system

Abstract

The system according to this embodiment aims to generate and notify users of personalized diet recipes in real time according to their diet progress. [Solution] The system according to the embodiment comprises a monitoring unit, a recipe generation unit, and a notification unit. The monitoring unit monitors the user's diet progress. The recipe generation unit generates diet recipes in real time based on the data collected by the monitoring unit. The notification unit notifies the user daily of the recipes generated by the recipe generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, personalized diet proposals according to the user's diet progress situation are not made in real time, and there is room for improvement.

[0005] The system according to the embodiment aims to generate and notify a personalized diet recipe in real time according to the user's diet progress situation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a recipe generation unit, and a notification unit. The monitoring unit monitors the user's diet progress. The recipe generation unit generates diet recipes in real time based on the data collected by the monitoring unit. The notification unit notifies the user daily of the recipes generated by the recipe generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate and notify users of personalized diet recipes in real time according to their diet progress. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The diet support system according to an embodiment of the present invention is a system that uses AI to support a user's diet. This diet support system generates diet recipes in real time according to the user's diet progress and notifies the user daily. Furthermore, for users who find it troublesome to create recipes themselves, it suggests the most suitable ready-made meals available at stores. In addition, by simply uploading an image of the meal the user has eaten, the system analyzes the image to determine the nutritional value, calories, and PFC balance of that meal and records the calories and nutrients consumed. Finally, it suggests a personalized meal considering the user's gender, age, dietary preferences, target weight, health status, exercise level, optimal PFC balance, etc. For example, the diet support system collects data such as the user's weight, meal content, and exercise amount, and the AI ​​analyzes it. By having the user input their weight daily and upload images of their meals, the AI ​​analyzes this data and understands the progress of their diet. Next, the AI ​​generates diet recipes in real time according to the user's diet progress. For example, if the user is approaching their target weight, it suggests a recipe with fewer calories. Also, if the user exercises, it suggests a recipe with a nutritional balance appropriate to the amount of exercise. This allows the user to eat the optimal meal tailored to their diet progress. Furthermore, for users who find creating recipes troublesome, the system suggests the best ready-made meals available at stores. For example, if a user enters "Tell me about low-calorie ready-made meals I can buy at a store," the AI ​​will suggest the best options based on that request. This makes it easy for users to stick to their diet. Additionally, users can simply upload images of their meals, and the system will analyze the images to determine the nutritional value, calories, and PFC balance of the meal, recording the calories and nutrients consumed. For example, when a user uploads an image of a meal, the AI ​​analyzes the image and calculates the nutritional value and calories of the meal. This makes it easy for users to record their meals. Finally, the system suggests personalized meals considering the user's gender, age, dietary preferences, target weight, health status, exercise level, and optimal PFC balance.For example, if a user enters information such as "female in her 30s, moderate exercise level, target weight 50kg," the AI ​​will suggest an optimal meal plan based on that information. This allows the user to eat meals that suit them and successfully lose weight. In this way, the diet support system can effectively support the user's weight loss efforts.

[0029] The diet support system according to this embodiment comprises a monitoring unit, a recipe generation unit, and a notification unit. The monitoring unit monitors the user's diet progress. The monitoring unit collects data such as the user's weight, body fat percentage, and calorie intake. The monitoring unit understands the progress of the diet by having the user input their weight daily and upload images of their meals. The recipe generation unit generates diet recipes in real time based on the data collected by the monitoring unit. For example, if the user is approaching their target weight, the recipe generation unit suggests a recipe with reduced calories. The recipe generation unit can also suggest a recipe with a nutritional balance appropriate to the amount of exercise the user has done. The recipe generation unit uses generation AI to generate optimal meals tailored to the user's diet progress. The notification unit notifies the user of the recipes generated by the recipe generation unit daily. The notification unit notifies the user of the recipes by methods such as email, push notifications, and SMS. The notification unit can also use AI to select the optimal notification method according to the user's schedule and preferences. As a result, the diet support system according to this embodiment can generate and notify users of recipes according to their diet progress.

[0030] The monitoring unit monitors the user's diet progress. Specifically, it collects data such as the user's weight, body fat percentage, and calorie intake. Users track their diet progress by entering their weight daily and uploading images of their meals. The monitoring unit centrally manages this data and tracks the user's diet progress in real time. For example, when a user enters their weight using a smartphone app, that data is sent to a cloud server and becomes accessible to the monitoring unit. Images of meals are analyzed using image recognition technology, and information on calorie intake and nutrients is automatically extracted. Furthermore, the monitoring unit can also collect the user's exercise data. For example, it can acquire data from smartwatches and fitness trackers to understand the user's exercise level and calorie expenditure. This allows the monitoring unit to comprehensively monitor the user's overall health and accurately evaluate their diet progress. In addition, the monitoring unit can provide feedback to the user based on the collected data. For example, it can send encouraging messages if weight is decreasing and provide advice for improvement if weight is increasing. This allows the monitoring unit to maintain the user's motivation and support their diet success.

[0031] The recipe generation unit generates diet recipes in real time based on data collected by the monitoring unit. Specifically, if the user is approaching their target weight, it suggests a low-calorie recipe. It can also suggest a recipe with a nutritional balance appropriate to the amount of exercise the user has done. The recipe generation unit uses a generation AI to create optimal meals tailored to the user's diet progress. The generation AI receives data such as the user's weight, body fat percentage, calorie intake, and exercise level as input, and generates the optimal recipe based on this data. For example, if the user has performed high-intensity exercise, the generation AI suggests a high-protein, low-calorie recipe. Furthermore, if the user prefers a specific ingredient, the AI ​​can prioritize generating recipes using that ingredient. The generation AI learns from past data and trends to provide recipes that are optimal for the user's preferences and diet goals. In addition, the recipe generation unit can accommodate the user's allergy information and dietary restrictions. For example, if the user requests a gluten-free diet, the generation AI will generate a gluten-free recipe. This allows the recipe generation unit to provide customized recipes that meet the user's individual needs.

[0032] The notification unit notifies users daily of recipes generated by the recipe generation unit. Specifically, it notifies users of recipes via methods such as email, push notifications, and SMS. The notification unit can also use AI to select the optimal notification method according to the user's schedule and preferences. For example, if a user wants to receive recipes at breakfast time, the notification unit will send a notification at that time. Also, if a user prefers a particular notification method, that method will be used preferentially. The notification unit can collect user feedback and continuously improve the content and timing of notifications. For example, if a user misses a notification, it will analyze the reason and adjust the timing of the next notification. The notification unit can also use multiple notification methods in combination to ensure information is delivered reliably. For example, if a push notification is not received, it will send another notification via SMS or email. In this way, the notification unit can provide users with recipes quickly and reliably, supporting their diet progress. Furthermore, the notification unit can send messages and reminders to maintain user motivation. For example, when a user approaches their target weight, it can send an encouraging message to celebrate their diet success. In this way, the notification unit can comprehensively support the user's diet and guide them to success.

[0033] The recipe generation unit can suggest the most suitable prepared foods available at stores. For example, if a user inputs "Tell me about low-calorie prepared foods available at stores," the AI ​​will suggest the most suitable prepared foods in response to that request. The recipe generation unit uses a generation AI to select the most suitable prepared foods from those available at stores, based on the user's diet progress. For example, the recipe generation unit refers to a store database and suggests the most suitable prepared foods considering calories and nutritional balance. This makes it possible to accommodate users who find creating recipes troublesome. Some or all of the above processes in the recipe generation unit may be performed using AI or not. For example, the recipe generation unit can input a store database into the generation AI and have the generation AI select the most suitable prepared foods.

[0034] The image analysis unit can analyze images of meals eaten by a user and determine the nutritional value, calories, and PFC balance of those meals. For example, when a user uploads an image of a meal they ate, the AI ​​analyzes the image and calculates the nutritional value and calories of the meal. The image analysis unit uses a generation AI to analyze the image of the meal and determine the nutritional value, calories, and PFC balance. For example, the image analysis unit uses deep learning technology to analyze the image of the meal and identify the type and quantity of ingredients. This allows for the automatic analysis of the nutritional value, calories, and PFC balance of the meal. Some or all of the above processing in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input an image of the meal into a generation AI and have the generation AI perform the analysis of nutritional value and calories.

[0035] The recording unit can record the calories and nutrients consumed based on the data analyzed by the image analysis unit. For example, the recording unit automatically records the nutritional value and calorie data of the meal analyzed by the image analysis unit. The recording unit uses a generation AI to record the calories and nutrients consumed based on the analyzed data. For example, the recording unit saves the data in a digital recording format so that the user can check it at any time. This allows for the automatic recording of calories and nutrients consumed. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the analyzed data into a generation AI and have the generation AI execute the recording method.

[0036] The suggestion unit can propose personalized meals by considering the user's gender, age, dietary preferences, target weight, health status, exercise level, optimal PFC balance, etc. For example, if the user inputs "female in her 30s, moderate exercise level, target weight 50kg," the AI ​​will propose an optimal meal plan based on that information. The suggestion unit uses a generation AI to propose personalized meals based on the user's individual information. For example, the suggestion unit analyzes the user's data and generates a nutritionally balanced meal plan. This allows it to propose the most suitable personalized meal for the user. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user data into a generation AI and have the generation AI execute personalized meal proposals.

[0037] The monitoring unit can analyze the user's past diet history and select the optimal monitoring method. For example, the monitoring unit may suggest a similar monitoring method based on the user's past successful diet methods. The monitoring unit may also suggest a different monitoring method to help the user avoid past unsuccessful diet methods. The monitoring unit uses generative AI to analyze the user's past diet history and select the optimal monitoring method. For example, the monitoring unit analyzes the user's past data to determine the most effective monitoring frequency. This allows the monitoring unit to select the optimal monitoring method based on the user's past diet history. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's past diet data into the generative AI and have the generative AI select the optimal monitoring method.

[0038] The monitoring unit can collect data while considering the user's lifestyle and stress level during monitoring. For example, the monitoring unit can adjust the timing of monitoring to match the user's lifestyle. The monitoring unit can also reduce data collection if the user's stress level is high. The monitoring unit uses generative AI to collect data while considering the user's lifestyle and stress level. For example, the monitoring unit comprehensively considers the user's lifestyle and stress level to select the optimal data collection method. This makes it possible to collect data according to the user's lifestyle and stress level. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's lifestyle data into the generative AI and have the generative AI execute the data collection method.

[0039] The monitoring unit can prioritize the collection of highly relevant data while considering the user's geographical location information during monitoring. For example, if the user is in a specific region, the monitoring unit will prioritize the collection of data related to that region. If the user is traveling, the monitoring unit can also prioritize the collection of data related to the travel destination. The monitoring unit uses generative AI to prioritize the collection of highly relevant data while considering the user's geographical location information. For example, the monitoring unit collects highly relevant data based on the user's current location. This allows for the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's geographical location data into the generative AI and have the generative AI perform the collection of highly relevant data.

[0040] The monitoring unit can analyze the user's social media activity and collect relevant data during monitoring. For example, the monitoring unit can collect monitoring data based on food information shared by the user on social media. The monitoring unit can also estimate stress levels and emotional states from the user's social media activity and adjust the monitoring data accordingly. The monitoring unit uses generative AI to analyze the user's social media activity and collect relevant data. For example, the monitoring unit can collect monitoring data based on information from health-related accounts that the user follows on social media. This allows the monitoring unit to collect relevant data based on the user's social media activity. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's social media data into a generative AI and have the generative AI collect relevant data.

[0041] The recipe generation unit can suggest recipes while considering the user's ingredient inventory. For example, the recipe generation unit can suggest the optimal recipe based on the ingredients the user has. The recipe generation unit can also list ingredients the user is lacking and suggest alternative ingredients. The recipe generation unit uses generation AI to suggest recipes while considering the user's ingredient inventory. For example, the recipe generation unit updates the user's ingredient inventory in real time and suggests recipes based on that. This allows it to suggest the optimal recipe based on the user's ingredient inventory. Some or all of the above processes in the recipe generation unit may be performed using AI or not. For example, the recipe generation unit can input the user's ingredient inventory data into the generation AI and have the generation AI perform recipe suggestions.

[0042] The recipe generation unit can customize recipes by taking into account the user's allergy information during recipe generation. For example, the recipe generation unit can suggest recipes that exclude ingredients the user is allergic to. The recipe generation unit can also suggest recipes using alternative ingredients based on the user's allergy information. The recipe generation unit uses generation AI to customize recipes by taking into account the user's allergy information. For example, the recipe generation unit updates the user's allergy information in real time and customizes recipes based on that information. This allows recipes to be customized based on the user's allergy information. Some or all of the above processes in the recipe generation unit may be performed using AI or not. For example, the recipe generation unit can input the user's allergy data into the generation AI and have the generation AI perform recipe customization.

[0043] The recipe generation unit can suggest recipes while considering the user's food preferences and past ratings. For example, the recipe generation unit can suggest new recipes based on recipes that the user has previously given high ratings to. The recipe generation unit can also analyze the user's food preferences and suggest recipes based on that. The recipe generation unit uses generation AI to suggest recipes while considering the user's food preferences and past ratings. For example, the recipe generation unit suggests the optimal recipe based on the user's past ratings. This allows the system to suggest the optimal recipe based on the user's food preferences and past ratings. Some or all of the above processes in the recipe generation unit may be performed using AI or not. For example, the recipe generation unit can input the user's past rating data into the generation AI and have the generation AI perform recipe suggestions.

[0044] The recipe generation unit can adjust the nutritional balance based on the user's exercise level when generating a recipe. For example, if the user exercises, the recipe generation unit will suggest a recipe with a nutritional balance appropriate to that exercise level. If the user does not exercise, the recipe generation unit can also suggest a recipe with fewer calories. The recipe generation unit uses a generation AI to adjust the nutritional balance based on the user's exercise level. For example, the recipe generation unit updates the user's exercise level in real time and adjusts the nutritional balance based on that. This allows the nutritional balance to be adjusted according to the user's exercise level. Some or all of the above processing in the recipe generation unit may be performed using AI or not. For example, the recipe generation unit can input the user's exercise data into the generation AI and have the generation AI perform the adjustment of the nutritional balance.

[0045] The notification unit can select the optimal notification method considering the user's schedule when sending a notification. For example, the notification unit can adjust the timing of notifications to match the user's schedule. The notification unit can also send concise notifications during busy times for the user. The notification unit uses a generation AI to select the optimal notification method considering the user's schedule. For example, the notification unit can send detailed notifications during times when the user is relaxed. This allows the notification unit to select the optimal notification method according to the user's schedule. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's schedule data into a generation AI and have the generation AI select the notification method.

[0046] The notification unit can analyze the user's past responses and customize the notification content when a notification is sent. For example, the notification unit can create new notification content based on notifications to which the user has previously responded favorably. The notification unit can also analyze the user's past responses and suggest the most suitable notification content. The notification unit uses generative AI to analyze the user's past responses and customize the notification content. For example, the notification unit adjusts the frequency and timing of notifications based on the user's past responses. This allows the notification content to be customized based on the user's past responses. Some or all of the above processes in the notification unit may be performed using AI or not. For example, the notification unit can input the user's past response data into the generative AI and have the generative AI perform the customization of the notification content.

[0047] The notification unit can select the optimal notification method when a notification is sent, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit will send a push notification. If the user is using a tablet, the notification unit can also send an in-app notification. The notification unit uses a generation AI to select the optimal notification method, taking into account the user's device information. For example, if the user is using a smartwatch, the notification unit will send a vibration notification. This allows the notification unit to select the optimal notification method based on the user's device information. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's device information into a generation AI and have the generation AI perform the selection of the notification method.

[0048] The notification unit can provide highly relevant notifications by considering the user's geographical location information. For example, if the user is in a specific region, the notification unit will provide notifications related to that region. If the user is traveling, the notification unit can also provide notifications related to the travel destination. The notification unit uses a generation AI to provide highly relevant notifications by considering the user's geographical location information. For example, if the user is at home, the notification unit will provide information about the area around their home. This allows the notification unit to provide highly relevant notifications based on the user's geographical location information. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's geographical location data into a generation AI and have the generation AI execute highly relevant notifications.

[0049] The image analysis unit can improve the accuracy of image analysis by referring to the user's past meal data. For example, the image analysis unit can improve the accuracy of analyzing similar meals based on the user's past meal data. The image analysis unit can also more accurately identify ingredients by referring to the user's past meal data. The image analysis unit can improve the accuracy of analysis by referring to the user's past meal data using a generating AI. For example, the image analysis unit can analyze the user's past meal data to improve the accuracy of estimating nutritional value and calories. This allows the image analysis unit to improve accuracy based on the user's past meal data. Some or all of the above processes in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input the user's past meal data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0050] The image analysis unit can apply different analysis algorithms depending on the type of meal the user ate during image analysis. For example, if the user ate Japanese food, the image analysis unit will apply an analysis algorithm specialized for Japanese food. If the user ate Western food, the image analysis unit can also apply an analysis algorithm specialized for Western food. The image analysis unit uses a generation AI to apply different analysis algorithms depending on the type of meal the user ate. For example, if the user ate Chinese food, the image analysis unit will apply an analysis algorithm specialized for Chinese food. This allows the optimal analysis algorithm to be applied according to the type of meal the user ate. Some or all of the above processing in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input the user's meal type data into the generation AI and have the generation AI execute the application of the analysis algorithm.

[0051] The image analysis unit can improve analysis accuracy by considering the user's food photography environment during image analysis. For example, if the user takes a picture in a dark place, the image analysis unit can improve analysis accuracy by adjusting the brightness of the image. If the user takes a picture in a bright place, the image analysis unit can also improve analysis accuracy by adjusting the contrast of the image. The image analysis unit uses a generation AI to improve analysis accuracy by considering the user's food photography environment. For example, if the user takes a picture outdoors, the image analysis unit can improve analysis accuracy by removing background noise. This allows for improved analysis accuracy based on the user's food photography environment. Some or all of the above processing in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input the user's shooting environment data into a generation AI and have the generation AI perform the improvement of analysis accuracy.

[0052] The image analysis unit can be equipped with a function to automatically measure the amount of food a user eats during image analysis. For example, the image analysis unit can automatically measure the amount of food from an image taken by the user. If the user uploads multiple images, the image analysis unit can also measure the amount of food from each image and sum them up. The image analysis unit can be equipped with a function to automatically measure the amount of food a user eats using a generating AI. For example, if the user manually inputs the amount of food, the image analysis unit can use that data to improve the accuracy of the automatic measurement. This allows the user's amount of food to be measured automatically. Some or all of the above processing in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input image data of the user's meal into a generating AI and have the generating AI perform the measurement of the amount of food.

[0053] The recording unit can select the optimal recording method by referring to the user's past data during recording. For example, the recording unit can suggest the optimal recording method based on the recording methods the user has used in the past. The recording unit can also analyze the user's past data and adjust the frequency and method of recording. The recording unit uses a generative AI to select the optimal recording method by referring to the user's past data. For example, the recording unit can improve the accuracy of recording based on the user's past data. This allows the recording unit to select the optimal recording method based on the user's past data. Some or all of the above processes in the recording unit may be performed using AI or not. For example, the recording unit can input the user's past data into a generative AI and have the generative AI perform the selection of a recording method.

[0054] The recording unit can adjust the frequency of data recording while taking into account the user's daily rhythm. For example, the recording unit can adjust the timing of recording to match the user's daily rhythm. The recording unit can also reduce the frequency of recording during busy times for the user. The recording unit uses a generation AI to adjust the frequency of data recording while taking into account the user's daily rhythm. For example, the recording unit can increase the frequency of recording during times when the user is relaxed. This allows the frequency of data recording to be adjusted according to the user's daily rhythm. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's daily rhythm data into a generation AI and have the generation AI perform the adjustment of the recording frequency.

[0055] The recording unit can select the optimal recording method while considering the user's device information. For example, if the user is using a smartphone, the recording unit can provide an in-app recording method. If the user is using a tablet, the recording unit can also provide a recording method optimized for a larger screen. The recording unit uses a generation AI to select the optimal recording method while considering the user's device information. For example, if the user is using a smartwatch, the recording unit can provide a concise and highly visible recording method. This allows the optimal recording method to be selected based on the user's device information. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's device information into a generation AI and have the generation AI perform the selection of the recording method.

[0056] The recording unit can record highly relevant data while considering the user's geographical location information. For example, if the user is in a specific region, the recording unit will prioritize recording data related to that region. If the user is traveling, the recording unit can also prioritize recording data related to the travel destination. The recording unit uses a generation AI to record highly relevant data while considering the user's geographical location information. For example, if the user is at home, the recording unit will prioritize recording data around the user's home. This allows the recording of highly relevant data based on the user's geographical location information. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's geographical location data into a generation AI and have the generation AI perform the recording of highly relevant data.

[0057] The suggestion unit can make optimal suggestions by referring to the user's past meal history when making suggestions. For example, the suggestion unit can suggest similar meals based on the user's past meal history. The suggestion unit can also analyze the user's past meal history and suggest nutritionally balanced meals. The suggestion unit uses generative AI to refer to the user's past meal history and make optimal suggestions. For example, the suggestion unit can refer to the user's past meal history to more accurately identify ingredients. This allows it to make optimal suggestions based on the user's past meal history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past meal history data into the generative AI and have the generative AI execute the suggestions.

[0058] The suggestion unit can customize its suggestions by taking into account the user's health condition. For example, if the user has a specific health condition, the suggestion unit can suggest a meal suitable for that condition. The suggestion unit can also suggest a nutritionally balanced meal based on the user's health condition. The suggestion unit uses generative AI to customize the suggestions by taking into account the user's health condition. For example, the suggestion unit updates the user's health condition in real time and customizes the suggestions based on that. This allows the suggestions to be customized based on the user's health condition. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's health condition data into the generative AI and have the generative AI perform the customization of the suggestions.

[0059] The suggestion unit can make optimal suggestions by considering the user's geographical location information when making suggestions. For example, if the user is in a specific region, the suggestion unit will make suggestions related to that region. If the user is traveling, the suggestion unit can also make suggestions related to the travel destination. The suggestion unit uses generative AI to make optimal suggestions by considering the user's geographical location information. For example, if the user is at home, the suggestion unit will make suggestions based on information about the area around their home. This allows the suggestion unit to make optimal suggestions based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location data into the generative AI and have the generative AI execute the suggestions.

[0060] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can customize suggestions based on food information shared by the user on social media. The suggestion unit can also analyze the user's food preferences and tastes from their social media activity and make suggestions based on that. The suggestion unit can use generative AI to analyze the user's social media activity and make relevant suggestions. For example, the suggestion unit can customize suggestions based on information from health-related accounts that the user follows on social media. This allows the suggestion unit to make relevant suggestions based on the user's social media activity. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's social media data into a generative AI and have the generative AI execute the suggestions.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The recipe generation unit can suggest recipes while considering the user's ingredient inventory. For example, it can suggest the most suitable recipe based on the ingredients the user has. It can also list ingredients the user is missing and suggest alternative ingredients. The recipe generation unit updates the user's ingredient inventory in real time and suggests recipes based on that information. This allows it to suggest the most suitable recipe based on the user's ingredient inventory.

[0063] The monitoring unit can analyze the user's past dieting history and select the optimal monitoring method. For example, it can suggest a similar monitoring method based on the user's past successful dieting methods. It can also suggest a different monitoring method to help the user avoid past unsuccessful dieting methods. The monitoring unit analyzes the user's past data to determine the most effective monitoring frequency. This allows it to select the optimal monitoring method based on the user's past dieting history.

[0064] The recipe generation unit can customize recipes by taking into account the user's allergy information during the recipe generation process. For example, it can suggest recipes that exclude ingredients the user is allergic to. It can also suggest recipes using alternative ingredients based on the user's allergy information. The recipe generation unit updates the user's allergy information in real time and customizes recipes based on that information. This allows recipes to be customized based on the user's allergy information.

[0065] The notification unit can select the most appropriate notification method, taking into account the user's schedule. For example, it can adjust the timing of notifications to match the user's schedule. It can also provide concise notifications during busy periods and detailed notifications during relaxed periods. This allows the system to select the most suitable notification method according to the user's schedule.

[0066] The image analysis unit can apply different analysis algorithms depending on the type of meal the user ate. For example, if the user ate Japanese food, it can apply an analysis algorithm specifically for Japanese food. If the user ate Western food, it can also apply an analysis algorithm specifically for Western food. If the user ate Chinese food, the image analysis unit can apply an analysis algorithm specifically for Chinese food. This allows the system to apply the most suitable analysis algorithm for the type of meal the user ate.

[0067] The suggestion department can customize the suggestions based on the user's health condition. For example, if a user has a specific health condition, it can suggest meals suitable for that condition. It can also suggest nutritionally balanced meals based on the user's health condition. The suggestion department updates the user's health condition in real time and customizes the suggestions accordingly. This allows for customized suggestions based on the user's health condition.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The monitoring unit monitors the user's diet progress. Specifically, it collects data such as the user's weight, body fat percentage, and calorie intake. Users input their weight daily and upload pictures of their meals, allowing the monitoring unit to track their diet progress. Step 2: The recipe generation unit generates diet recipes in real time based on the data collected by the monitoring unit. For example, if the user is approaching their target weight, it will suggest a low-calorie recipe. It can also suggest a recipe with a nutritional balance appropriate to the amount of exercise the user has done. The recipe generation unit uses a generation AI to create optimal meals tailored to the user's diet progress. Step 3: The notification unit notifies users daily of the recipes generated by the recipe generation unit. The notification unit notifies users of the recipes via methods such as email, push notifications, and SMS. Furthermore, the notification unit can use AI to select the optimal notification method according to the user's schedule and preferences.

[0070] (Example of form 2) The diet support system according to an embodiment of the present invention is a system that uses AI to support a user's diet. This diet support system generates diet recipes in real time according to the user's diet progress and notifies the user daily. Furthermore, for users who find it troublesome to create recipes themselves, it suggests the most suitable ready-made meals available at stores. In addition, by simply uploading an image of the meal the user has eaten, the system analyzes the image to determine the nutritional value, calories, and PFC balance of that meal and records the calories and nutrients consumed. Finally, it suggests a personalized meal considering the user's gender, age, dietary preferences, target weight, health status, exercise level, optimal PFC balance, etc. For example, the diet support system collects data such as the user's weight, meal content, and exercise amount, and the AI ​​analyzes it. By having the user input their weight daily and upload images of their meals, the AI ​​analyzes this data and understands the progress of their diet. Next, the AI ​​generates diet recipes in real time according to the user's diet progress. For example, if the user is approaching their target weight, it suggests a recipe with fewer calories. Also, if the user exercises, it suggests a recipe with a nutritional balance appropriate to the amount of exercise. This allows the user to eat the optimal meal tailored to their diet progress. Furthermore, for users who find creating recipes troublesome, the system suggests the best ready-made meals available at stores. For example, if a user enters "Tell me about low-calorie ready-made meals I can buy at a store," the AI ​​will suggest the best options based on that request. This makes it easy for users to stick to their diet. Additionally, users can simply upload images of their meals, and the system will analyze the images to determine the nutritional value, calories, and PFC balance of the meal, recording the calories and nutrients consumed. For example, when a user uploads an image of a meal, the AI ​​analyzes the image and calculates the nutritional value and calories of the meal. This makes it easy for users to record their meals. Finally, the system suggests personalized meals considering the user's gender, age, dietary preferences, target weight, health status, exercise level, and optimal PFC balance.For example, if a user enters information such as "female in her 30s, moderate exercise level, target weight 50kg," the AI ​​will suggest an optimal meal plan based on that information. This allows the user to eat meals that suit them and successfully lose weight. In this way, the diet support system can effectively support the user's weight loss efforts.

[0071] The diet support system according to this embodiment comprises a monitoring unit, a recipe generation unit, and a notification unit. The monitoring unit monitors the user's diet progress. The monitoring unit collects data such as the user's weight, body fat percentage, and calorie intake. The monitoring unit understands the progress of the diet by having the user input their weight daily and upload images of their meals. The recipe generation unit generates diet recipes in real time based on the data collected by the monitoring unit. For example, if the user is approaching their target weight, the recipe generation unit suggests a recipe with reduced calories. The recipe generation unit can also suggest a recipe with a nutritional balance appropriate to the amount of exercise the user has done. The recipe generation unit uses generation AI to generate optimal meals tailored to the user's diet progress. The notification unit notifies the user of the recipes generated by the recipe generation unit daily. The notification unit notifies the user of the recipes by methods such as email, push notifications, and SMS. The notification unit can also use AI to select the optimal notification method according to the user's schedule and preferences. As a result, the diet support system according to this embodiment can generate and notify users of recipes according to their diet progress.

[0072] The monitoring unit monitors the user's diet progress. Specifically, it collects data such as the user's weight, body fat percentage, and calorie intake. Users track their diet progress by entering their weight daily and uploading images of their meals. The monitoring unit centrally manages this data and tracks the user's diet progress in real time. For example, when a user enters their weight using a smartphone app, that data is sent to a cloud server and becomes accessible to the monitoring unit. Images of meals are analyzed using image recognition technology, and information on calorie intake and nutrients is automatically extracted. Furthermore, the monitoring unit can also collect the user's exercise data. For example, it can acquire data from smartwatches and fitness trackers to understand the user's exercise level and calorie expenditure. This allows the monitoring unit to comprehensively monitor the user's overall health and accurately evaluate their diet progress. In addition, the monitoring unit can provide feedback to the user based on the collected data. For example, it can send encouraging messages if weight is decreasing and provide advice for improvement if weight is increasing. This allows the monitoring unit to maintain the user's motivation and support their diet success.

[0073] The recipe generation unit generates diet recipes in real time based on data collected by the monitoring unit. Specifically, if the user is approaching their target weight, it suggests a low-calorie recipe. It can also suggest a recipe with a nutritional balance appropriate to the amount of exercise the user has done. The recipe generation unit uses a generation AI to create optimal meals tailored to the user's diet progress. The generation AI receives data such as the user's weight, body fat percentage, calorie intake, and exercise level as input, and generates the optimal recipe based on this data. For example, if the user has performed high-intensity exercise, the generation AI suggests a high-protein, low-calorie recipe. Furthermore, if the user prefers a specific ingredient, the AI ​​can prioritize generating recipes using that ingredient. The generation AI learns from past data and trends to provide recipes that are optimal for the user's preferences and diet goals. In addition, the recipe generation unit can accommodate the user's allergy information and dietary restrictions. For example, if the user requests a gluten-free diet, the generation AI will generate a gluten-free recipe. This allows the recipe generation unit to provide customized recipes that meet the user's individual needs.

[0074] The notification unit notifies users daily of recipes generated by the recipe generation unit. Specifically, it notifies users of recipes via methods such as email, push notifications, and SMS. The notification unit can also use AI to select the optimal notification method according to the user's schedule and preferences. For example, if a user wants to receive recipes at breakfast time, the notification unit will send a notification at that time. Also, if a user prefers a particular notification method, that method will be used preferentially. The notification unit can collect user feedback and continuously improve the content and timing of notifications. For example, if a user misses a notification, it will analyze the reason and adjust the timing of the next notification. The notification unit can also use multiple notification methods in combination to ensure information is delivered reliably. For example, if a push notification is not received, it will send another notification via SMS or email. In this way, the notification unit can provide users with recipes quickly and reliably, supporting their diet progress. Furthermore, the notification unit can send messages and reminders to maintain user motivation. For example, when a user approaches their target weight, it can send an encouraging message to celebrate their diet success. In this way, the notification unit can comprehensively support the user's diet and guide them to success.

[0075] The recipe generation unit can suggest the most suitable prepared foods available at stores. For example, if a user inputs "Tell me about low-calorie prepared foods available at stores," the AI ​​will suggest the most suitable prepared foods in response to that request. The recipe generation unit uses a generation AI to select the most suitable prepared foods from those available at stores, based on the user's diet progress. For example, the recipe generation unit refers to a store database and suggests the most suitable prepared foods considering calories and nutritional balance. This makes it possible to accommodate users who find creating recipes troublesome. Some or all of the above processes in the recipe generation unit may be performed using AI or not. For example, the recipe generation unit can input a store database into the generation AI and have the generation AI select the most suitable prepared foods.

[0076] The image analysis unit can analyze images of meals eaten by a user and determine the nutritional value, calories, and PFC balance of those meals. For example, when a user uploads an image of a meal they ate, the AI ​​analyzes the image and calculates the nutritional value and calories of the meal. The image analysis unit uses a generation AI to analyze the image of the meal and determine the nutritional value, calories, and PFC balance. For example, the image analysis unit uses deep learning technology to analyze the image of the meal and identify the type and quantity of ingredients. This allows for the automatic analysis of the nutritional value, calories, and PFC balance of the meal. Some or all of the above processing in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input an image of the meal into a generation AI and have the generation AI perform the analysis of nutritional value and calories.

[0077] The recording unit can record the calories and nutrients consumed based on the data analyzed by the image analysis unit. For example, the recording unit automatically records the nutritional value and calorie data of the meal analyzed by the image analysis unit. The recording unit uses a generation AI to record the calories and nutrients consumed based on the analyzed data. For example, the recording unit saves the data in a digital recording format so that the user can check it at any time. This allows for the automatic recording of calories and nutrients consumed. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the analyzed data into a generation AI and have the generation AI execute the recording method.

[0078] The suggestion unit can propose personalized meals by considering the user's gender, age, dietary preferences, target weight, health status, exercise level, optimal PFC balance, etc. For example, if the user inputs "female in her 30s, moderate exercise level, target weight 50kg," the AI ​​will propose an optimal meal plan based on that information. The suggestion unit uses a generation AI to propose personalized meals based on the user's individual information. For example, the suggestion unit analyzes the user's data and generates a nutritionally balanced meal plan. This allows it to propose the most suitable personalized meal for the user. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user data into a generation AI and have the generation AI execute personalized meal proposals.

[0079] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring unit can reduce the monitoring frequency to alleviate the user's burden. If the user is relaxed, the monitoring unit can also increase the monitoring frequency to collect more detailed data. The monitoring unit uses generative AI to estimate the user's emotions and adjusts the monitoring frequency based on those emotions. For example, the monitoring unit uses facial recognition technology to estimate the user's emotions and adjusts the monitoring frequency based on the emotion score. This allows the monitoring frequency to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0080] The monitoring unit can analyze the user's past diet history and select the optimal monitoring method. For example, the monitoring unit may suggest a similar monitoring method based on the user's past successful diet methods. The monitoring unit may also suggest a different monitoring method to help the user avoid past unsuccessful diet methods. The monitoring unit uses generative AI to analyze the user's past diet history and select the optimal monitoring method. For example, the monitoring unit analyzes the user's past data to determine the most effective monitoring frequency. This allows the monitoring unit to select the optimal monitoring method based on the user's past diet history. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's past diet data into the generative AI and have the generative AI select the optimal monitoring method.

[0081] The monitoring unit can collect data while considering the user's lifestyle and stress level during monitoring. For example, the monitoring unit can adjust the timing of monitoring to match the user's lifestyle. The monitoring unit can also reduce data collection if the user's stress level is high. The monitoring unit uses generative AI to collect data while considering the user's lifestyle and stress level. For example, the monitoring unit comprehensively considers the user's lifestyle and stress level to select the optimal data collection method. This makes it possible to collect data according to the user's lifestyle and stress level. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's lifestyle data into the generative AI and have the generative AI execute the data collection method.

[0082] The monitoring unit can estimate the user's emotions and prioritize monitoring data based on the estimated emotions. For example, if the user is stressed, the monitoring unit will prioritize collecting only important data. If the user is relaxed, the monitoring unit can also prioritize collecting detailed data. The monitoring unit uses generative AI to estimate the user's emotions and prioritizes monitoring data based on those emotions. For example, the monitoring unit uses facial recognition technology to estimate the user's emotions and prioritizes monitoring data based on the emotion score. This allows the monitoring unit to prioritize monitoring data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0083] The monitoring unit can prioritize the collection of highly relevant data while considering the user's geographical location information during monitoring. For example, if the user is in a specific region, the monitoring unit will prioritize the collection of data related to that region. If the user is traveling, the monitoring unit can also prioritize the collection of data related to the travel destination. The monitoring unit uses generative AI to prioritize the collection of highly relevant data while considering the user's geographical location information. For example, the monitoring unit collects highly relevant data based on the user's current location. This allows for the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's geographical location data into the generative AI and have the generative AI perform the collection of highly relevant data.

[0084] The monitoring unit can analyze the user's social media activity and collect relevant data during monitoring. For example, the monitoring unit can collect monitoring data based on food information shared by the user on social media. The monitoring unit can also estimate stress levels and emotional states from the user's social media activity and adjust the monitoring data accordingly. The monitoring unit uses generative AI to analyze the user's social media activity and collect relevant data. For example, the monitoring unit can collect monitoring data based on information from health-related accounts that the user follows on social media. This allows the monitoring unit to collect relevant data based on the user's social media activity. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's social media data into a generative AI and have the generative AI collect relevant data.

[0085] The recipe generation unit can estimate the user's emotions and adjust the way the recipe is presented based on the estimated emotions. For example, if the user is stressed, the recipe generation unit will suggest a simple and easy recipe. If the user is relaxed, the recipe generation unit can also suggest a recipe with detailed instructions. The recipe generation unit uses a generative AI to estimate the user's emotions and adjust the way the recipe is presented based on those emotions. For example, the recipe generation unit uses facial recognition technology to estimate the user's emotions and adjusts the way the recipe is presented based on the emotion score. This allows the recipe to be presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the recipe generation unit may be performed using AI or not. For example, the recipe generation unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0086] The recipe generation unit can suggest recipes while considering the user's ingredient inventory. For example, the recipe generation unit can suggest the optimal recipe based on the ingredients the user has. The recipe generation unit can also list ingredients the user is lacking and suggest alternative ingredients. The recipe generation unit uses generation AI to suggest recipes while considering the user's ingredient inventory. For example, the recipe generation unit updates the user's ingredient inventory in real time and suggests recipes based on that. This allows it to suggest the optimal recipe based on the user's ingredient inventory. Some or all of the above processes in the recipe generation unit may be performed using AI or not. For example, the recipe generation unit can input the user's ingredient inventory data into the generation AI and have the generation AI perform recipe suggestions.

[0087] The recipe generation unit can customize recipes by taking into account the user's allergy information during recipe generation. For example, the recipe generation unit can suggest recipes that exclude ingredients the user is allergic to. The recipe generation unit can also suggest recipes using alternative ingredients based on the user's allergy information. The recipe generation unit uses generation AI to customize recipes by taking into account the user's allergy information. For example, the recipe generation unit updates the user's allergy information in real time and customizes recipes based on that information. This allows recipes to be customized based on the user's allergy information. Some or all of the above processes in the recipe generation unit may be performed using AI or not. For example, the recipe generation unit can input the user's allergy data into the generation AI and have the generation AI perform recipe customization.

[0088] The recipe generation unit can estimate the user's emotions and adjust the difficulty of the recipe based on the estimated emotions. For example, if the user is feeling stressed, the recipe generation unit may suggest an easy recipe. If the user is relaxed, the recipe generation unit may also suggest a challenging recipe. The recipe generation unit uses a generation AI to estimate the user's emotions and adjust the difficulty of the recipe based on those emotions. For example, the recipe generation unit uses facial recognition technology to estimate the user's emotions and adjusts the difficulty of the recipe based on the emotion score. This allows the difficulty of the recipe to be adjusted according to 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 is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the recipe generation unit may be performed using AI or not. For example, the recipe generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0089] The recipe generation unit can suggest recipes while considering the user's food preferences and past ratings. For example, the recipe generation unit can suggest new recipes based on recipes that the user has previously given high ratings to. The recipe generation unit can also analyze the user's food preferences and suggest recipes based on that. The recipe generation unit uses generation AI to suggest recipes while considering the user's food preferences and past ratings. For example, the recipe generation unit suggests the optimal recipe based on the user's past ratings. This allows the system to suggest the optimal recipe based on the user's food preferences and past ratings. Some or all of the above processes in the recipe generation unit may be performed using AI or not. For example, the recipe generation unit can input the user's past rating data into the generation AI and have the generation AI perform recipe suggestions.

[0090] The recipe generation unit can adjust the nutritional balance based on the user's exercise level when generating a recipe. For example, if the user exercises, the recipe generation unit will suggest a recipe with a nutritional balance appropriate to that exercise level. If the user does not exercise, the recipe generation unit can also suggest a recipe with fewer calories. The recipe generation unit uses a generation AI to adjust the nutritional balance based on the user's exercise level. For example, the recipe generation unit updates the user's exercise level in real time and adjusts the nutritional balance based on that. This allows the nutritional balance to be adjusted according to the user's exercise level. Some or all of the above processing in the recipe generation unit may be performed using AI or not. For example, the recipe generation unit can input the user's exercise data into the generation AI and have the generation AI perform the adjustment of the nutritional balance.

[0091] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit can reduce the frequency of notifications to alleviate the user's burden. If the user is relaxed, the notification unit can also increase the frequency of notifications and provide more detailed information. The notification unit uses generative AI to estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, the notification unit can use facial recognition technology to estimate the user's emotions and adjust the timing of notifications based on the emotion score. This allows the timing of notifications to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0092] The notification unit can select the optimal notification method considering the user's schedule when sending a notification. For example, the notification unit can adjust the timing of notifications to match the user's schedule. The notification unit can also send concise notifications during busy times for the user. The notification unit uses a generation AI to select the optimal notification method considering the user's schedule. For example, the notification unit can send detailed notifications during times when the user is relaxed. This allows the notification unit to select the optimal notification method according to the user's schedule. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's schedule data into a generation AI and have the generation AI select the notification method.

[0093] The notification unit can analyze the user's past responses and customize the notification content when a notification is sent. For example, the notification unit can create new notification content based on notifications to which the user has previously responded favorably. The notification unit can also analyze the user's past responses and suggest the most suitable notification content. The notification unit uses generative AI to analyze the user's past responses and customize the notification content. For example, the notification unit adjusts the frequency and timing of notifications based on the user's past responses. This allows the notification content to be customized based on the user's past responses. Some or all of the above processes in the notification unit may be performed using AI or not. For example, the notification unit can input the user's past response data into the generative AI and have the generative AI perform the customization of the notification content.

[0094] The notification unit can estimate the user's emotions and determine the priority of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize only important notifications. If the user is relaxed, the notification unit may also prioritize detailed notifications. The notification unit uses generative AI to estimate the user's emotions and determine the priority of notifications based on those emotions. For example, the notification unit uses facial recognition technology to estimate the user's emotions and determines the priority of notifications based on the emotion score. This allows the notification unit to determine the priority of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The notification unit can select the optimal notification method when a notification is sent, taking into account the user's device information. For example, if the user is using a smartphone, the notification unit will send a push notification. If the user is using a tablet, the notification unit can also send an in-app notification. The notification unit uses a generation AI to select the optimal notification method, taking into account the user's device information. For example, if the user is using a smartwatch, the notification unit will send a vibration notification. This allows the notification unit to select the optimal notification method based on the user's device information. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's device information into a generation AI and have the generation AI perform the selection of the notification method.

[0096] The notification unit can provide highly relevant notifications by considering the user's geographical location information. For example, if the user is in a specific region, the notification unit will provide notifications related to that region. If the user is traveling, the notification unit can also provide notifications related to the travel destination. The notification unit uses a generation AI to provide highly relevant notifications by considering the user's geographical location information. For example, if the user is at home, the notification unit will provide information about the area around their home. This allows the notification unit to provide highly relevant notifications based on the user's geographical location information. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the user's geographical location data into a generation AI and have the generation AI execute highly relevant notifications.

[0097] The image analysis unit can estimate the user's emotions and adjust the accuracy of the image analysis based on the estimated emotions. For example, if the user is stressed, the image analysis unit can perform a simplified analysis to reduce the user's burden. If the user is relaxed, the image analysis unit can also perform a detailed analysis to improve accuracy. The image analysis unit uses generative AI to estimate the user's emotions and adjusts the accuracy of the image analysis based on those emotions. For example, the image analysis unit uses facial expression recognition technology to estimate the user's emotions and adjusts the accuracy of the image analysis based on the emotion score. This allows the accuracy of the image analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0098] The image analysis unit can improve the accuracy of image analysis by referring to the user's past meal data. For example, the image analysis unit can improve the accuracy of analyzing similar meals based on the user's past meal data. The image analysis unit can also more accurately identify ingredients by referring to the user's past meal data. The image analysis unit can improve the accuracy of analysis by referring to the user's past meal data using a generating AI. For example, the image analysis unit can analyze the user's past meal data to improve the accuracy of estimating nutritional value and calories. This allows the image analysis unit to improve accuracy based on the user's past meal data. Some or all of the above processes in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input the user's past meal data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0099] The image analysis unit can apply different analysis algorithms depending on the type of meal the user ate during image analysis. For example, if the user ate Japanese food, the image analysis unit will apply an analysis algorithm specialized for Japanese food. If the user ate Western food, the image analysis unit can also apply an analysis algorithm specialized for Western food. The image analysis unit uses a generation AI to apply different analysis algorithms depending on the type of meal the user ate. For example, if the user ate Chinese food, the image analysis unit will apply an analysis algorithm specialized for Chinese food. This allows the optimal analysis algorithm to be applied according to the type of meal the user ate. Some or all of the above processing in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input the user's meal type data into the generation AI and have the generation AI execute the application of the analysis algorithm.

[0100] The image analysis unit can estimate the user's emotions and determine the priority of image analysis based on the estimated emotions. For example, if the user is stressed, the image analysis unit will prioritize analyzing only important images. If the user is relaxed, the image analysis unit may also prioritize detailed image analysis. The image analysis unit uses generative AI to estimate the user's emotions and determines the priority of image analysis based on those emotions. For example, the image analysis unit uses facial expression recognition technology to estimate the user's emotions and determines the priority of image analysis based on the emotion score. This allows the priority of image analysis to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0101] The image analysis unit can improve analysis accuracy by considering the user's food photography environment during image analysis. For example, if the user takes a picture in a dark place, the image analysis unit can improve analysis accuracy by adjusting the brightness of the image. If the user takes a picture in a bright place, the image analysis unit can also improve analysis accuracy by adjusting the contrast of the image. The image analysis unit uses a generation AI to improve analysis accuracy by considering the user's food photography environment. For example, if the user takes a picture outdoors, the image analysis unit can improve analysis accuracy by removing background noise. This allows for improved analysis accuracy based on the user's food photography environment. Some or all of the above processing in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input the user's shooting environment data into a generation AI and have the generation AI perform the improvement of analysis accuracy.

[0102] The image analysis unit can be equipped with a function to automatically measure the amount of food a user eats during image analysis. For example, the image analysis unit can automatically measure the amount of food from an image taken by the user. If the user uploads multiple images, the image analysis unit can also measure the amount of food from each image and sum them up. The image analysis unit can be equipped with a function to automatically measure the amount of food a user eats using a generating AI. For example, if the user manually inputs the amount of food, the image analysis unit can use that data to improve the accuracy of the automatic measurement. This allows the user's amount of food to be measured automatically. Some or all of the above processing in the image analysis unit may be performed using AI or not. For example, the image analysis unit can input image data of the user's meal into a generating AI and have the generating AI perform the measurement of the amount of food.

[0103] The recording unit can estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, if the user is stressed, the recording unit can provide a simple recording method. If the user is relaxed, the recording unit can also provide a detailed recording method. The recording unit uses generative AI to estimate the user's emotions and adjust the recording method based on those emotions. For example, the recording unit uses facial recognition technology to estimate the user's emotions and adjusts the recording method based on the emotion score. This allows the recording method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0104] The recording unit can select the optimal recording method by referring to the user's past data during recording. For example, the recording unit can suggest the optimal recording method based on the recording methods the user has used in the past. The recording unit can also analyze the user's past data and adjust the frequency and method of recording. The recording unit uses a generative AI to select the optimal recording method by referring to the user's past data. For example, the recording unit can improve the accuracy of recording based on the user's past data. This allows the recording unit to select the optimal recording method based on the user's past data. Some or all of the above processes in the recording unit may be performed using AI or not. For example, the recording unit can input the user's past data into a generative AI and have the generative AI perform the selection of a recording method.

[0105] The recording unit can adjust the frequency of data recording while taking into account the user's daily rhythm. For example, the recording unit can adjust the timing of recording to match the user's daily rhythm. The recording unit can also reduce the frequency of recording during busy times for the user. The recording unit uses a generation AI to adjust the frequency of data recording while taking into account the user's daily rhythm. For example, the recording unit can increase the frequency of recording during times when the user is relaxed. This allows the frequency of data recording to be adjusted according to the user's daily rhythm. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's daily rhythm data into a generation AI and have the generation AI perform the adjustment of the recording frequency.

[0106] The recording unit can estimate the user's emotions and prioritize recorded data based on the estimated emotions. For example, if the user is stressed, the recording unit will prioritize recording only important data. If the user is relaxed, the recording unit can also prioritize recording detailed data. The recording unit uses generative AI to estimate the user's emotions and prioritize recorded data based on those emotions. For example, the recording unit uses facial recognition technology to estimate the user's emotions and prioritizes recorded data based on the emotion score. This allows the recording unit to prioritize recorded data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0107] The recording unit can select the optimal recording method while considering the user's device information. For example, if the user is using a smartphone, the recording unit can provide an in-app recording method. If the user is using a tablet, the recording unit can also provide a recording method optimized for a larger screen. The recording unit uses a generation AI to select the optimal recording method while considering the user's device information. For example, if the user is using a smartwatch, the recording unit can provide a concise and highly visible recording method. This allows the optimal recording method to be selected based on the user's device information. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's device information into a generation AI and have the generation AI perform the selection of the recording method.

[0108] The recording unit can record highly relevant data while considering the user's geographical location information. For example, if the user is in a specific region, the recording unit will prioritize recording data related to that region. If the user is traveling, the recording unit can also prioritize recording data related to the travel destination. The recording unit uses a generation AI to record highly relevant data while considering the user's geographical location information. For example, if the user is at home, the recording unit will prioritize recording data around the user's home. This allows the recording of highly relevant data based on the user's geographical location information. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the user's geographical location data into a generation AI and have the generation AI perform the recording of highly relevant data.

[0109] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will offer simple and easy suggestions. If the user is relaxed, the suggestion unit can also offer more detailed suggestions. The suggestion unit uses generative AI to estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, the suggestion unit can use facial recognition technology to estimate the user's emotions and adjust the way it presents its suggestions based on the emotion score. This allows the suggestion unit to adjust the way it presents its suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0110] The suggestion unit can make optimal suggestions by referring to the user's past meal history when making suggestions. For example, the suggestion unit can suggest similar meals based on the user's past meal history. The suggestion unit can also analyze the user's past meal history and suggest nutritionally balanced meals. The suggestion unit uses generative AI to refer to the user's past meal history and make optimal suggestions. For example, the suggestion unit can refer to the user's past meal history to more accurately identify ingredients. This allows it to make optimal suggestions based on the user's past meal history. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's past meal history data into the generative AI and have the generative AI execute the suggestions.

[0111] The suggestion unit can customize its suggestions by taking into account the user's health condition. For example, if the user has a specific health condition, the suggestion unit can suggest a meal suitable for that condition. The suggestion unit can also suggest a nutritionally balanced meal based on the user's health condition. The suggestion unit uses generative AI to customize the suggestions by taking into account the user's health condition. For example, the suggestion unit updates the user's health condition in real time and customizes the suggestions based on that. This allows the suggestions to be customized based on the user's health condition. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's health condition data into the generative AI and have the generative AI perform the customization of the suggestions.

[0112] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize only important suggestions. If the user is relaxed, the suggestion unit may also prioritize detailed suggestions. The suggestion unit uses generative AI to estimate the user's emotions and prioritize suggestions based on those emotions. For example, the suggestion unit uses facial recognition technology to estimate the user's emotions and prioritizes suggestions based on the emotion score. This allows the suggestion unit to prioritize suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial data into a generative AI and have the generative AI perform emotion estimation.

[0113] The suggestion unit can make optimal suggestions by considering the user's geographical location information when making suggestions. For example, if the user is in a specific region, the suggestion unit will make suggestions related to that region. If the user is traveling, the suggestion unit can also make suggestions related to the travel destination. The suggestion unit uses generative AI to make optimal suggestions by considering the user's geographical location information. For example, if the user is at home, the suggestion unit will make suggestions based on information about the area around their home. This allows the suggestion unit to make optimal suggestions based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's geographical location data into the generative AI and have the generative AI execute the suggestions.

[0114] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can customize suggestions based on food information shared by the user on social media. The suggestion unit can also analyze the user's food preferences and tastes from their social media activity and make suggestions based on that. The suggestion unit can use generative AI to analyze the user's social media activity and make relevant suggestions. For example, the suggestion unit can customize suggestions based on information from health-related accounts that the user follows on social media. This allows the suggestion unit to make relevant suggestions based on the user's social media activity. Some or all of the above processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's social media data into a generative AI and have the generative AI execute the suggestions.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring frequency can be reduced to lessen the user's burden. If the user is relaxed, the monitoring frequency can be increased to collect more detailed data. The monitoring unit uses facial recognition technology to estimate the user's emotions and adjusts the monitoring frequency based on the emotion score. This allows the monitoring frequency to be adjusted according to the user's emotions.

[0117] The recipe generation unit can suggest recipes while considering the user's ingredient inventory. For example, it can suggest the most suitable recipe based on the ingredients the user has. It can also list ingredients the user is missing and suggest alternative ingredients. The recipe generation unit updates the user's ingredient inventory in real time and suggests recipes based on that information. This allows it to suggest the most suitable recipe based on the user's ingredient inventory.

[0118] The image analysis unit can estimate the user's emotions and adjust the accuracy of the image analysis based on the estimated emotions. For example, if the user is stressed, a simplified analysis can be performed to reduce the user's burden. If the user is relaxed, a more detailed analysis can be performed to improve accuracy. The image analysis unit estimates the user's emotions using facial recognition technology and adjusts the accuracy of the image analysis based on the emotion score. This allows the accuracy of the image analysis to be adjusted according to the user's emotions.

[0119] The recording unit can estimate the user's emotions and adjust the recording method based on the estimated emotions. For example, if the user is stressed, it can provide a simple recording method. If the user is relaxed, it can also provide a more detailed recording method. The recording unit estimates the user's emotions using facial recognition technology and adjusts the recording method based on the emotion score. This allows the recording method to be adjusted according to the user's emotions.

[0120] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, it can offer simple and easy suggestions. If the user is relaxed, it can offer more detailed suggestions. The suggestion unit uses facial recognition technology to estimate the user's emotions and adjusts the way it presents suggestions based on the emotion score. This allows the suggestion unit to adjust its presentation according to the user's emotions.

[0121] The monitoring unit can analyze the user's past dieting history and select the optimal monitoring method. For example, it can suggest a similar monitoring method based on the user's past successful dieting methods. It can also suggest a different monitoring method to help the user avoid past unsuccessful dieting methods. The monitoring unit analyzes the user's past data to determine the most effective monitoring frequency. This allows it to select the optimal monitoring method based on the user's past dieting history.

[0122] The recipe generation unit can customize recipes by taking into account the user's allergy information during the recipe generation process. For example, it can suggest recipes that exclude ingredients the user is allergic to. It can also suggest recipes using alternative ingredients based on the user's allergy information. The recipe generation unit updates the user's allergy information in real time and customizes recipes based on that information. This allows recipes to be customized based on the user's allergy information.

[0123] The notification unit can select the most appropriate notification method, taking into account the user's schedule. For example, it can adjust the timing of notifications to match the user's schedule. It can also provide concise notifications during busy periods and detailed notifications during relaxed periods. This allows the system to select the most suitable notification method according to the user's schedule.

[0124] The image analysis unit can apply different analysis algorithms depending on the type of meal the user ate. For example, if the user ate Japanese food, it can apply an analysis algorithm specifically for Japanese food. If the user ate Western food, it can also apply an analysis algorithm specifically for Western food. If the user ate Chinese food, the image analysis unit can apply an analysis algorithm specifically for Chinese food. This allows the system to apply the most suitable analysis algorithm for the type of meal the user ate.

[0125] The suggestion department can customize the suggestions based on the user's health condition. For example, if a user has a specific health condition, it can suggest meals suitable for that condition. It can also suggest nutritionally balanced meals based on the user's health condition. The suggestion department updates the user's health condition in real time and customizes the suggestions accordingly. This allows for customized suggestions based on the user's health condition.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The monitoring unit monitors the user's diet progress. Specifically, it collects data such as the user's weight, body fat percentage, and calorie intake. Users input their weight daily and upload pictures of their meals, allowing the monitoring unit to track their diet progress. Step 2: The recipe generation unit generates diet recipes in real time based on the data collected by the monitoring unit. For example, if the user is approaching their target weight, it will suggest a low-calorie recipe. It can also suggest a recipe with a nutritional balance appropriate to the amount of exercise the user has done. The recipe generation unit uses a generation AI to create optimal meals tailored to the user's diet progress. Step 3: The notification unit notifies users daily of the recipes generated by the recipe generation unit. The notification unit notifies users of the recipes via methods such as email, push notifications, and SMS. Furthermore, the notification unit can use AI to select the optimal notification method according to the user's schedule and preferences.

[0128] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0129] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0130] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0131] Each of the multiple elements described above, including the monitoring unit, recipe generation unit, notification unit, image analysis unit, recording unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit collects the user's weight and meal content using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A analyzes the data. The recipe generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a recipe according to the user's diet progress. The notification unit notifies the user of the recipe using the output device 40 of the smart device 14. The image analysis unit analyzes images of meals using the camera 42 of the smart device 14 and calculates nutritional value and calories. The recording unit records the data analyzed by the specific processing unit 290 of the data processing unit 12. The suggestion unit proposes an optimal meal plan to the user using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] As shown in Figure 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.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the monitoring unit, recipe generation unit, notification unit, image analysis unit, recording unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit collects the user's weight and meal content using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A analyzes the data. The recipe generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a recipe according to the user's diet progress. The notification unit notifies the user of the recipe using the speaker 240 of the smart glasses 214. The image analysis unit analyzes images of meals using the camera 42 of the smart glasses 214 and calculates nutritional value and calories. The recording unit records the data analyzed by the specific processing unit 290 of the data processing unit 12. The suggestion unit proposes an optimal meal plan to the user using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the monitoring unit, recipe generation unit, notification unit, image analysis unit, recording unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit collects the user's weight and meal content using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A analyzes the data. The recipe generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a recipe according to the user's diet progress. The notification unit notifies the user of the recipe using the speaker 240 of the headset terminal 314. The image analysis unit analyzes images of meals using the camera 42 of the headset terminal 314 and calculates nutritional value and calories. The recording unit records the data analyzed by the specific processing unit 290 of the data processing unit 12. The suggestion unit proposes an optimal meal plan to the user using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0168] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0170] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0171] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0172] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] Each of the multiple elements described above, including the monitoring unit, recipe generation unit, notification unit, image analysis unit, recording unit, and suggestion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit collects the user's weight and meal content using the camera 42 and microphone 238 of the robot 414, and the control unit 46A analyzes the data. The recipe generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a recipe according to the user's diet progress. The notification unit notifies the user of the recipe using the speaker 240 of the robot 414. The image analysis unit analyzes images of the meal using the camera 42 of the robot 414 and calculates the nutritional value and calories. The recording unit records the data analyzed by the specific processing unit 290 of the data processing unit 12. The suggestion unit proposes an optimal meal plan to the user using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0181] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0183] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0184] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0185] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0189] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0190] 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.

[0191] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0192] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0193] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0194] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0196] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0197] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0198] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0199] (Note 1) A monitoring unit that monitors the user's diet progress, A recipe generation unit that generates diet recipes in real time based on data collected by the monitoring unit, The system includes a notification unit that notifies the user daily of the recipes generated by the recipe generation unit. A system characterized by the following features. (Note 2) The recipe generation unit, We suggest the best prepared foods available at the store. The system described in Appendix 1, characterized by the features described herein. (Note 3) Equipped with an image analysis unit, The aforementioned image analysis unit, The system analyzes images of meals the user has eaten to determine the nutritional value, calories, and macronutrient balance (PFC balance) of those meals. The system described in Appendix 1, characterized by the features described herein. (Note 4) Equipped with a recording unit, The aforementioned recording unit is Based on the data analyzed by the aforementioned image analysis unit, the system records the calories and nutrients consumed. The system described in Appendix 3, characterized by the features described herein. (Note 5) Equipped with a proposal department, The aforementioned proposal section is, We propose personalized meals that take into account the user's gender, age, dietary preferences, target weight, health status, exercise level, and optimal macronutrient balance (PFC balance). The system described in Appendix 1, characterized by the features described herein. (Note 6) The monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The monitoring unit, Analyze the user's past dieting history and select the optimal monitoring method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The monitoring unit, During monitoring, data is collected while taking into account the user's lifestyle and stress level. The system described in Appendix 1, characterized by the features described herein. (Note 9) The monitoring unit, It estimates user sentiment and prioritizes monitoring data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The monitoring unit, During monitoring, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The monitoring unit, During monitoring, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The recipe generation unit, The system estimates the user's emotions and adjusts the way recipes are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The recipe generation unit, When generating recipes, the system suggests recipes while taking into account the user's current ingredient inventory. The system described in Appendix 1, characterized by the features described herein. (Note 14) The recipe generation unit, When generating a recipe, customize it to take into account the user's allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The recipe generation unit, The system estimates the user's emotions and adjusts the difficulty level of recipes based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The recipe generation unit, When generating recipes, the system suggests recipes that take into account the user's food preferences and past ratings. The system described in Appendix 1, characterized by the features described herein. (Note 17) The recipe generation unit, When generating recipes, the nutritional balance is adjusted based on the user's activity level. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, When sending notifications, the system will select the most suitable notification method, taking the user's schedule into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When sending notifications, the system analyzes the user's past responses to customize the notification content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When sending notifications, the system takes the user's geographical location into consideration to deliver more relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned image analysis unit, It estimates the user's emotions and adjusts the accuracy of image analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned image analysis unit, During image analysis, the system improves analysis accuracy by referencing the user's past meal data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned image analysis unit, During image analysis, different analysis algorithms are applied depending on the type of meal the user is eating. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned image analysis unit, It estimates the user's emotions and determines the priority of image analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned image analysis unit, When analyzing images, we improve the accuracy of the analysis by taking into account the environment in which the user's meal was photographed. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned image analysis unit, We will add a feature that automatically measures the amount of food the user eats during image analysis. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned recording unit is The system estimates the user's emotions and adjusts the recording method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned recording unit is During recording, the system selects the optimal recording method by referring to the user's past data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned recording unit is During recording, the frequency of data recording is adjusted to take into account the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned recording unit is The system estimates the user's emotions and prioritizes recorded data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned recording unit is During recording, the optimal recording method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned recording unit is During recording, the system takes the user's geographical location into consideration to record the most relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned proposal section is, When making suggestions, the system refers to the user's past meal history to provide the most suitable recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned proposal section is, When making a proposal, customize the proposal content to take the user's health condition into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned proposal section is, When making a proposal, we take the user's geographical location into consideration to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A monitoring unit that monitors the user's diet progress, A recipe generation unit that generates diet recipes in real time based on data collected by the monitoring unit, The system includes a notification unit that notifies the user daily of the recipes generated by the recipe generation unit. A system characterized by the following features.

2. The recipe generation unit, We suggest the best prepared foods available at the store. The system according to feature 1.

3. Equipped with an image analysis unit, The aforementioned image analysis unit, The system analyzes images of meals the user has eaten to determine the nutritional value, calories, and macronutrient balance (PFC balance) of those meals. The system according to feature 1.

4. Equipped with a recording unit, The aforementioned recording unit is Based on the data analyzed by the aforementioned image analysis unit, the system records the calories and nutrients consumed. The system according to claim 3.

5. Equipped with a proposal department, The aforementioned proposal section is, We propose personalized meals that take into account the user's gender, age, dietary preferences, target weight, health status, exercise level, and optimal macronutrient balance (PFC balance). The system according to feature 1.

6. The monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system according to feature 1.

7. The monitoring unit, Analyze the user's past dieting history and select the optimal monitoring method. The system according to feature 1.

8. The monitoring unit, During monitoring, data is collected while taking into account the user's lifestyle and stress level. The system according to feature 1.

9. The monitoring unit, It estimates user sentiment and prioritizes monitoring data based on the estimated user sentiment. The system according to feature 1.

10. The monitoring unit, During monitoring, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system according to feature 1.

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