system

Abstract

The system according to the embodiment aims to allow a user to easily record what they eat and to provide dietary management and advice based on their goals. [Solution] A system according to an embodiment includes a photographing unit, an analysis unit, an input unit, and a management unit. The photographing unit takes a photo of what the user eats. The analysis unit analyzes the photo taken by the photographing unit and identifies ingredients, calories, and nutrients. The input unit inputs the user's target weight or health goal. The management unit manages and advises the user on their diet based on the information identified by the analysis unit and the goal input by the input unit.

Description

[Technical Field]

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

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

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

[0004] Previous technology required users to manually enter data to manage their diet and receive advice, which was a time-consuming process.

[0005] The system according to the embodiment aims to allow a user to easily record what they eat and to provide dietary management and advice based on their goals. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, an analysis unit, an input unit, and a management unit. The photographing unit takes a photo of what the user has eaten. The analysis unit analyzes the photo taken by the photographing unit and identifies ingredients, calories, and nutrients. The input unit inputs the user's target weight or health goal. The management unit manages and advises the user on the diet based on the information identified by the analysis unit and the goal input by the input unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily record what they eat and provides dietary management and advice based on their goals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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 diet management system according to an embodiment of the present invention allows a user to take photos of what they eat, analyze them using a generation AI, identify ingredients, calories, and nutrients, and provide dietary management and advice based on the user's target weight and health goals. This system allows users to manage their diet without any special operations by simply taking photos of what they eat using an app. For example, a user takes photos of the bread and salad they had for breakfast and sends them to the generation AI. The generation AI recognizes the ingredients in the photos and calculates their calories and nutrients. For example, it identifies the carbohydrate, fat, and protein content in a photo of bread, and the vitamin and mineral content in a photo of salad. Furthermore, when a user inputs their target weight and health goals into the app, the generation AI uses that information to manage their diet and provide advice. For example, a user may set a goal such as "I want to lose 2 kg in one month." This information is sent to the generation AI. The generation AI then manages and advises the user based on their goals. For example, if a user's calorie intake exceeds their target, the generation AI may provide advice such as, "Today's calorie intake is above target. Have a lighter dinner." Additionally, if the nutritional balance is unbalanced, the system will provide specific advice such as, "You're lacking in vitamin C. Eat some fruit." This system allows users to easily manage their diet and support a healthy lifestyle. It will be a useful tool for a wide range of users, from busy businessmen to health-conscious elderly people. This allows the diet management system to manage and provide advice on diet simply by having the user take a photo of what they have eaten.

[0029] A diet management system according to an embodiment includes a photographing unit, an analysis unit, an input unit, and a management unit. The photographing unit photographs the food a user eats. Photographs of the food a user eats include, but are not limited to, breakfast, lunch, dinner, and snacks. The photographing unit may, for example, use a smartphone camera to photograph the food. The photographing unit may also use a digital camera or a tablet camera to photograph the food. The photographing unit may also photograph the food at high resolution to obtain detailed information. For example, the photographing unit may photograph the food at high resolution so that even the finer details of the ingredients can be recognized. The analysis unit may use a generative AI to analyze the photograph taken by the photographing unit and identify the ingredients, calories, and nutrients. The analysis unit may, for example, use image recognition technology to recognize the ingredients in the photograph. The analysis unit may also use a machine learning algorithm to identify the type of ingredient. The analysis unit may also refer to a database to calculate the calories and nutrients of ingredients. For example, the analysis unit may refer to a food composition database to calculate the calories and nutrients of ingredients. The input unit inputs the user's target weight and health goals. The input unit inputs the target weight and health goals using, for example, a smartphone app. The input unit can also input the target weight and health goals using a tablet or PC. Furthermore, the input unit can support voice input and gesture input. For example, the input unit inputs the user's target weight and health goals using voice recognition technology. The management unit manages and provides dietary advice based on the information identified by the analysis unit and the goals input by the input unit. For example, the management unit provides specific advice if the user's calorie intake exceeds the goal. The management unit can also provide specific advice if the nutritional balance is unbalanced. Furthermore, the management unit can encrypt or anonymize data to protect the user's privacy. For example, the management unit encrypts the user's data using AES encryption or RSA encryption. This allows the diet management system according to the embodiment to provide dietary management and advice simply by the user taking a photo of what they eat.

[0030] The analysis unit can recognize ingredients in a photograph and identify their calories or nutrients. The analysis unit can, for example, use image recognition technology to recognize ingredients in a photograph. For example, the analysis unit can identify the type of ingredient using a machine learning algorithm. The analysis unit can also refer to a database to calculate the calories and nutrients of ingredients. For example, the analysis unit can calculate the calories and nutrients of ingredients by referring to a food component database. The analysis unit can also analyze the appearance and characteristics of ingredients to identify calories and nutrients. For example, the analysis unit can analyze the color and shape of ingredients to identify calories and nutrients. This allows detailed meal information to be provided by identifying the calories and nutrients of ingredients in a photograph. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input image data of ingredients in a photograph into the generation AI and have the generation AI recognize the ingredients and identify their calories and nutrients.

[0031] The management unit can manage and provide dietary advice based on the user's goals. For example, if the user's calorie intake exceeds the goal, the management unit can provide specific advice. For example, the management unit can provide advice such as, "Today's calorie intake exceeds the goal. Have a lighter dinner." The management unit can also provide specific advice if the user's nutritional balance is unbalanced. For example, the management unit can provide advice such as, "You're lacking in vitamin C. Eat some fruit." Furthermore, the management unit can encrypt or anonymize data to protect the user's privacy. For example, the management unit can encrypt the user's data using AES encryption or RSA encryption. This allows for personalized health goals to be met by providing dietary management and advice based on the user's goals. Some or all of the above-described processing in the management unit can be performed using, or without, a generation AI. For example, the management unit can input the user's goal data into the generation AI and have the generation AI perform dietary management and advice.

[0032] The management unit can provide specific advice when the user's calorie intake exceeds the target. For example, the management unit may provide advice such as, "Today's calorie intake exceeds the target. Have a lighter dinner." The management unit can also suggest dietary changes when the user's calorie intake exceeds the target. For example, the management unit may provide a suggestion such as, "Choose low-calorie ingredients for tomorrow's breakfast." Furthermore, the management unit can also suggest exercise when the user's calorie intake exceeds the target. For example, the management unit may provide a suggestion such as, "Take a 30-minute walk after dinner." This makes it easier for the user to adjust their diet by providing specific advice when the user's calorie intake exceeds the target. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit may input the user's calorie data into the generation AI and cause the generation AI to generate specific advice.

[0033] The management unit can provide specific advice when the user's nutritional balance is unbalanced. For example, the management unit may provide advice such as, "You're lacking in vitamin C. Eat more fruit." The management unit can also suggest dietary changes when the user's nutritional balance is unbalanced. For example, the management unit may provide a suggestion such as, "Eat more vegetables at your next meal." The management unit can also suggest supplements when the user's nutritional balance is unbalanced. For example, the management unit may provide a suggestion such as, "Take a vitamin C supplement." This supports a healthy diet by providing specific advice when the user's nutritional balance is unbalanced. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit may input the user's nutritional data into the generation AI and cause the generation AI to generate specific advice.

[0034] The system can encrypt or anonymize data to protect user privacy. For example, the system encrypts data to protect user privacy. For example, the system encrypts user data using AES encryption or RSA encryption. The system can also anonymize data to protect user privacy. For example, the system anonymizes user data using data masking or pseudo-anonymization. Furthermore, the system can control data access to protect user privacy. For example, the system limits the authority to access user data so that only specific users can access the data. This allows data encryption or anonymization to protect user privacy, allowing for safe use. Some or all of the above-mentioned processing in the system may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the system can input user data into a generation AI and have the generation AI encrypt or anonymize the data.

[0035] When taking a photo, the photographing unit can select the optimal photographing method by referring to the user's past meal history. The photographing unit selects the optimal photographing method by referring to the user's past meal history, for example. For example, the photographing unit analyzes photos of meals taken by the user in the past and suggests the method that will capture the photos most clearly. The photographing unit can also identify, from the user's past meal history, time periods when specific ingredients are often photographed and encourage the user to take photos during those time periods. The photographing unit can also suggest angles and lighting conditions that best capture specific ingredients based on the user's past meal history. This allows the optimal photographing method to be selected by referring to the user's past meal history. Some or all of the above-described processing in the photographing unit may be performed using, or without, a generation AI. For example, the photographing unit can input the user's past meal history data into the generation AI and have the generation AI select the optimal photographing method.

[0036] The photographing unit can be added with a function to automatically recognize the freshness and cooking method of ingredients when photographing. The photographing unit, for example, automatically recognizes the freshness and cooking method of ingredients when photographing. For example, the photographing unit analyzes the color and texture of ingredients to evaluate freshness. The photographing unit can also automatically recognize and record cooking methods (grilling, boiling, frying, etc.). The photographing unit can also analyze how ingredients are cut and presented to identify the cooking method. This allows for automatic recognition of the freshness and cooking method of ingredients, making it possible to provide more detailed meal information. Some or all of the above-described processing in the photographing unit may be performed using, or without, a generation AI, for example. For example, the photographing unit can input image data of ingredients into a generation AI and have the generation AI recognize the freshness and cooking method.

[0037] The photographing unit can prioritize photographing highly relevant meals by taking into account the user's geographical location information when photographing. For example, the photographing unit prioritizes photographing highly relevant meals by taking into account the user's geographical location information when photographing. For example, if the user is in a specific area, the photographing unit can prioritize photographing local specialties of that area. Furthermore, if the user is traveling, the photographing unit can prioritize photographing specialty dishes of the travel destination. Furthermore, if the user is participating in a specific event, the photographing unit can prioritize photographing meals related to the event. In this way, highly relevant meal information can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the photographing unit may be performed using, or without, a generation AI. For example, the photographing unit can input the user's geographical location information to the generation AI and cause the generation AI to determine the priority order of highly relevant meals.

[0038] The photographing unit can analyze the user's social media activity and photograph related meals when photographing. For example, the photographing unit can analyze the user's social media activity and photograph related meals when photographing. For example, the photographing unit can analyze photos of meals shared by the user on social media and prioritize photographing similar meals. The photographing unit can also prioritize photographing meals introduced by influencers the user follows. The photographing unit can also photograph related meals based on food-related groups and events in which the user participates. This makes it possible to provide related meal information by analyzing the user's social media activity. Some or all of the above-described processing in the photographing unit can be performed using, or without, a generation AI. For example, the photographing unit can input the user's social media activity data into the generation AI and have the generation AI photograph related meals.

[0039] The analysis unit can add a function to identify the origin and producer information of ingredients during analysis. The analysis unit, for example, identifies the origin and producer information of ingredients during analysis. For example, the analysis unit analyzes the barcode or two-dimensional code (e.g., QR Code (registered trademark)) of an ingredient to identify the origin information. The analysis unit can also analyze the information written on the ingredient's packaging to identify the producer information. The analysis unit can also analyze the appearance and characteristics of an ingredient to estimate the origin and producer. This makes it possible to provide more detailed meal information by identifying the origin and producer information of ingredients. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data from the barcode or two-dimensional code of an ingredient into the generation AI and have the generation AI identify the origin and producer information.

[0040] The analysis unit can add a function to automatically detect allergen information of ingredients during analysis. The analysis unit automatically detects allergen information of ingredients during analysis, for example. For example, the analysis unit analyzes the ingredient list of ingredients to identify allergen information. The analysis unit can also analyze the appearance and characteristics of ingredients to estimate allergens. The analysis unit can also automatically detect allergen information listed on ingredient packaging. This makes it easier to take measures against allergies by automatically detecting allergen information of ingredients. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input ingredient list data of ingredients into the generation AI and have the generation AI detect allergen information.

[0041] The analysis unit can determine the analysis priority by referring to the user's past dietary history during analysis. For example, the analysis unit can determine the analysis priority by referring to the user's past dietary history during analysis. For example, the analysis unit prioritizes analysis of ingredients that the user frequently ingested in the past. Furthermore, if the user's past dietary history indicates a deficiency of a specific nutrient, the analysis unit can prioritize analysis of ingredients containing that nutrient. Furthermore, the analysis unit can prioritize analysis of specific ingredients based on the user's past dietary history. Thus, the analysis priority can be determined by referring to the user's past dietary history. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's past dietary history data into the generation AI and have the generation AI determine the analysis priority.

[0042] The analysis unit can customize the analysis results by taking into account the user's health condition during analysis. For example, the analysis unit customizes the analysis results by taking into account the user's health condition during analysis. For example, if the user has a specific health condition (e.g., diabetes), the analysis unit can prioritize providing nutrient information related to that condition. Furthermore, if the user has a specific health goal (e.g., weight loss), the analysis unit can also provide analysis results related to that goal. Furthermore, the analysis unit can customize the analysis results for specific ingredients based on the user's health condition. This makes it possible to provide more appropriate analysis results by taking the user's health condition into consideration. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's health condition data into the generation AI and have the generation AI customize the analysis results.

[0043] The input unit can select the optimal input method by referring to the user's past health data at the time of input. For example, the input unit can select the optimal input method by referring to the user's past health data at the time of input. For example, the input unit can suggest the most effective input method based on the user's past health data. Furthermore, if the input unit determines that a specific input method (e.g., voice input) is effective based on the user's past health data, the input unit can preferentially suggest that method. Furthermore, the input unit can analyze the user's past health data and select the optimal input method. In this way, the optimal input method can be selected by referring to the user's past health data. Some or all of the above-mentioned processing in the input unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the input unit can input the user's past health data to the generation AI and have the generation AI select the optimal input method.

[0044] The input unit may be added with a function to support voice input or gesture input during input. The input unit supports, for example, voice input or gesture input during input. For example, the input unit may allow the user to input a target weight or health goal by voice. The input unit may also allow the user to input a target weight or health goal by gesture. The input unit may also allow the user to input a target weight or health goal by combining voice input and gesture input. This supports voice input and gesture input, making it possible to provide a wider variety of input methods. Some or all of the above-described processing in the input unit may be performed using, or without, a generation AI. For example, the input unit may input the user's voice data or gesture data into the generation AI and cause the generation AI to support the input method.

[0045] The input unit can select the optimal input method by taking into account the user's device information when inputting. For example, the input unit selects the optimal input method by taking into account the user's device information when inputting. For example, if the user is using a smartphone, the input unit prioritizes touch input. Furthermore, if the user is using a tablet, the input unit can provide an input method optimized for a large screen. Furthermore, if the user is using a smartwatch, the input unit can provide an input method that is simple and highly visible. This allows the optimal input method to be provided by taking into account the user's device information. Some or all of the above-described processing in the input unit may be performed using, or without, a generation AI. For example, the input unit can input the user's device information to the generation AI and cause the generation AI to select the optimal input method.

[0046] The input unit can customize input data taking into account the user's lifestyle habits at the time of input. The input unit, for example, customizes input data taking into account the user's lifestyle habits at the time of input. For example, the input unit provides a simplified input method when the user leads a busy lifestyle. The input unit can also provide detailed input options when the user leads a healthy lifestyle. The input unit can also suggest optimal input data based on the user's lifestyle habits. This enables more appropriate data input by taking the user's lifestyle habits into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can input the user's lifestyle habit data into the generation AI and cause the generation AI to customize the input data.

[0047] The management unit can select the optimal health management method by referring to the user's past health data during management. For example, the management unit can select the optimal health management method by referring to the user's past health data during management. For example, the management unit can suggest the most effective health management method based on the user's past health data. Furthermore, if the management unit determines that a specific health management method (e.g., calorie restriction) is effective based on the user's past health data, it can preferentially suggest that method. The management unit can also analyze the user's past health data and select the optimal health management method. In this way, the optimal health management method can be provided by referring to the user's past health data. Some or all of the above-mentioned processing in the management unit can be performed, for example, using a generation AI or without using a generation AI. For example, the management unit can input the user's past health data into the generation AI and have the generation AI select the optimal health management method.

[0048] The management unit can customize the management method during management, taking into account the user's lifestyle and activity level. For example, the management unit customizes the management method during management, taking into account the user's lifestyle and activity level. For example, if the user exercises frequently, the management unit can provide a dietary management method that corresponds to the user's activity level. Furthermore, if the user lives a desk-based lifestyle, the management unit can also provide a dietary management method that is suited to the user's lifestyle. Furthermore, the management unit can customize an optimal dietary management method based on the user's lifestyle and activity level. This enables more appropriate management by taking into account the user's lifestyle and activity level. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input data on the user's lifestyle and activity level into the generation AI and have the generation AI customize the management method.

[0049] The management unit can select the optimal management method by taking into account the user's geographical location information during management. For example, the management unit selects the optimal management method by taking into account the user's geographical location information during management. For example, if the user is in a specific area, the management unit can provide a management method based on the ingredients and food culture of that area. Furthermore, if the user is traveling, the management unit can also provide a management method suitable for the dining environment of the travel destination. Furthermore, the management unit can select the optimal dietary management method based on the user's geographical location information. In this way, the optimal management method can be provided by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal management method.

[0050] During management, the management unit can analyze the user's social media activity and suggest a management method. For example, during management, the management unit can analyze the user's social media activity and suggest a management method. For example, the management unit can analyze photos of meals shared by the user on social media and suggest a similar diet management method. The management unit can also suggest a diet management method introduced by an influencer the user follows. The management unit can also suggest an optimal diet management method based on diet-related groups and events in which the user participates. This allows for a more appropriate diet management method to be provided by analyzing the user's social media activity. Some or all of the above-described processing in the management unit can be performed using, or without, a generation AI. For example, the management unit can input the user's social media activity data into the generation AI and have the generation AI execute a diet management method suggestion.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The management unit can analyze the user's meal history and learn the user's preferences based on the frequency of intake of specific ingredients and dishes. For example, the management unit can identify ingredients that the user frequently consumes and suggest recipes that include those ingredients. The management unit can also analyze trends in the user's favorite dishes and suggest similar dishes. Furthermore, the management unit can make suggestions to increase the variety of meals based on the user's preferences. This enables meal management tailored to the user's preferences and improves meal satisfaction.

[0053] The analysis unit can analyze seasonal food intake trends based on the user's dietary history. For example, the analysis unit can identify ingredients that the user consumes in large amounts in the summer and suggest recipes suitable for that summer. The analysis unit can also identify ingredients that the user consumes in large amounts in the winter and suggest recipes suitable for that winter. Furthermore, the analysis unit can take into account the nutritional balance of each season and suggest nutritional supplements appropriate for the season. This enables dietary management according to the season and supports a healthy diet.

[0054] The management unit can evaluate the allergy risk of specific ingredients based on the user's dietary history. For example, the management unit can collect allergy information on ingredients the user has previously consumed and identify ingredients with a high allergy risk. The management unit can also evaluate the allergy risk of ingredients the user is about to consume and issue warnings. Furthermore, the management unit can suggest alternative ingredients with a low allergy risk. This reduces the allergy risk and enables safe dietary management.

[0055] The management unit can manage food storage methods and expiration dates based on the user's meal history. For example, the management unit can suggest storage methods for ingredients purchased by the user and notify the user of appropriate storage methods. The management unit can also manage expiration dates for ingredients and notify the user of ingredients that are approaching their expiration date. Furthermore, the management unit can suggest recipes that use ingredients that are close to their expiration date. This reduces food waste and enables efficient meal management.

[0056] The management unit can automatically generate a food ingredient shopping list based on the user's meal history. For example, the management unit can create a shopping list by listing ingredients frequently used by the user. The management unit can also add necessary ingredients to the list based on the user's meal plan. Furthermore, the management unit can manage ingredient inventory and automatically add necessary ingredients to the list. This allows for efficient ingredient purchasing and makes meal management easier.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The camera takes photos of what the user eats. Photos of what the user eats can include breakfast, lunch, dinner, snacks, etc. The camera can take photos of meals using a smartphone camera, digital camera, or tablet camera. Furthermore, the camera takes high-resolution photos of meals so that even the fine details of the ingredients can be recognized. Step 2: The analysis unit uses the generation AI to analyze the photo taken by the photography unit and identify the ingredients, calories, and nutrients. The analysis unit uses image recognition technology and machine learning algorithms to recognize the ingredients in the photo and identify the type of ingredient. The analysis unit then refers to a food ingredient database to calculate the calories and nutrients of the ingredients. Step 3: The input unit inputs the user's target weight and health goals. The input unit can input the target weight and health goals using a smartphone app, tablet, or PC. In addition, the input unit supports voice input and gesture input, and the user's target weight and health goals can also be input using voice recognition technology. Step 4: The management unit manages and advises the user on their diet based on the information identified by the analysis unit and the goals entered by the input unit. The management unit provides specific advice if the user's calorie intake exceeds the goal or if their nutritional balance is unbalanced. Furthermore, the management unit encrypts and anonymizes data to protect the user's privacy.

[0059] (Example 2) A diet management system according to an embodiment of the present invention allows a user to take photos of what they eat, analyze them using a generation AI, identify ingredients, calories, and nutrients, and provide dietary management and advice based on the user's target weight and health goals. This system allows users to manage their diet without any special operations by simply taking photos of what they eat using an app. For example, a user takes photos of the bread and salad they had for breakfast and sends them to the generation AI. The generation AI recognizes the ingredients in the photos and calculates their calories and nutrients. For example, it identifies the carbohydrate, fat, and protein content in a photo of bread, and the vitamin and mineral content in a photo of salad. Furthermore, when a user inputs their target weight and health goals into the app, the generation AI uses that information to manage their diet and provide advice. For example, a user may set a goal such as "I want to lose 2 kg in one month." This information is sent to the generation AI. The generation AI then manages and advises the user based on their goals. For example, if a user's calorie intake exceeds their target, the generation AI may provide advice such as, "Today's calorie intake is above target. Have a lighter dinner." Additionally, if the nutritional balance is unbalanced, the system will provide specific advice such as, "You're lacking in vitamin C. Eat some fruit." This system allows users to easily manage their diet and support a healthy lifestyle. It will be a useful tool for a wide range of users, from busy businessmen to health-conscious elderly people. This allows the diet management system to manage and provide advice on diet simply by having the user take a photo of what they have eaten.

[0060] A diet management system according to an embodiment includes a photographing unit, an analysis unit, an input unit, and a management unit. The photographing unit photographs the food a user eats. Photographs of the food a user eats include, but are not limited to, breakfast, lunch, dinner, and snacks. The photographing unit may, for example, use a smartphone camera to photograph the food. The photographing unit may also use a digital camera or a tablet camera to photograph the food. The photographing unit may also photograph the food at high resolution to obtain detailed information. For example, the photographing unit may photograph the food at high resolution so that even the finer details of the ingredients can be recognized. The analysis unit may use a generative AI to analyze the photograph taken by the photographing unit and identify the ingredients, calories, and nutrients. The analysis unit may, for example, use image recognition technology to recognize the ingredients in the photograph. The analysis unit may also use a machine learning algorithm to identify the type of ingredient. The analysis unit may also refer to a database to calculate the calories and nutrients of ingredients. For example, the analysis unit may refer to a food composition database to calculate the calories and nutrients of ingredients. The input unit inputs the user's target weight and health goals. The input unit inputs the target weight and health goals using, for example, a smartphone app. The input unit can also input the target weight and health goals using a tablet or PC. Furthermore, the input unit can support voice input and gesture input. For example, the input unit inputs the user's target weight and health goals using voice recognition technology. The management unit manages and provides dietary advice based on the information identified by the analysis unit and the goals input by the input unit. For example, the management unit provides specific advice if the user's calorie intake exceeds the goal. The management unit can also provide specific advice if the nutritional balance is unbalanced. Furthermore, the management unit can encrypt or anonymize data to protect the user's privacy. For example, the management unit encrypts the user's data using AES encryption or RSA encryption. This allows the diet management system according to the embodiment to provide dietary management and advice simply by the user taking a photo of what they eat.

[0061] The analysis unit can recognize ingredients in a photograph and identify their calories or nutrients. The analysis unit can, for example, use image recognition technology to recognize ingredients in a photograph. For example, the analysis unit can identify the type of ingredient using a machine learning algorithm. The analysis unit can also refer to a database to calculate the calories and nutrients of ingredients. For example, the analysis unit can calculate the calories and nutrients of ingredients by referring to a food component database. The analysis unit can also analyze the appearance and characteristics of ingredients to identify calories and nutrients. For example, the analysis unit can analyze the color and shape of ingredients to identify calories and nutrients. This allows detailed meal information to be provided by identifying the calories and nutrients of ingredients in a photograph. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input image data of ingredients in a photograph into the generation AI and have the generation AI recognize the ingredients and identify their calories and nutrients.

[0062] The management unit can manage and provide dietary advice based on the user's goals. For example, if the user's calorie intake exceeds the goal, the management unit can provide specific advice. For example, the management unit can provide advice such as, "Today's calorie intake exceeds the goal. Have a lighter dinner." The management unit can also provide specific advice if the user's nutritional balance is unbalanced. For example, the management unit can provide advice such as, "You're lacking in vitamin C. Eat some fruit." Furthermore, the management unit can encrypt or anonymize data to protect the user's privacy. For example, the management unit can encrypt the user's data using AES encryption or RSA encryption. This allows for personalized health goals to be met by providing dietary management and advice based on the user's goals. Some or all of the above-described processing in the management unit can be performed using, or without, a generation AI. For example, the management unit can input the user's goal data into the generation AI and have the generation AI perform dietary management and advice.

[0063] The management unit can provide specific advice when the user's calorie intake exceeds the target. For example, the management unit may provide advice such as, "Today's calorie intake exceeds the target. Have a lighter dinner." The management unit can also suggest dietary changes when the user's calorie intake exceeds the target. For example, the management unit may provide a suggestion such as, "Choose low-calorie ingredients for tomorrow's breakfast." Furthermore, the management unit can also suggest exercise when the user's calorie intake exceeds the target. For example, the management unit may provide a suggestion such as, "Take a 30-minute walk after dinner." This makes it easier for the user to adjust their diet by providing specific advice when the user's calorie intake exceeds the target. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit may input the user's calorie data into the generation AI and cause the generation AI to generate specific advice.

[0064] The management unit can provide specific advice when the user's nutritional balance is unbalanced. For example, the management unit may provide advice such as, "You're lacking in vitamin C. Eat more fruit." The management unit can also suggest dietary changes when the user's nutritional balance is unbalanced. For example, the management unit may provide a suggestion such as, "Eat more vegetables at your next meal." The management unit can also suggest supplements when the user's nutritional balance is unbalanced. For example, the management unit may provide a suggestion such as, "Take a vitamin C supplement." This supports a healthy diet by providing specific advice when the user's nutritional balance is unbalanced. Some or all of the above-described processing in the management unit may be performed using, or without, a generation AI. For example, the management unit may input the user's nutritional data into the generation AI and cause the generation AI to generate specific advice.

[0065] The system can encrypt or anonymize data to protect user privacy. For example, the system encrypts data to protect user privacy. For example, the system encrypts user data using AES encryption or RSA encryption. The system can also anonymize data to protect user privacy. For example, the system anonymizes user data using data masking or pseudo-anonymization. Furthermore, the system can control data access to protect user privacy. For example, the system limits the authority to access user data so that only specific users can access the data. This allows data encryption or anonymization to protect user privacy, allowing for safe use. Some or all of the above-mentioned processing in the system may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the system can input user data into a generation AI and have the generation AI encrypt or anonymize the data.

[0066] The image capture unit can estimate the user's emotions and adjust the timing of capturing images based on the estimated user emotions. For example, the image capture unit can estimate the user's emotions and adjust the timing of capturing images based on the estimated user emotions. For example, if the user is feeling stressed, the image capture unit can prompt the user to capture an image at a time when the user is relaxed. If the user is excited, the image capture unit can also immediately capture an image to capture that moment. If the user is tired, the image capture unit can also prompt the user to capture an image during a break. This allows for more appropriate timing of capturing images by adjusting the timing of capturing images based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 image capture unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the image capture unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of capturing images.

[0067] When taking a photo, the photographing unit can select the optimal photographing method by referring to the user's past meal history. The photographing unit selects the optimal photographing method by referring to the user's past meal history, for example. For example, the photographing unit analyzes photos of meals taken by the user in the past and suggests the method that will capture the photos most clearly. The photographing unit can also identify, from the user's past meal history, time periods when specific ingredients are often photographed and encourage the user to take photos during those time periods. The photographing unit can also suggest angles and lighting conditions that best capture specific ingredients based on the user's past meal history. This allows the optimal photographing method to be selected by referring to the user's past meal history. Some or all of the above-described processing in the photographing unit may be performed using, or without, a generation AI. For example, the photographing unit can input the user's past meal history data into the generation AI and have the generation AI select the optimal photographing method.

[0068] The photographing unit can be added with a function to automatically recognize the freshness and cooking method of ingredients when photographing. The photographing unit, for example, automatically recognizes the freshness and cooking method of ingredients when photographing. For example, the photographing unit analyzes the color and texture of ingredients to evaluate freshness. The photographing unit can also automatically recognize and record cooking methods (grilling, boiling, frying, etc.). The photographing unit can also analyze how ingredients are cut and presented to identify the cooking method. This allows for automatic recognition of the freshness and cooking method of ingredients, making it possible to provide more detailed meal information. Some or all of the above-described processing in the photographing unit may be performed using, or without, a generation AI, for example. For example, the photographing unit can input image data of ingredients into a generation AI and have the generation AI recognize the freshness and cooking method.

[0069] The photographing unit can estimate the user's emotions and determine the priority of meals to be photographed based on the estimated user emotions. The photographing unit, for example, estimates the user's emotions and determines the priority of meals to be photographed based on the estimated user emotions. For example, if the user is feeling stressed, the photographing unit can prioritize photographing ingredients that have a relaxing effect. Also, if the user is excited, the photographing unit can prioritize photographing ingredients that are suitable for replenishing energy. Also, if the user is tired, the photographing unit can prioritize photographing ingredients that are highly nutritious. This allows for more appropriate meal information to be provided by determining the priority of meals to be photographed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 photographing unit may be performed using, for example, the generation AI. For example, the photographing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of meals to be photographed.

[0070] The photographing unit can prioritize photographing highly relevant meals by taking into account the user's geographical location information when photographing. For example, the photographing unit prioritizes photographing highly relevant meals by taking into account the user's geographical location information when photographing. For example, if the user is in a specific area, the photographing unit can prioritize photographing local specialties of that area. Furthermore, if the user is traveling, the photographing unit can prioritize photographing specialty dishes of the travel destination. Furthermore, if the user is participating in a specific event, the photographing unit can prioritize photographing meals related to the event. In this way, highly relevant meal information can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the photographing unit may be performed using, or without, a generation AI. For example, the photographing unit can input the user's geographical location information to the generation AI and cause the generation AI to determine the priority order of highly relevant meals.

[0071] The photographing unit can analyze the user's social media activity and photograph related meals when photographing. For example, the photographing unit can analyze the user's social media activity and photograph related meals when photographing. For example, the photographing unit can analyze photos of meals shared by the user on social media and prioritize photographing similar meals. The photographing unit can also prioritize photographing meals introduced by influencers the user follows. The photographing unit can also photograph related meals based on food-related groups and events in which the user participates. This makes it possible to provide related meal information by analyzing the user's social media activity. Some or all of the above-described processing in the photographing unit can be performed using, or without, a generation AI. For example, the photographing unit can input the user's social media activity data into the generation AI and have the generation AI photograph related meals.

[0072] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide detailed nutrient information. If the user is in a hurry, the analysis unit can also perform a simplified analysis and provide only key calorie information. If the user is excited, the analysis unit can also provide a visually easy-to-understand analysis result. This allows for more appropriate analysis results to be provided by adjusting the accuracy of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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, the generation AI, or without the generation 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.

[0073] The analysis unit can add a function to identify the origin and producer information of ingredients during analysis. The analysis unit, for example, identifies the origin and producer information of ingredients during analysis. For example, the analysis unit analyzes the barcode or two-dimensional code (e.g., QR code) of an ingredient to identify the origin information. The analysis unit can also analyze the information written on the ingredient's packaging to identify the producer information. The analysis unit can also analyze the appearance and characteristics of an ingredient to estimate the origin and producer. This makes it possible to provide more detailed meal information by identifying the origin and producer information of ingredients. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data from the barcode or two-dimensional code of an ingredient into the generation AI and have the generation AI identify the origin and producer information.

[0074] The analysis unit can add a function to automatically detect allergen information of ingredients during analysis. The analysis unit automatically detects allergen information of ingredients during analysis, for example. For example, the analysis unit analyzes the ingredient list of ingredients to identify allergen information. The analysis unit can also analyze the appearance and characteristics of ingredients to estimate allergens. The analysis unit can also automatically detect allergen information listed on ingredient packaging. This makes it easier to take measures against allergies by automatically detecting allergen information of ingredients. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input ingredient list data of ingredients into the generation AI and have the generation AI detect allergen information.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can 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 nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for more appropriate display by adjusting the display method of the analysis results based on 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 these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation 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.

[0076] The analysis unit can determine the analysis priority by referring to the user's past dietary history during analysis. For example, the analysis unit can determine the analysis priority by referring to the user's past dietary history during analysis. For example, the analysis unit prioritizes analysis of ingredients that the user frequently ingested in the past. Furthermore, if the user's past dietary history indicates a deficiency of a specific nutrient, the analysis unit can prioritize analysis of ingredients containing that nutrient. Furthermore, the analysis unit can prioritize analysis of specific ingredients based on the user's past dietary history. Thus, the analysis priority can be determined by referring to the user's past dietary history. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's past dietary history data into the generation AI and have the generation AI determine the analysis priority.

[0077] The analysis unit can customize the analysis results by taking into account the user's health condition during analysis. For example, the analysis unit customizes the analysis results by taking into account the user's health condition during analysis. For example, if the user has a specific health condition (e.g., diabetes), the analysis unit can prioritize providing nutrient information related to that condition. Furthermore, if the user has a specific health goal (e.g., weight loss), the analysis unit can also provide analysis results related to that goal. Furthermore, the analysis unit can customize the analysis results for specific ingredients based on the user's health condition. This makes it possible to provide more appropriate analysis results by taking the user's health condition into consideration. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's health condition data into the generation AI and have the generation AI customize the analysis results.

[0078] The input unit can estimate a user's emotion and adjust the input interface based on the estimated user emotion. For example, the input unit can estimate a user's emotion and adjust the input interface based on the estimated user emotion. For example, when a user is stressed, the input unit can provide a simple interface and minimize input steps. Furthermore, when a user is relaxed, the input unit can provide detailed input options and suggest customizable input methods. Furthermore, when a user is in a hurry, the input unit can prioritize voice input and enable the user to quickly enter a target weight or health goal. This allows the input interface to be adjusted based on the user's emotion, thereby providing a more user-friendly interface. 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 input unit can be performed using, for example, the generation AI. For example, the input unit can input user emotion data into the generation AI and have the generation AI adjust the input interface.

[0079] The input unit can select the optimal input method by referring to the user's past health data at the time of input. For example, the input unit can select the optimal input method by referring to the user's past health data at the time of input. For example, the input unit can suggest the most effective input method based on the user's past health data. Furthermore, if the input unit determines that a specific input method (e.g., voice input) is effective based on the user's past health data, the input unit can preferentially suggest that method. Furthermore, the input unit can analyze the user's past health data and select the optimal input method. In this way, the optimal input method can be selected by referring to the user's past health data. Some or all of the above-mentioned processing in the input unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the input unit can input the user's past health data to the generation AI and have the generation AI select the optimal input method.

[0080] The input unit may be added with a function to support voice input or gesture input during input. The input unit supports, for example, voice input or gesture input during input. For example, the input unit may allow the user to input a target weight or health goal by voice. The input unit may also allow the user to input a target weight or health goal by gesture. The input unit may also allow the user to input a target weight or health goal by combining voice input and gesture input. This supports voice input and gesture input, making it possible to provide a wider variety of input methods. Some or all of the above-described processing in the input unit may be performed using, or without, a generation AI. For example, the input unit may input the user's voice data or gesture data into the generation AI and cause the generation AI to support the input method.

[0081] The input unit can estimate the user's emotions and prioritize the input data based on the estimated user emotions. The input unit, for example, estimates the user's emotions and prioritizes the input data based on the estimated user emotions. For example, when the user is stressed, the input unit prioritizes input of only important data. Furthermore, when the user is relaxed, the input unit can also input detailed data. Furthermore, when the user is in a hurry, the input unit can prioritize data that can be input quickly. This enables more appropriate data input by prioritizing the input data based on 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 input unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the input unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the input data.

[0082] The input unit can select the optimal input method by taking into account the user's device information when inputting. For example, the input unit selects the optimal input method by taking into account the user's device information when inputting. For example, if the user is using a smartphone, the input unit prioritizes touch input. Furthermore, if the user is using a tablet, the input unit can provide an input method optimized for a large screen. Furthermore, if the user is using a smartwatch, the input unit can provide an input method that is simple and highly visible. This allows the optimal input method to be provided by taking into account the user's device information. Some or all of the above-described processing in the input unit may be performed using, or without, a generation AI. For example, the input unit can input the user's device information to the generation AI and cause the generation AI to select the optimal input method.

[0083] The input unit can customize input data taking into account the user's lifestyle habits at the time of input. The input unit, for example, customizes input data taking into account the user's lifestyle habits at the time of input. For example, the input unit provides a simplified input method when the user leads a busy lifestyle. The input unit can also provide detailed input options when the user leads a healthy lifestyle. The input unit can also suggest optimal input data based on the user's lifestyle habits. This enables more appropriate data input by taking the user's lifestyle habits into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the input unit can input the user's lifestyle habit data into the generation AI and cause the generation AI to customize the input data.

[0084] The management unit can estimate the user's emotions and adjust the management method based on the estimated user emotions. For example, the management unit can estimate the user's emotions and adjust the management method based on the estimated user emotions. For example, if the user is feeling stressed, the management unit can provide a simple and easy-to-understand management method. If the user is relaxed, the management unit can also provide detailed management options. If the user is in a hurry, the management unit can also provide a quick management method. This enables more appropriate management by adjusting the management method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI 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 management unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the management unit can input the user's emotion data into the generation AI and have the generation AI adjust the management method.

[0085] The management unit can select the optimal health management method by referring to the user's past health data during management. For example, the management unit can select the optimal health management method by referring to the user's past health data during management. For example, the management unit can suggest the most effective health management method based on the user's past health data. Furthermore, if the management unit determines that a specific health management method (e.g., calorie restriction) is effective based on the user's past health data, it can preferentially suggest that method. The management unit can also analyze the user's past health data and select the optimal health management method. In this way, the optimal health management method can be provided by referring to the user's past health data. Some or all of the above-mentioned processing in the management unit can be performed, for example, using a generation AI or without using a generation AI. For example, the management unit can input the user's past health data into the generation AI and have the generation AI select the optimal health management method.

[0086] The management unit can customize the management method during management, taking into account the user's lifestyle and activity level. For example, the management unit customizes the management method during management, taking into account the user's lifestyle and activity level. For example, if the user exercises frequently, the management unit can provide a dietary management method that corresponds to the user's activity level. Furthermore, if the user lives a desk-based lifestyle, the management unit can also provide a dietary management method that is suited to the user's lifestyle. Furthermore, the management unit can customize an optimal dietary management method based on the user's lifestyle and activity level. This enables more appropriate management by taking into account the user's lifestyle and activity level. Some or all of the above-described processing in the management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the management unit can input data on the user's lifestyle and activity level into the generation AI and have the generation AI customize the management method.

[0087] The management unit can estimate the user's emotions and determine management priorities based on the estimated user emotions. For example, the management unit estimates the user's emotions and determines management priorities based on the estimated user emotions. For example, if the user is feeling stressed, the management unit prioritizes processing only important management items. Furthermore, if the user is relaxed, the management unit can process detailed management items. Furthermore, if the user is in a hurry, the management unit can prioritize management items that can be processed quickly. This enables more appropriate management by determining management priorities based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as 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 management unit can be performed using, for example, the generation AI. For example, the management unit can input the user's emotion data into the generation AI and have the generation AI determine the management priorities.

[0088] The management unit can select the optimal management method by taking into account the user's geographical location information during management. For example, the management unit selects the optimal management method by taking into account the user's geographical location information during management. For example, if the user is in a specific area, the management unit can provide a management method based on the ingredients and food culture of that area. Furthermore, if the user is traveling, the management unit can also provide a management method suitable for the dining environment of the travel destination. Furthermore, the management unit can select the optimal dietary management method based on the user's geographical location information. In this way, the optimal management method can be provided by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the management unit may be performed using, or without, a generation AI. For example, the management unit can input the user's geographical location information into the generation AI and have the generation AI select the optimal management method.

[0089] During management, the management unit can analyze the user's social media activity and suggest a management method. For example, during management, the management unit can analyze the user's social media activity and suggest a management method. For example, the management unit can analyze photos of meals shared by the user on social media and suggest a similar diet management method. The management unit can also suggest a diet management method introduced by an influencer the user follows. The management unit can also suggest an optimal diet management method based on diet-related groups and events in which the user participates. This allows for a more appropriate diet management method to be provided by analyzing the user's social media activity. Some or all of the above-described processing in the management unit can be performed using, or without, a generation AI. For example, the management unit can input the user's social media activity data into the generation AI and have the generation AI execute a diet management method suggestion. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described photographing unit, analysis unit, input unit, management unit, and emotion estimation function, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit takes a photo of a meal using the camera 42 of the smart device 14 and estimates the user's emotion. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies ingredients, calories, and nutrients using a generation AI. The input unit inputs the user's target weight and health goals using the reception device 38 of the smart device 14. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages and advises the user on their diet based on the information identified by the analysis unit and the goals input by the input unit. The emotion estimation function is realized by the control unit 46A of the smart device 14 and adjusts the timing of photographing based on the user's emotion. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described photographing unit, analysis unit, input unit, management unit, and emotion estimation function, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit takes a photo of a meal using the camera 42 of the smart glasses 214 and estimates the user's emotion. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies ingredients, calories, and nutrients using a generative AI. The input unit inputs the user's target weight and health goals using the microphone 238 of the smart glasses 214. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages and advises the user on the diet based on the information identified by the analysis unit and the goals input by the input unit. The emotion estimation function is realized by the control unit 46A of the smart glasses 214 and adjusts the timing of photographing based on the user's emotion. === Hard Collateral 1-3 === Each of the multiple elements, including the above-described photographing unit, analysis unit, input unit, management unit, and emotion estimation function, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the photographing unit takes a photo of a meal using the camera 42 of the headset-type terminal 314 and estimates the user's emotion. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies ingredients, calories, and nutrients using a generation AI. The input unit inputs the user's target weight and health goals using the microphone 238 of the headset-type terminal 314. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages and advises the user on diet based on the information identified by the analysis unit and the goals input by the input unit. The emotion estimation function is realized by the control unit 46A of the headset-type terminal 314 and adjusts the timing of photographing based on the user's emotion. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned photographing unit, analysis unit, input unit, management unit, and emotion estimation function, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit takes a photo of a meal using the camera 42 of the robot 414 and estimates the user's emotion. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies ingredients, calories, and nutrients using a generative AI. The input unit inputs the user's target weight and health goals using the microphone 238 of the robot 414. The management unit is realized by the specific processing unit 290 of the data processing device 12 and manages and advises the user on the diet based on the information identified by the analysis unit and the goals input by the input unit. The emotion estimation function is realized by the control unit 46A of the robot 414 and adjusts the timing of photographing based on the user's emotion.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The management unit can analyze the user's meal history and learn the user's preferences based on the frequency of intake of specific ingredients and dishes. For example, the management unit can identify ingredients that the user frequently consumes and suggest recipes that include those ingredients. The management unit can also analyze trends in the user's favorite dishes and suggest similar dishes. Furthermore, the management unit can make suggestions to increase the variety of meals based on the user's preferences. This enables meal management tailored to the user's preferences and improves meal satisfaction.

[0092] The analysis unit can analyze seasonal food intake trends based on the user's dietary history. For example, the analysis unit can identify ingredients that the user consumes in large amounts in the summer and suggest recipes suitable for that summer. The analysis unit can also identify ingredients that the user consumes in large amounts in the winter and suggest recipes suitable for that winter. Furthermore, the analysis unit can take into account the nutritional balance of each season and suggest nutritional supplements appropriate for the season. This enables dietary management according to the season and supports a healthy diet.

[0093] The management unit can evaluate the allergy risk of specific ingredients based on the user's dietary history. For example, the management unit can collect allergy information on ingredients the user has previously consumed and identify ingredients with a high allergy risk. The management unit can also evaluate the allergy risk of ingredients the user is about to consume and issue warnings. Furthermore, the management unit can suggest alternative ingredients with a low allergy risk. This reduces the allergy risk and enables safe dietary management.

[0094] The management unit can manage food storage methods and expiration dates based on the user's meal history. For example, the management unit can suggest storage methods for ingredients purchased by the user and notify the user of appropriate storage methods. The management unit can also manage expiration dates for ingredients and notify the user of ingredients that are approaching their expiration date. Furthermore, the management unit can suggest recipes that use ingredients that are close to their expiration date. This reduces food waste and enables efficient meal management.

[0095] The management unit can automatically generate a food ingredient shopping list based on the user's meal history. For example, the management unit can create a shopping list by listing ingredients frequently used by the user. The management unit can also add necessary ingredients to the list based on the user's meal plan. Furthermore, the management unit can manage ingredient inventory and automatically add necessary ingredients to the list. This allows for efficient ingredient purchasing and makes meal management easier.

[0096] The management unit can estimate the user's emotions and suggest meals based on the estimated user's emotions. For example, if the user is feeling stressed, the management unit can suggest recipes using ingredients that have a relaxing effect. Also, if the user is tired, the management unit can suggest recipes using ingredients that are suitable for replenishing energy. Furthermore, if the user is happy, the management unit can suggest a special meal. This makes it possible to suggest meals based on the user's emotions, thereby improving meal satisfaction.

[0097] The analysis unit can estimate the user's emotions and select ingredients based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can prioritize selecting ingredients that have a relaxing effect. Also, if the user is excited, the analysis unit can prioritize selecting ingredients that are suitable for replenishing energy. Furthermore, if the user is tired, the analysis unit can prioritize selecting ingredients that are highly nutritious. This makes it possible to select ingredients according to the user's emotions, allowing for more appropriate dietary management.

[0098] The input unit can estimate the user's emotion and customize the input method based on the estimated user's emotion. For example, the input unit can provide a simple and intuitive input method when the user is stressed. The input unit can also provide detailed input options when the user is relaxed. Furthermore, the input unit can also provide a method that allows quick input when the user is in a hurry. This provides an input method that corresponds to the user's emotion, improving usability.

[0099] The management unit can estimate the user's emotions and suggest meal timings based on the estimated user emotions. For example, if the user is feeling stressed, the management unit can suggest a meal timing that will allow the user to relax. Also, if the user is tired, the management unit can suggest a meal timing that is suitable for replenishing energy. Furthermore, if the user is excited, the management unit can suggest a meal timing that will allow the user to calm down. In this way, meal timings are suggested according to the user's emotions, maximizing the effect of the meal.

[0100] The analysis unit can estimate the user's emotions and adjust the nutritional balance of the meal based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can suggest ingredients containing nutrients that help relieve stress. Also, if the user is tired, the analysis unit can suggest ingredients containing nutrients that are suitable for replenishing energy. Furthermore, if the user is relaxed, the analysis unit can suggest ingredients containing nutrients that have a relaxing effect. This makes it possible to adjust the nutritional balance according to the user's emotions, and provides a healthy meal.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The camera takes photos of what the user eats. Photos of what the user eats can include breakfast, lunch, dinner, snacks, etc. The camera can take photos of meals using a smartphone camera, digital camera, or tablet camera. Furthermore, the camera takes high-resolution photos of meals so that even the fine details of the ingredients can be recognized. Step 2: The analysis unit uses the generation AI to analyze the photo taken by the photography unit and identify the ingredients, calories, and nutrients. The analysis unit uses image recognition technology and machine learning algorithms to recognize the ingredients in the photo and identify the type of ingredient. The analysis unit then refers to a food ingredient database to calculate the calories and nutrients of the ingredients. Step 3: The input unit inputs the user's target weight and health goals. The input unit can input the target weight and health goals using a smartphone app, tablet, or PC. In addition, the input unit supports voice input and gesture input, and the user's target weight and health goals can also be input using voice recognition technology. Step 4: The management unit manages and advises the user on their diet based on the information identified by the analysis unit and the goals entered by the input unit. The management unit provides specific advice if the user's calorie intake exceeds the goal or if their nutritional balance is unbalanced. Furthermore, the management unit encrypts and anonymizes data to protect the user's privacy.

[0103] 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.

[0104] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a photographing unit that takes a photograph of what the user has eaten; an analysis unit that analyzes the photograph taken by the photographing unit and identifies ingredients, calories, and nutrients; an input unit for inputting a user's target weight or health goal; a management unit that manages and advises dietary intake based on the information identified by the analysis unit and the goal input by the input unit; Equipped with A system characterized by:

2. The analysis unit Recognize ingredients in photos and identify their calorie or nutrient content 2. The system of claim 1.

3. The management unit Provide dietary management and advice based on user goals 2. The system of claim 1.

4. The management unit Provide specific advice when users exceed their calorie goal 2. The system of claim 1.

5. The management unit Providing specific advice when nutritional imbalance occurs 2. The system of claim 1.

6. The system comprises: Encrypting or anonymizing data to protect user privacy 2. The system of claim 1.

7. The imaging unit is Estimate the user's emotions and adjust the timing of taking photos based on the estimated user emotions.

2. The system of claim 1.

8. The imaging unit is When taking a photo, the system selects the optimal shooting method by referring to the user's past meal history.

2. The system of claim 1.

Images