System

Abstract

An object of a system according to an embodiment is to accurately grasp nutritional values of meals and propose a well-balanced menu.SOLUTION: A system according to an embodiment includes an analysis part, a calculation part, an analysis part and a proposal part. The analysis unit analyzes the picture of the meal. The calculation unit calculates a nutritional value on the basis of the ingredient specified by the analysis unit. The analysis unit analyzes the nutrition value calculated by the calculation unit. The proposal unit proposes a menu on the basis of the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately grasp the nutritional value of meals and propose balanced menus.

[0005] The system according to the embodiment aims to accurately grasp the nutritional value of meals and propose balanced menus. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a calculation unit, an analysis unit, and a suggestion unit. The analysis unit analyzes a photo of a meal. The calculation unit calculates nutritional values ​​based on ingredients identified by the analysis unit. The analysis unit analyzes the nutritional values ​​calculated by the calculation unit. The suggestion unit suggests a menu based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately grasp the nutritional value of meals and propose balanced menus. [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 nutritional value calculation system according to an embodiment of the present invention automatically analyzes photos of meals, calculates and analyzes nutritional values, and proposes optimal menus. In the nutritional value calculation system, a user takes a photo of a meal and inputs it into the tool. Next, AI analyzes the photo to identify the types and amounts of ingredients. The AI ​​calculates nutritional values ​​based on the identified ingredients and analyzes nutritional deficiencies and excess intakes. Finally, the AI ​​proposes optimal menus to the user based on the analysis results. For example, a nutritional value calculation system allows a user to input a photo of a meal. For example, the nutritional value calculation system allows a user to take a photo of a meal with a smartphone camera and upload the photo to the tool. Next, the nutritional value calculation system uses AI to analyze the input photo. The AI ​​identifies ingredients in the photo using image recognition technology. For example, the AI ​​identifies ingredients such as vegetables, meat, and fish in the photo and estimates the amount of each. The nutritional value calculation system then calculates nutritional values ​​based on the identified ingredients. For example, the system calculates vitamin and mineral content based on the type and amount of vegetables, and protein and fat content based on the type and amount of meat and fish. Next, the nutritional value calculation system identifies nutrient deficiencies and excess intakes based on the calculated nutritional values. For example, it identifies cases where vitamin C is deficient or fat intake is excessive. Next, the nutritional value calculation system suggests optimal menus for the user based on the analysis results. For example, it suggests menus that include ingredients to compensate for nutrient deficiencies or ingredients to reduce excess intakes of nutrients. This allows the nutritional value calculation system to enable the user to eat a balanced diet. This allows the user to easily understand the nutritional value of their meals and lead a healthy diet. For example, this is very useful for users who are on a diet or who need to consume specific nutrients. Furthermore, knowing the nutritional value of meals in detail can also be useful for health management and disease prevention.

[0029] The nutritional value calculation system according to the embodiment includes an analysis unit, a calculation unit, an analysis unit, and a proposal unit. The analysis unit analyzes a photo of a meal. The analysis unit identifies ingredients in the photo, for example, using image recognition technology. For example, the analysis unit identifies ingredients such as vegetables, meat, and fish in the photo and estimates the amount of each. The analysis unit can also use AI to identify the type and amount of ingredients. For example, the analysis unit can identify ingredients using deep learning. The calculation unit calculates nutritional value based on the ingredients identified by the analysis unit. The calculation unit calculates vitamin and mineral content based on the type and amount of vegetables, for example. The calculation unit can also calculate protein and lipid content based on the type and amount of meat or fish. For example, the calculation unit can calculate nutritional value using a food database. The analysis unit analyzes the nutritional value calculated by the calculation unit. For example, the analysis unit identifies nutritional deficiencies or excessive intakes based on the calculated nutritional value. The analysis unit can also analyze nutritional value using statistical methods. For example, the analysis unit identifies nutritional deficiencies or excess intakes of nutrients compared with the recommended intake amount. The suggestion unit suggests an optimal menu based on the analysis results obtained by the analysis unit. The suggestion unit suggests, for example, a menu including ingredients to compensate for nutritional deficiencies or ingredients to reduce excessive nutritional intake. The suggestion unit can also suggest menus taking into account the user's preferences and allergy information. For example, the suggestion unit suggests menus including favorite ingredients based on user input. In this way, the nutritional value calculation system according to the embodiment can analyze photos of meals, calculate and analyze nutritional values, and suggest optimal menus.

[0030] The nutritional value calculation system includes a reception unit through which a user inputs a photo of a meal. The reception unit inputs the photo of the meal. For example, the reception unit takes a photo of the meal using a smartphone camera and uploads the photo to the tool. The reception unit can also input a photo of the meal using a digital camera or tablet. For example, the reception unit automatically recognizes a photo taken by the user and processes it as input data. This allows the user to easily input a photo of the meal. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input a photo taken by the user to a generation AI and have the generation AI recognize the photo.

[0031] The suggestion unit includes a display unit that displays a menu suggested to the user. The display unit displays the menu suggested to the user. The display unit displays the menu on, for example, a smartphone or tablet screen. The display unit can also display the menu on a computer monitor or television screen. For example, the display unit provides a graphical interface so that the user can visually confirm the suggested menu. This allows the user to visually confirm the suggested menu. Some or all of the above-described processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can input data of the suggested menu into a generation AI and have the generation AI execute a visual display.

[0032] The suggestion unit includes a storage unit that stores the proposed menu. The storage unit stores the proposed menu. The storage unit stores the menu in, for example, cloud storage or local storage. The storage unit can also store the menu on the user's device. For example, the storage unit stores the proposed menu in a database so that it can be referenced later. This allows the user to reference the proposed menu later. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input data of the proposed menu into a generation AI and have the generation AI execute the storage process.

[0033] The analysis unit can identify ingredients in a photograph using image recognition technology. Image recognition technology includes, but is not limited to, deep learning and object detection algorithms. For example, the analysis unit can identify ingredients in a photograph using deep learning. The analysis unit can also identify ingredients using object detection algorithms. The analysis unit can also identify the type and amount of ingredients using image recognition technology. For example, the analysis unit can identify ingredients such as vegetables, meat, and fish in a photograph and estimate the amount of each ingredient. This allows the ingredients in the photograph to be accurately identified. Some or all of the above-described 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 photo data into a generation AI and have the generation AI identify the ingredients.

[0034] The calculation unit can calculate nutritional values ​​based on the identified ingredients. Nutritional value calculations include, but are not limited to, a food database and a calculation algorithm. For example, the calculation unit calculates the nutritional value of the identified ingredients using a food database. The calculation unit can also calculate nutritional value using a calculation algorithm. The calculation unit can also calculate vitamin and mineral content based on the identified ingredients. For example, the calculation unit calculates vitamin and mineral content based on the type and amount of vegetables. The calculation unit can also calculate protein and lipid content based on the type and amount of meat or fish. This allows accurate nutritional value calculation based on the ingredients. Some or all of the above-described processing in the calculation unit can be performed using, for example, AI, or without AI. For example, the calculation unit can input data on the identified ingredients into a generation AI and have the generation AI calculate the nutritional value.

[0035] The analysis unit can identify nutrient deficiencies or excess intakes based on the calculated nutritional values. Examples of identifying nutrient deficiencies or excess intakes include, but are not limited to, recommended intakes and excess intake thresholds. For example, the analysis unit can identify nutrient deficiencies or excess intakes compared to recommended intakes. The analysis unit can also identify excess intakes based on excess intake thresholds. The analysis unit can also analyze nutritional values ​​using statistical methods. For example, the analysis unit can identify nutrient deficiencies or excess intakes based on the calculated nutritional values. This allows for the identification of nutrient deficiencies or excess intakes. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input calculated nutritional value data into the generation AI and cause the generation AI to identify nutrient deficiencies or excess intakes.

[0036] The suggestion unit can suggest an optimal menu based on the analysis results. The optimal menu suggestion may include, but is not limited to, nutritional balance and user preferences, for example. The suggestion unit may suggest, for example, a menu that includes ingredients to supplement nutritional deficiencies or ingredients to reduce excessive nutritional intake. The suggestion unit may also suggest a menu taking into consideration the user's preferences and allergy information. For example, the suggestion unit may suggest a menu that includes favorite ingredients based on the user's input. This allows the suggestion of an optimal menu for the user. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input data of the analysis results into a generation AI and cause the generation AI to suggest an optimal menu.

[0037] During analysis, the analysis unit can correct the analysis results based on the freshness of the ingredients and the cooking method. For example, if the ingredients are very fresh, the analysis unit corrects the nutritional value to be higher. Furthermore, if the cooking method is baking, the analysis unit can also correct the results taking into account the loss of vitamin C. Furthermore, if the cooking method is boiling, the analysis unit can also correct the results taking into account the elution of minerals. In this way, the analysis results can be corrected taking into account the freshness of the ingredients and the cooking method. 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 data on the freshness of the ingredients and the cooking method into the generation AI, and have the generation AI correct the analysis results.

[0038] During analysis, the analysis unit can take fluctuations in nutritional value into account by referring to the origin information of the ingredients. For example, if the origin is an organically grown region, the analysis unit corrects the nutritional value to be higher. Furthermore, if the origin is far away, the analysis unit can also correct the nutritional value by taking into account the loss of nutritional value during transportation. Furthermore, if the origin is under specific climatic conditions, the analysis unit can also correct the nutritional value by taking into account the influence of those conditions. In this way, fluctuations in nutritional value can be corrected by taking into account the origin information of the ingredients. 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 data on the origin information of the ingredients into the generation AI and have the generation AI correct the fluctuations in nutritional value.

[0039] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past dietary history. The analysis unit improves the accuracy of the analysis, for example, based on data on ingredients the user has previously consumed. The analysis unit can also consider the intake tendency of specific nutrients from the user's past dietary history during analysis. The analysis unit can also consider the combination of ingredients during analysis based on the user's past dietary history. This can improve the accuracy of the analysis by referring to the user's past dietary history. 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 data on the user's past dietary history into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0040] The analysis unit can provide analysis results taking into account the allergen information of ingredients during analysis. For example, the analysis unit can display the risk of allergic reactions based on the allergen information contained in ingredients. The analysis unit can also suggest alternative ingredients based on the allergen information. The analysis unit can also suggest cooking methods to avoid allergic reactions based on the allergen information. In this way, analysis results can be provided taking into account the allergen information of ingredients. 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 data on the allergen information of ingredients into the generation AI and have the generation AI provide the analysis results.

[0041] During analysis, the analysis unit can correct the analysis results by referring to seasonal information about the ingredients. The analysis unit corrects the analysis results, for example, based on information about ingredients whose nutritional value varies depending on the season. The analysis unit can also correct the nutritional value of seasonal ingredients to be higher based on the seasonal information. The analysis unit can also analyze based on the seasonal information, taking into account fluctuations in nutritional value due to storage methods. This allows the analysis results to be corrected by referring to the seasonal information about the ingredients. 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 data about seasonal information about ingredients into the generation AI and have the generation AI correct the analysis results.

[0042] During analysis, the analysis unit can customize the analysis method according to the user's dietary goals. For example, if the goal is to lose weight, the analysis unit can emphasize analysis of calories and lipids. Furthermore, if the goal is to build muscle, the analysis unit can also emphasize analysis of protein and amino acids. Furthermore, if the goal is to maintain health, the analysis unit can also analyze balanced nutritional values. This allows the analysis method to be customized according to the user's dietary goals. 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 data on the user's dietary goals into the generation AI and have the generation AI customize the analysis method.

[0043] The calculation unit can correct the nutritional value by taking into account the cooking method of the ingredients when making calculations. For example, if the cooking method is baking, the calculation unit corrects the nutritional value by taking into account the decrease in vitamin C. The calculation unit can also correct the nutritional value by taking into account the elution of minerals if the cooking method is boiling. The calculation unit can also correct the nutritional value by taking into account the increase in fat if the cooking method is frying. In this way, the nutritional value can be corrected by taking into account the cooking method of the ingredients. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on the cooking method into the generation AI and have the generation AI perform the correction of the nutritional value.

[0044] The calculation unit can take into account fluctuations in nutritional value by referring to the storage state of the ingredients when making calculations. For example, in the case of frozen ingredients, the calculation unit corrects the nutritional value by taking into account the loss of vitamins. In the case of canned ingredients, the calculation unit can also correct by taking into account fluctuations in nutritional value during storage. In the case of fresh ingredients, the calculation unit can also correct by taking into account fluctuations in nutritional value depending on the storage period. In this way, fluctuations in nutritional value can be taken into account by referring to the storage state of the ingredients. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on the storage state into the generation AI and have the generation AI correct for fluctuations in nutritional value.

[0045] During calculation, the calculation unit can improve the accuracy of the calculation by referring to the user's past nutritional intake history. For example, the calculation unit performs calculations based on the user's past nutritional intake history, taking into account the intake tendency of specific nutrients. The calculation unit can also perform calculations based on the user's past nutritional intake history, taking into account nutritional balance. The calculation unit can also perform calculations based on the user's past nutritional intake history, taking into account the combination of ingredients. This can improve the accuracy of the calculation by referring to the user's past nutritional intake history. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on the user's past nutritional intake history into the generation AI and cause the generation AI to improve the accuracy of the calculation.

[0046] The calculation unit can correct the nutritional value during calculation, taking into account the organic cultivation information of the ingredients. For example, in the case of organically grown ingredients, the calculation unit corrects the nutritional value higher. The calculation unit can also correct the nutritional value based on the organic cultivation information, taking into account the risk of pesticide residues. The calculation unit can also correct the nutritional value based on the organic cultivation information, taking into account the quality of the ingredients. In this way, the nutritional value can be corrected taking into account the organic cultivation information of the ingredients. Some or all of the above-mentioned processing in the calculation unit may be performed using AI, for example, or may be performed without using AI. For example, the calculation unit can input organic cultivation information data into the generation AI and have the generation AI correct the nutritional value.

[0047] The calculation unit can correct the nutritional value by referring to the degree of processing of the ingredients during calculation. For example, in the case of raw ingredients, the calculation unit calculates the nutritional value as is. In addition, in the case of frozen ingredients, the calculation unit can also correct the nutritional value by taking into account fluctuations in nutritional value during storage. In the case of canned ingredients, the calculation unit can also correct the nutritional value by taking into account fluctuations in nutritional value during storage. In this way, the nutritional value can be corrected by referring to the degree of processing of the ingredients. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on the degree of processing into the generation AI and have the generation AI correct the nutritional value.

[0048] The calculation unit can customize nutritional values ​​according to the user's health condition during calculation. For example, if the user has an allergy, the calculation unit can exclude the nutritional values ​​of ingredients containing allergens. The calculation unit can also limit the intake of specific nutrients based on the user's medical history. The calculation unit can also emphasize necessary nutrients based on the user's health condition during calculation. This allows nutritional values ​​to be customized according to the user's health condition. Some or all of the above-mentioned processing in the calculation unit may be performed using AI, for example, or may be performed without using AI. For example, the calculation unit can input data on the user's health condition into the generation AI and have the generation AI customize the nutritional values.

[0049] During analysis, the analysis unit can correct the required amount of nutrients taking into account the user's age and gender. The analysis unit, for example, corrects the amount of required nutrients according to the user's age. The analysis unit can also correct the amount of required nutrients according to the user's gender. The analysis unit can also customize the required amount of nutrients based on the user's age and gender. This allows the required amount of nutrients to be corrected taking into account the user's age and gender. 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 data on the user's age and gender into the generation AI and cause the generation AI to correct the required amount of nutrients.

[0050] During analysis, the analysis unit can correct the required amount of nutritional value by referring to the user's activity level. The analysis unit, for example, corrects the required amount of calories according to the user's amount of exercise. The analysis unit can also correct the required amount of protein according to the user's activity level. The analysis unit can also customize the required amount of nutritional value based on the user's activity level. This makes it possible to correct the required amount of nutritional value by referring to the user's activity level. 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 data on the user's activity level into the generation AI and cause the generation AI to correct the required amount of nutritional value.

[0051] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past health checkup results. For example, the analysis unit corrects the required amount of a specific nutrient based on the user's past health checkup results. The analysis unit can also perform analysis taking nutritional balance into consideration based on the user's health checkup results. The analysis unit can also customize the required amount of nutrition according to the user's health condition based on the user's past health checkup results. This can improve the accuracy of the analysis by referring to the user's past health checkup results. 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 data on the user's past health checkup results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0052] During analysis, the analysis unit can correct the required amount of nutritional value by taking into account the frequency of the user's meals. For example, if the user eats three meals a day, the analysis unit distributes the nutritional value of each meal evenly. Furthermore, if the user eats two meals a day, the analysis unit can also set the nutritional value of each meal to be higher. Furthermore, if the user has irregular meals, the analysis unit can also make corrections by taking into account the overall nutritional balance. In this way, the required amount of nutritional value can be corrected by taking into account the frequency of the user's meals. 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 data on the user's meal frequency into the generation AI and cause the generation AI to correct the required amount of nutritional value.

[0053] During analysis, the analysis unit can correct the required amount of nutrients by referring to the user's lifestyle. For example, if the user is a smoker, the analysis unit can set a higher required amount of vitamin C. Furthermore, if the user is a heavy drinker, the analysis unit can also set a higher required amount of nutrients that support liver function. Furthermore, the analysis unit can customize the required amount of specific nutrients based on the user's lifestyle. This allows the required amount of nutrients to be corrected by referring to the user's lifestyle. 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 data on the user's lifestyle into the generation AI and cause the generation AI to correct the required amount of nutrients.

[0054] During analysis, the analysis unit can customize the required nutritional amounts by taking into account the user's genetic information. For example, the analysis unit can set the required amounts of specific nutrients based on the user's genetic information. The analysis unit can also correct the nutritional amounts by taking into account the risk of specific diseases from the user's genetic information. The analysis unit can also customize the required amounts of individual nutrients based on the user's genetic information. This makes it possible to customize the required nutritional amounts by taking into account the user's genetic information. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input the user's genetic information data into the generation AI and cause the generation AI to customize the required nutritional amounts.

[0055] When proposing, the suggestion unit can customize a menu taking into consideration the user's ingredient preferences and allergy information. For example, the suggestion unit can suggest a menu giving priority to the user's favorite ingredients. The suggestion unit can also suggest a menu that does not contain allergens based on the user's allergy information. The suggestion unit can also suggest a menu taking into consideration the user's preference trends based on the user's past eating history. This allows the menu to be customized taking into consideration the user's ingredient preferences and allergy information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's ingredient preferences and allergy information into the generation AI and have the generation AI customize the menu.

[0056] When proposing, the suggestion unit can adjust the menu according to the user's dietary purpose. For example, if the purpose is to lose weight, the suggestion unit can suggest a low-calorie menu. Furthermore, if the purpose is to build muscle, the suggestion unit can also suggest a high-protein menu. Furthermore, if the purpose is to maintain health, the suggestion unit can also suggest a balanced menu. In this way, the menu can be adjusted according to the user's dietary purpose. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input data on the user's dietary purpose into the generation AI and have the generation AI adjust the menu.

[0057] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past meal history. The suggestion unit, for example, suggests a menu including favorite ingredients based on the user's past meal history. The suggestion unit can also suggest a menu taking into consideration nutritional balance based on the user's past meal history. The suggestion unit can also suggest a menu taking into consideration ingredient combinations based on the user's past meal history. This can improve the accuracy of the suggestion by referring to the user's past meal history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data of the user's past meal history into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0058] When proposing a menu, the suggestion unit can customize the menu taking into consideration the availability of ingredients in the user's geographical area. For example, the suggestion unit can suggest a menu based on ingredients available in the user's area. The suggestion unit can also suggest a menu based on ingredients that are seasonal in the user's area. The suggestion unit can also suggest a menu based on local specialties in the user's area. This allows the menu to be customized taking into consideration the availability of ingredients in the user's geographical area. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the availability of ingredients in the user's geographical area into the generation AI and cause the generation AI to customize the menu.

[0059] When proposing, the suggestion unit can adjust the menu according to the user's mealtimes. For example, for breakfast, the suggestion unit can suggest a menu that emphasizes energy replenishment. For lunch, the suggestion unit can also suggest a balanced menu. For dinner, the suggestion unit can also suggest a menu that is easy to digest. This allows the menu to be adjusted according to the user's mealtimes. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the user's mealtimes into the generation AI and have the generation AI adjust the menu.

[0060] When proposing, the suggestion unit can customize a menu taking into consideration the user's cultural background and eating habits. For example, the suggestion unit suggests a menu based on ingredients that correspond to the user's cultural background. The suggestion unit can also suggest a menu that includes the user's favorite ingredients based on the user's eating habits. The suggestion unit can also suggest a menu that avoids specific ingredients by taking into consideration the user's cultural background. This allows the menu to be customized taking into consideration the user's cultural background and eating habits. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the user's cultural background and eating habits into the generation AI and cause the generation AI to customize the menu.

[0061] The reception unit can suggest the optimal input method by referring to the user's past input history when receiving the input. For example, the reception unit automatically displays ingredients that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest ingredients to be used in a specific time period from the user's past input history. This makes it possible to suggest the optimal input method by referring to the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input data from the user's past input history into a generation AI and have the generation AI suggest the optimal input method.

[0062] The reception unit can select the optimal input means by taking into consideration the user's device information when receiving the input. For example, if the user is using a smartphone, the reception unit prioritizes touch input. Furthermore, if the user is using a tablet, the reception unit can provide an input method optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can prioritize voice input. This allows the optimal input means to be selected by taking into consideration the user's device information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's device information into the generation AI and cause the generation AI to select the optimal input means.

[0063] The reception unit can customize the interface according to the user's input method when receiving the input. For example, if the user uses voice input, the reception unit provides an interface optimized for voice recognition. Furthermore, if the user uses text input, the reception unit can also provide an interface optimized for keyboard input. Furthermore, if the user uses image input, the reception unit can also provide an interface optimized for image recognition. This allows the interface to be customized according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's input method to a generation AI and have the generation AI customize the interface.

[0064] The reception unit can provide a multilingual interface according to the user's language setting at the time of reception. The reception unit automatically sets the interface language based on, for example, the language setting of the user's device. The reception unit can also provide a language switching function when the user uses multiple languages. The reception unit can also provide an interface in a specific language when the user selects that language. This makes it possible to provide a multilingual interface according to the user's language setting. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's language setting into a generation AI and cause the generation AI to provide a multilingual interface.

[0065] The display unit can select the optimal display method by referring to the user's past browsing history when displaying. The display unit customizes the display content based on, for example, information that the user has preferred to view in the past. The display unit can also preferentially display specific information from the user's past browsing history. The display unit can also provide a display method that matches the user's visual preferences based on the user's past browsing history. This allows the optimal display method to be selected by referring to the user's past browsing history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data of the user's past browsing history to a generation AI and have the generation AI select the optimal display method.

[0066] The display unit can select the optimal display means when displaying, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a display method that is simple and highly visible. This allows the optimal display means to be selected taking into account the user's device information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data on the user's device information into the generation AI and cause the generation AI to select the optimal display means.

[0067] The display unit can provide a multilingual display according to the user's language setting when displaying. The display unit, for example, automatically sets the display language based on the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also provide a display in a specific language when the user selects that language. This makes it possible to provide a multilingual display according to the user's language setting. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data of the user's language setting into a generation AI and cause the generation AI to provide a multilingual display.

[0068] The display unit can provide a display customized according to the user's visual preferences when displaying. The display unit customizes the display content based on, for example, the user's preferred color shade. The display unit can also adjust the font size and style according to the user's visual preferences. The display unit can also provide an optimal display method based on the user's past selection history. This makes it possible to provide a display customized according to the user's visual preferences. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data on the user's visual preferences into a generation AI and cause the generation AI to provide a customized display.

[0069] The storage unit can select the optimal storage method by referring to the user's past storage history when saving. The storage unit customizes the storage method, for example, based on data that the user frequently saved in the past. The storage unit can also prioritize saving specific data from the user's past storage history. The storage unit can also provide the optimal storage format based on the user's past storage history. This allows the optimal storage method to be selected by referring to the user's past storage history. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input data of the user's past storage history to a generation AI and have the generation AI select the optimal storage method.

[0070] The storage unit can select the optimal storage means when saving data by taking into account the user's device information. For example, if the user is using a smartphone, the storage unit provides a storage method according to the device's storage capacity. Furthermore, if the user is using a tablet, the storage unit can also provide large-capacity data storage. Furthermore, if the user is using cloud storage, the storage unit can automatically save data to the cloud. This allows the optimal storage means to be selected by taking into account the user's device information. Some or all of the above-described processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input the user's device information data into the generation AI and have the generation AI select the optimal storage means.

[0071] The storage unit can provide a function for automatically backing up the user's data when saving the data. For example, the storage unit automatically backs up the data to the cloud when the user saves the data. The storage unit can also periodically back up the data stored on the user's device. The storage unit can also automatically perform backups at times specified by the user. This allows the user's data to be automatically backed up. Some or all of the above-described processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input backup data of the user's data to the generation AI and have the generation AI perform the backup.

[0072] The storage unit can provide an encryption function to enhance the security of the user's data when it is stored. For example, the storage unit can automatically encrypt data when the user stores it. The storage unit can also encrypt data stored on the user's device. The storage unit can also encrypt and store data specified by the user. This makes it possible to provide an encryption function to enhance the security of the user's data. Some or all of the above-mentioned processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input the data to be encrypted to a generation AI and have the generation AI perform the encryption.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] When analyzing a user's meal photos, the analysis unit can consider not only the color and shape of ingredients, but also the texture and temperature of the ingredients. For example, the analysis unit can identify the cooking method, such as fried or simmered, by analyzing the texture of the ingredients. The analysis unit can also identify the type of dish, such as cold salad or hot soup, by detecting the temperature of the ingredients. Furthermore, the analysis unit can track changes in the color of the ingredients to understand the progress of cooking. This allows the analysis unit to identify ingredients in more detail and accurately.

[0075] When calculating nutritional values ​​based on identified ingredients, the calculation unit can also take into account the origin and cultivation method of the ingredients. For example, the calculation unit can correct the nutritional value higher for organically grown ingredients. In addition, if the origin is far away, the calculation unit can also correct the value taking into account the loss of nutritional value during transportation. Furthermore, the calculation unit can also correct the value taking into account the nutritional value of ingredients grown under specific climatic conditions. This allows the calculation unit to accurately calculate nutritional values ​​taking into account the cultivation method and origin information of the ingredients.

[0076] The analysis unit can identify nutritional deficiencies or excess intakes based on the calculated nutritional values, taking into account the user's health condition and lifestyle habits. For example, if the user is a smoker, the analysis unit can set a higher required amount of vitamin C. Also, if the user is a heavy drinker, the analysis unit can set a higher required amount of nutrients that support liver function. Furthermore, the analysis unit can correct the required amount of calories and protein depending on the user's level of exercise. This enables the analysis unit to analyze nutritional values ​​taking into account the user's health condition and lifestyle habits.

[0077] When analyzing a photo of a meal, the analysis unit can correct the analysis results based on the freshness of the ingredients and the cooking method. For example, if the ingredients are very fresh, the analysis unit can correct the nutritional value to be higher. In addition, if the cooking method is baking, the analysis unit can also correct the results taking into account the loss of vitamin C. Furthermore, if the cooking method is boiling, the analysis unit can also correct the results taking into account the elution of minerals. This allows the analysis unit to provide accurate analysis results that take into account the freshness of the ingredients and the cooking method.

[0078] When calculating nutritional values ​​based on identified ingredients, the calculation unit can improve the accuracy of the calculation by referring to the user's past nutritional intake history. For example, the calculation unit can calculate the nutritional values ​​by taking into account the intake tendency of specific nutrients based on the user's past nutritional intake history. The calculation unit can also calculate the nutritional values ​​by taking into account nutritional balance based on the user's past nutritional intake history. Furthermore, the calculation unit can calculate the nutritional values ​​by taking into account the combination of ingredients based on the user's past nutritional intake history. This allows the calculation unit to accurately calculate nutritional values ​​by referring to the user's past nutritional intake history.

[0079] The analysis unit can correct the required amount of nutrients based on the calculated nutritional values, taking into account the user's age and gender. For example, the analysis unit can correct the amount of nutrients required according to the user's age. The analysis unit can also correct the amount of nutrients required according to the user's gender. Furthermore, the analysis unit can customize the required amount of nutrients based on the user's age and gender. This enables the analysis unit to analyze nutritional values ​​taking into account the user's age and gender.

[0080] When saving a suggested menu, the suggestion unit can select the optimal saving method by referring to the user's past saving history. For example, the suggestion unit can customize the saving method based on data that the user has frequently saved in the past. The suggestion unit can also prioritize saving specific data from the user's past saving history. Furthermore, the suggestion unit can provide the optimal saving format based on the user's past saving history. This allows the suggestion unit to save data optimally by referring to the user's past saving history.

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

[0082] Step 1: The analyzer analyzes a photo of the meal. It uses image recognition technology to identify the ingredients in the photo and estimate the amount of each. For example, deep learning can be used to identify the ingredients. Step 2: The calculation unit calculates nutritional values ​​based on the ingredients identified by the analysis unit. Using a food database, the calculation unit calculates the vitamin and mineral content based on the type and amount of vegetables, and the protein and fat content based on the type and amount of meat and fish. Step 3: The analysis unit analyzes the nutritional values ​​calculated by the calculation unit. Based on the calculated nutritional values, the analysis unit identifies any nutritional values ​​that are deficient or excessively ingested. It can also identify any nutritional values ​​that are deficient or excessively ingested by comparing them with the recommended intake amount. Step 4: The suggestion unit proposes an optimal menu based on the analysis results obtained by the analysis unit. The suggestion unit proposes a menu that includes ingredients to compensate for nutritional deficiencies or ingredients to reduce excessive nutritional intake. It can also propose a menu that takes into account the user's preferences and allergy information.

[0083] (Example 2) A nutritional value calculation system according to an embodiment of the present invention automatically analyzes photos of meals, calculates and analyzes nutritional values, and proposes optimal menus. In the nutritional value calculation system, a user takes a photo of a meal and inputs it into the tool. Next, AI analyzes the photo to identify the types and amounts of ingredients. The AI ​​calculates nutritional values ​​based on the identified ingredients and analyzes nutritional deficiencies and excess intakes. Finally, the AI ​​proposes optimal menus to the user based on the analysis results. For example, a nutritional value calculation system allows a user to input a photo of a meal. For example, the nutritional value calculation system allows a user to take a photo of a meal with a smartphone camera and upload the photo to the tool. Next, the nutritional value calculation system uses AI to analyze the input photo. The AI ​​identifies ingredients in the photo using image recognition technology. For example, the AI ​​identifies ingredients such as vegetables, meat, and fish in the photo and estimates the amount of each. The nutritional value calculation system then calculates nutritional values ​​based on the identified ingredients. For example, the system calculates vitamin and mineral content based on the type and amount of vegetables, and protein and fat content based on the type and amount of meat and fish. Next, the nutritional value calculation system identifies nutrient deficiencies and excess intakes based on the calculated nutritional values. For example, it identifies cases where vitamin C is deficient or fat intake is excessive. Next, the nutritional value calculation system suggests optimal menus for the user based on the analysis results. For example, it suggests menus that include ingredients to compensate for nutrient deficiencies or ingredients to reduce excess intakes of nutrients. This allows the nutritional value calculation system to enable the user to eat a balanced diet. This allows the user to easily understand the nutritional value of their meals and lead a healthy diet. For example, this is very useful for users who are on a diet or who need to consume specific nutrients. Furthermore, knowing the nutritional value of meals in detail can also be useful for health management and disease prevention.

[0084] The nutritional value calculation system according to the embodiment includes an analysis unit, a calculation unit, an analysis unit, and a proposal unit. The analysis unit analyzes a photo of a meal. The analysis unit identifies ingredients in the photo, for example, using image recognition technology. For example, the analysis unit identifies ingredients such as vegetables, meat, and fish in the photo and estimates the amount of each. The analysis unit can also use AI to identify the type and amount of ingredients. For example, the analysis unit can identify ingredients using deep learning. The calculation unit calculates nutritional value based on the ingredients identified by the analysis unit. The calculation unit calculates vitamin and mineral content based on the type and amount of vegetables, for example. The calculation unit can also calculate protein and lipid content based on the type and amount of meat or fish. For example, the calculation unit can calculate nutritional value using a food database. The analysis unit analyzes the nutritional value calculated by the calculation unit. For example, the analysis unit identifies nutritional deficiencies or excessive intakes based on the calculated nutritional value. The analysis unit can also analyze nutritional value using statistical methods. For example, the analysis unit identifies nutritional deficiencies or excess intakes of nutrients compared with the recommended intake amount. The suggestion unit suggests an optimal menu based on the analysis results obtained by the analysis unit. The suggestion unit suggests, for example, a menu including ingredients to compensate for nutritional deficiencies or ingredients to reduce excessive nutritional intake. The suggestion unit can also suggest menus taking into account the user's preferences and allergy information. For example, the suggestion unit suggests menus including favorite ingredients based on user input. In this way, the nutritional value calculation system according to the embodiment can analyze photos of meals, calculate and analyze nutritional values, and suggest optimal menus.

[0085] The nutritional value calculation system includes a reception unit through which a user inputs a photo of a meal. The reception unit inputs the photo of the meal. For example, the reception unit takes a photo of the meal using a smartphone camera and uploads the photo to the tool. The reception unit can also input a photo of the meal using a digital camera or tablet. For example, the reception unit automatically recognizes a photo taken by the user and processes it as input data. This allows the user to easily input a photo of the meal. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input a photo taken by the user to a generation AI and have the generation AI recognize the photo.

[0086] The suggestion unit includes a display unit that displays a menu suggested to the user. The display unit displays the menu suggested to the user. The display unit displays the menu on, for example, a smartphone or tablet screen. The display unit can also display the menu on a computer monitor or television screen. For example, the display unit provides a graphical interface so that the user can visually confirm the suggested menu. This allows the user to visually confirm the suggested menu. Some or all of the above-described processing in the display unit may be performed, for example, using AI or may be performed without using AI. For example, the display unit can input data of the suggested menu into a generation AI and have the generation AI execute a visual display.

[0087] The suggestion unit includes a storage unit that stores the proposed menu. The storage unit stores the proposed menu. The storage unit stores the menu in, for example, cloud storage or local storage. The storage unit can also store the menu on the user's device. For example, the storage unit stores the proposed menu in a database so that it can be referenced later. This allows the user to reference the proposed menu later. Some or all of the above-described processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input data of the proposed menu into a generation AI and have the generation AI execute the storage process.

[0088] The analysis unit can identify ingredients in a photograph using image recognition technology. Image recognition technology includes, but is not limited to, deep learning and object detection algorithms. For example, the analysis unit can identify ingredients in a photograph using deep learning. The analysis unit can also identify ingredients using object detection algorithms. The analysis unit can also identify the type and amount of ingredients using image recognition technology. For example, the analysis unit can identify ingredients such as vegetables, meat, and fish in a photograph and estimate the amount of each ingredient. This allows the ingredients in the photograph to be accurately identified. Some or all of the above-described 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 photo data into a generation AI and have the generation AI identify the ingredients.

[0089] The calculation unit can calculate nutritional values ​​based on the identified ingredients. Nutritional value calculations include, but are not limited to, a food database and a calculation algorithm. For example, the calculation unit calculates the nutritional value of the identified ingredients using a food database. The calculation unit can also calculate nutritional value using a calculation algorithm. The calculation unit can also calculate vitamin and mineral content based on the identified ingredients. For example, the calculation unit calculates vitamin and mineral content based on the type and amount of vegetables. The calculation unit can also calculate protein and lipid content based on the type and amount of meat or fish. This allows accurate nutritional value calculation based on the ingredients. Some or all of the above-described processing in the calculation unit can be performed using, for example, AI, or without AI. For example, the calculation unit can input data on the identified ingredients into a generation AI and have the generation AI calculate the nutritional value.

[0090] The analysis unit can identify nutrient deficiencies or excess intakes based on the calculated nutritional values. Examples of identifying nutrient deficiencies or excess intakes include, but are not limited to, recommended intakes and excess intake thresholds. For example, the analysis unit can identify nutrient deficiencies or excess intakes compared to recommended intakes. The analysis unit can also identify excess intakes based on excess intake thresholds. The analysis unit can also analyze nutritional values ​​using statistical methods. For example, the analysis unit can identify nutrient deficiencies or excess intakes based on the calculated nutritional values. This allows for the identification of nutrient deficiencies or excess intakes. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input calculated nutritional value data into the generation AI and cause the generation AI to identify nutrient deficiencies or excess intakes.

[0091] The suggestion unit can suggest an optimal menu based on the analysis results. The optimal menu suggestion may include, but is not limited to, nutritional balance and user preferences, for example. The suggestion unit may suggest, for example, a menu that includes ingredients to supplement nutritional deficiencies or ingredients to reduce excessive nutritional intake. The suggestion unit may also suggest a menu taking into consideration the user's preferences and allergy information. For example, the suggestion unit may suggest a menu that includes favorite ingredients based on the user's input. This allows the suggestion of an optimal menu for the user. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input data of the analysis results into a generation AI and cause the generation AI to suggest an optimal menu.

[0092] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can increase the analysis accuracy and provide detailed nutritional value information. Furthermore, if the user is relaxed, the analysis unit can maintain the analysis accuracy at normal levels and provide standard nutritional value information. Furthermore, if the user is in a hurry, the analysis unit can reduce the analysis accuracy and provide quick nutritional value information. This allows the analysis accuracy 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 emotion data into the generation AI and have the generation AI adjust the analysis accuracy.

[0093] During analysis, the analysis unit can correct the analysis results based on the freshness of the ingredients and the cooking method. For example, if the ingredients are very fresh, the analysis unit corrects the nutritional value to be higher. Furthermore, if the cooking method is baking, the analysis unit can also correct the results taking into account the loss of vitamin C. Furthermore, if the cooking method is boiling, the analysis unit can also correct the results taking into account the elution of minerals. In this way, the analysis results can be corrected taking into account the freshness of the ingredients and the cooking method. 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 data on the freshness of the ingredients and the cooking method into the generation AI, and have the generation AI correct the analysis results.

[0094] During analysis, the analysis unit can take fluctuations in nutritional value into account by referring to the origin information of the ingredients. For example, if the origin is an organically grown region, the analysis unit corrects the nutritional value to be higher. Furthermore, if the origin is far away, the analysis unit can also correct the nutritional value by taking into account the loss of nutritional value during transportation. Furthermore, if the origin is under specific climatic conditions, the analysis unit can also correct the nutritional value by taking into account the influence of those conditions. In this way, fluctuations in nutritional value can be corrected by taking into account the origin information of the ingredients. 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 data on the origin information of the ingredients into the generation AI and have the generation AI correct the fluctuations in nutritional value.

[0095] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past dietary history. The analysis unit improves the accuracy of the analysis, for example, based on data on ingredients the user has previously consumed. The analysis unit can also consider the intake tendency of specific nutrients from the user's past dietary history during analysis. The analysis unit can also consider the combination of ingredients during analysis based on the user's past dietary history. This can improve the accuracy of the analysis by referring to the user's past dietary history. 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 data on the user's past dietary history into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0096] 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, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the display method of the analysis results 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 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 can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0097] The analysis unit can provide analysis results taking into account the allergen information of ingredients during analysis. For example, the analysis unit can display the risk of allergic reactions based on the allergen information contained in ingredients. The analysis unit can also suggest alternative ingredients based on the allergen information. The analysis unit can also suggest cooking methods to avoid allergic reactions based on the allergen information. In this way, analysis results can be provided taking into account the allergen information of ingredients. 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 data on the allergen information of ingredients into the generation AI and have the generation AI provide the analysis results.

[0098] During analysis, the analysis unit can correct the analysis results by referring to seasonal information about the ingredients. The analysis unit corrects the analysis results, for example, based on information about ingredients whose nutritional value varies depending on the season. The analysis unit can also correct the nutritional value of seasonal ingredients to be higher based on the seasonal information. The analysis unit can also analyze based on the seasonal information, taking into account fluctuations in nutritional value due to storage methods. This allows the analysis results to be corrected by referring to the seasonal information about the ingredients. 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 data about seasonal information about ingredients into the generation AI and have the generation AI correct the analysis results.

[0099] During analysis, the analysis unit can customize the analysis method according to the user's dietary goals. For example, if the goal is to lose weight, the analysis unit can emphasize analysis of calories and lipids. Furthermore, if the goal is to build muscle, the analysis unit can also emphasize analysis of protein and amino acids. Furthermore, if the goal is to maintain health, the analysis unit can also analyze balanced nutritional values. This allows the analysis method to be customized according to the user's dietary goals. 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 data on the user's dietary goals into the generation AI and have the generation AI customize the analysis method.

[0100] The calculation unit can estimate the user's emotions and adjust the level of detail of the nutritional value calculation based on the estimated user's emotions. For example, if the user is feeling stressed, the calculation unit can perform a detailed nutritional value calculation and provide specific values. Alternatively, if the user is relaxed, the calculation unit can perform a standard nutritional value calculation and provide general values. Alternatively, if the user is in a hurry, the calculation unit can perform a simplified nutritional value calculation and provide values ​​that focus on the main points. This allows the level of detail of the nutritional value calculation 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-mentioned processing in the calculation unit can be performed using, for example, an AI, or without an AI. For example, the calculation unit can input the user's emotion data into the generation AI and have the generation AI adjust the level of detail of the nutritional value calculation.

[0101] The calculation unit can correct the nutritional value by taking into account the cooking method of the ingredients when making calculations. For example, if the cooking method is baking, the calculation unit corrects the nutritional value by taking into account the decrease in vitamin C. The calculation unit can also correct the nutritional value by taking into account the elution of minerals if the cooking method is boiling. The calculation unit can also correct the nutritional value by taking into account the increase in fat if the cooking method is frying. In this way, the nutritional value can be corrected by taking into account the cooking method of the ingredients. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on the cooking method into the generation AI and have the generation AI perform the correction of the nutritional value.

[0102] The calculation unit can take into account fluctuations in nutritional value by referring to the storage state of the ingredients when making calculations. For example, in the case of frozen ingredients, the calculation unit corrects the nutritional value by taking into account the loss of vitamins. In the case of canned ingredients, the calculation unit can also correct by taking into account fluctuations in nutritional value during storage. In the case of fresh ingredients, the calculation unit can also correct by taking into account fluctuations in nutritional value depending on the storage period. In this way, fluctuations in nutritional value can be taken into account by referring to the storage state of the ingredients. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on the storage state into the generation AI and have the generation AI correct for fluctuations in nutritional value.

[0103] During calculation, the calculation unit can improve the accuracy of the calculation by referring to the user's past nutritional intake history. For example, the calculation unit performs calculations based on the user's past nutritional intake history, taking into account the intake tendency of specific nutrients. The calculation unit can also perform calculations based on the user's past nutritional intake history, taking into account nutritional balance. The calculation unit can also perform calculations based on the user's past nutritional intake history, taking into account the combination of ingredients. This can improve the accuracy of the calculation by referring to the user's past nutritional intake history. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on the user's past nutritional intake history into the generation AI and cause the generation AI to improve the accuracy of the calculation.

[0104] The calculation unit can estimate the user's emotions and determine the priority of nutritional value calculations based on the estimated user's emotions. For example, if the user is feeling stressed, the calculation unit can prioritize vitamin and mineral calculations. The calculation unit can also perform overall nutritional value calculations if the user is relaxed. The calculation unit can also prioritize calculation of major nutrients if the user is in a hurry. This allows the priority of nutritional value calculations to be determined 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-described processing in the calculation unit can be performed using, for example, an AI, or without an AI. For example, the calculation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of nutritional value calculations.

[0105] The calculation unit can correct the nutritional value during calculation, taking into account the organic cultivation information of the ingredients. For example, in the case of organically grown ingredients, the calculation unit corrects the nutritional value higher. The calculation unit can also correct the nutritional value based on the organic cultivation information, taking into account the risk of pesticide residues. The calculation unit can also correct the nutritional value based on the organic cultivation information, taking into account the quality of the ingredients. In this way, the nutritional value can be corrected taking into account the organic cultivation information of the ingredients. Some or all of the above-mentioned processing in the calculation unit may be performed using AI, for example, or may be performed without using AI. For example, the calculation unit can input organic cultivation information data into the generation AI and have the generation AI correct the nutritional value.

[0106] The calculation unit can correct the nutritional value by referring to the degree of processing of the ingredients during calculation. For example, in the case of raw ingredients, the calculation unit calculates the nutritional value as is. In addition, in the case of frozen ingredients, the calculation unit can also correct the nutritional value by taking into account fluctuations in nutritional value during storage. In the case of canned ingredients, the calculation unit can also correct the nutritional value by taking into account fluctuations in nutritional value during storage. In this way, the nutritional value can be corrected by referring to the degree of processing of the ingredients. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input data on the degree of processing into the generation AI and have the generation AI correct the nutritional value.

[0107] The calculation unit can customize nutritional values ​​according to the user's health condition during calculation. For example, if the user has an allergy, the calculation unit can exclude the nutritional values ​​of ingredients containing allergens. The calculation unit can also limit the intake of specific nutrients based on the user's medical history. The calculation unit can also emphasize necessary nutrients based on the user's health condition during calculation. This allows nutritional values ​​to be customized according to the user's health condition. Some or all of the above-mentioned processing in the calculation unit may be performed using AI, for example, or may be performed without using AI. For example, the calculation unit can input data on the user's health condition into the generation AI and have the generation AI customize the nutritional values.

[0108] 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, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the display method of the analysis results 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 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 can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0109] During analysis, the analysis unit can correct the required amount of nutrients taking into account the user's age and gender. The analysis unit, for example, corrects the amount of required nutrients according to the user's age. The analysis unit can also correct the amount of required nutrients according to the user's gender. The analysis unit can also customize the required amount of nutrients based on the user's age and gender. This allows the required amount of nutrients to be corrected taking into account the user's age and gender. 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 data on the user's age and gender into the generation AI and cause the generation AI to correct the required amount of nutrients.

[0110] During analysis, the analysis unit can correct the required amount of nutritional value by referring to the user's activity level. The analysis unit, for example, corrects the required amount of calories according to the user's amount of exercise. The analysis unit can also correct the required amount of protein according to the user's activity level. The analysis unit can also customize the required amount of nutritional value based on the user's activity level. This makes it possible to correct the required amount of nutritional value by referring to the user's activity level. 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 data on the user's activity level into the generation AI and cause the generation AI to correct the required amount of nutritional value.

[0111] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past health checkup results. For example, the analysis unit corrects the required amount of a specific nutrient based on the user's past health checkup results. The analysis unit can also perform analysis taking nutritional balance into consideration based on the user's health checkup results. The analysis unit can also customize the required amount of nutrition according to the user's health condition based on the user's past health checkup results. This can improve the accuracy of the analysis by referring to the user's past health checkup results. 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 data on the user's past health checkup results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0112] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize displaying vitamin and mineral deficiencies. The analysis unit can also display overall nutritional balance if the user is relaxed. The analysis unit can also prioritize displaying deficiencies or excesses of major nutrients if the user is in a hurry. This allows the analysis results to be prioritized 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 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the analysis results.

[0113] During analysis, the analysis unit can correct the required amount of nutritional value by taking into account the frequency of the user's meals. For example, if the user eats three meals a day, the analysis unit distributes the nutritional value of each meal evenly. Furthermore, if the user eats two meals a day, the analysis unit can also set the nutritional value of each meal to be higher. Furthermore, if the user has irregular meals, the analysis unit can also make corrections by taking into account the overall nutritional balance. In this way, the required amount of nutritional value can be corrected by taking into account the frequency of the user's meals. 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 data on the user's meal frequency into the generation AI and cause the generation AI to correct the required amount of nutritional value.

[0114] During analysis, the analysis unit can correct the required amount of nutrients by referring to the user's lifestyle. For example, if the user is a smoker, the analysis unit can set a higher required amount of vitamin C. Furthermore, if the user is a heavy drinker, the analysis unit can also set a higher required amount of nutrients that support liver function. Furthermore, the analysis unit can customize the required amount of specific nutrients based on the user's lifestyle. This allows the required amount of nutrients to be corrected by referring to the user's lifestyle. 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 data on the user's lifestyle into the generation AI and cause the generation AI to correct the required amount of nutrients.

[0115] During analysis, the analysis unit can customize the required nutritional amounts by taking into account the user's genetic information. For example, the analysis unit can set the required amounts of specific nutrients based on the user's genetic information. The analysis unit can also correct the nutritional amounts by taking into account the risk of specific diseases from the user's genetic information. The analysis unit can also customize the required amounts of individual nutrients based on the user's genetic information. This makes it possible to customize the required nutritional amounts by taking into account the user's genetic information. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input the user's genetic information data into the generation AI and cause the generation AI to customize the required nutritional amounts.

[0116] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can make simple, highly visible suggestions. If the user is relaxed, the suggestion unit can also make suggestions that include detailed information. If the user is in a hurry, the suggestion unit can also make suggestions that focus on the main points. This allows the way suggestions are expressed 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 suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way suggestions are expressed.

[0117] When proposing, the suggestion unit can customize a menu taking into consideration the user's ingredient preferences and allergy information. For example, the suggestion unit can suggest a menu giving priority to the user's favorite ingredients. The suggestion unit can also suggest a menu that does not contain allergens based on the user's allergy information. The suggestion unit can also suggest a menu taking into consideration the user's preference trends based on the user's past eating history. This allows the menu to be customized taking into consideration the user's ingredient preferences and allergy information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's ingredient preferences and allergy information into the generation AI and have the generation AI customize the menu.

[0118] When proposing, the suggestion unit can adjust the menu according to the user's dietary purpose. For example, if the purpose is to lose weight, the suggestion unit can suggest a low-calorie menu. Furthermore, if the purpose is to build muscle, the suggestion unit can also suggest a high-protein menu. Furthermore, if the purpose is to maintain health, the suggestion unit can also suggest a balanced menu. In this way, the menu can be adjusted according to the user's dietary purpose. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input data on the user's dietary purpose into the generation AI and have the generation AI adjust the menu.

[0119] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past meal history. The suggestion unit, for example, suggests a menu including favorite ingredients based on the user's past meal history. The suggestion unit can also suggest a menu taking into consideration nutritional balance based on the user's past meal history. The suggestion unit can also suggest a menu taking into consideration ingredient combinations based on the user's past meal history. This can improve the accuracy of the suggestion by referring to the user's past meal history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data of the user's past meal history into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0120] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggestions that have a relaxing effect. Furthermore, if the user is relaxed, the suggestion unit can also prioritize suggestions that emphasize nutritional balance. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggestions that are easy to prepare. This allows the priority of suggestions 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 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 suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of suggestions.

[0121] When proposing a menu, the suggestion unit can customize the menu taking into consideration the availability of ingredients in the user's geographical area. For example, the suggestion unit can suggest a menu based on ingredients available in the user's area. The suggestion unit can also suggest a menu based on ingredients that are seasonal in the user's area. The suggestion unit can also suggest a menu based on local specialties in the user's area. This allows the menu to be customized taking into consideration the availability of ingredients in the user's geographical area. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the availability of ingredients in the user's geographical area into the generation AI and cause the generation AI to customize the menu.

[0122] When proposing, the suggestion unit can adjust the menu according to the user's mealtimes. For example, for breakfast, the suggestion unit can suggest a menu that emphasizes energy replenishment. For lunch, the suggestion unit can also suggest a balanced menu. For dinner, the suggestion unit can also suggest a menu that is easy to digest. This allows the menu to be adjusted according to the user's mealtimes. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the user's mealtimes into the generation AI and have the generation AI adjust the menu.

[0123] When proposing, the suggestion unit can customize a menu taking into consideration the user's cultural background and eating habits. For example, the suggestion unit suggests a menu based on ingredients that correspond to the user's cultural background. The suggestion unit can also suggest a menu that includes the user's favorite ingredients based on the user's eating habits. The suggestion unit can also suggest a menu that avoids specific ingredients by taking into consideration the user's cultural background. This allows the menu to be customized taking into consideration the user's cultural background and eating habits. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data on the user's cultural background and eating habits into the generation AI and cause the generation AI to customize the menu.

[0124] The reception unit can estimate the user's emotions and adjust the reception interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple, highly visible interface. Furthermore, if the user is relaxed, the reception unit can provide an interface that includes detailed information. Furthermore, if the user is in a hurry, the reception unit can provide an interface that focuses on the main points. This allows the reception interface 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-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the reception interface.

[0125] The reception unit can suggest the optimal input method by referring to the user's past input history when receiving the input. For example, the reception unit automatically displays ingredients that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest ingredients to be used in a specific time period from the user's past input history. This makes it possible to suggest the optimal input method by referring to the user's past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input data from the user's past input history into a generation AI and have the generation AI suggest the optimal input method.

[0126] The reception unit can select the optimal input means by taking into consideration the user's device information when receiving the input. For example, if the user is using a smartphone, the reception unit prioritizes touch input. Furthermore, if the user is using a tablet, the reception unit can provide an input method optimized for a large screen. Furthermore, if the user is using a smartwatch, the reception unit can prioritize voice input. This allows the optimal input means to be selected by taking into consideration the user's device information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's device information into the generation AI and cause the generation AI to select the optimal input means.

[0127] The reception unit can estimate the user's emotions and determine reception priorities based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize a simple input method. Furthermore, if the user is relaxed, the reception unit can also provide detailed input options. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input. This allows reception priorities to be determined 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 reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the reception priorities.

[0128] The reception unit can customize the interface according to the user's input method when receiving the input. For example, if the user uses voice input, the reception unit provides an interface optimized for voice recognition. Furthermore, if the user uses text input, the reception unit can also provide an interface optimized for keyboard input. Furthermore, if the user uses image input, the reception unit can also provide an interface optimized for image recognition. This allows the interface to be customized according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's input method to a generation AI and have the generation AI customize the interface.

[0129] The reception unit can provide a multilingual interface according to the user's language setting at the time of reception. The reception unit automatically sets the interface language based on, for example, the language setting of the user's device. The reception unit can also provide a language switching function when the user uses multiple languages. The reception unit can also provide an interface in a specific language when the user selects that language. This makes it possible to provide a multilingual interface according to the user's language setting. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's language setting into a generation AI and cause the generation AI to provide a multilingual interface.

[0130] The display unit can estimate the user's emotions and adjust the display content based on the estimated user emotions. For example, when the user is feeling stressed, the display unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the display unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the display unit can provide a display method that focuses on the main points. This allows the display content to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, for example, 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 display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's emotion data into the generation AI and have the generation AI adjust the display content.

[0131] The display unit can select the optimal display method by referring to the user's past browsing history when displaying. The display unit customizes the display content based on, for example, information that the user has preferred to view in the past. The display unit can also preferentially display specific information from the user's past browsing history. The display unit can also provide a display method that matches the user's visual preferences based on the user's past browsing history. This allows the optimal display method to be selected by referring to the user's past browsing history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data of the user's past browsing history to a generation AI and have the generation AI select the optimal display method.

[0132] The display unit can select the optimal display means when displaying, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a display method that is simple and highly visible. This allows the optimal display means to be selected taking into account the user's device information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data on the user's device information into the generation AI and cause the generation AI to select the optimal display means.

[0133] The display unit can estimate the user's emotions and determine display priorities based on the estimated user emotions. For example, when the user is feeling stressed, the display unit can prioritize displaying important information. Furthermore, when the user is relaxed, the display unit can display detailed information. Furthermore, when the user is in a hurry, the display unit can prioritize displaying information that focuses on the main points. This allows display priorities to be determined 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 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 display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's emotion data into the generation AI and have the generation AI determine the display priorities.

[0134] The display unit can provide a multilingual display according to the user's language setting when displaying. The display unit, for example, automatically sets the display language based on the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. The display unit can also provide a display in a specific language when the user selects that language. This makes it possible to provide a multilingual display according to the user's language setting. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data of the user's language setting into a generation AI and cause the generation AI to provide a multilingual display.

[0135] The display unit can provide a display customized according to the user's visual preferences when displaying. The display unit customizes the display content based on, for example, the user's preferred color shade. The display unit can also adjust the font size and style according to the user's visual preferences. The display unit can also provide an optimal display method based on the user's past selection history. This makes it possible to provide a display customized according to the user's visual preferences. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data on the user's visual preferences into a generation AI and cause the generation AI to provide a customized display.

[0136] The storage unit can estimate the user's emotions and determine the priority of data to be saved based on the estimated user emotions. For example, when the user is feeling stressed, the storage unit prioritizes saving important data. Furthermore, when the user is relaxed, the storage unit can also save detailed data. Furthermore, when the user is in a hurry, the storage unit can prioritize saving data that covers the essential points. This allows the priority of data to be saved to be determined 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 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 storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of data to be saved.

[0137] The storage unit can select the optimal storage method by referring to the user's past storage history when saving. The storage unit customizes the storage method, for example, based on data that the user frequently saved in the past. The storage unit can also prioritize saving specific data from the user's past storage history. The storage unit can also provide the optimal storage format based on the user's past storage history. This allows the optimal storage method to be selected by referring to the user's past storage history. Some or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input data of the user's past storage history to a generation AI and have the generation AI select the optimal storage method.

[0138] The storage unit can select the optimal storage means when saving data by taking into account the user's device information. For example, if the user is using a smartphone, the storage unit provides a storage method according to the device's storage capacity. Furthermore, if the user is using a tablet, the storage unit can also provide large-capacity data storage. Furthermore, if the user is using cloud storage, the storage unit can automatically save data to the cloud. This allows the optimal storage means to be selected by taking into account the user's device information. Some or all of the above-described processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input the user's device information data into the generation AI and have the generation AI select the optimal storage means.

[0139] The storage unit can estimate the user's emotions and adjust the display method of the stored data based on the estimated user emotions. For example, if the user is feeling stressed, the storage unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the storage unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the storage unit can provide a display method that focuses on the main points. This allows the display method of the stored data 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 storage unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the storage unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the stored data.

[0140] The storage unit can provide a function for automatically backing up the user's data when saving the data. For example, the storage unit automatically backs up the data to the cloud when the user saves the data. The storage unit can also periodically back up the data stored on the user's device. The storage unit can also automatically perform backups at times specified by the user. This allows the user's data to be automatically backed up. Some or all of the above-described processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input backup data of the user's data to the generation AI and have the generation AI perform the backup.

[0141] The storage unit can provide an encryption function to enhance the security of the user's data when it is stored. For example, the storage unit can automatically encrypt data when the user stores it. The storage unit can also encrypt data stored on the user's device. The storage unit can also encrypt and store data specified by the user. This makes it possible to provide an encryption function to enhance the security of the user's data. Some or all of the above-mentioned processing in the storage unit may be performed using AI, for example, or may be performed without using AI. For example, the storage unit can input the data to be encrypted to a generation AI and have the generation AI perform the encryption. === Hard Collateral 1-1 === Each of the multiple elements, including the analysis unit, calculation unit, analysis unit, suggestion unit, reception unit, display unit, and storage unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit takes a photo of a meal using the camera 42 of the smart device 14, and the specific processing unit 290 of the data processing device 12 identifies ingredients using image recognition technology. The calculation unit calculates nutritional values ​​using the specific processing unit 290 of the data processing device 12. The analysis unit analyzes nutritional values ​​using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests an optimal menu using the specific processing unit 290 of the data processing device 12. The reception unit inputs a photo of the meal using the control unit 46A of the smart device 14. The display unit displays the suggested menu on the display 40A of the smart device 14. The storage unit saves the suggested menu in the storage 32 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the analysis unit, calculation unit, analysis unit, suggestion unit, reception unit, display unit, and storage unit, described above, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit takes a photo of a meal using the camera 42 of the smart glasses 214, and the specific processing unit 290 of the data processing device 12 identifies ingredients using image recognition technology. The calculation unit calculates nutritional values ​​using the specific processing unit 290 of the data processing device 12. The analysis unit analyzes nutritional values ​​using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests an optimal menu using the specific processing unit 290 of the data processing device 12. The reception unit inputs a photo of the meal using the control unit 46A of the smart glasses 214. The display unit displays the suggested menu on the display of the smart glasses 214. The storage unit saves the suggested menu in the storage 32 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the analysis unit, calculation unit, analysis unit, suggestion unit, reception unit, display unit, and storage unit described above is realized, for example, in at least one of the headset terminal 314 and the data processing device 12. For example, the analysis unit takes a photo of a meal using the camera 42 of the headset terminal 314, and the specific processing unit 290 of the data processing device 12 identifies the ingredients using image recognition technology. The calculation unit calculates nutritional values ​​using the specific processing unit 290 of the data processing device 12. The analysis unit analyzes nutritional values ​​using the specific processing unit 290 of the data processing device 12. The suggestion unit suggests an optimal menu using the specific processing unit 290 of the data processing device 12. The reception unit inputs a photo of the meal using the control unit 46A of the headset terminal 314. The display unit displays the suggested menu on the display 343 of the headset terminal 314. The storage unit saves the suggested menu in the storage 32 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the analysis unit, calculation unit, analysis unit, proposal unit, reception unit, display unit, and storage unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit takes a photo of a meal using the camera 42 of the robot 414, and the specific processing unit 290 of the data processing device 12 identifies the ingredients using image recognition technology. The calculation unit calculates nutritional values ​​using the specific processing unit 290 of the data processing device 12. The analysis unit analyzes nutritional values ​​using the specific processing unit 290 of the data processing device 12. The proposal unit proposes an optimal menu using the specific processing unit 290 of the data processing device 12. The reception unit inputs a photo of the meal using the control unit 46A of the robot 414. The display unit displays the proposed menu on the display of the robot 414. The storage unit saves the proposed menu in the storage 32 of the data processing device 12.

[0142] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0143] When analyzing a user's meal photos, the analysis unit can consider not only the color and shape of ingredients, but also the texture and temperature of the ingredients. For example, the analysis unit can identify the cooking method, such as fried or simmered, by analyzing the texture of the ingredients. The analysis unit can also identify the type of dish, such as cold salad or hot soup, by detecting the temperature of the ingredients. Furthermore, the analysis unit can track changes in the color of the ingredients to understand the progress of cooking. This allows the analysis unit to identify ingredients in more detail and accurately.

[0144] When calculating nutritional values ​​based on identified ingredients, the calculation unit can also take into account the origin and cultivation method of the ingredients. For example, the calculation unit can correct the nutritional value higher for organically grown ingredients. In addition, if the origin is far away, the calculation unit can also correct the value taking into account the loss of nutritional value during transportation. Furthermore, the calculation unit can also correct the value taking into account the nutritional value of ingredients grown under specific climatic conditions. This allows the calculation unit to accurately calculate nutritional values ​​taking into account the cultivation method and origin information of the ingredients.

[0145] The analysis unit can identify nutritional deficiencies or excess intakes based on the calculated nutritional values, taking into account the user's health condition and lifestyle habits. For example, if the user is a smoker, the analysis unit can set a higher required amount of vitamin C. Also, if the user is a heavy drinker, the analysis unit can set a higher required amount of nutrients that support liver function. Furthermore, the analysis unit can correct the required amount of calories and protein depending on the user's level of exercise. This enables the analysis unit to analyze nutritional values ​​taking into account the user's health condition and lifestyle habits.

[0146] When displaying a menu suggested to a user, the suggestion unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can provide a simple, highly visible display method. If the user is relaxed, the suggestion unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the suggestion unit can also provide a display method that focuses on the main points. In this way, the suggestion unit can provide an optimal display method according to the user's emotions.

[0147] When saving a suggested menu, the suggestion unit can estimate the user's emotions and determine the priority of data to be saved based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize saving important data. Also, if the user is relaxed, the suggestion unit can save data including detailed data. Furthermore, if the user is in a hurry, the suggestion unit can prioritize saving data that focuses on the main points. This enables the suggestion unit to save optimal data according to the user's emotions.

[0148] When analyzing a photo of a meal, the analysis unit can correct the analysis results based on the freshness of the ingredients and the cooking method. For example, if the ingredients are very fresh, the analysis unit can correct the nutritional value to be higher. In addition, if the cooking method is baking, the analysis unit can also correct the results taking into account the loss of vitamin C. Furthermore, if the cooking method is boiling, the analysis unit can also correct the results taking into account the elution of minerals. This allows the analysis unit to provide accurate analysis results that take into account the freshness of the ingredients and the cooking method.

[0149] When calculating nutritional values ​​based on identified ingredients, the calculation unit can improve the accuracy of the calculation by referring to the user's past nutritional intake history. For example, the calculation unit can calculate the nutritional values ​​by taking into account the intake tendency of specific nutrients based on the user's past nutritional intake history. The calculation unit can also calculate the nutritional values ​​by taking into account nutritional balance based on the user's past nutritional intake history. Furthermore, the calculation unit can calculate the nutritional values ​​by taking into account the combination of ingredients based on the user's past nutritional intake history. This allows the calculation unit to accurately calculate nutritional values ​​by referring to the user's past nutritional intake history.

[0150] The analysis unit can correct the required amount of nutrients based on the calculated nutritional values, taking into account the user's age and gender. For example, the analysis unit can correct the amount of nutrients required according to the user's age. The analysis unit can also correct the amount of nutrients required according to the user's gender. Furthermore, the analysis unit can customize the required amount of nutrients based on the user's age and gender. This enables the analysis unit to analyze nutritional values ​​taking into account the user's age and gender.

[0151] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can make simple, highly visible suggestions. If the user is relaxed, the suggestion unit can make suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can make suggestions that focus on the main points. In this way, the suggestion unit can provide an optimal way of expressing suggestions according to the user's emotions.

[0152] When saving a suggested menu, the suggestion unit can select the optimal saving method by referring to the user's past saving history. For example, the suggestion unit can customize the saving method based on data that the user has frequently saved in the past. The suggestion unit can also prioritize saving specific data from the user's past saving history. Furthermore, the suggestion unit can provide the optimal saving format based on the user's past saving history. This allows the suggestion unit to save data optimally by referring to the user's past saving history.

[0153] The processing flow of the second embodiment will be briefly explained below.

[0154] Step 1: The analyzer analyzes a photo of the meal. It uses image recognition technology to identify the ingredients in the photo and estimate the amount of each. For example, deep learning can be used to identify the ingredients. Step 2: The calculation unit calculates nutritional values ​​based on the ingredients identified by the analysis unit. Using a food database, the calculation unit calculates the vitamin and mineral content based on the type and amount of vegetables, and the protein and fat content based on the type and amount of meat and fish. Step 3: The analysis unit analyzes the nutritional values ​​calculated by the calculation unit. Based on the calculated nutritional values, the analysis unit identifies any nutritional values ​​that are deficient or excessively ingested. It can also identify any nutritional values ​​that are deficient or excessively ingested by comparing them with the recommended intake amount. Step 4: The suggestion unit proposes an optimal menu based on the analysis results obtained by the analysis unit. The suggestion unit proposes a menu that includes ingredients to compensate for nutritional deficiencies or ingredients to reduce excessive nutritional intake. It can also propose a menu that takes into account the user's preferences and allergy information.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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 AI 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0176] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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 AI 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.

[0189] 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.

[0190] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0191] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0192] 7, a 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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).

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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 AI 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.

[0206] 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.

[0207] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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).

[0212] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0213] 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."

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] [Explanation of symbols]

[0227] 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. an analysis unit that analyzes food photos; a calculation unit that calculates nutritional values ​​based on the ingredients identified by the analysis unit; an analysis unit that analyzes the nutritional value calculated by the calculation unit; a suggestion unit that suggests a menu based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. A reception unit is provided where the user can input a photo of the meal.

2. The system of claim 1.

3. The proposal unit It has a display unit that displays menu suggestions to the user.

2. The system of claim 1.

4. The proposal unit Equipped with a storage section to store proposed menus 2. The system of claim 1.

5. The analysis unit Identifying ingredients in photos using image recognition technology 2. The system of claim 1.

6. The calculation unit Calculate nutritional values ​​based on identified ingredients 2. The system of claim 1.

7. The analysis unit Identify nutritional deficiencies and excesses based on calculated nutritional values 2. The system of claim 1.

8. The proposal unit Propose optimal menus based on analysis results 2. The system of claim 1.

9. The analysis unit Estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions.

2. The system of claim 1.

Images