology and calculate the nutrients of each ingredient. The analysis unit can also classify the type of meal using image classification technology. The identification unit uses AI to identify missing nutrients based on the results of the analysis by the analysis unit. For example, the identification unit can identify whether a nutrient such as vitamin C, iron, or calcium is missing. The identification unit can identify missing nutrients based on, for example, nutrient reference values. The identification unit can also identify missing nutrients by referring to the user's past dietary data. The generation unit uses AI to generate recipes that supplement the missing nutrients identified by the identification unit. For example, if a user is deficient in vitamin C, a recipe using ingredients that are rich in vitamin C is generated. The generation unit can generate a recipe that includes, for example, specific types and amounts of ingredients and cooking methods. The generation unit can also improve the accuracy of the generated recipe by referring to the user's past dietary data. The provision unit provides the user with the recipe generated by the generation unit. For example, the recipe can be displayed through a smartphone app. The provision unit can also improve the provision method by reflecting user feedback. In this way, the nutrient suggestion system according to the embodiment allows the user to easily achieve a balanced diet. For example, people on a diet or those who want to consume more of a specific nutrient can easily achieve nutritional balance. Furthermore, recording the contents of meals can also be useful for long-term health management.

[0030] The analysis unit can recognize the contents of a meal using image analysis technology. Image analysis technology includes, for example, object detection technology and image classification technology. The analysis unit can, for example, use object detection technology to identify ingredients and calculate the nutrients for each. The analysis unit can also classify the type of meal using image classification technology. For example, the analysis unit can identify ingredients such as vegetables, meat, fish, and carbohydrates and calculate the nutrients for each. In this way, the image analysis technology can accurately recognize the contents of a meal. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a photo of a meal into the generation AI and have the generation AI identify the ingredients and calculate the nutrients.

[0031] The identification unit can identify whether at least one of vitamin C, iron, and calcium is deficient. The identification unit identifies the deficient nutrient based on, for example, a reference value for the nutrient. For example, the identification unit can identify whether vitamin C is deficient based on a reference value for vitamin C. The identification unit can also identify whether iron is deficient based on a reference value for iron. The identification unit can also identify whether calcium is deficient based on a reference value for calcium. This makes it possible to accurately identify the deficient nutrient. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the nutrient data analyzed by the analysis unit into the generation AI and cause the generation AI to identify the deficient nutrient.

[0032] The generation unit can generate recipes to supplement deficient nutrients. For example, if there is a vitamin C deficiency, the generation unit generates recipes using ingredients that are high in vitamin C. For example, the generation unit generates recipes using ingredients that are high in vitamin C, such as oranges or broccoli. Furthermore, if there is an iron deficiency, the generation unit can also generate recipes using ingredients that are high in iron. For example, the generation unit generates recipes using ingredients that are high in iron, such as spinach or liver. Furthermore, if there is a calcium deficiency, the generation unit can also generate recipes using ingredients that are high in calcium. For example, the generation unit generates recipes using ingredients that are high in calcium, such as milk or cheese. In this way, recipes to supplement deficient nutrients can be generated. Some or all of the above-described processing by the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input data on deficient nutrients identified by the identification unit into the generation AI and cause the generation AI to generate a recipe.

[0033] The generation unit can generate a recipe that includes specific types or amounts of ingredients and cooking methods. For example, if a user is deficient in vitamin C, the generation unit generates a recipe that includes types and amounts of ingredients that are high in vitamin C and cooking methods. For example, when generating a recipe for a salad using oranges, the generation unit generates a recipe that includes the amount of oranges, how to cut them, how to make a dressing, etc. Furthermore, when a user is deficient in iron, the generation unit can generate a recipe that includes types and amounts of ingredients that are high in iron and cooking methods. For example, when generating a recipe for a smoothie using spinach, the generation unit generates a recipe that includes the amount of spinach, types of other ingredients, how to use a blender, etc. Furthermore, when a user is deficient in calcium, the generation unit can generate a recipe that includes types and amounts of ingredients that are high in calcium and cooking methods. For example, when generating a recipe for a stew using milk, the generation unit generates a recipe that includes the amount of milk, types of other ingredients, cooking procedures, etc. In this way, a recipe that includes specific types and amounts of ingredients and cooking methods can be generated. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the missing nutrient data identified by the identification unit into the generation AI and cause the generation AI to generate a recipe including specific types and amounts of ingredients and cooking methods.

[0034] The providing unit can suggest the generated recipe to the user. The providing unit suggests the generated recipe to the user, for example, through a smartphone app. For example, the providing unit displays details of the recipe so that the user can easily check the recipe. The providing unit can also improve the presentation method by reflecting user feedback. For example, the providing unit collects ratings and comments made by users on the recipe and improves the content of the recipe and the presentation method based on the ratings and comments. This makes it possible to suggest the generated recipe to the user. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the recipe data generated by the generation unit to a generation AI and cause the generation AI to execute a method for suggesting the recipe to the user.

[0035] The photographing unit can automatically adjust the angle of the food and lighting conditions when photographing to take the optimal photograph. For example, the photographing unit can automatically adjust the angle of the food to photograph the food in its entirety. The photographing unit can also automatically adjust the lighting conditions to photograph the food so that the colors of the ingredients look vivid. The photographing unit can also adjust the brightness of the background to photograph the food so that the food stands out. In this way, the optimal photograph can be taken by automatically adjusting the angle of the food and lighting conditions. Some or all of the above-described processing in the photographing unit may be performed using, or without, AI, for example. For example, the photographing unit can input a photo of the food into a generation AI and have the generation AI adjust the angle and lighting conditions.

[0036] The photographing unit can automatically select different photographing modes depending on the type of food when photographing. For example, for vegetable dishes, the photographing unit selects a mode that makes the food appear more colorful. Furthermore, for meat dishes, the photographing unit can select a mode that conveys the juiciness. Furthermore, for desserts, the photographing unit can select a mode that brings out the sweetness. This makes it possible to automatically select the optimal photographing mode depending on the type of food. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input a photo of the food into the generation AI and have the generation AI select the photographing mode.

[0037] When taking a photo, the photographing unit can suggest optimal photographing settings by referring to the user's past photographing history. The photographing unit, for example, automatically applies photographing settings that the user previously preferred. The photographing unit can also suggest optimal angles and lighting conditions based on the user's past photographing history. The photographing unit can also suggest optimal settings based on evaluations of photos the user has taken in the past. In this way, optimal photographing settings can be suggested by referring to the user's past photographing history. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the user's past photographing data into a generation AI and have the generation AI suggest optimal photographing settings.

[0038] When taking a photo, the photographing unit can prioritize photographing highly relevant meals by taking into account the user's geographical location information. For example, if the user is in a specific area, the photographing unit can prioritize photographing local specialties of that area. Furthermore, if the user is traveling, the photographing unit can prioritize photographing local specialty dishes. Furthermore, if the user is participating in a specific event, the photographing unit can prioritize photographing meals related to that event. In this way, highly relevant meals can be photographed preferentially by taking into account the user's geographical location information. Some or all of the above-described processing in the photographing unit may be performed using AI, for example, or may be performed without using AI. For example, the photographing unit can input the user's geographical location data into the generation AI and cause the generation AI to prioritize photographing highly relevant meals.

[0039] The photographing unit can analyze the user's social media activity and photograph related meals when photographing. For example, the photographing unit prioritizes photographing meals that the user has shared on social media. The photographing unit can also analyze the content of the user's social media posts and photograph related meals. The photographing unit can also photograph related meals by referring to the activity of the user's friends on social media. In this way, related meals can be photographed by analyzing the user's social media activity. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the user's social media data into the generation AI and have the generation AI photograph related meals.

[0040] The photographing unit can customize the photographing method by reflecting the user's past feedback when photographing. For example, the photographing unit automatically applies a photographing method that the user previously preferred. The photographing unit can also suggest an optimal photographing method based on the user's past feedback. The photographing unit can also customize the photographing method based on the user's evaluation of photos taken in the past. In this way, the optimal photographing method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the photographing unit may be performed using AI, for example, or may be performed without using AI. For example, the photographing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the photographing method.

[0041] During analysis, the analysis unit can break down the components of a meal in detail to identify nutrients. For example, the analysis unit can break down the components of a meal in detail to identify the content of each nutrient. The analysis unit can also break down the components of a meal to identify the content of vitamins and minerals. The analysis unit can also break down the components of a meal to identify the content of proteins and lipids. In this way, by breaking down the components of a meal in detail, the content of each nutrient can be identified. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input meal component data into the generation AI and cause the generation AI to identify nutrients.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the type of meal. For example, in the case of a vegetable dish, the analysis unit applies an analysis algorithm for vitamins and minerals. In addition, the analysis unit can also apply an analysis algorithm for proteins and lipids in the case of a meat dish. In addition, the analysis unit can also apply an analysis algorithm for carbohydrates and calories in the case of a dessert. This makes it possible to apply the optimal analysis algorithm depending on the type of meal. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input meal type data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past dietary data. The analysis unit, for example, improves the accuracy of the analysis by referring to the user's past dietary data. The analysis unit can also analyze nutrient intake trends from the user's past dietary data. The analysis unit can also correct the analysis results based on the user's past dietary data. In this way, the accuracy of the analysis can be improved by referring to the user's past dietary data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past dietary data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can perform the analysis while taking into account the geographical distribution of meals. For example, the analysis unit analyzes regional nutrients while taking into account the geographical distribution of meals. The analysis unit can also analyze regional ingredients while taking into account the geographical distribution of meals. The analysis unit can also analyze regional cooking methods while taking into account the geographical distribution of meals. In this way, regional nutrients and ingredients can be accurately analyzed by taking into account the geographical distribution of meals. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographical distribution data to a generation AI and have the generation AI perform the analysis.

[0045] The analysis unit can improve the accuracy of the analysis by referring to diet-related literature during analysis. The analysis unit can improve the accuracy of the analysis by, for example, referring to diet-related literature. The analysis unit can also correct the nutrient content from diet-related literature. The analysis unit can also correct the analysis results based on diet-related literature. In this way, by referring to diet-related literature, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0046] The analysis unit can perform the analysis while taking into account the market value of the meal. For example, the analysis unit can analyze the nutrients of expensive ingredients while taking into account the market value of the meal. The analysis unit can also analyze the nutrients of inexpensive ingredients while taking into account the market value of the meal. The analysis unit can also analyze ingredients with high cost performance while taking into account the market value of the meal. In this way, ingredients with high cost performance can be analyzed by taking into account the market value of the meal. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input market value data into the generation AI and have the generation AI perform the analysis.

[0047] During the identification, the identification unit can break down the components of the meal in detail to identify the missing nutrients. For example, the identification unit can break down the components of the meal in detail to identify a deficiency in each nutrient. The identification unit can also break down the components of the meal to identify a deficiency in vitamins or minerals. The identification unit can also break down the components of the meal to identify a deficiency in protein or lipids. In this way, by breaking down the components of the meal in detail, the missing nutrients can be accurately identified. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input meal component data into the generation AI and cause the generation AI to identify the missing nutrients.

[0048] During identification, the identification unit can apply different identification algorithms depending on the type of meal. For example, in the case of a vegetable dish, the identification unit can apply an identification algorithm for vitamins and minerals. In addition, in the case of a meat dish, the identification unit can also apply an identification algorithm for protein and fat. In addition, in the case of a dessert, the identification unit can also apply an identification algorithm for carbohydrates and calories. This makes it possible to apply the optimal identification algorithm depending on the type of meal. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input meal type data into the generation AI and cause the generation AI to apply the identification algorithm.

[0049] During identification, the identification unit can improve the accuracy of the identification by referring to the user's past dietary data. The identification unit, for example, improves the accuracy of the identification by referring to the user's past dietary data. The identification unit can also analyze nutrient intake trends from the user's past dietary data. The identification unit can also correct the identification result based on the user's past dietary data. In this way, the accuracy of the identification can be improved by referring to the user's past dietary data. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's past dietary data into the generation AI and cause the generation AI to improve the accuracy of the identification.

[0050] During the identification, the identification unit can identify nutrient deficiencies by taking into account the geographical distribution of meals. For example, the identification unit can identify nutrient deficiencies specific to a region by taking into account the geographical distribution of meals. The identification unit can also identify nutrient deficiencies specific to a region by taking into account the geographical distribution of meals. The identification unit can also identify nutrient deficiencies caused by cooking methods specific to a region by taking into account the geographical distribution of meals. In this way, by taking into account the geographical distribution of meals, it is possible to accurately identify nutrient deficiencies specific to a region. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input geographical distribution data into the generation AI and cause the generation AI to identify nutrient deficiencies.

[0051] During identification, the identification unit can improve the accuracy of identifying the missing nutrients by referring to diet-related literature. The identification unit can, for example, refer to diet-related literature to improve the accuracy of identifying nutrient deficiencies. The identification unit can also correct the nutrient content from diet-related literature. The identification unit can also correct the identification result based on diet-related literature. In this way, by referring to diet-related literature, the accuracy of identifying the missing nutrients can be improved. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of identification.

[0052] During identification, the identification unit can identify deficient nutrients by taking into account the market value of the meal. For example, the identification unit can identify deficient nutrients in expensive ingredients by taking into account the market value of the meal. The identification unit can also identify deficient nutrients in inexpensive ingredients by taking into account the market value of the meal. The identification unit can also identify deficient nutrients in cost-effective ingredients by taking into account the market value of the meal. In this way, by taking into account the market value of the meal, it is possible to accurately identify deficient nutrients in expensive ingredients and inexpensive ingredients. Some or all of the above-described processing in the identification unit may be performed using, or without, AI, for example. For example, the identification unit can input market value data into the generation AI and cause the generation AI to identify deficient nutrients.

[0053] During generation, the generation unit can generate a recipe that breaks down the components of a meal in detail to supplement any missing nutrients. For example, the generation unit can generate a recipe that breaks down the components of a meal in detail to supplement any missing nutrients. The generation unit can also generate a recipe that breaks down the components of a meal to supplement vitamins and minerals. The generation unit can also generate a recipe that breaks down the components of a meal to supplement proteins and lipids. In this way, a recipe can be generated that supplements any missing nutrients by breaking down the components of a meal in detail. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input meal component data into the generation AI and cause the generation AI to generate a recipe.

[0054] During generation, the generation unit can apply different generation algorithms depending on the type of meal. For example, in the case of vegetable dishes, the generation unit applies an algorithm that generates a recipe that supplements vitamins and minerals. In addition, in the case of meat dishes, the generation unit can also apply an algorithm that generates a recipe that supplements protein and fat. In addition, in the case of desserts, the generation unit can also apply an algorithm that generates a recipe that supplements carbohydrates and calories. This makes it possible to apply the optimal generation algorithm depending on the type of meal. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input meal type data into the generation AI and cause the generation AI to apply the generation algorithm.

[0055] During generation, the generation unit can improve the accuracy of generation by referring to the user's past dietary data. The generation unit, for example, improves the accuracy of generation by referring to the user's past dietary data. The generation unit can also analyze nutrient intake trends from the user's past dietary data. The generation unit can also correct the generation results based on the user's past dietary data. In this way, the accuracy of generation can be improved by referring to the user's past dietary data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past dietary data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0056] The generation unit can generate recipes taking into account the geographical distribution of meals. For example, the generation unit can generate recipes using ingredients specific to a region taking into account the geographical distribution of meals. The generation unit can also generate recipes using cooking methods specific to a region taking into account the geographical distribution of meals. The generation unit can also generate recipes that supplement nutrients specific to a region taking into account the geographical distribution of meals. In this way, recipes using ingredients and cooking methods specific to a region can be generated by taking into account the geographical distribution of meals. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input geographical distribution data to the generation AI and cause the generation AI to generate a recipe.

[0057] The generation unit can improve the accuracy of generation by referring to diet-related literature during generation. The generation unit can, for example, improve the accuracy of generation by referring to diet-related literature. The generation unit can also correct the nutrient content from diet-related literature. The generation unit can also correct the generation result based on diet-related literature. In this way, by referring to diet-related literature, the accuracy of generation can be improved. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0058] The generation unit can generate a recipe taking into account the market value of the meal. For example, the generation unit can generate a recipe using expensive ingredients taking into account the market value of the meal. The generation unit can also generate a recipe using inexpensive ingredients taking into account the market value of the meal. The generation unit can also generate a recipe using ingredients with high cost performance taking into account the market value of the meal. In this way, by taking into account the market value of the meal, recipes using expensive ingredients and inexpensive ingredients can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input market value data into the generation AI and cause the generation AI to generate a recipe.

[0059] When providing a recipe, the providing unit can select the optimal serving method by referring to the user's past recipe usage history. The providing unit, for example, refers to the user's past recipe usage history and provides the optimal recipe. The providing unit can also provide recipes that use the user's favorite ingredients from the user's past recipe usage history. The providing unit can also select the optimal serving method based on the user's past recipe usage history. In this way, the optimal recipe can be provided by referring to the user's past recipe usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past recipe usage history data into the generation AI and cause the generation AI to select the optimal serving method.

[0060] The providing unit can customize the means for providing the recipe based on the user's current living situation when providing the recipe. For example, the providing unit can provide an easy-to-make recipe taking into account the user's current living situation. The providing unit can also provide a time-consuming but nutritious recipe taking into account the user's current living situation. The providing unit can also provide a recipe using specific ingredients taking into account the user's current living situation. This makes it possible to provide an optimal recipe by taking into account the user's current living situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's living situation data into the generation AI and cause the generation AI to customize the means for providing the recipe.

[0061] The providing unit can improve the providing method by reflecting user feedback when providing the food. The providing unit improves the providing method based on, for example, user feedback. The providing unit can also improve the content of the recipe based on user feedback. The providing unit can also customize the providing means based on user feedback. In this way, the providing method can be improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to improve the providing method.

[0062] The providing unit can provide an optimal recipe by taking into account the user's geographical location information. For example, the providing unit can provide a recipe using ingredients specific to a region by taking into account the user's geographical location information. The providing unit can also provide a recipe using a cooking method specific to a region by taking into account the user's geographical location information. The providing unit can also provide a recipe that supplements nutrients specific to a region by taking into account the user's geographical location information. In this way, recipes using ingredients and cooking methods specific to a region can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input geographical location data to a generation AI and cause the generation AI to provide a recipe.

[0063] At the time of provision, the providing unit can analyze the user's social media activity and provide related recipes. For example, the providing unit can provide recipes related to meals shared by the user on social media. The providing unit can also analyze the content of the user's social media posts and provide related recipes. The providing unit can also provide related recipes by referring to the activity of the user's friends on social media. In this way, related recipes can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media data into a generation AI and cause the generation AI to provide related recipes.

[0064] The providing unit can customize the providing method by reflecting the user's past feedback when providing the food. The providing unit customizes the providing method based on, for example, the user's past feedback. The providing unit can also customize the contents of the recipe based on the user's past feedback. The providing unit can also customize the providing means based on the user's past feedback. In this way, the providing method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the providing method.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The analysis unit can also evaluate the freshness of ingredients when analyzing photos of the user's meals. For example, the analysis unit can analyze the color and texture of vegetables to evaluate their freshness. The analysis unit can also analyze the color and luster of meat and fish to evaluate their freshness. Furthermore, the analysis unit can suggest to the user to use fresher ingredients based on the freshness of the ingredients. This allows the user to use fresher and more nutritious ingredients.

[0067] The identification unit can also identify allergen information for ingredients when analyzing photos of the user's meals. For example, the identification unit can identify allergens, such as nuts and dairy products, contained in the meal. The identification unit can also refer to the user's allergy information to identify ingredients containing allergens. Furthermore, the identification unit can suggest alternative ingredients to avoid ingredients containing allergens. This allows the user to avoid allergic reactions.

[0068] When analyzing photos of meals taken by a user, the generation unit can also generate recipes taking into account the seasonality of ingredients. For example, the generation unit generates recipes using seasonal ingredients. The generation unit can also generate recipes taking into account seasonal variations in nutrients. Furthermore, the generation unit can generate recipes taking into account the ease of obtaining ingredients in each season. This allows users to enjoy nutritionally balanced meals according to the season.

[0069] The providing unit can also suggest food storage methods when analyzing photos of the user's meals. For example, the providing unit can suggest appropriate storage methods for vegetables and fruits. The providing unit can also suggest freezing methods for meat and fish. Furthermore, the providing unit can also suggest storage methods for cooked meals. This allows the user to extend the shelf life of ingredients and reduce food waste.

[0070] The analysis unit can also identify origin information for ingredients when analyzing photos of the user's meals. For example, the analysis unit can analyze the label or packaging information of ingredients to identify the origin. The analysis unit can also analyze the characteristics of ingredients to estimate the origin. Furthermore, the analysis unit can suggest to the user to use locally produced ingredients based on the origin information. This allows the user to use fresh, locally produced ingredients.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The user takes a photo of the meal using the camera. For example, the user can take a photo of the meal using a smartphone camera. The camera can also automatically adjust the type of camera, shooting angle, and lighting conditions to take the optimal photo. Step 2: The analysis unit uses AI to analyze the photos taken by the photography unit. For example, it uses image analysis technology to recognize the contents of the meal. For example, the analysis unit can use object detection technology to identify ingredients and calculate the nutritional value of each. The analysis unit can also classify the type of meal using image classification technology. Step 3: The identification unit uses AI to identify the nutrients that are lacking based on the results of the analysis by the analysis unit. For example, it identifies whether there is a deficiency in nutrients such as vitamin C, iron, or calcium. The identification unit can identify the nutrients that are lacking based on, for example, the standard values ​​of the nutrients. The identification unit can also identify the nutrients that are lacking by referring to the user's past dietary data. Step 4: The generation unit uses AI to generate recipes that supplement the missing nutrients identified by the identification unit. For example, if there is a vitamin C deficiency, the generation unit generates recipes using ingredients that are rich in vitamin C. The generation unit can generate recipes that include, for example, specific types and amounts of ingredients and cooking methods. The generation unit can also refer to the user's past dietary data to improve the accuracy of the generated recipes. Step 5: The providing unit provides the recipe generated by the generating unit to the user. For example, the recipe can be displayed through a smartphone app. The providing unit can also reflect user feedback and improve the providing method.

[0073] (Example 2) A nutrient recommendation system according to an embodiment of the present invention allows a user to take a photo of a meal, which is analyzed by an AI to identify any missing nutrients. The system then generates and provides the user with a recipe to supplement the missing nutrients. In this nutrient recommendation system, a user takes a photo of a meal, which is then analyzed by an AI to recognize the contents of the meal and identify which nutrients are missing. The AI ​​then generates and recommends a recipe to the user to supplement the missing nutrients. For example, in a nutrient recommendation system, a user takes a photo of a meal. The photo is input into an AI. The AI ​​uses image analysis technology to recognize the contents of the meal. For example, it identifies ingredients such as vegetables, meat, fish, and carbohydrates and calculates the nutrients for each. The AI ​​then analyzes the contents of the meal to identify which nutrients are missing. For example, if a nutrient such as vitamin C, iron, or calcium is missing, it identifies that nutrient. The AI ​​then generates a recipe to supplement the missing nutrients. For example, if a user is missing vitamin C, a recipe using ingredients rich in vitamin C is suggested. The recipe includes specific ingredient types and amounts, cooking methods, etc. Finally, the AI ​​suggests the generated recipe to the user. The user can use the suggested recipes as a reference to prepare a balanced meal. In this way, the nutrient suggestion system helps the user maintain a healthy diet. In this way, the nutrient suggestion system makes it easy for the user to achieve a balanced diet. For example, it can easily help people on a diet or who want to increase their intake of specific nutrients to achieve a balanced diet. In addition, recording the contents of meals can also be useful for long-term health management.

[0074] The nutrient recommendation system according to the embodiment includes a photographing unit, an analysis unit, an identification unit, a generation unit, and a provision unit. The photographing unit allows a user to photograph a meal. For example, the photograph can be taken using a smartphone camera. The photographing unit can also automatically adjust the type of camera, the photographing angle, and the lighting conditions to capture the optimal photograph. The analysis unit uses AI to analyze the photograph taken by the photographing unit. For example, the analysis unit can recognize the contents of the meal using image analysis technology. For example, the analysis unit can identify ingredients using object detection technology and calculate the nutrients of each ingredient. The analysis unit can also classify the type of meal using image classification technology. The identification unit uses AI to identify missing nutrients based on the results of the analysis by the analysis unit. For example, the identification unit can identify whether a nutrient such as vitamin C, iron, or calcium is missing. The identification unit can identify missing nutrients based on, for example, nutrient reference values. The identification unit can also identify missing nutrients by referring to the user's past dietary data. The generation unit uses AI to generate recipes that supplement the missing nutrients identified by the identification unit. For example, if a user is deficient in vitamin C, a recipe using ingredients that are rich in vitamin C is generated. The generation unit can generate a recipe that includes, for example, specific types and amounts of ingredients and cooking methods. The generation unit can also improve the accuracy of the generated recipe by referring to the user's past dietary data. The provision unit provides the user with the recipe generated by the generation unit. For example, the recipe can be displayed through a smartphone app. The provision unit can also improve the provision method by reflecting user feedback. In this way, the nutrient suggestion system according to the embodiment allows the user to easily achieve a balanced diet. For example, people on a diet or those who want to consume more of a specific nutrient can easily achieve nutritional balance. Furthermore, recording the contents of meals can also be useful for long-term health management.

[0075] The analysis unit can recognize the contents of a meal using image analysis technology. Image analysis technology includes, for example, object detection technology and image classification technology. The analysis unit can, for example, use object detection technology to identify ingredients and calculate the nutrients for each. The analysis unit can also classify the type of meal using image classification technology. For example, the analysis unit can identify ingredients such as vegetables, meat, fish, and carbohydrates and calculate the nutrients for each. In this way, the image analysis technology can accurately recognize the contents of a meal. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a photo of a meal into the generation AI and have the generation AI identify the ingredients and calculate the nutrients.

[0076] The identification unit can identify whether at least one of vitamin C, iron, and calcium is deficient. The identification unit identifies the deficient nutrient based on, for example, a reference value for the nutrient. For example, the identification unit can identify whether vitamin C is deficient based on a reference value for vitamin C. The identification unit can also identify whether iron is deficient based on a reference value for iron. The identification unit can also identify whether calcium is deficient based on a reference value for calcium. This makes it possible to accurately identify the deficient nutrient. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the nutrient data analyzed by the analysis unit into the generation AI and cause the generation AI to identify the deficient nutrient.

[0077] The generation unit can generate recipes to supplement deficient nutrients. For example, if there is a vitamin C deficiency, the generation unit generates recipes using ingredients that are high in vitamin C. For example, the generation unit generates recipes using ingredients that are high in vitamin C, such as oranges or broccoli. Furthermore, if there is an iron deficiency, the generation unit can also generate recipes using ingredients that are high in iron. For example, the generation unit generates recipes using ingredients that are high in iron, such as spinach or liver. Furthermore, if there is a calcium deficiency, the generation unit can also generate recipes using ingredients that are high in calcium. For example, the generation unit generates recipes using ingredients that are high in calcium, such as milk or cheese. In this way, recipes to supplement deficient nutrients can be generated. Some or all of the above-described processing by the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input data on deficient nutrients identified by the identification unit into the generation AI and cause the generation AI to generate a recipe.

[0078] The generation unit can generate a recipe that includes specific types or amounts of ingredients and cooking methods. For example, if a user is deficient in vitamin C, the generation unit generates a recipe that includes types and amounts of ingredients that are high in vitamin C and cooking methods. For example, when generating a recipe for a salad using oranges, the generation unit generates a recipe that includes the amount of oranges, how to cut them, how to make a dressing, etc. Furthermore, when a user is deficient in iron, the generation unit can generate a recipe that includes types and amounts of ingredients that are high in iron and cooking methods. For example, when generating a recipe for a smoothie using spinach, the generation unit generates a recipe that includes the amount of spinach, types of other ingredients, how to use a blender, etc. Furthermore, when a user is deficient in calcium, the generation unit can generate a recipe that includes types and amounts of ingredients that are high in calcium and cooking methods. For example, when generating a recipe for a stew using milk, the generation unit generates a recipe that includes the amount of milk, types of other ingredients, cooking procedures, etc. In this way, a recipe that includes specific types and amounts of ingredients and cooking methods can be generated. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the missing nutrient data identified by the identification unit into the generation AI and cause the generation AI to generate a recipe including specific types and amounts of ingredients and cooking methods.

[0079] The providing unit can suggest the generated recipe to the user. The providing unit suggests the generated recipe to the user, for example, through a smartphone app. For example, the providing unit displays details of the recipe so that the user can easily check the recipe. The providing unit can also improve the presentation method by reflecting user feedback. For example, the providing unit collects ratings and comments made by users on the recipe and improves the content of the recipe and the presentation method based on the ratings and comments. This makes it possible to suggest the generated recipe to the user. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the recipe data generated by the generation unit to a generation AI and cause the generation AI to execute a method for suggesting the recipe to the user.

[0080] The nutrient recommendation system includes a camera unit that estimates a user's emotions and adjusts the timing of photographing a meal based on the estimated user emotions. For example, if the user is relaxed, the camera unit automatically takes a photo when the meal is ready. Furthermore, if the user is in a hurry, the camera unit can quickly take a photo once all the meal ingredients are ready. Furthermore, if the user is feeling stressed, the camera unit can adjust the timing of photographing so that the entire meal is visible. This allows the meal to be photographed at the optimal timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the camera unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the camera unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion and adjust the timing of photographing.

[0081] The photographing unit can automatically adjust the angle of the food and lighting conditions when photographing to take the optimal photograph. For example, the photographing unit can automatically adjust the angle of the food to photograph the food in its entirety. The photographing unit can also automatically adjust the lighting conditions to photograph the food so that the colors of the ingredients look vivid. The photographing unit can also adjust the brightness of the background to photograph the food so that the food stands out. In this way, the optimal photograph can be taken by automatically adjusting the angle of the food and lighting conditions. Some or all of the above-described processing in the photographing unit may be performed using, or without, AI, for example. For example, the photographing unit can input a photo of the food into a generation AI and have the generation AI adjust the angle and lighting conditions.

[0082] The photographing unit can automatically select different photographing modes depending on the type of food when photographing. For example, for vegetable dishes, the photographing unit selects a mode that makes the food appear more colorful. Furthermore, for meat dishes, the photographing unit can select a mode that conveys the juiciness. Furthermore, for desserts, the photographing unit can select a mode that brings out the sweetness. This makes it possible to automatically select the optimal photographing mode depending on the type of food. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input a photo of the food into the generation AI and have the generation AI select the photographing mode.

[0083] When taking a photo, the photographing unit can suggest optimal photographing settings by referring to the user's past photographing history. The photographing unit, for example, automatically applies photographing settings that the user previously preferred. The photographing unit can also suggest optimal angles and lighting conditions based on the user's past photographing history. The photographing unit can also suggest optimal settings based on evaluations of photos the user has taken in the past. In this way, optimal photographing settings can be suggested by referring to the user's past photographing history. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the user's past photographing data into a generation AI and have the generation AI suggest optimal photographing settings.

[0084] The photographing unit can estimate the user's emotions and prioritize the meals to be photographed based on the estimated user's emotions. For example, when the user is relaxed, the photographing unit selects meals to be photographed taking into consideration the overall balance. Furthermore, when the user is in a hurry, the photographing unit can prioritize photographing main ingredients. Furthermore, when the user is feeling stressed, the photographing unit can prioritize photographing nutritious ingredients. This allows the prioritization of meals to be photographed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or without AI. For example, the photographing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions and prioritize the meals to be photographed.

[0085] When taking a photo, the photographing unit can prioritize photographing highly relevant meals by taking into account the user's geographical location information. For example, if the user is in a specific area, the photographing unit can prioritize photographing local specialties of that area. Furthermore, if the user is traveling, the photographing unit can prioritize photographing local specialty dishes. Furthermore, if the user is participating in a specific event, the photographing unit can prioritize photographing meals related to that event. In this way, highly relevant meals can be photographed preferentially by taking into account the user's geographical location information. Some or all of the above-described processing in the photographing unit may be performed using AI, for example, or may be performed without using AI. For example, the photographing unit can input the user's geographical location data into the generation AI and cause the generation AI to prioritize photographing highly relevant meals.

[0086] The photographing unit can analyze the user's social media activity and photograph related meals when photographing. For example, the photographing unit prioritizes photographing meals that the user has shared on social media. The photographing unit can also analyze the content of the user's social media posts and photograph related meals. The photographing unit can also photograph related meals by referring to the activity of the user's friends on social media. In this way, related meals can be photographed by analyzing the user's social media activity. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the user's social media data into the generation AI and have the generation AI photograph related meals.

[0087] The photographing unit can customize the photographing method by reflecting the user's past feedback when photographing. For example, the photographing unit automatically applies a photographing method that the user previously preferred. The photographing unit can also suggest an optimal photographing method based on the user's past feedback. The photographing unit can also customize the photographing method based on the user's evaluation of photos taken in the past. In this way, the optimal photographing method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the photographing unit may be performed using AI, for example, or may be performed without using AI. For example, the photographing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the photographing method.

[0088] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a quick analysis when the user is in a hurry. The analysis unit can also perform an analysis focusing on important nutrients when the user is stressed. This allows the accuracy of the analysis to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions and adjust the accuracy of the analysis.

[0089] During analysis, the analysis unit can break down the components of a meal in detail to identify nutrients. For example, the analysis unit can break down the components of a meal in detail to identify the content of each nutrient. The analysis unit can also break down the components of a meal to identify the content of vitamins and minerals. The analysis unit can also break down the components of a meal to identify the content of proteins and lipids. In this way, by breaking down the components of a meal in detail, the content of each nutrient can be identified. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input meal component data into the generation AI and cause the generation AI to identify nutrients.

[0090] During analysis, the analysis unit can apply different analysis algorithms depending on the type of meal. For example, in the case of a vegetable dish, the analysis unit applies an analysis algorithm for vitamins and minerals. In addition, the analysis unit can also apply an analysis algorithm for proteins and lipids in the case of a meat dish. In addition, the analysis unit can also apply an analysis algorithm for carbohydrates and calories in the case of a dessert. This makes it possible to apply the optimal analysis algorithm depending on the type of meal. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input meal type data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0091] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past dietary data. The analysis unit, for example, improves the accuracy of the analysis by referring to the user's past dietary data. The analysis unit can also analyze nutrient intake trends from the user's past dietary data. The analysis unit can also correct the analysis results based on the user's past dietary data. In this way, the accuracy of the analysis can be improved by referring to the user's past dietary data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past dietary data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can display detailed analysis results when the user is relaxed. The analysis unit can also display concise analysis results when the user is in a hurry. The analysis unit can also display analysis results focusing on important nutrients when the user is stressed. This allows the display method of the analysis results to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions and adjust the display method of the analysis results.

[0093] The analysis unit can perform the analysis while taking into account the geographical distribution of meals. For example, the analysis unit analyzes regional nutrients while taking into account the geographical distribution of meals. The analysis unit can also analyze regional ingredients while taking into account the geographical distribution of meals. The analysis unit can also analyze regional cooking methods while taking into account the geographical distribution of meals. In this way, regional nutrients and ingredients can be accurately analyzed by taking into account the geographical distribution of meals. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographical distribution data to a generation AI and have the generation AI perform the analysis.

[0094] The analysis unit can improve the accuracy of the analysis by referring to diet-related literature during analysis. The analysis unit can improve the accuracy of the analysis by, for example, referring to diet-related literature. The analysis unit can also correct the nutrient content from diet-related literature. The analysis unit can also correct the analysis results based on diet-related literature. In this way, by referring to diet-related literature, the accuracy of the analysis can be improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0095] The analysis unit can perform the analysis while taking into account the market value of the meal. For example, the analysis unit can analyze the nutrients of expensive ingredients while taking into account the market value of the meal. The analysis unit can also analyze the nutrients of inexpensive ingredients while taking into account the market value of the meal. The analysis unit can also analyze ingredients with high cost performance while taking into account the market value of the meal. In this way, ingredients with high cost performance can be analyzed by taking into account the market value of the meal. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input market value data into the generation AI and have the generation AI perform the analysis.

[0096] The identification unit can estimate the user's emotions and adjust the method for identifying missing nutrients based on the estimated user emotions. For example, when the user is relaxed, the identification unit can identify detailed nutrients. When the user is in a hurry, the identification unit can also identify nutrients focusing on major nutrients. When the user is stressed, the identification unit can also identify nutrients focusing on important nutrients. This allows the method for identifying missing nutrients to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the identification unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions and adjust the identification method.

[0097] During the identification, the identification unit can break down the components of the meal in detail to identify the missing nutrients. For example, the identification unit can break down the components of the meal in detail to identify a deficiency in each nutrient. The identification unit can also break down the components of the meal to identify a deficiency in vitamins or minerals. The identification unit can also break down the components of the meal to identify a deficiency in protein or lipids. In this way, by breaking down the components of the meal in detail, the missing nutrients can be accurately identified. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input meal component data into the generation AI and cause the generation AI to identify the missing nutrients.

[0098] During identification, the identification unit can apply different identification algorithms depending on the type of meal. For example, in the case of a vegetable dish, the identification unit can apply an identification algorithm for vitamins and minerals. In addition, in the case of a meat dish, the identification unit can also apply an identification algorithm for protein and fat. In addition, in the case of a dessert, the identification unit can also apply an identification algorithm for carbohydrates and calories. This makes it possible to apply the optimal identification algorithm depending on the type of meal. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input meal type data into the generation AI and cause the generation AI to apply the identification algorithm.

[0099] During identification, the identification unit can improve the accuracy of the identification by referring to the user's past dietary data. The identification unit, for example, improves the accuracy of the identification by referring to the user's past dietary data. The identification unit can also analyze nutrient intake trends from the user's past dietary data. The identification unit can also correct the identification result based on the user's past dietary data. In this way, the accuracy of the identification can be improved by referring to the user's past dietary data. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the user's past dietary data into the generation AI and cause the generation AI to improve the accuracy of the identification.

[0100] The identification unit can estimate the user's emotions and adjust the display method of missing nutrients based on the estimated user emotions. For example, when the user is relaxed, the identification unit can display detailed information about missing nutrients. Furthermore, when the user is in a hurry, the identification unit can also provide a concise display method that focuses on important nutrients when the user is stressed. This allows the display method of missing nutrients to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the identification unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the identification unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion and adjust the display method.

[0101] During the identification, the identification unit can identify nutrient deficiencies by taking into account the geographical distribution of meals. For example, the identification unit can identify nutrient deficiencies specific to a region by taking into account the geographical distribution of meals. The identification unit can also identify nutrient deficiencies specific to a region by taking into account the geographical distribution of meals. The identification unit can also identify nutrient deficiencies caused by cooking methods specific to a region by taking into account the geographical distribution of meals. In this way, by taking into account the geographical distribution of meals, it is possible to accurately identify nutrient deficiencies specific to a region. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input geographical distribution data into the generation AI and cause the generation AI to identify nutrient deficiencies.

[0102] During identification, the identification unit can improve the accuracy of identifying the missing nutrients by referring to diet-related literature. The identification unit can, for example, refer to diet-related literature to improve the accuracy of identifying nutrient deficiencies. The identification unit can also correct the nutrient content from diet-related literature. The identification unit can also correct the identification result based on diet-related literature. In this way, by referring to diet-related literature, the accuracy of identifying the missing nutrients can be improved. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or can be performed without using AI. For example, the identification unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of identification.

[0103] During identification, the identification unit can identify deficient nutrients by taking into account the market value of the meal. For example, the identification unit can identify deficient nutrients in expensive ingredients by taking into account the market value of the meal. The identification unit can also identify deficient nutrients in inexpensive ingredients by taking into account the market value of the meal. The identification unit can also identify deficient nutrients in cost-effective ingredients by taking into account the market value of the meal. In this way, by taking into account the market value of the meal, it is possible to accurately identify deficient nutrients in expensive ingredients and inexpensive ingredients. Some or all of the above-described processing in the identification unit may be performed using, or without, AI, for example. For example, the identification unit can input market value data into the generation AI and cause the generation AI to identify deficient nutrients.

[0104] The generation unit can estimate the user's emotions and adjust the recipe generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a detailed recipe. If the user is in a hurry, the generation unit can also generate a simple and quick recipe. If the user is stressed, the generation unit can also generate a nutritious recipe. This allows the recipe generation method to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion and adjust the generation method.

[0105] During generation, the generation unit can generate a recipe that breaks down the components of a meal in detail to supplement any missing nutrients. For example, the generation unit can generate a recipe that breaks down the components of a meal in detail to supplement any missing nutrients. The generation unit can also generate a recipe that breaks down the components of a meal to supplement vitamins and minerals. The generation unit can also generate a recipe that breaks down the components of a meal to supplement proteins and lipids. In this way, a recipe can be generated that supplements any missing nutrients by breaking down the components of a meal in detail. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input meal component data into the generation AI and cause the generation AI to generate a recipe.

[0106] During generation, the generation unit can apply different generation algorithms depending on the type of meal. For example, in the case of vegetable dishes, the generation unit applies an algorithm that generates a recipe that supplements vitamins and minerals. In addition, in the case of meat dishes, the generation unit can also apply an algorithm that generates a recipe that supplements protein and fat. In addition, in the case of desserts, the generation unit can also apply an algorithm that generates a recipe that supplements carbohydrates and calories. This makes it possible to apply the optimal generation algorithm depending on the type of meal. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input meal type data into the generation AI and cause the generation AI to apply the generation algorithm.

[0107] During generation, the generation unit can improve the accuracy of generation by referring to the user's past dietary data. The generation unit, for example, improves the accuracy of generation by referring to the user's past dietary data. The generation unit can also analyze nutrient intake trends from the user's past dietary data. The generation unit can also correct the generation results based on the user's past dietary data. In this way, the accuracy of generation can be improved by referring to the user's past dietary data. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past dietary data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0108] The generation unit can estimate the user's emotions and adjust the recipe display method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can display a detailed recipe. If the user is in a hurry, the generation unit can also display a concise recipe. If the user is stressed, the generation unit can also display a recipe that focuses on important nutrients. This allows the recipe display method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion and adjust the display method.

[0109] The generation unit can generate recipes taking into account the geographical distribution of meals. For example, the generation unit can generate recipes using ingredients specific to a region taking into account the geographical distribution of meals. The generation unit can also generate recipes using cooking methods specific to a region taking into account the geographical distribution of meals. The generation unit can also generate recipes that supplement nutrients specific to a region taking into account the geographical distribution of meals. In this way, recipes using ingredients and cooking methods specific to a region can be generated by taking into account the geographical distribution of meals. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input geographical distribution data to the generation AI and cause the generation AI to generate a recipe.

[0110] The generation unit can improve the accuracy of generation by referring to diet-related literature during generation. The generation unit can, for example, improve the accuracy of generation by referring to diet-related literature. The generation unit can also correct the nutrient content from diet-related literature. The generation unit can also correct the generation result based on diet-related literature. In this way, by referring to diet-related literature, the accuracy of generation can be improved. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0111] The generation unit can generate a recipe taking into account the market value of the meal. For example, the generation unit can generate a recipe using expensive ingredients taking into account the market value of the meal. The generation unit can also generate a recipe using inexpensive ingredients taking into account the market value of the meal. The generation unit can also generate a recipe using ingredients with high cost performance taking into account the market value of the meal. In this way, by taking into account the market value of the meal, recipes using expensive ingredients and inexpensive ingredients can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input market value data into the generation AI and cause the generation AI to generate a recipe.

[0112] The providing unit can estimate the user's emotions and adjust the recipe presentation method based on the estimated user emotions. For example, if the user is relaxed, the providing unit can provide a detailed recipe. If the user is in a hurry, the providing unit can also provide a concise recipe. If the user is stressed, the providing unit can also provide a recipe that focuses on important nutrients. This allows the recipe presentation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion and adjust the presentation method.

[0113] When providing a recipe, the providing unit can select the optimal serving method by referring to the user's past recipe usage history. The providing unit, for example, refers to the user's past recipe usage history and provides the optimal recipe. The providing unit can also provide recipes that use the user's favorite ingredients from the user's past recipe usage history. The providing unit can also select the optimal serving method based on the user's past recipe usage history. In this way, the optimal recipe can be provided by referring to the user's past recipe usage history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past recipe usage history data into the generation AI and cause the generation AI to select the optimal serving method.

[0114] The providing unit can customize the means for providing the recipe based on the user's current living situation when providing the recipe. For example, the providing unit can provide an easy-to-make recipe taking into account the user's current living situation. The providing unit can also provide a time-consuming but nutritious recipe taking into account the user's current living situation. The providing unit can also provide a recipe using specific ingredients taking into account the user's current living situation. This makes it possible to provide an optimal recipe by taking into account the user's current living situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's living situation data into the generation AI and cause the generation AI to customize the means for providing the recipe.

[0115] The providing unit can improve the providing method by reflecting user feedback when providing the food. The providing unit improves the providing method based on, for example, user feedback. The providing unit can also improve the content of the recipe based on user feedback. The providing unit can also customize the providing means based on user feedback. In this way, the providing method can be improved by reflecting user feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to improve the providing method.

[0116] The providing unit can estimate the user's emotions and prioritize recipes based on the estimated user emotions. For example, if the user is relaxed, the providing unit can prioritize detailed recipes. Furthermore, if the user is in a hurry, the providing unit can prioritize concise recipes. Furthermore, if the user is stressed, the providing unit can prioritize recipes that focus on important nutrients. This allows recipe prioritization to be determined according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions and prioritize recipes.

[0117] The providing unit can provide an optimal recipe by taking into account the user's geographical location information. For example, the providing unit can provide a recipe using ingredients specific to a region by taking into account the user's geographical location information. The providing unit can also provide a recipe using a cooking method specific to a region by taking into account the user's geographical location information. The providing unit can also provide a recipe that supplements nutrients specific to a region by taking into account the user's geographical location information. In this way, recipes using ingredients and cooking methods specific to a region can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input geographical location data to a generation AI and cause the generation AI to provide a recipe.

[0118] At the time of provision, the providing unit can analyze the user's social media activity and provide related recipes. For example, the providing unit can provide recipes related to meals shared by the user on social media. The providing unit can also analyze the content of the user's social media posts and provide related recipes. The providing unit can also provide related recipes by referring to the activity of the user's friends on social media. In this way, related recipes can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input social media data into a generation AI and cause the generation AI to provide related recipes.

[0119] The providing unit can customize the providing method by reflecting the user's past feedback when providing the food. The providing unit customizes the providing method based on, for example, the user's past feedback. The providing unit can also customize the contents of the recipe based on the user's past feedback. The providing unit can also customize the providing means based on the user's past feedback. In this way, the providing method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the providing method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned photographing unit, analysis unit, identification unit, generation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit takes a photo of a meal using the camera 42 of the smart device 14. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the taken photo using AI. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies missing nutrients based on the analysis results. The generation unit is realized by the identification processing unit 290 of the data processing device 12 and generates a recipe to supplement the missing nutrients. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated recipe to the user. The photographing unit has a function of estimating the user's emotions and adjusting the timing of shooting based on the estimated emotions. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, identification unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit takes a photo of a meal using the camera 42 of the smart glasses 214. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the taken photo using AI. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies deficient nutrients based on the analysis results. The generation unit is realized by the identification processing unit 290 of the data processing device 12 and generates a recipe to supplement the deficient nutrients. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the generated recipe to the user. The photographing unit has a function of estimating the user's emotions and adjusting the timing of shooting based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, identification unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the photographing unit takes a photo of a meal using the camera 42 of the headset-type terminal 314. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the taken photo using AI. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies deficient nutrients based on the analysis results. The generation unit is realized by the identification processing unit 290 of the data processing device 12 and generates a recipe to supplement the deficient nutrients. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides the generated recipe to the user. The photographing unit has a function of estimating the user's emotions and adjusting the timing of photographing based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, identification unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit takes a photo of a meal using the camera 42 of the robot 414. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the taken photo using AI. The identification unit is realized by the identification processing unit 290 of the data processing device 12 and identifies missing nutrients based on the analysis results. The generation unit is realized by the identification processing unit 290 of the data processing device 12 and generates a recipe to supplement the missing nutrients. The provision unit is realized by the control unit 46A of the robot 414 and provides the generated recipe to the user. The photographing unit has a function of estimating the user's emotions and adjusting the timing of photographing based on the estimated emotions.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The analysis unit can also evaluate the freshness of ingredients when analyzing photos of the user's meals. For example, the analysis unit can analyze the color and texture of vegetables to evaluate their freshness. The analysis unit can also analyze the color and luster of meat and fish to evaluate their freshness. Furthermore, the analysis unit can suggest to the user to use fresher ingredients based on the freshness of the ingredients. This allows the user to use fresher and more nutritious ingredients.

[0122] The identification unit can also identify allergen information for ingredients when analyzing photos of the user's meals. For example, the identification unit can identify allergens, such as nuts and dairy products, contained in the meal. The identification unit can also refer to the user's allergy information to identify ingredients containing allergens. Furthermore, the identification unit can suggest alternative ingredients to avoid ingredients containing allergens. This allows the user to avoid allergic reactions.

[0123] When analyzing photos of meals taken by a user, the generation unit can also generate recipes taking into account the seasonality of ingredients. For example, the generation unit generates recipes using seasonal ingredients. The generation unit can also generate recipes taking into account seasonal variations in nutrients. Furthermore, the generation unit can generate recipes taking into account the ease of obtaining ingredients in each season. This allows users to enjoy nutritionally balanced meals according to the season.

[0124] The providing unit can also suggest food storage methods when analyzing photos of the user's meals. For example, the providing unit can suggest appropriate storage methods for vegetables and fruits. The providing unit can also suggest freezing methods for meat and fish. Furthermore, the providing unit can also suggest storage methods for cooked meals. This allows the user to extend the shelf life of ingredients and reduce food waste.

[0125] The analysis unit can also identify origin information for ingredients when analyzing photos of the user's meals. For example, the analysis unit can analyze the label or packaging information of ingredients to identify the origin. The analysis unit can also analyze the characteristics of ingredients to estimate the origin. Furthermore, the analysis unit can suggest to the user to use locally produced ingredients based on the origin information. This allows the user to use fresh, locally produced ingredients.

[0126] The analysis unit can estimate the user's emotions and adjust the nutritional balance of the meal based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can suggest ingredients that are rich in nutrients that help relieve stress. Also, if the user is tired, the analysis unit can suggest ingredients that are rich in nutrients that help replenish energy. Furthermore, if the user is relaxed, the analysis unit can suggest ingredients that are rich in nutrients that have a relaxing effect. This allows the user to enjoy a nutritionally balanced meal that suits their emotions.

[0127] The identification unit can estimate the user's emotions and adjust the calories of the meal based on the estimated user's emotions. For example, if the user is on a diet, the identification unit can suggest low-calorie ingredients. Also, if the user has just exercised, the identification unit can suggest high-calorie ingredients. Furthermore, if the user is feeling stressed, the identification unit can suggest low-calorie ingredients that will help relieve stress. This allows the user to control calories according to their emotions.

[0128] The generation unit can estimate the user's emotions and adjust the appearance of the meal based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can suggest a colorful and visually appealing meal. If the user is in a hurry, the generation unit can also suggest a simple and quick meal. Furthermore, if the user is feeling stressed, the generation unit can also suggest a meal with a calming appearance. This allows the user to enjoy a visually appealing meal that suits their emotions.

[0129] The providing unit can estimate the user's emotions and adjust the timing of providing the meal based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide the meal at a timing that allows the user to enjoy the meal at a leisurely pace. Also, if the user is in a hurry, the providing unit can provide the meal quickly. Furthermore, if the user is feeling stressed, the providing unit can provide the meal at a timing that allows the user to relax. This allows the user to enjoy the meal at the optimal timing according to their emotions.

[0130] The providing unit can estimate the user's emotions and adjust the amount of food based on the estimated user's emotions. For example, the providing unit can provide an appropriate amount of food when the user is relaxed. The providing unit can also provide a small amount of food when the user is in a hurry. Furthermore, the providing unit can also provide an appropriate amount of food that helps relieve stress when the user is feeling stressed. This allows the user to enjoy an appropriate amount of food according to their emotions.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The user takes a photo of the meal using the camera. For example, the user can take a photo of the meal using a smartphone camera. The camera can also automatically adjust the type of camera, shooting angle, and lighting conditions to take the optimal photo. Step 2: The analysis unit uses AI to analyze the photos taken by the photography unit. For example, it uses image analysis technology to recognize the contents of the meal. For example, the analysis unit can use object detection technology to identify ingredients and calculate the nutritional value of each. The analysis unit can also classify the type of meal using image classification technology. Step 3: The identification unit uses AI to identify the nutrients that are lacking based on the results of the analysis by the analysis unit. For example, it identifies whether there is a deficiency in nutrients such as vitamin C, iron, or calcium. The identification unit can identify the nutrients that are lacking based on, for example, the standard values ​​of the nutrients. The identification unit can also identify the nutrients that are lacking by referring to the user's past dietary data. Step 4: The generation unit uses AI to generate recipes that supplement the missing nutrients identified by the identification unit. For example, if there is a vitamin C deficiency, the generation unit generates recipes using ingredients that are rich in vitamin C. The generation unit can generate recipes that include, for example, specific types and amounts of ingredients and cooking methods. The generation unit can also refer to the user's past dietary data to improve the accuracy of the generated recipes. Step 5: The providing unit provides the recipe generated by the generating unit to the user. For example, the recipe can be displayed through a smartphone app. The providing unit can also reflect user feedback and improve the providing method.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 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 photography department that takes photos of meals, an analysis unit that analyzes the photograph taken by the photographing unit; an identifying unit that identifies a nutrient that is lacking based on the analysis result by the analyzing unit; a generation unit that generates a recipe for supplementing the missing nutrients identified by the identification unit; a providing unit that provides the recipe generated by the generating unit to a user. A system characterized by:

2. The analysis unit Recognizing food contents using image analysis technology 2. The system of claim 1.

3. The identification unit Identify deficiencies in at least one of the following nutrients: Vitamin C, iron, or calcium 2. The system of claim 1.

4. The generation unit Generate recipes to fill in missing nutrients 2. The system of claim 1.

5. The generation unit Generate recipes that include specific ingredients and cooking methods 2. The system of claim 1.

6. The providing unit Suggest the generated recipe to the user 2. The system of claim 1.

7. The imaging unit is Estimates the user's emotions and adjusts the timing of taking photos of meals based on the estimated user emotions.

2. The system of claim 1.

8. The imaging unit is When taking a photo, the camera automatically adjusts the angle of the meal and lighting conditions to capture the best possible photo.

2. The system of claim 1.

Images