system

Abstract

The system according to this embodiment aims to analyze the nutritional balance of a meal and support individual health management. [Solution] The system according to the embodiment comprises an analysis unit, an advice unit, a checking unit, and a provision unit. The analysis unit analyzes a photograph of a meal. The advice unit provides advice on nutritional balance and points for improvement based on the results analyzed by the analysis unit. The checking unit checks the user's health status. The provision unit provides personalized advice based on the data obtained by the checking unit.

Description

Technical Field

[0006]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to easily grasp the nutritional balance of meals and perform individual health management.

[0005] The system according to the embodiment aims to analyze the nutritional balance of meals and support individual health management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, an advice unit, a checking unit, and a provision unit. The analysis unit analyzes a photograph of a meal. The advice unit provides advice on nutritional balance and areas for improvement based on the results analyzed by the analysis unit. The checking unit checks the user's health status. The provision unit provides personalized advice based on the data obtained by the checking unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the nutritional balance of a meal and support individual health management. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied 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 health management system according to an embodiment of the present invention is a free app called "Smart Health Log" that allows users to easily record their daily meals and health status, and an AI agent called "Smart Health Buddy" supports individual health management. With this health management system, when a user takes a picture of their meal and uploads it, the AI ​​analyzes the meal content and advises on nutritional balance and areas for improvement. The user also records their health status by answering simple questions (e.g., sleep duration, stress level, exercise level). Based on this, the AI ​​suggests improvements to nutrition and lifestyle habits. The AI ​​agent "Smart Health Buddy" provides nutritional advice in real time and gives immediate feedback based on the analysis results of the meal photos. For example, it may make specific suggestions such as, "You seem to be lacking in vitamin C. Try adding fruit to your next meal!" It also analyzes past data and makes suggestions tailored to the user's health status. For example, it may make suggestions such as, "You seem to be accumulating fatigue recently. Try to get more rest early." Furthermore, it presents a daily meal plan based on the user's preferences and nutritional status. For example, it may make suggestions such as, "How about 'Salmon and Vegetable Foil Bake' tonight? It's a well-balanced menu." In addition, the app will support continued use by integrating gamification. For example, it will provide motivational features such as, "You're just one day away from achieving a week-long streak! Let's keep it up and build a great healthy habit!" This app uses natural language processing (NLP) to enable natural conversations with users and employs a generative AI-based model to generate personalized responses. It also utilizes image recognition technology to analyze the nutrients in food photos and integrates and analyzes food data, health status data, and user behavior logs. Furthermore, it will achieve scalable data processing by utilizing cloud infrastructure. The development steps for this app will begin with creating a prototype and integrating basic AI agent dialogue functions with image recognition technology. Next, the accuracy of dialogue and advice will be improved by incorporating user feedback. Finally, it will be released as a freemium model, with paid features being introduced gradually.The target audience includes health-conscious individuals in their 20s to 40s (especially women), those who want to support their family's health management, individuals interested in diet and nutrition management, and healthcare professionals and researchers (from a data provision perspective). The monetization strategy will involve a premium plan (offering advanced AI reports, expert consultations, and personalized plans), provision of anonymized data, and advertising revenue (displaying advertisements for health foods and nutritional supplements). Expected effects include improved user satisfaction, increased user numbers, and diversified revenue streams. The AI ​​agent will provide 24 / 7 personal coaching support, and the gamification and engaging AI suggestions will improve user retention. Furthermore, stable profits will be achieved through data sales, advertising, and premium plan revenue models. This will enable the health management system to support users' daily health management and encourage continued use.

[0029] The health management system according to this embodiment comprises an analysis unit, an advice unit, a checking unit, and a provision unit. The analysis unit analyzes photographs of meals. The analysis unit analyzes photographs of meals using, for example, image recognition technology and extracts information on nutrients. The analysis unit can analyze photographs of meals using, for example, deep learning and estimate the content of each nutrient. The analysis unit can also analyze photographs of meals and identify the types and quantities of ingredients. The advice unit provides advice on nutritional balance and points for improvement based on the results analyzed by the analysis unit. The advice unit can, for example, suggest a balanced meal if the nutritional balance is skewed. The advice unit can also suggest ingredients containing a particular nutrient if that nutrient is deficient. The advice unit can also, for example, specifically indicate points for improving the diet and provide advice that is easy for the user to implement. The checking unit checks the user's health status. The checking unit analyzes health data entered by the user (e.g., weight, blood pressure, blood glucose level) and evaluates the health status. The checking unit can also identify health risks based on the user's health status. The checking unit can, for example, periodically monitor the user's health status and issue an alert if an abnormality is detected. The provisioning unit provides personalized advice based on the data obtained by the checking unit. The provisioning unit can, for example, propose a meal plan tailored to the user's health status. The provisioning unit can also, for example, present a specific action plan based on the user's health goals. The provisioning unit can also, for example, provide advice tailored to the user's preferences and lifestyle. As a result, the health management system according to this embodiment can comprehensively manage the user's health status and provide individualized advice.

[0030] The analysis unit analyzes photographs of meals. For example, it uses image recognition technology to analyze the photos and extract nutritional information. Specifically, it can use a convolutional neural network (CNN) as the image recognition technology. The CNN recognizes the types and shapes of ingredients from the meal photos and extracts the nutrients of each ingredient by referencing a database. Furthermore, by using deep learning, it can analyze the meal photos and estimate the content of each nutrient with high accuracy. For example, it can analyze the amount of ingredients in the meal photo at the pixel level and calculate the content of nutrients such as calories, protein, fat, and carbohydrates. It can also identify the types and quantities of ingredients by analyzing the meal photos. For example, it can identify ingredients such as vegetables, meat, and fish in the photo and estimate their respective quantities. This allows the analysis unit to understand the nutritional balance of the meals consumed by the user in detail. Furthermore, the analysis unit can use not only meal photos but also meal content and recipe information entered by the user for analysis. This allows for the extraction of more accurate nutritional information and a comprehensive evaluation of the user's diet. The analysis department centrally manages this data and collaborates with other departments to support users' health management.

[0031] The advice department provides advice on nutritional balance and areas for improvement based on the results analyzed by the analysis department. Specifically, it evaluates the user's diet based on the nutrient information provided by the analysis department and proposes a balanced diet. For example, if the user's diet is heavy in carbohydrates, the advice department will suggest adding foods rich in protein and vegetables. If a specific nutrient is deficient, it can also suggest foods rich in that nutrient. For example, if there is a vitamin C deficiency, the advice department will advise consuming foods rich in vitamin C, such as oranges and broccoli. Furthermore, the advice department provides specific points for dietary improvement and offers advice that is easy for the user to implement. For example, it provides specific advice on meal quantity, timing, and cooking methods to support the user in putting these changes into practice in their daily life. The advice department can also provide advice tailored to the user's preferences and lifestyle. For example, if the user is busy, it will suggest healthy recipes that are easy to prepare. In this way, the advice department can improve the user's diet and support a healthy lifestyle.

[0032] The monitoring unit checks the user's health status. Specifically, it analyzes health data entered by the user (e.g., weight, blood pressure, blood sugar levels) and evaluates their health status. For example, based on weight data regularly entered by the user, it graphs weight fluctuations to understand trends in their health status. It also analyzes blood pressure and blood sugar level data to determine if they are within the normal range. Based on this data, the monitoring unit can also identify the user's health risks. For example, if blood pressure is high, it notifies the user of the risk of hypertension and advises them to see a doctor. Furthermore, the monitoring unit can regularly monitor the user's health status and issue alerts if there are any abnormalities. For example, if weight increases or decreases rapidly, or if blood pressure rises sharply, it will alert the user and urge them to take action early. The monitoring unit centrally manages this data and comprehensively evaluates the user's health status. In addition to analyzing the user's health data, the monitoring unit can also consider information such as the user's lifestyle and diet to perform a more accurate health assessment. This allows the monitoring unit to understand the user's health status in detail and provide appropriate advice.

[0033] The service provider offers personalized advice based on data obtained by the checking department. Specifically, it proposes meal plans tailored to the user's health condition. For example, it creates and provides a healthy meal plan based on the user's weight, blood pressure, and blood sugar data. It can also present specific action plans based on the user's health goals. For example, it provides advice on calorie restriction and exercise to users who want to lose weight, and advice on low-sodium diets and stress management to users who want to lower their blood pressure. Furthermore, the service provider can provide advice tailored to the user's preferences and lifestyle. For example, if a user likes a particular ingredient, it will suggest a healthy recipe using that ingredient. Also, if a user is busy, it will provide a meal plan that can be prepared in a short time. Through this advice, the service provider supports users in leading a healthy life. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it adjusts the content of the next advice based on the results of the advice the user has followed. In this way, the service provider can provide users with the best possible advice and support their health management.

[0034] The real-time advice unit provides nutritional advice in real time. For example, the real-time advice unit can analyze a photo of a meal and provide advice on nutritional balance and areas for improvement on the spot. For example, when a photo of a meal is uploaded, the real-time advice unit can immediately display the analysis results and provide specific advice. For example, the real-time advice unit can also suggest nutrients to be consumed in the next meal based on the contents of the meal. This allows for immediate feedback to the user by providing nutritional advice in real time. Some or all of the above processing in the real-time advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the real-time advice unit can input a photo of a meal into a generative AI, and the generative AI can output the analysis results.

[0035] The monitoring unit performs health monitoring and proposes action plans. For example, the monitoring unit periodically collects user health data and monitors their health status. For example, the monitoring unit can analyze user health data and identify health risks. For example, the monitoring unit can also propose specific action plans based on the user's health status. In this way, it supports the user's health management by performing health monitoring and proposing action plans. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input user health data into a generative AI, which can then identify health risks and propose an action plan.

[0036] The menu suggestion unit provides customized menu suggestions. For example, the menu suggestion unit suggests a daily meal plan based on the user's preferences and nutritional status. For example, the menu suggestion unit can suggest a balanced menu according to the user's health condition. For example, the menu suggestion unit can also suggest a menu that does not contain allergens, taking into account the user's allergy information. In this way, by providing customized menu suggestions, it offers a meal plan that matches the user's preferences and nutritional status. Some or all of the above processing in the menu suggestion unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the menu suggestion unit can input the user's preferences and nutritional status into a generating AI, which can then suggest a customized menu.

[0037] The Motivation Unit provides motivation maintenance functions. For example, the Motivation Unit visualizes the user's progress in health management and provides a sense of accomplishment. For example, the Motivation Unit can provide rewards when the user achieves a goal. For example, the Motivation Unit can also assist the user in setting goals to continue health management. In this way, by providing motivation maintenance functions, it promotes continued use by the user. Some or all of the above processes in the Motivation Unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the Motivation Unit can input the user's health management progress into a generative AI, and the generative AI can provide feedback to give a sense of accomplishment.

[0038] The conversational unit uses natural language processing to enable natural conversations with the user. For example, the conversational unit generates natural responses to user input. For example, the conversational unit can provide appropriate answers to user questions. For example, the conversational unit can also provide advice on health management through dialogue with the user. In this way, natural conversations with the user are achieved by using natural language processing. Some or all of the above processing in the conversational unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the conversational unit can input user input into a generative AI, and the generative AI can generate natural responses.

[0039] The image recognition unit performs nutritional analysis of food photos using image recognition technology. For example, the image recognition unit analyzes food photos and identifies the content of each nutrient. For example, the image recognition unit can analyze food photos using deep learning to identify the types and quantities of ingredients. For example, the image recognition unit can analyze food photos and evaluate the nutritional balance. In this way, nutritional analysis of food photos is performed by utilizing image recognition technology. Some or all of the above processing in the image recognition unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the image recognition unit can input a food photo into a generative AI, and the generative AI can identify the content of nutrients.

[0040] The data integration unit integrates and analyzes meal data, health status data, and user activity logs. For example, the data integration unit integrates meal data and health status data to evaluate the user's nutritional status. For example, the data integration unit can analyze user activity logs to identify areas for improvement in health management. The data integration unit can also perform comprehensive health management by integrating meal data, health status data, and activity logs. This allows for comprehensive health management by integrating and analyzing meal data, health status data, and user activity logs. Some or all of the above processing in the data integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data integration unit can input meal data, health status data, and activity logs into a generative AI, which can then perform comprehensive health management.

[0041] The cloud processing unit performs scalable data processing using cloud infrastructure. For example, the cloud processing unit efficiently processes large amounts of data using cloud infrastructure. For example, the cloud processing unit can store and analyze data using cloud infrastructure. For example, the cloud processing unit can also back up and recover data using cloud infrastructure. In this way, scalable data processing is performed by utilizing cloud infrastructure. Some or all of the above processing in the cloud processing unit may be performed using, for example, generative AI, or without generative AI. For example, the cloud processing unit can use cloud infrastructure to enable generative AI to analyze data.

[0042] The analysis unit improves the accuracy of its analysis by referring to the user's past eating history when analyzing food photos. For example, the analysis unit compares the nutritional balance of the current meal with the nutritional balance of meals the user has consumed in the past and provides analysis results. For example, the analysis unit can analyze the intake trends of specific nutrients from the user's past eating history and point out any excess or deficiency of nutrients in the current meal. For example, the analysis unit can also refer to the user's past eating history and analyze changes in nutritional balance when the same ingredients are used. This improves the accuracy of the analysis by referring to the user's past eating history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the user's past eating history into a generating AI, which can then improve the accuracy of the analysis.

[0043] The analysis unit performs its analysis of food photographs while considering the origin and quality information of the ingredients. For example, the analysis unit analyzes differences in nutritional value based on the origin information of the ingredients and provides the results. For example, the analysis unit can analyze differences in nutritional value while considering quality information of the ingredients (organically grown, pesticide-free, etc.). For example, the analysis unit can also analyze health risks (pesticide residue, etc.) based on the origin and quality information of the ingredients and provide the results. By considering the origin and quality information of the ingredients, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the origin and quality information of the ingredients into a generating AI, and the generating AI can perform the analysis.

[0044] The analysis unit performs analysis of food photos while taking into account the user's allergy information. For example, the analysis unit identifies allergens contained in the food based on the user's allergy information and provides analysis results. For example, the analysis unit can suggest alternative ingredients that do not contain allergens, taking into account the user's allergy information. For example, the analysis unit can evaluate the safety of the food based on the user's allergy information and provide analysis results. This makes it possible to suggest ingredients that do not contain allergens by taking into account the user's allergy information. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's allergy information into a generating AI, and the generating AI can perform the analysis.

[0045] The analysis unit analyzes food photos while taking into account the user's food preferences and tastes. For example, the analysis unit can identify preferred ingredients from the user's past meal history and reflect this in the analysis results. For example, the analysis unit can suggest recipes using the user's preferred ingredients based on their preferences. For example, the analysis unit can also suggest meal plans using the user's preferred ingredients while maintaining nutritional balance, taking into account the user's food preferences. This provides more personalized analysis results by considering the user's food preferences and tastes. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's food preferences and tastes into a generative AI, which can then perform the analysis.

[0046] The advice unit customizes the content of the advice based on the user's health goals when providing advice. For example, the advice unit can provide advice on calorie restriction based on the user's health goal (e.g., weight loss). For example, the advice unit can provide advice on protein intake based on the user's health goal (e.g., muscle building). For example, the advice unit can suggest foods with relaxing effects based on the user's health goal (e.g., stress reduction). By customizing the content of the advice based on the user's health goals, more effective advice is provided. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit can input the user's health goals into a generative AI, which can then customize the content of the advice.

[0047] The advice unit, when providing advice, presents specific points for improvement based on the user's dietary history. For example, the advice unit can identify nutrient deficiencies or excesses from the user's past dietary history and present points for improvement. For example, the advice unit can propose a balanced meal plan based on the user's dietary history. For example, the advice unit can analyze the user's dietary history and suggest specific foods to increase the intake of certain nutrients. This provides more practical advice by presenting specific points for improvement based on the user's dietary history. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit can input the user's dietary history into a generative AI, which can then present specific points for improvement.

[0048] The advice unit provides advice while considering the user's lifestyle and habits. For example, the advice unit can suggest appropriate meal times based on the user's lifestyle (e.g., night owl). For example, the advice unit can provide exercise advice based on the user's lifestyle (e.g., desk work). For example, the advice unit can suggest healthy options when eating out based on the user's lifestyle (e.g., frequent eating out). This allows for more practical advice by considering the user's lifestyle and habits. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit can input the user's lifestyle and habits into a generative AI, which can then provide advice.

[0049] The advice unit provides nutritional balance advice while considering the user's exercise history. For example, the advice unit can provide advice on energy replenishment based on the user's exercise history (e.g., running). For example, the advice unit can provide advice on protein intake based on the user's exercise history (e.g., strength training). For example, the advice unit can suggest foods with relaxing effects based on the user's exercise history (e.g., yoga). By considering the user's exercise history, it can provide more appropriate nutritional balance advice. Some or all of the above processing in the advice unit may be performed using, for example, a generating AI, or without a generating AI. For example, the advice unit can input the user's exercise history into a generating AI, and the generating AI can provide nutritional balance advice.

[0050] The checking unit optimizes the check items by referring to the user's past health data when checking the user's health status. The checking unit can, for example, identify specific health risks from the user's past health data and add check items. The checking unit can, for example, omit unnecessary check items based on the user's past health data. The checking unit can also, for example, refer to the user's past health data and perform checks that focus on specific health indicators. In this way, the check items are optimized by referring to the user's past health data. Some or all of the above processing in the checking unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the checking unit can input the user's past health data into a generating AI, and the generating AI can optimize the check items.

[0051] The checking unit performs health status checks while considering the user's living environment and stress level. For example, the checking unit can add a stress check by considering the user's living environment (e.g., workplace stress). For example, the checking unit can perform a mental health check by considering the user's living environment (e.g., family situation). For example, the checking unit can also provide advice for stress reduction based on the user's stress level. This allows for a more appropriate health status check by considering the user's living environment and stress level. Some or all of the above processing in the checking unit may be performed using, for example, a generating AI, or without a generating AI. For example, the checking unit can input the user's living environment and stress level into a generating AI, which can then perform the check.

[0052] The checking unit performs health status checks while considering the user's family history and genetic information. For example, the checking unit may consider the user's family history (e.g., heart disease) and add a check for heart disease risk. For example, the checking unit may perform a check for diabetes risk based on the user's genetic information (e.g., diabetes risk). For example, the checking unit may refer to the user's family history and genetic information and perform checks that focus on specific health risks. This allows for more appropriate health status checks by considering the user's family history and genetic information. Some or all of the above processing in the checking unit may be performed using, for example, a generating AI, or without a generating AI. For example, the checking unit can input the user's family history and genetic information into a generating AI, which can then perform the checks.

[0053] The checking unit performs health status checks while considering the user's exercise habits and sleep patterns. For example, the checking unit considers the user's exercise habits (e.g., running three times a week) and performs health checks according to the amount of exercise. For example, the checking unit can add checks to evaluate sleep quality based on the user's sleep patterns (e.g., irregular sleep). For example, the checking unit can also optimize check items for evaluating health risks by referring to the user's exercise habits and sleep patterns. This allows for more appropriate health status checks by considering the user's exercise habits and sleep patterns. Some or all of the above processing in the checking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the checking unit can input the user's exercise habits and sleep patterns into a generative AI, which can then perform the checks.

[0054] The service provider optimizes the content of the advice provided by referring to the user's past advice history. For example, the service provider prioritizes providing advice that has been effective based on the user's past advice history. For example, the service provider can avoid repeating the same advice based on the user's past advice history. For example, the service provider can refer to the user's past advice history and focus on providing advice on areas where no improvement has been seen. In this way, the service provider optimizes the content of the advice provided by referring to the user's past advice history. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the service provider can input the user's past advice history into a generating AI, and the generating AI can optimize the content of the advice provided.

[0055] The service provider, when providing advice, presents a specific action plan based on the user's health goals. For example, the service provider can present a specific meal plan based on the user's health goal (e.g., weight loss). For example, the service provider can present a specific exercise plan based on the user's health goal (e.g., muscle strengthening). For example, the service provider can present a specific relaxation plan based on the user's health goal (e.g., stress reduction). By presenting a specific action plan based on the user's health goals, the service provider can provide more effective advice. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's health goals into a generative AI, which can then present a specific action plan.

[0056] The service provider takes into account the user's lifestyle and daily schedule when providing advice. For example, the service provider may suggest appropriate meal times based on the user's lifestyle (e.g., night owl). For example, the service provider may provide easy-to-implement advice based on the user's daily schedule (e.g., busy weekdays). For example, the service provider may also provide advice that can be implemented within a reasonable scope, taking into account the user's lifestyle and schedule. This allows for more practical advice by considering the user's lifestyle and daily schedule. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider may input the user's lifestyle and daily schedule into a generative AI, which can then provide advice.

[0057] The service provider integrates the user's dietary and exercise history to provide comprehensive advice. For example, the service provider integrates the user's dietary and exercise history to provide advice that considers the balance between nutritional balance and exercise volume. For example, the service provider can present a specific action plan for health improvement based on the user's dietary and exercise history. For example, the service provider can refer to the user's dietary and exercise history to evaluate their overall health status and provide advice. By integrating the user's dietary and exercise history, the service provider can provide more comprehensive advice. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's dietary and exercise history into a generative AI, which can then provide comprehensive advice.

[0058] The real-time advice unit monitors the user's current health status in real time and provides advice when offering real-time advice. For example, the real-time advice unit can monitor the user's current health status (e.g., heart rate) in real time and provide appropriate advice. For example, the real-time advice unit can evaluate health risks based on the user's current health status (e.g., blood pressure) and provide advice. For example, the real-time advice unit can monitor the user's current health status in real time and adjust the content of the advice as needed. This allows for the provision of more appropriate advice by monitoring the user's current health status in real time. Some or all of the above processing in the real-time advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the real-time advice unit can input the user's current health status data into a generative AI, which can then provide advice.

[0059] The real-time advice unit provides advice while considering the user's current activity status. For example, the real-time advice unit considers the user's current activity status (e.g., exercising) and provides appropriate advice. For example, the real-time advice unit can provide relaxing advice based on the user's current activity status (e.g., resting). For example, the real-time advice unit can refer to the user's current activity status and provide advice at the optimal time. This allows for the provision of more appropriate advice by considering the user's current activity status. Some or all of the above processing in the real-time advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the real-time advice unit can input the user's current activity status data into a generative AI, which can then provide advice.

[0060] The monitoring unit optimizes monitoring items by referring to the user's past health data during monitoring. For example, the monitoring unit can identify specific health risks from the user's past health data and add monitoring items. For example, the monitoring unit can omit unnecessary monitoring items based on the user's past health data. For example, the monitoring unit can refer to the user's past health data and perform monitoring that focuses on specific health indicators. This optimizes monitoring items by referring to the user's past health data. Some or all of the above processing in the monitoring unit may be performed using, for example, a generating AI, or without a generating AI. For example, the monitoring unit can input the user's past health data into a generating AI, which can then optimize the monitoring items.

[0061] The monitoring unit performs monitoring while considering the user's living environment and stress level. For example, the monitoring unit can add stress monitoring by considering the user's living environment (e.g., workplace stress). For example, the monitoring unit can perform mental health monitoring by considering the user's living environment (e.g., family situation). For example, the monitoring unit can also provide advice for stress reduction based on the user's stress level. This allows for more appropriate monitoring by considering the user's living environment and stress level. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the user's living environment and stress level into a generative AI, which can then perform the monitoring.

[0062] The menu suggestion unit optimizes its suggestions by referring to the user's past meal history. For example, the menu suggestion unit can suggest a menu that considers nutritional balance based on the user's past meal history. For example, the menu suggestion unit can suggest a menu that uses the same ingredients based on the user's past meal history. For example, the menu suggestion unit can also suggest a menu that supplements specific nutrients by referring to the user's past meal history. In this way, the suggestions are optimized by referring to the user's past meal history. Some or all of the above processing in the menu suggestion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the menu suggestion unit can input the user's past meal history into a generation AI, which can then optimize the suggestions.

[0063] The menu suggestion unit considers the user's food preferences and allergy information when suggesting menus. For example, the menu suggestion unit can suggest menus using the user's favorite ingredients. For example, the menu suggestion unit can suggest menus that do not contain allergens based on the user's allergy information. For example, the menu suggestion unit can suggest balanced menus considering the user's food preferences and allergy information. In this way, by considering the user's food preferences and allergy information, it can suggest more appropriate menus. Some or all of the above processing in the menu suggestion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the menu suggestion unit can input the user's food preferences and allergy information into a generation AI, and the generation AI can make suggestions.

[0064] The Motivation Unit selects the optimal motivation maintenance method by referring to the user's past behavioral history when maintaining motivation. For example, the Motivation Unit prioritizes providing motivation maintenance methods that have been effective based on the user's past behavioral history. For example, the Motivation Unit can prevent the user from repeating the same methods based on the user's past behavioral history. For example, the Motivation Unit can refer to the user's past behavioral history and provide motivation maintenance methods that focus on areas where no improvement was seen. In this way, the Motivation Unit selects the optimal motivation maintenance method by referring to the user's past behavioral history. Some or all of the above processing in the Motivation Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Motivation Unit can input the user's past behavioral history into a generative AI, and the generative AI can select the optimal method.

[0065] The Motivation Unit provides methods for maintaining motivation, taking into account the user's lifestyle and daily schedule. For example, the Motivation Unit can suggest appropriate motivation maintenance methods based on the user's lifestyle (e.g., night owl). For example, the Motivation Unit can provide easy-to-implement motivation maintenance methods based on the user's daily schedule (e.g., busy weekdays). For example, the Motivation Unit can also provide motivation maintenance methods that can be implemented within a reasonable scope, taking into account the user's lifestyle and schedule. This allows for the provision of more appropriate motivation maintenance methods by considering the user's lifestyle and daily schedule. Some or all of the above processing in the Motivation Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Motivation Unit can input the user's lifestyle and daily schedule into a generative AI, which can then provide maintenance methods.

[0066] The conversation unit achieves natural conversation by referring to the user's past conversation history during a conversation. For example, the conversation unit prioritizes providing conversation content that was effective based on the user's past conversation history. For example, the conversation unit can avoid repeating the same content based on the user's past conversation history. For example, the conversation unit can refer to the user's past conversation history and provide conversation content that focuses on areas where no improvement was seen. In this way, natural conversation is achieved by referring to the user's past conversation history. Data for the above processing in the conversation unit is collected. The monitoring unit identifies specific health risks and adds monitoring items based on the user's past health data. For example, the monitoring unit can omit unnecessary monitoring items based on the user's past health data. For example, the monitoring unit can refer to the user's past health data and perform monitoring that focuses on specific health indicators. In this way, monitoring items are optimized by referring to the user's past health data. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or without using generative AI. For example, the monitoring unit can input the user's past health data into a generating AI, which can then optimize the monitoring items.

[0067] The conversation unit conducts conversations while considering the user's lifestyle and daily schedule. For example, the conversation unit conducts conversations at appropriate times based on the user's lifestyle (e.g., night owl). For example, the conversation unit can provide conversation content that can be easily performed based on the user's daily schedule (e.g., busy weekdays). For example, the conversation unit can also provide conversation content that can be performed within a reasonable range, taking into account the user's lifestyle and schedule. This allows for more appropriate conversations by considering the user's lifestyle and daily schedule. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input the user's lifestyle and daily schedule into a generative AI, which can then conduct the conversation.

[0068] The image recognition unit improves recognition accuracy by referring to the user's past meal photos during image recognition. For example, the image recognition unit can identify the same ingredients from the user's past meal photos to improve recognition accuracy. For example, the image recognition unit can evaluate the nutritional value of a specific ingredient based on the user's past meal photos. The image recognition unit can also refer to the user's past meal photos and provide feedback to improve recognition accuracy. In this way, recognition accuracy is improved by referring to the user's past meal photos. Some or all of the above processing in the image recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the image recognition unit can input the user's past meal photos into a generative AI, which can then improve recognition accuracy.

[0069] The image recognition unit performs recognition while considering the user's food preferences and allergy information. For example, the image recognition unit may prioritize the recognition of the user's preferred foods. For example, the image recognition unit may prioritize the recognition of allergen-free foods based on the user's allergy information. For example, considering the user's food preferences and allergy information, the image recognition unit uses image recognition technology to perform nutritional analysis of food photos. For example, the image recognition unit analyzes food photos and identifies the content of each nutrient. For example, the image recognition unit may use deep learning to analyze food photos and identify the types and quantities of ingredients. For example, the image recognition unit may analyze food photos and evaluate the nutritional balance. In this way, nutritional analysis of food photos is performed by utilizing image recognition technology. Some or all of the above processing in the image recognition unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the image recognition unit can input a food photo into a generative AI, and the generative AI can identify the content of nutrients.

[0070] The data integration unit improves integration accuracy by referencing the user's past data during data integration. For example, the data integration unit can identify similar patterns from the user's past data to improve integration accuracy. For example, the data integration unit can evaluate specific data points based on the user's past data. The data integration unit can also provide feedback to improve integration accuracy by referencing the user's past data. This improves integration accuracy by referencing the user's past data. Some or all of the above processing in the data integration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data integration unit can input the user's past data into a generative AI, which can then improve integration accuracy.

[0071] The data integration unit performs data integration while considering the user's lifestyle and daily schedule. For example, the data integration unit integrates data at an appropriate time based on the user's lifestyle (e.g., night owl). For example, the data integration unit can provide a data integration method that can be easily executed based on the user's daily schedule (e.g., busy weekdays). For example, the data integration unit can also provide a data integration method that can be executed within a reasonable scope, taking into account the user's lifestyle and schedule. This allows for more appropriate data integration by considering the user's lifestyle and daily schedule. Some or all of the above processing in the data integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data integration unit can input the user's lifestyle and daily schedule into a generative AI, which can then perform the integration.

[0072] The cloud processing unit improves processing accuracy by referencing the user's past data during cloud processing. For example, the cloud processing unit can identify similar patterns from the user's past data and improve processing accuracy. For example, the cloud processing unit can evaluate specific data points based on the user's past data. The cloud processing unit can also refer to the user's past data and provide feedback to improve processing accuracy. This improves processing accuracy by referencing the user's past data. Some or all of the above processing in the cloud processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the cloud processing unit can input the user's past data into a generative AI, which can then improve processing accuracy.

[0073] The cloud processing unit considers the user's lifestyle and daily schedule when performing cloud processing. For example, the cloud processing unit performs cloud processing at the appropriate time based on the user's lifestyle (e.g., night owl). The cloud processing unit needs to clearly define easy-to-execute cloud processing methods based on the user's daily schedule (e.g., busy weekdays). For example, how to record meals, how to save data, etc.

[0074] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0075] The analysis unit can improve the accuracy of its analysis of food photos by referring to the user's past eating history. For example, it can compare the nutritional balance of the current meal with the nutritional balance of meals the user has eaten in the past and provide analysis results. It can also analyze the intake trends of specific nutrients from the user's past eating history and point out any excesses or deficiencies in the current meal's nutrients. Furthermore, it can refer to the user's past eating history and analyze how the nutritional balance changes when the same ingredients are used. In this way, the accuracy of the analysis can be improved by referring to the user's past eating history.

[0076] The advice function can customize the content of advice based on the user's health goals. For example, it can provide advice on calorie restriction based on the user's health goal (e.g., weight loss). It can also provide advice on protein intake based on the user's health goal (e.g., muscle building). Furthermore, it can suggest foods with relaxing effects based on the user's health goal (e.g., stress reduction). By customizing the advice based on the user's health goals, it can provide more effective advice.

[0077] The checking function can optimize check items by referring to the user's past health data during health status checks. For example, it can identify specific health risks from the user's past health data and add check items accordingly. It can also omit unnecessary check items based on the user's past health data. Furthermore, it can perform checks that focus on specific health indicators by referring to the user's past health data. In this way, the check items can be optimized by referring to the user's past health data.

[0078] The service provider can optimize the advice provided by referring to the user's past advice history. For example, it can prioritize providing advice that has been effective based on the user's past advice history. It can also avoid repeating the same advice based on the user's past advice history. Furthermore, it can refer to the user's past advice history and focus advice on areas where no improvement has been seen. In this way, the service provider can optimize the advice provided by referring to the user's past advice history.

[0079] The cloud processing unit can improve processing accuracy by referencing the user's past data during cloud processing. For example, it can identify similar patterns from the user's past data to improve processing accuracy. It can also evaluate specific data points based on the user's past data. Furthermore, it can provide feedback to improve processing accuracy by referencing the user's past data. In this way, processing accuracy can be improved by referencing the user's past data.

[0080] The following briefly describes the processing flow for example form 1.

[0081] Step 1: The analysis unit analyzes the photo of the meal. The analysis unit can, for example, use image recognition technology to analyze the photo of the meal and extract information about nutrients. The analysis unit can, for example, use deep learning to analyze the photo of the meal and estimate the amount of each nutrient. The analysis unit can, for example, analyze the photo of the meal and identify the type and quantity of ingredients. Step 2: The advice unit provides advice on nutritional balance and areas for improvement based on the results analyzed by the analysis unit. For example, if the nutritional balance is skewed, the advice unit will suggest a balanced diet. For example, if a specific nutrient is lacking, the advice unit can also suggest foods containing that nutrient. For example, the advice unit can also provide specific points for improving the diet and offer advice that is easy for the user to implement. Step 3: The checking unit checks the user's health status. The checking unit analyzes the health data entered by the user (e.g., weight, blood pressure, blood sugar level) and evaluates the user's health status. The checking unit can also identify health risks based on the user's health status. The checking unit can also periodically monitor the user's health status and issue alerts if any abnormalities are detected. Step 4: The service provider provides personalized advice based on the data obtained by the checking unit. For example, the service provider may suggest a meal plan tailored to the user's health condition. The service provider may also present a specific action plan based on the user's health goals. The service provider may also provide advice tailored to the user's preferences and lifestyle.

[0082] (Example of form 2) The health management system according to an embodiment of the present invention is a free app called "Smart Health Log" that allows users to easily record their daily meals and health status, and an AI agent called "Smart Health Buddy" supports individual health management. With this health management system, when a user takes a picture of their meal and uploads it, the AI ​​analyzes the meal content and advises on nutritional balance and areas for improvement. The user also records their health status by answering simple questions (e.g., sleep duration, stress level, exercise level). Based on this, the AI ​​suggests improvements to nutrition and lifestyle habits. The AI ​​agent "Smart Health Buddy" provides nutritional advice in real time and gives immediate feedback based on the analysis results of the meal photos. For example, it may make specific suggestions such as, "You seem to be lacking in vitamin C. Try adding fruit to your next meal!" It also analyzes past data and makes suggestions tailored to the user's health status. For example, it may make suggestions such as, "You seem to be accumulating fatigue recently. Try to get more rest early." Furthermore, it presents a daily meal plan based on the user's preferences and nutritional status. For example, it may make suggestions such as, "How about 'Salmon and Vegetable Foil Bake' tonight? It's a well-balanced menu." In addition, the app will support continued use by integrating gamification. For example, it will provide motivational features such as, "You're just one day away from achieving a week-long streak! Let's keep it up and build a great healthy habit!" This app uses natural language processing (NLP) to enable natural conversations with users and employs a generative AI-based model to generate personalized responses. It also utilizes image recognition technology to analyze the nutrients in food photos and integrates and analyzes food data, health status data, and user behavior logs. Furthermore, it will achieve scalable data processing by utilizing cloud infrastructure. The development steps for this app will begin with creating a prototype and integrating basic AI agent dialogue functions with image recognition technology. Next, the accuracy of dialogue and advice will be improved by incorporating user feedback. Finally, it will be released as a freemium model, with paid features being introduced gradually.The target audience includes health-conscious individuals in their 20s to 40s (especially women), those who want to support their family's health management, individuals interested in diet and nutrition management, and healthcare professionals and researchers (from a data provision perspective). The monetization strategy will involve a premium plan (offering advanced AI reports, expert consultations, and personalized plans), provision of anonymized data, and advertising revenue (displaying advertisements for health foods and nutritional supplements). Expected effects include improved user satisfaction, increased user numbers, and diversified revenue streams. The AI ​​agent will provide 24 / 7 personal coaching support, and the gamification and engaging AI suggestions will improve user retention. Furthermore, stable profits will be achieved through data sales, advertising, and premium plan revenue models. This will enable the health management system to support users' daily health management and encourage continued use.

[0083] The health management system according to this embodiment comprises an analysis unit, an advice unit, a checking unit, and a provision unit. The analysis unit analyzes photographs of meals. The analysis unit analyzes photographs of meals using, for example, image recognition technology and extracts information on nutrients. The analysis unit can analyze photographs of meals using, for example, deep learning and estimate the content of each nutrient. The analysis unit can also analyze photographs of meals and identify the types and quantities of ingredients. The advice unit provides advice on nutritional balance and points for improvement based on the results analyzed by the analysis unit. The advice unit can, for example, suggest a balanced meal if the nutritional balance is skewed. The advice unit can also suggest ingredients containing a particular nutrient if that nutrient is deficient. The advice unit can also, for example, specifically indicate points for improving the diet and provide advice that is easy for the user to implement. The checking unit checks the user's health status. The checking unit analyzes health data entered by the user (e.g., weight, blood pressure, blood glucose level) and evaluates the health status. The checking unit can also identify health risks based on the user's health status. The checking unit can, for example, periodically monitor the user's health status and issue an alert if an abnormality is detected. The provisioning unit provides personalized advice based on the data obtained by the checking unit. The provisioning unit can, for example, propose a meal plan tailored to the user's health status. The provisioning unit can also, for example, present a specific action plan based on the user's health goals. The provisioning unit can also, for example, provide advice tailored to the user's preferences and lifestyle. As a result, the health management system according to this embodiment can comprehensively manage the user's health status and provide individualized advice.

[0084] The analysis unit analyzes photographs of meals. For example, it uses image recognition technology to analyze the photos and extract nutritional information. Specifically, it can use a convolutional neural network (CNN) as the image recognition technology. The CNN recognizes the types and shapes of ingredients from the meal photos and extracts the nutrients of each ingredient by referencing a database. Furthermore, by using deep learning, it can analyze the meal photos and estimate the content of each nutrient with high accuracy. For example, it can analyze the amount of ingredients in the meal photo at the pixel level and calculate the content of nutrients such as calories, protein, fat, and carbohydrates. It can also identify the types and quantities of ingredients by analyzing the meal photos. For example, it can identify ingredients such as vegetables, meat, and fish in the photo and estimate their respective quantities. This allows the analysis unit to understand the nutritional balance of the meals consumed by the user in detail. Furthermore, the analysis unit can use not only meal photos but also meal content and recipe information entered by the user for analysis. This allows for the extraction of more accurate nutritional information and a comprehensive evaluation of the user's diet. The analysis department centrally manages this data and collaborates with other departments to support users' health management.

[0085] The advice department provides advice on nutritional balance and areas for improvement based on the results analyzed by the analysis department. Specifically, it evaluates the user's diet based on the nutrient information provided by the analysis department and proposes a balanced diet. For example, if the user's diet is heavy in carbohydrates, the advice department will suggest adding foods rich in protein and vegetables. If a specific nutrient is deficient, it can also suggest foods rich in that nutrient. For example, if there is a vitamin C deficiency, the advice department will advise consuming foods rich in vitamin C, such as oranges and broccoli. Furthermore, the advice department provides specific points for dietary improvement and offers advice that is easy for the user to implement. For example, it provides specific advice on meal quantity, timing, and cooking methods to support the user in putting these changes into practice in their daily life. The advice department can also provide advice tailored to the user's preferences and lifestyle. For example, if the user is busy, it will suggest healthy recipes that are easy to prepare. In this way, the advice department can improve the user's diet and support a healthy lifestyle.

[0086] The monitoring unit checks the user's health status. Specifically, it analyzes health data entered by the user (e.g., weight, blood pressure, blood sugar levels) and evaluates their health status. For example, based on weight data regularly entered by the user, it graphs weight fluctuations to understand trends in their health status. It also analyzes blood pressure and blood sugar level data to determine if they are within the normal range. Based on this data, the monitoring unit can also identify the user's health risks. For example, if blood pressure is high, it notifies the user of the risk of hypertension and advises them to see a doctor. Furthermore, the monitoring unit can regularly monitor the user's health status and issue alerts if there are any abnormalities. For example, if weight increases or decreases rapidly, or if blood pressure rises sharply, it will alert the user and urge them to take action early. The monitoring unit centrally manages this data and comprehensively evaluates the user's health status. In addition to analyzing the user's health data, the monitoring unit can also consider information such as the user's lifestyle and diet to perform a more accurate health assessment. This allows the monitoring unit to understand the user's health status in detail and provide appropriate advice.

[0087] The service provider offers personalized advice based on data obtained by the checking department. Specifically, it proposes meal plans tailored to the user's health condition. For example, it creates and provides a healthy meal plan based on the user's weight, blood pressure, and blood sugar data. It can also present specific action plans based on the user's health goals. For example, it provides advice on calorie restriction and exercise to users who want to lose weight, and advice on low-sodium diets and stress management to users who want to lower their blood pressure. Furthermore, the service provider can provide advice tailored to the user's preferences and lifestyle. For example, if a user likes a particular ingredient, it will suggest a healthy recipe using that ingredient. Also, if a user is busy, it will provide a meal plan that can be prepared in a short time. Through this advice, the service provider supports users in leading a healthy life. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it adjusts the content of the next advice based on the results of the advice the user has followed. In this way, the service provider can provide users with the best possible advice and support their health management.

[0088] The real-time advice unit provides nutritional advice in real time. For example, the real-time advice unit can analyze a photo of a meal and provide advice on nutritional balance and areas for improvement on the spot. For example, when a photo of a meal is uploaded, the real-time advice unit can immediately display the analysis results and provide specific advice. For example, the real-time advice unit can also suggest nutrients to be consumed in the next meal based on the contents of the meal. This allows for immediate feedback to the user by providing nutritional advice in real time. Some or all of the above processing in the real-time advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the real-time advice unit can input a photo of a meal into a generative AI, and the generative AI can output the analysis results.

[0089] The monitoring unit performs health monitoring and proposes action plans. For example, the monitoring unit periodically collects user health data and monitors their health status. For example, the monitoring unit can analyze user health data and identify health risks. For example, the monitoring unit can also propose specific action plans based on the user's health status. In this way, it supports the user's health management by performing health monitoring and proposing action plans. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input user health data into a generative AI, which can then identify health risks and propose an action plan.

[0090] The menu suggestion unit provides customized menu suggestions. For example, the menu suggestion unit suggests a daily meal plan based on the user's preferences and nutritional status. For example, the menu suggestion unit can suggest a balanced menu according to the user's health condition. For example, the menu suggestion unit can also suggest a menu that does not contain allergens, taking into account the user's allergy information. In this way, by providing customized menu suggestions, it offers a meal plan that matches the user's preferences and nutritional status. Some or all of the above processing in the menu suggestion unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the menu suggestion unit can input the user's preferences and nutritional status into a generating AI, which can then suggest a customized menu.

[0091] The Motivation Unit provides motivation maintenance functions. For example, the Motivation Unit visualizes the user's progress in health management and provides a sense of accomplishment. For example, the Motivation Unit can provide rewards when the user achieves a goal. For example, the Motivation Unit can also assist the user in setting goals to continue health management. In this way, by providing motivation maintenance functions, it promotes continued use by the user. Some or all of the above processes in the Motivation Unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the Motivation Unit can input the user's health management progress into a generative AI, and the generative AI can provide feedback to give a sense of accomplishment.

[0092] The conversational unit uses natural language processing to enable natural conversations with the user. For example, the conversational unit generates natural responses to user input. For example, the conversational unit can provide appropriate answers to user questions. For example, the conversational unit can also provide advice on health management through dialogue with the user. In this way, natural conversations with the user are achieved by using natural language processing. Some or all of the above processing in the conversational unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the conversational unit can input user input into a generative AI, and the generative AI can generate natural responses.

[0093] The image recognition unit performs nutritional analysis of food photos using image recognition technology. For example, the image recognition unit analyzes food photos and identifies the content of each nutrient. For example, the image recognition unit can analyze food photos using deep learning to identify the types and quantities of ingredients. For example, the image recognition unit can analyze food photos and evaluate the nutritional balance. In this way, nutritional analysis of food photos is performed by utilizing image recognition technology. Some or all of the above processing in the image recognition unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the image recognition unit can input a food photo into a generative AI, and the generative AI can identify the content of nutrients.

[0094] The data integration unit integrates and analyzes meal data, health status data, and user activity logs. For example, the data integration unit integrates meal data and health status data to evaluate the user's nutritional status. For example, the data integration unit can analyze user activity logs to identify areas for improvement in health management. The data integration unit can also perform comprehensive health management by integrating meal data, health status data, and activity logs. This allows for comprehensive health management by integrating and analyzing meal data, health status data, and user activity logs. Some or all of the above processing in the data integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data integration unit can input meal data, health status data, and activity logs into a generative AI, which can then perform comprehensive health management.

[0095] The cloud processing unit performs scalable data processing using cloud infrastructure. For example, the cloud processing unit efficiently processes large amounts of data using cloud infrastructure. For example, the cloud processing unit can store and analyze data using cloud infrastructure. For example, the cloud processing unit can also back up and recover data using cloud infrastructure. In this way, scalable data processing is performed by utilizing cloud infrastructure. Some or all of the above processing in the cloud processing unit may be performed using, for example, generative AI, or without generative AI. For example, the cloud processing unit can use cloud infrastructure to enable generative AI to analyze data.

[0096] The analysis unit estimates the user's emotions and adjusts the analysis method of the food photos based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a concise summary of the analysis results in a visually easy-to-understand format. For example, if the user is relaxed, the analysis unit can provide analysis results that include detailed nutritional information and background information on ingredients. For example, if the user is in a hurry, the analysis unit can provide a concise analysis result that highlights only the deficiencies or excesses of important nutrients. This allows for more appropriate analysis results by adjusting the analysis method of the food photos based on 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 analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the analysis method.

[0097] The analysis unit improves the accuracy of its analysis by referring to the user's past eating history when analyzing food photos. For example, the analysis unit compares the nutritional balance of the current meal with the nutritional balance of meals the user has consumed in the past and provides analysis results. For example, the analysis unit can analyze the intake trends of specific nutrients from the user's past eating history and point out any excess or deficiency of nutrients in the current meal. For example, the analysis unit can also refer to the user's past eating history and analyze changes in nutritional balance when the same ingredients are used. This improves the accuracy of the analysis by referring to the user's past eating history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the user's past eating history into a generating AI, which can then improve the accuracy of the analysis.

[0098] The analysis unit performs its analysis of food photographs while considering the origin and quality information of the ingredients. For example, the analysis unit analyzes differences in nutritional value based on the origin information of the ingredients and provides the results. For example, the analysis unit can analyze differences in nutritional value while considering quality information of the ingredients (organically grown, pesticide-free, etc.). For example, the analysis unit can also analyze health risks (pesticide residue, etc.) based on the origin and quality information of the ingredients and provide the results. By considering the origin and quality information of the ingredients, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the origin and quality information of the ingredients into a generating AI, and the generating AI can perform the analysis.

[0099] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit summarizes the analysis results concisely and provides them in a visually easy-to-understand format. For example, if the user is relaxed, the analysis unit can provide analysis results that include detailed nutritional information and background information on ingredients. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that highlight only the deficiencies or excesses of important nutrients. This allows for the provision of more appropriate information by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the display method.

[0100] The analysis unit performs analysis of food photos while taking into account the user's allergy information. For example, the analysis unit identifies allergens contained in the food based on the user's allergy information and provides analysis results. For example, the analysis unit can suggest alternative ingredients that do not contain allergens, taking into account the user's allergy information. For example, the analysis unit can evaluate the safety of the food based on the user's allergy information and provide analysis results. This makes it possible to suggest ingredients that do not contain allergens by taking into account the user's allergy information. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's allergy information into a generating AI, and the generating AI can perform the analysis.

[0101] The analysis unit analyzes food photos while taking into account the user's food preferences and tastes. For example, the analysis unit can identify preferred ingredients from the user's past meal history and reflect this in the analysis results. For example, the analysis unit can suggest recipes using the user's preferred ingredients based on their preferences. For example, the analysis unit can also suggest meal plans using the user's preferred ingredients while maintaining nutritional balance, taking into account the user's food preferences. This provides more personalized analysis results by considering the user's food preferences and tastes. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the user's food preferences and tastes into a generative AI, which can then perform the analysis.

[0102] The advice unit estimates the user's emotions and adjusts the way it expresses the advice based on the estimated emotions. For example, if the user is stressed, the advice unit will provide advice in gentle language. For example, if the user is relaxed, the advice unit may provide advice that includes detailed explanations. For example, if the user is in a hurry, the advice unit may provide concise and to-the-point advice. In this way, by adjusting the way the advice is expressed based on the user's emotions, more appropriate advice is provided. 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 advice unit may be performed using a generative AI, or not using a generative AI. For example, the advice unit can input user emotion data into a generative AI, which can then adjust the way it is expressed.

[0103] The advice unit customizes the content of the advice based on the user's health goals when providing advice. For example, the advice unit can provide advice on calorie restriction based on the user's health goal (e.g., weight loss). For example, the advice unit can provide advice on protein intake based on the user's health goal (e.g., muscle building). For example, the advice unit can suggest foods with relaxing effects based on the user's health goal (e.g., stress reduction). By customizing the content of the advice based on the user's health goals, more effective advice is provided. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit can input the user's health goals into a generative AI, which can then customize the content of the advice.

[0104] The advice unit, when providing advice, presents specific points for improvement based on the user's dietary history. For example, the advice unit can identify nutrient deficiencies or excesses from the user's past dietary history and present points for improvement. For example, the advice unit can propose a balanced meal plan based on the user's dietary history. For example, the advice unit can analyze the user's dietary history and suggest specific foods to increase the intake of certain nutrients. This provides more practical advice by presenting specific points for improvement based on the user's dietary history. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit can input the user's dietary history into a generative AI, which can then present specific points for improvement.

[0105] The advice unit estimates the user's emotions and adjusts the timing of advice based on the estimated emotions. For example, if the user is stressed, the advice unit will provide advice during a time when the user can relax. For example, if the user is relaxed, the advice unit may provide advice that includes detailed explanations. For example, if the user is in a hurry, the advice unit may provide concise and to-the-point advice. By adjusting the timing of advice based on the user's emotions, advice is provided at a more appropriate time. 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 advice unit may be performed using a generative AI, or not using a generative AI. For example, the advice unit can input user emotion data into a generative AI, which can then adjust the timing.

[0106] The advice unit provides advice while considering the user's lifestyle and habits. For example, the advice unit can suggest appropriate meal times based on the user's lifestyle (e.g., night owl). For example, the advice unit can provide exercise advice based on the user's lifestyle (e.g., desk work). For example, the advice unit can suggest healthy options when eating out based on the user's lifestyle (e.g., frequent eating out). This allows for more practical advice by considering the user's lifestyle and habits. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit can input the user's lifestyle and habits into a generative AI, which can then provide advice.

[0107] The advice unit provides nutritional balance advice while considering the user's exercise history. For example, the advice unit can provide advice on energy replenishment based on the user's exercise history (e.g., running). For example, the advice unit can provide advice on protein intake based on the user's exercise history (e.g., strength training). For example, the advice unit can suggest foods with relaxing effects based on the user's exercise history (e.g., yoga). By considering the user's exercise history, it can provide more appropriate nutritional balance advice. Some or all of the above processing in the advice unit may be performed using, for example, a generating AI, or without a generating AI. For example, the advice unit can input the user's exercise history into a generating AI, and the generating AI can provide nutritional balance advice.

[0108] The checking unit estimates the user's emotions and adjusts the frequency of health checks based on the estimated emotions. For example, if the user is stressed, the checking unit reduces the frequency to alleviate the burden. For example, if the user is relaxed, the checking unit can increase the frequency to collect more detailed data. For example, if the user is in a hurry, the checking unit can present only concise check items. This allows for health checks at a more appropriate frequency by adjusting the frequency of health checks based on 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 checking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the checking unit can input user emotion data into a generative AI, which can then adjust the frequency.

[0109] The checking unit optimizes the check items by referring to the user's past health data when checking the user's health status. The checking unit can, for example, identify specific health risks from the user's past health data and add check items. The checking unit can, for example, omit unnecessary check items based on the user's past health data. The checking unit can also, for example, refer to the user's past health data and perform checks that focus on specific health indicators. In this way, the check items are optimized by referring to the user's past health data. Some or all of the above processing in the checking unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the checking unit can input the user's past health data into a generating AI, and the generating AI can optimize the check items.

[0110] The checking unit performs health status checks while considering the user's living environment and stress level. For example, the checking unit can add a stress check by considering the user's living environment (e.g., workplace stress). For example, the checking unit can perform a mental health check by considering the user's living environment (e.g., family situation). For example, the checking unit can also provide advice for stress reduction based on the user's stress level. This allows for a more appropriate health status check by considering the user's living environment and stress level. Some or all of the above processing in the checking unit may be performed using, for example, a generating AI, or without a generating AI. For example, the checking unit can input the user's living environment and stress level into a generating AI, which can then perform the check.

[0111] The checking unit estimates the user's emotions and adjusts the display method of the check results based on the estimated emotions. For example, if the user is stressed, the checking unit displays the results in a concise and visually easy-to-understand format. For example, if the user is relaxed, the checking unit can display results that include detailed explanations. For example, if the user is in a hurry, the checking unit can display results that highlight only the important points. This allows for more appropriate information to be provided by adjusting the display method of the check results based on 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 checking unit may be performed using a generative AI, or not. For example, the checking unit can input user emotion data into a generative AI, which can then adjust the display method.

[0112] The checking unit performs health status checks while considering the user's family history and genetic information. For example, the checking unit may consider the user's family history (e.g., heart disease) and add a check for heart disease risk. For example, the checking unit may perform a check for diabetes risk based on the user's genetic information (e.g., diabetes risk). For example, the checking unit may refer to the user's family history and genetic information and perform checks that focus on specific health risks. This allows for more appropriate health status checks by considering the user's family history and genetic information. Some or all of the above processing in the checking unit may be performed using, for example, a generating AI, or without a generating AI. For example, the checking unit can input the user's family history and genetic information into a generating AI, which can then perform the checks.

[0113] The checking unit performs health status checks while considering the user's exercise habits and sleep patterns. For example, the checking unit considers the user's exercise habits (e.g., running three times a week) and performs health checks according to the amount of exercise. For example, the checking unit can add checks to evaluate sleep quality based on the user's sleep patterns (e.g., irregular sleep). For example, the checking unit can also optimize check items for evaluating health risks by referring to the user's exercise habits and sleep patterns. This allows for more appropriate health status checks by considering the user's exercise habits and sleep patterns. Some or all of the above processing in the checking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the checking unit can input the user's exercise habits and sleep patterns into a generative AI, which can then perform the checks.

[0114] The service provider estimates the user's emotions and adjusts the timing of advice delivery based on the estimated emotions. For example, if the user is feeling stressed, the service provider will provide advice during a time when the user can relax. For example, if the user is relaxed, the service provider may provide advice that includes detailed explanations. For example, if the user is in a hurry, the service provider may provide concise and to-the-point advice. By adjusting the timing of advice delivery based on the user's emotions, advice is provided at a more appropriate time. 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 service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI, which can then adjust the timing.

[0115] The service provider optimizes the content of the advice provided by referring to the user's past advice history. For example, the service provider prioritizes providing advice that has been effective based on the user's past advice history. For example, the service provider can avoid repeating the same advice based on the user's past advice history. For example, the service provider can refer to the user's past advice history and focus on providing advice on areas where no improvement has been seen. In this way, the service provider optimizes the content of the advice provided by referring to the user's past advice history. Some or all of the above processing in the service provider may be performed using, for example, a generating AI, or without using a generating AI. For example, the service provider can input the user's past advice history into a generating AI, and the generating AI can optimize the content of the advice provided.

[0116] The service provider, when providing advice, presents a specific action plan based on the user's health goals. For example, the service provider can present a specific meal plan based on the user's health goal (e.g., weight loss). For example, the service provider can present a specific exercise plan based on the user's health goal (e.g., muscle strengthening). For example, the service provider can present a specific relaxation plan based on the user's health goal (e.g., stress reduction). By presenting a specific action plan based on the user's health goals, the service provider can provide more effective advice. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's health goals into a generative AI, which can then present a specific action plan.

[0117] The service provider estimates the user's emotions and prioritizes advice based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize stress reduction advice. For example, if the user is relaxed, the service provider may prioritize health improvement advice. For example, if the user is in a hurry, the service provider may prioritize advice focusing only on the most important points. This allows for more appropriate advice to be provided by prioritizing advice based on 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 service provider may be performed using a generative AI, or not. For example, the service provider can input user emotion data into a generative AI, which can then determine the priorities.

[0118] The service provider takes into account the user's lifestyle and daily schedule when providing advice. For example, the service provider may suggest appropriate meal times based on the user's lifestyle (e.g., night owl). For example, the service provider may provide easy-to-implement advice based on the user's daily schedule (e.g., busy weekdays). For example, the service provider may also provide advice that can be implemented within a reasonable scope, taking into account the user's lifestyle and schedule. This allows for more practical advice by considering the user's lifestyle and daily schedule. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider may input the user's lifestyle and daily schedule into a generative AI, which can then provide advice.

[0119] The service provider integrates the user's dietary and exercise history to provide comprehensive advice. For example, the service provider integrates the user's dietary and exercise history to provide advice that considers the balance between nutritional balance and exercise volume. For example, the service provider can present a specific action plan for health improvement based on the user's dietary and exercise history. For example, the service provider can refer to the user's dietary and exercise history to evaluate their overall health status and provide advice. By integrating the user's dietary and exercise history, the service provider can provide more comprehensive advice. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's dietary and exercise history into a generative AI, which can then provide comprehensive advice.

[0120] The real-time advice unit estimates the user's emotions and adjusts the content of the real-time advice based on the estimated emotions. For example, if the user is stressed, the real-time advice unit provides relaxing advice. For example, if the user is relaxed, the real-time advice unit can provide advice that includes detailed explanations. For example, if the user is in a hurry, the real-time advice unit can provide concise and to-the-point advice. In this way, by adjusting the content of the real-time advice based on the user's emotions, more appropriate advice is provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The 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 real-time advice unit may be performed using a generative AI, or not using a generative AI. For example, the real-time advice unit can input user emotion data into a generative AI, which can then adjust the content.

[0121] The real-time advice unit monitors the user's current health status in real time and provides advice when offering real-time advice. For example, the real-time advice unit can monitor the user's current health status (e.g., heart rate) in real time and provide appropriate advice. For example, the real-time advice unit can evaluate health risks based on the user's current health status (e.g., blood pressure) and provide advice. For example, the real-time advice unit can monitor the user's current health status in real time and adjust the content of the advice as needed. This allows for the provision of more appropriate advice by monitoring the user's current health status in real time. Some or all of the above processing in the real-time advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the real-time advice unit can input the user's current health status data into a generative AI, which can then provide advice.

[0122] The real-time advice unit estimates the user's emotions and adjusts the timing of real-time advice based on the estimated emotions. For example, if the user is feeling stressed, the real-time advice unit will provide advice during a time when the user can relax. For example, if the user is relaxed, the real-time advice unit can provide advice that includes detailed explanations. For example, if the user is in a hurry, the real-time advice unit can provide concise and to-the-point advice. By adjusting the timing of real-time advice based on the user's emotions, advice is provided at a more appropriate time. 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 real-time advice unit may be performed using a generative AI, or not using a generative AI. For example, the real-time advice unit can input user emotion data into a generative AI, which can then adjust the timing.

[0123] The real-time advice unit provides advice while considering the user's current activity status. For example, the real-time advice unit considers the user's current activity status (e.g., exercising) and provides appropriate advice. For example, the real-time advice unit can provide relaxing advice based on the user's current activity status (e.g., resting). For example, the real-time advice unit can refer to the user's current activity status and provide advice at the optimal time. This allows for the provision of more appropriate advice by considering the user's current activity status. Some or all of the above processing in the real-time advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the real-time advice unit can input the user's current activity status data into a generative AI, which can then provide advice.

[0124] The monitoring unit estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring unit may reduce the monitoring frequency to alleviate the burden. For example, if the user is relaxed, the monitoring unit may increase the monitoring frequency to collect more detailed data. For example, if the user is in a hurry, the monitoring unit may present only concise monitoring items. This allows for more appropriate monitoring by adjusting the monitoring frequency based on 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 processing in the monitoring unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the monitoring unit may input user emotion data into a generative AI, which can then adjust the frequency.

[0125] The monitoring unit optimizes monitoring items by referring to the user's past health data during monitoring. For example, the monitoring unit can identify specific health risks from the user's past health data and add monitoring items. For example, the monitoring unit can omit unnecessary monitoring items based on the user's past health data. For example, the monitoring unit can refer to the user's past health data and perform monitoring that focuses on specific health indicators. This optimizes monitoring items by referring to the user's past health data. Some or all of the above processing in the monitoring unit may be performed using, for example, a generating AI, or without a generating AI. For example, the monitoring unit can input the user's past health data into a generating AI, which can then optimize the monitoring items.

[0126] The monitoring unit estimates the user's emotions and adjusts the display method of the monitoring results based on the estimated user emotions. For example, if the user is stressed, the monitoring unit displays the results in a concise and visually easy-to-understand format. For example, if the user is relaxed, the monitoring unit can display results that include detailed explanations. For example, if the user is in a hurry, the monitoring unit can display results that highlight only the important points. This allows for more appropriate information to be provided by adjusting the display method of the monitoring results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 a generative AI, or not using a generative AI. For example, the monitoring unit can input user emotion data into a generative AI, which can then adjust the display method.

[0127] The monitoring unit performs monitoring while considering the user's living environment and stress level. For example, the monitoring unit can add stress monitoring by considering the user's living environment (e.g., workplace stress). For example, the monitoring unit can perform mental health monitoring by considering the user's living environment (e.g., family situation). For example, the monitoring unit can also provide advice for stress reduction based on the user's stress level. This allows for more appropriate monitoring by considering the user's living environment and stress level. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit can input the user's living environment and stress level into a generative AI, which can then perform the monitoring.

[0128] The menu suggestion unit estimates the user's emotions and adjusts the menu suggestions based on the estimated emotions. For example, if the user is stressed, the menu suggestion unit may suggest a menu using relaxing ingredients. If the user is relaxed, the menu suggestion unit may suggest a menu with detailed nutritional information. If the user is in a hurry, the menu suggestion unit may suggest a menu that is easy to prepare. In this way, by adjusting the menu suggestions based on the user's emotions, a more appropriate menu is suggested. 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 menu suggestion unit may be performed using a generative AI, or not using a generative AI. For example, the menu suggestion unit can input user emotion data into a generative AI, which can then adjust the content.

[0129] The menu suggestion unit optimizes its suggestions by referring to the user's past meal history. For example, the menu suggestion unit can suggest a menu that considers nutritional balance based on the user's past meal history. For example, the menu suggestion unit can suggest a menu that uses the same ingredients based on the user's past meal history. For example, the menu suggestion unit can also suggest a menu that supplements specific nutrients by referring to the user's past meal history. In this way, the suggestions are optimized by referring to the user's past meal history. Some or all of the above processing in the menu suggestion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the menu suggestion unit can input the user's past meal history into a generation AI, which can then optimize the suggestions.

[0130] The menu suggestion unit estimates the user's emotions and adjusts the timing of menu suggestions based on the estimated emotions. For example, if the user is stressed, the menu suggestion unit will suggest a menu during a time when the user can relax. For example, if the user is relaxed, the menu suggestion unit may suggest a menu with detailed explanations. For example, if the user is in a hurry, the menu suggestion unit may suggest a concise and to-the-point menu. By adjusting the timing of menu suggestions based on the user's emotions, the system suggests menus at a more appropriate time. 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 menu suggestion unit may be performed using, for example, generative AI, or not using generative AI. For example, the menu suggestion unit can input user emotion data into the generative AI, which can then adjust the timing.

[0131] The menu suggestion unit considers the user's food preferences and allergy information when suggesting menus. For example, the menu suggestion unit can suggest menus using the user's favorite ingredients. For example, the menu suggestion unit can suggest menus that do not contain allergens based on the user's allergy information. For example, the menu suggestion unit can suggest balanced menus considering the user's food preferences and allergy information. In this way, by considering the user's food preferences and allergy information, it can suggest more appropriate menus. Some or all of the above processing in the menu suggestion unit may be performed using, for example, a generation AI, or without a generation AI. For example, the menu suggestion unit can input the user's food preferences and allergy information into a generation AI, and the generation AI can make suggestions.

[0132] The motivation unit estimates the user's emotions and adjusts the method of maintaining motivation based on the estimated user emotions. For example, if the user is stressed, the motivation unit maintains motivation in a relaxing way. For example, if the user is relaxed, the motivation unit can maintain motivation in a way that includes detailed explanations. For example, if the user is in a hurry, the motivation unit can maintain motivation in a concise and to-the-point way. In this way, motivation is maintained in a more appropriate way by adjusting the method of maintaining motivation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 motivation unit may be performed using a generative AI, or not using a generative AI. For example, the motivation unit can input user emotion data into a generative AI, and the generative AI can adjust the method.

[0133] The Motivation Unit selects the optimal motivation maintenance method by referring to the user's past behavioral history when maintaining motivation. For example, the Motivation Unit prioritizes providing motivation maintenance methods that have been effective based on the user's past behavioral history. For example, the Motivation Unit can prevent the user from repeating the same methods based on the user's past behavioral history. For example, the Motivation Unit can refer to the user's past behavioral history and provide motivation maintenance methods that focus on areas where no improvement was seen. In this way, the Motivation Unit selects the optimal motivation maintenance method by referring to the user's past behavioral history. Some or all of the above processing in the Motivation Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Motivation Unit can input the user's past behavioral history into a generative AI, and the generative AI can select the optimal method.

[0134] The motivation unit estimates the user's emotions and adjusts the timing of motivation maintenance based on the estimated emotions. For example, if the user is stressed, the motivation unit provides methods for maintaining motivation during relaxing times. For example, if the user is relaxed, the motivation unit can maintain motivation in a way that includes detailed explanations. For example, if the user is in a hurry, the motivation unit can maintain motivation in a concise and to-the-point way. This allows for motivation maintenance at a more appropriate time by adjusting the timing of motivation maintenance based on 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 motivation unit may be performed using a generative AI, or not. For example, the motivation unit can input user emotion data into a generative AI, which can then adjust the timing.

[0135] The Motivation Unit provides methods for maintaining motivation, taking into account the user's lifestyle and daily schedule. For example, the Motivation Unit can suggest appropriate motivation maintenance methods based on the user's lifestyle (e.g., night owl). For example, the Motivation Unit can provide easy-to-implement motivation maintenance methods based on the user's daily schedule (e.g., busy weekdays). For example, the Motivation Unit can also provide motivation maintenance methods that can be implemented within a reasonable scope, taking into account the user's lifestyle and schedule. This allows for the provision of more appropriate motivation maintenance methods by considering the user's lifestyle and daily schedule. Some or all of the above processing in the Motivation Unit may be performed using, for example, a generative AI, or without a generative AI. For example, the Motivation Unit can input the user's lifestyle and daily schedule into a generative AI, which can then provide maintenance methods.

[0136] The conversational unit estimates the user's emotions and adjusts the conversation content based on the estimated emotions. For example, if the user is stressed, the conversational unit will engage in conversation with relaxing content. If the user is relaxed, the conversational unit can engage in conversation with detailed explanations. If the user is in a hurry, the conversational unit can engage in conversation with concise and to-the-point content. In this way, by adjusting the conversation content based on the user's emotions, a more appropriate conversation is provided. 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 conversational unit may be performed using a generative AI, or not using a generative AI. For example, the conversational unit can input user emotion data into a generative AI, which can then adjust the content.

[0137] The conversation unit achieves natural conversation by referring to the user's past conversation history during a conversation. For example, the conversation unit prioritizes providing conversation content that was effective based on the user's past conversation history. For example, the conversation unit can avoid repeating the same content based on the user's past conversation history. For example, the conversation unit can refer to the user's past conversation history and provide conversation content that focuses on areas where no improvement was seen. In this way, natural conversation is achieved by referring to the user's past conversation history. Data for the above processing in the conversation unit is collected. The monitoring unit identifies specific health risks and adds monitoring items based on the user's past health data. For example, the monitoring unit can omit unnecessary monitoring items based on the user's past health data. For example, the monitoring unit can refer to the user's past health data and perform monitoring that focuses on specific health indicators. In this way, monitoring items are optimized by referring to the user's past health data. Some or all of the above processing in the monitoring unit may be performed using, for example, generative AI, or without using generative AI. For example, the monitoring unit can input the user's past health data into a generating AI, which can then optimize the monitoring items.

[0138] The conversation unit estimates the user's emotions and adjusts the timing of the conversation based on the estimated emotions. For example, if the user is stressed, the conversation unit will engage in conversation during a time when the user can relax. For example, if the user is relaxed, the conversation unit can engage in conversation with detailed explanations. For example, if the user is in a hurry, the conversation unit can engage in conversation with concise and to-the-point content. In this way, by adjusting the timing of the conversation based on the user's emotions, conversations are conducted at more appropriate times. 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 conversation unit may be performed using a generative AI, or not using a generative AI. For example, the conversation unit can input user emotion data into a generative AI, which can then adjust the timing.

[0139] The conversation unit conducts conversations while considering the user's lifestyle and daily schedule. For example, the conversation unit conducts conversations at appropriate times based on the user's lifestyle (e.g., night owl). For example, the conversation unit can provide conversation content that can be easily performed based on the user's daily schedule (e.g., busy weekdays). For example, the conversation unit can also provide conversation content that can be performed within a reasonable range, taking into account the user's lifestyle and schedule. This allows for more appropriate conversations by considering the user's lifestyle and daily schedule. Some or all of the above processing in the conversation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the conversation unit can input the user's lifestyle and daily schedule into a generative AI, which can then conduct the conversation.

[0140] The image recognition unit estimates the user's emotions and adjusts the accuracy of image recognition based on the estimated emotions. For example, if the user is stressed, the image recognition unit provides the image recognition results in a concise and visually easy-to-understand format. For example, if the user is relaxed, the image recognition unit can provide image recognition results that include detailed explanations. For example, if the user is in a hurry, the image recognition unit can also provide image recognition results that highlight only the important points. This allows for more appropriate image recognition by adjusting the accuracy of image recognition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 image recognition unit may be performed using a generative AI, or not using a generative AI. For example, the image recognition unit can input user emotion data into a generative AI, which can then adjust the accuracy.

[0141] The image recognition unit improves recognition accuracy by referring to the user's past meal photos during image recognition. For example, the image recognition unit can identify the same ingredients from the user's past meal photos to improve recognition accuracy. For example, the image recognition unit can evaluate the nutritional value of a specific ingredient based on the user's past meal photos. The image recognition unit can also refer to the user's past meal photos and provide feedback to improve recognition accuracy. In this way, recognition accuracy is improved by referring to the user's past meal photos. Some or all of the above processing in the image recognition unit may be performed using, for example, a generative AI, or without a generative AI. For example, the image recognition unit can input the user's past meal photos into a generative AI, which can then improve recognition accuracy.

[0142] The image recognition unit estimates the user's emotions and adjusts the display method of the image recognition results based on the estimated emotions. For example, if the user is stressed, the image recognition unit displays the results in a concise and visually easy-to-understand format. For example, if the user is relaxed, the image recognition unit can display results that include detailed explanations. For example, if the user is in a hurry, the image recognition unit can display results that highlight only the important points. This allows for more appropriate information to be provided by adjusting the display method of the image recognition results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 image recognition unit may be performed using a generative AI, or not using a generative AI. For example, the image recognition unit can input user emotion data into a generative AI, which can then adjust the display method.

[0143] The image recognition unit performs recognition while considering the user's food preferences and allergy information. For example, the image recognition unit may prioritize the recognition of the user's preferred foods. For example, the image recognition unit may prioritize the recognition of allergen-free foods based on the user's allergy information. For example, considering the user's food preferences and allergy information, the image recognition unit uses image recognition technology to perform nutritional analysis of food photos. For example, the image recognition unit analyzes food photos and identifies the content of each nutrient. For example, the image recognition unit may use deep learning to analyze food photos and identify the types and quantities of ingredients. For example, the image recognition unit may analyze food photos and evaluate the nutritional balance. In this way, nutritional analysis of food photos is performed by utilizing image recognition technology. Some or all of the above processing in the image recognition unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the image recognition unit can input a food photo into a generative AI, and the generative AI can identify the content of nutrients.

[0144] The data integration unit estimates the user's emotions and adjusts the data integration method based on the estimated emotions. For example, if the user is stressed, the data integration unit integrates the data in a concise and visually easy-to-understand format. For example, if the user is relaxed, the data integration unit can perform data integration that includes detailed explanations. For example, if the user is in a hurry, the data integration unit can perform data integration that highlights only the important points. This allows for more appropriate data integration by adjusting the data integration method based on 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 data integration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data integration unit can input user emotion data into a generative AI, which can then adjust the method.

[0145] The data integration unit improves integration accuracy by referencing the user's past data during data integration. For example, the data integration unit can identify similar patterns from the user's past data to improve integration accuracy. For example, the data integration unit can evaluate specific data points based on the user's past data. The data integration unit can also provide feedback to improve integration accuracy by referencing the user's past data. This improves integration accuracy by referencing the user's past data. Some or all of the above processing in the data integration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the data integration unit can input the user's past data into a generative AI, which can then improve integration accuracy.

[0146] The data integration unit estimates the user's emotions and adjusts how the data integration results are displayed based on the estimated emotions. For example, if the user is stressed, the data integration unit displays the results in a concise and visually easy-to-understand format. For example, if the user is relaxed, the data integration unit may display results that include detailed explanations. For example, if the user is in a hurry, the data integration unit may display results that highlight only the important points. This allows for more appropriate information to be provided by adjusting how the data integration results are displayed based on 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 data integration unit may be performed using or without a generative AI. For example, the data integration unit can input user emotion data into a generative AI, which can then adjust the display method.

[0147] The data integration unit performs data integration while considering the user's lifestyle and daily schedule. For example, the data integration unit integrates data at an appropriate time based on the user's lifestyle (e.g., night owl). For example, the data integration unit can provide a data integration method that can be easily executed based on the user's daily schedule (e.g., busy weekdays). For example, the data integration unit can also provide a data integration method that can be executed within a reasonable scope, taking into account the user's lifestyle and schedule. This allows for more appropriate data integration by considering the user's lifestyle and daily schedule. Some or all of the above processing in the data integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data integration unit can input the user's lifestyle and daily schedule into a generative AI, which can then perform the integration.

[0148] The cloud processing unit estimates the user's emotions and adjusts the cloud processing method based on the estimated emotions. For example, if the user is stressed, the cloud processing unit provides the cloud processing results in a concise and visually easy-to-understand format. For example, if the user is relaxed, the cloud processing unit can provide cloud processing results that include detailed explanations. For example, if the user is in a hurry, the cloud processing unit can also provide cloud processing results that highlight only the important points. This allows for more appropriate cloud processing by adjusting the cloud processing method based on 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 processing described above in the cloud processing unit may be performed using or without a generative AI. For example, the cloud processing unit can input user emotion data into a generative AI, which can then adjust the method.

[0149] The cloud processing unit improves processing accuracy by referencing the user's past data during cloud processing. For example, the cloud processing unit can identify similar patterns from the user's past data and improve processing accuracy. For example, the cloud processing unit can evaluate specific data points based on the user's past data. The cloud processing unit can also refer to the user's past data and provide feedback to improve processing accuracy. This improves processing accuracy by referencing the user's past data. Some or all of the above processing in the cloud processing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the cloud processing unit can input the user's past data into a generative AI, which can then improve processing accuracy.

[0150] The cloud processing unit estimates the user's emotions and adjusts how the cloud processing results are displayed based on the estimated emotions. For example, if the user is stressed, the cloud processing unit displays the results in a concise and visually easy-to-understand format. For example, if the user is relaxed, the cloud processing unit can display results that include detailed explanations. For example, if the user is in a hurry, the cloud processing unit can display results that highlight only the important points. This allows for more appropriate information to be provided by adjusting how the cloud processing results are displayed based on 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 processing described above in the cloud processing unit may be performed using a generative AI, or not using a generative AI. For example, the cloud processing unit can input user emotion data into a generative AI, which can then adjust the display method.

[0151] The cloud processing unit considers the user's lifestyle and daily schedule when performing cloud processing. For example, the cloud processing unit performs cloud processing at the appropriate time based on the user's lifestyle (e.g., night owl). The cloud processing unit needs to clearly define easy-to-execute cloud processing methods based on the user's daily schedule (e.g., busy weekdays). For example, how to record meals, how to save data, etc.

[0152] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0153] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on those emotions. For example, if the user is stressed, the analysis results can be summarized concisely and presented in a visually easy-to-understand format. If the user is relaxed, the analysis results can include detailed nutritional information and background information on ingredients. Furthermore, if the user is in a hurry, the analysis results can be summarized to highlight only deficiencies or excesses of important nutrients. By adjusting how the analysis results are displayed based on the user's emotions, more appropriate information can be provided.

[0154] The real-time advice unit can estimate the user's emotions and adjust the advice based on those emotions. For example, if the user is feeling stressed, it can provide relaxing advice. If the user is relaxed, it can provide advice with more detailed explanations. Furthermore, if the user is in a hurry, it can provide concise and to-the-point advice. In this way, by adjusting the advice based on the user's emotions, it can provide more appropriate advice.

[0155] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on those emotions. For example, if the user is stressed, the monitoring frequency can be reduced to lessen the burden. Conversely, if the user is relaxed, the monitoring frequency can be increased to collect more detailed data. Furthermore, if the user is in a hurry, only concise monitoring items can be presented. This allows for more appropriate monitoring frequency by adjusting the monitoring frequency based on the user's emotions.

[0156] The menu suggestion function can estimate the user's emotions and adjust the menu suggestions based on those emotions. For example, if the user is stressed, it can suggest a menu using ingredients that promote relaxation. If the user is relaxed, it can suggest a menu with detailed nutritional information. Furthermore, if the user is in a hurry, it can suggest a menu that is easy to prepare. In this way, by adjusting the menu suggestions based on the user's emotions, it can suggest more appropriate menus.

[0157] The motivation unit can estimate the user's emotions and adjust the method of maintaining motivation based on those emotions. For example, if the user is stressed, it can maintain motivation in a relaxing way. If the user is relaxed, it can maintain motivation in a way that includes detailed explanations. Furthermore, if the user is in a hurry, it can maintain motivation in a concise and to-the-point way. In this way, by adjusting the method of maintaining motivation based on the user's emotions, motivation can be maintained in a more appropriate manner.

[0158] The analysis unit can improve the accuracy of its analysis of food photos by referring to the user's past eating history. For example, it can compare the nutritional balance of the current meal with the nutritional balance of meals the user has eaten in the past and provide analysis results. It can also analyze the intake trends of specific nutrients from the user's past eating history and point out any excesses or deficiencies in the current meal's nutrients. Furthermore, it can refer to the user's past eating history and analyze how the nutritional balance changes when the same ingredients are used. In this way, the accuracy of the analysis can be improved by referring to the user's past eating history.

[0159] The advice function can customize the content of advice based on the user's health goals. For example, it can provide advice on calorie restriction based on the user's health goal (e.g., weight loss). It can also provide advice on protein intake based on the user's health goal (e.g., muscle building). Furthermore, it can suggest foods with relaxing effects based on the user's health goal (e.g., stress reduction). By customizing the advice based on the user's health goals, it can provide more effective advice.

[0160] The checking function can optimize check items by referring to the user's past health data during health status checks. For example, it can identify specific health risks from the user's past health data and add check items accordingly. It can also omit unnecessary check items based on the user's past health data. Furthermore, it can perform checks that focus on specific health indicators by referring to the user's past health data. In this way, the check items can be optimized by referring to the user's past health data.

[0161] The service provider can optimize the advice provided by referring to the user's past advice history. For example, it can prioritize providing advice that has been effective based on the user's past advice history. It can also avoid repeating the same advice based on the user's past advice history. Furthermore, it can refer to the user's past advice history and focus advice on areas where no improvement has been seen. In this way, the service provider can optimize the advice provided by referring to the user's past advice history.

[0162] The cloud processing unit can improve processing accuracy by referencing the user's past data during cloud processing. For example, it can identify similar patterns from the user's past data to improve processing accuracy. It can also evaluate specific data points based on the user's past data. Furthermore, it can provide feedback to improve processing accuracy by referencing the user's past data. In this way, processing accuracy can be improved by referencing the user's past data.

[0163] The following briefly describes the processing flow for example form 2.

[0164] Step 1: The analysis unit analyzes the photo of the meal. The analysis unit can, for example, use image recognition technology to analyze the photo of the meal and extract information about nutrients. The analysis unit can, for example, use deep learning to analyze the photo of the meal and estimate the amount of each nutrient. The analysis unit can, for example, analyze the photo of the meal and identify the type and quantity of ingredients. Step 2: The advice unit provides advice on nutritional balance and areas for improvement based on the results analyzed by the analysis unit. For example, if the nutritional balance is skewed, the advice unit will suggest a balanced diet. For example, if a specific nutrient is lacking, the advice unit can also suggest foods containing that nutrient. For example, the advice unit can also provide specific points for improving the diet and offer advice that is easy for the user to implement. Step 3: The checking unit checks the user's health status. The checking unit analyzes the health data entered by the user (e.g., weight, blood pressure, blood sugar level) and evaluates the user's health status. The checking unit can also identify health risks based on the user's health status. The checking unit can also periodically monitor the user's health status and issue alerts if any abnormalities are detected. Step 4: The service provider provides personalized advice based on the data obtained by the checking unit. For example, the service provider may suggest a meal plan tailored to the user's health condition. The service provider may also present a specific action plan based on the user's health goals. The service provider may also provide advice tailored to the user's preferences and lifestyle.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] For example, each of the multiple elements, including the analysis unit, advice unit, check unit, and provision unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit takes a picture of the meal using the camera 42 of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The advice unit is implemented, for example, using the specific processing unit 290 of the data processing unit 12 and provides advice on nutritional balance and points for improvement based on the analysis results. The check unit collects the user's health data using the control unit 46A of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The provision unit is implemented, for example, using the specific processing unit 290 of the data processing unit 12 and provides personalized advice according to the user's health condition. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0169] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] For example, each of the multiple elements, including the analysis unit, advice unit, check unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit takes a picture of the meal using the camera 42 of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing unit 12. The advice unit is implemented, for example, using the specific processing unit 290 of the data processing unit 12 and provides advice on nutritional balance and points for improvement based on the analysis results. The check unit collects the user's health data using the control unit 46A of the smart glasses 214 and analyzes it using the specific processing unit 290 of the data processing unit 12. The provision unit is implemented, for example, using the specific processing unit 290 of the data processing unit 12 and provides personalized advice according to the user's health condition. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0185] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.).

[0197] 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.

[0198] 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.

[0199] 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.

[0200] For example, each of the multiple elements, including the analysis unit, advice unit, check unit, and provision unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit takes a picture of the meal using the camera 42 of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The advice unit is implemented, for example, using the specific processing unit 290 of the data processing unit 12 and provides advice on nutritional balance and points for improvement based on the analysis results. The check unit collects the user's health data using the control unit 46A of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The provision unit is implemented, for example, using the specific processing unit 290 of the data processing unit 12 and provides personalized advice according to the user's health condition. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0201] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] For example, each of the multiple elements, including the analysis unit, advice unit, check unit, and provision unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the analysis unit takes a picture of the meal using the camera 42 of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The advice unit is implemented, for example, using the specific processing unit 290 of the data processing unit 12 and provides advice on nutritional balance and points for improvement based on the analysis results. The check unit collects the user's health data using the control unit 46A of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The provision unit is implemented, for example, using the specific processing unit 290 of the data processing unit 12 and provides personalized advice according to the user's health condition. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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."

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] (Note 1) The analysis unit analyzes the photos of the food, Based on the results analyzed by the aforementioned analysis unit, an advice unit provides advice on nutritional balance and areas for improvement. A check unit that checks the user's health status, A providing unit that provides personalized advice based on the data obtained by the checking unit, Equipped with A system characterized by the following features. (Note 2) Equipped with a real-time advice section that provides nutritional advice in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has a monitoring department that conducts health monitoring and proposes action plans. The system described in Appendix 1, characterized by the features described herein. (Note 4) We have a menu planning department that provides customized menu suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a motivation department that provides a function to maintain motivation. The system described in Appendix 1, characterized by the features described herein. (Note 6) It features a conversational section that uses natural language processing to enable natural conversations with the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) It is equipped with an image recognition unit that uses image recognition technology to analyze the nutrients in food photographs. The system described in Appendix 1, characterized by the features described herein. (Note 8) It includes a data integration unit that integrates and analyzes meal data, health status data, and user behavior logs. The system described in Appendix 1, characterized by the features described herein. (Note 9) Equipped with a cloud processing unit that performs scalable data processing using cloud infrastructure. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the analysis method of food photos based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing food photos, we improve the accuracy of the analysis by referring to the user's past meal history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing food photos, the analysis takes into account the origin and quality information of the ingredients. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing food photos, the analysis takes into account the user's allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing food photos, the analysis takes into account the user's food preferences and tastes. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned advice section, When providing advice, customize the content of the advice based on the user's health goals. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned advice section, When providing advice, specific areas for improvement are presented based on the user's dietary history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice section, It estimates the user's emotions and adjusts the timing of advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advice section, When providing advice, we take into account the user's lifestyle and habits. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advice section, When providing advice, we will consider the user's exercise history to offer advice on nutritional balance. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned checking unit is The system estimates the user's emotions and adjusts the frequency of health checks based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned checking unit is When performing a health check, the system optimizes the check items by referring to the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned checking unit is When checking a user's health status, the check will take into account their living environment and stress level. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned checking unit is The system estimates the user's emotions and adjusts how the check results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned checking unit is During health status checks, the system takes into account the user's family history and genetic information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned checking unit is During the health check, the system takes into account the user's exercise habits and sleep patterns. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts the timing of advice delivery based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing advice, we optimize the content by referring to the user's past advice history. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing advice, present a specific action plan based on the user's health goals. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing advice, we take into consideration the user's lifestyle and daily schedule. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing advice, we integrate the user's dietary and exercise history to provide comprehensive advice. The system described in Appendix 1, characterized by the features described herein. (Note 34) The real-time advice unit estimates the user's emotion and adjusts the content of real-time advice based on the estimated emotion of the user. The system according to Appendix 2, characterized in that it is as described above. (Appendix 35) The real-time advice unit monitors the user's current health status in real time and gives advice when providing real-time advice. The system according to Appendix 2, characterized in that it is as described above. (Appendix 36) The real-time advice unit estimates the user's emotion and adjusts the timing of real-time advice based on the estimated emotion of the user. The system according to Appendix 2, characterized in that it is as described above. (Appendix 37) The real-time advice unit gives advice considering the user's current activity status when providing real-time advice. The system according to Appendix 2, characterized in that it is as described above. (Appendix 38) The monitoring unit estimates the user's emotion and adjusts the monitoring frequency based on the estimated emotion of the user. The system according to Appendix 1, characterized in that it is as described above. (Appendix 39) The monitoring unit optimizes the monitoring items by referring to the user's past health data during monitoring. The system according to Appendix 1, characterized in that it is as described above. (Appendix 40) The monitoring unit estimates the user's emotion and adjusts the display method of the monitoring results based on the estimated emotion of the user. The system according to Appendix 1, characterized in that it is as described above. (Appendix 41) The monitoring unit During monitoring, the user's living environment and stress level should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned menu proposal department, The system estimates the user's emotions and adjusts the menu suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned menu proposal department, When suggesting menus, the system optimizes the suggestions by referencing the user's past meal history. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned menu proposal department, The system estimates the user's emotions and adjusts the timing of menu suggestions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned menu proposal department, When suggesting menus, we take into account the user's food preferences and allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned Motivation Department It estimates the user's emotions and adjusts methods for maintaining motivation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 47) The aforementioned Motivation Department When maintaining motivation, the system selects the optimal motivation maintenance method by referring to the user's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 48) The aforementioned Motivation Department It estimates the user's emotions and adjusts the timing of motivation maintenance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 49) The motivation unit provides a maintenance method in consideration of the user's life rhythm and daily schedule when maintaining motivation. The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 50) The conversation unit estimates the user's emotion and adjusts the content of the conversation based on the estimated emotion of the user. The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 51) The conversation unit realizes a natural conversation by referring to the user's past conversation history during the conversation. The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 52) The conversation unit estimates the user's emotion and adjusts the timing of the conversation based on the estimated emotion of the user. The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 53) <{ The conversation unit conducts a conversation considering the user's life rhythm and daily schedule during the conversation. The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 54) The image recognition unit estimates the user's emotion and adjusts the accuracy of image recognition based on the estimated emotion of the user. The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 55) The image recognition unit improves the recognition accuracy by referring to the user's past meal photos during image recognition. The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 56) The image recognition unit estimates the user's emotion and adjusts the display method of the image recognition result based on the estimated emotion of the user. The system according to Supplementary Note 1, characterized by the above. (Supplementary Note 57) The image recognition unit, During image recognition, the system takes into account the user's food preferences and allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 58) The aforementioned data integration unit, We estimate user sentiment and adjust the data integration method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 59) The aforementioned data integration unit, When integrating data, referencing users' historical data improves integration accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 60) The aforementioned data integration unit, It estimates the user's emotions and adjusts how the data integration results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 61) The aforementioned data integration unit, When integrating data, the integration process takes into account the user's lifestyle and daily schedule. The system described in Appendix 1, characterized by the features described herein. (Note 62) The aforementioned cloud processing unit, It estimates the user's emotions and adjusts the cloud processing method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 63) The aforementioned cloud processing unit, When processing in the cloud, we improve processing accuracy by referencing the user's past data. The system described in Appendix 1, characterized by the features described herein. (Note 64) The aforementioned cloud processing unit, It estimates the user's emotions and adjusts how cloud processing results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 65) The aforementioned cloud processing unit, When processing in the cloud, the system takes into account the user's lifestyle and daily schedule. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0237] 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. The analysis unit analyzes the photos of the food, Based on the results analyzed by the aforementioned analysis unit, an advice unit provides advice on nutritional balance and areas for improvement. A check unit that checks the user's health status, A providing unit that provides personalized advice based on the data obtained by the checking unit, Equipped with A system characterized by the following features.

2. Equipped with a real-time advice section that provides nutritional advice in real time. The system according to feature 1.

3. It has a monitoring department that conducts health monitoring and proposes action plans. The system according to feature 1.

4. We have a menu planning department that provides customized menu suggestions. The system according to feature 1.

5. It includes a motivation department that provides a function to maintain motivation. The system according to feature 1.

6. It features a conversational section that uses natural language processing to enable natural conversations with the user. The system according to feature 1.

7. It is equipped with an image recognition unit that uses image recognition technology to analyze the nutrients in food photographs. The system according to feature 1.

8. It includes a data integration unit that integrates and analyzes meal data, health status data, and user behavior logs. The system according to feature 1.

9. Equipped with a cloud processing unit that performs scalable data processing using cloud infrastructure. The system according to feature 1.

10. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the analysis method of food photos based on the estimated user emotions. The system according to feature 1.

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