system

Abstract

The system according to this embodiment aims to analyze the user's lifestyle data and propose an optimal health improvement plan. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, a reception unit, and a diagnosis unit. The data collection unit collects data such as the user's lifestyle data, exercise records, dietary records, and sleep patterns. The analysis unit analyzes the data collected by the data collection unit and evaluates the user's health status. The proposal unit proposes an optimal health improvement plan to the user based on the health status evaluated by the analysis unit. The reception unit inputs the user's symptoms based on the health improvement plan proposed by the proposal unit. The diagnosis unit performs an initial diagnosis based on the symptoms input by the reception unit and recommends that the user visit an appropriate medical institution.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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, comprehensive analysis of users' lifestyle data and health data and individual proposal of optimal health improvement plans have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze users' lifestyle data and propose an optimal health improvement plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a reception unit, and a diagnosis unit. The data collection unit collects data such as the user's lifestyle, exercise records, dietary records, and sleep patterns. The analysis unit analyzes the data collected by the data collection unit and evaluates the user's health status. The proposal unit proposes an optimal health improvement plan to the user based on the health status evaluated by the analysis unit. The reception unit inputs the user's symptoms based on the health improvement plan proposed by the proposal unit. The diagnosis unit performs an initial diagnosis based on the symptoms entered by the reception unit and recommends that the user visit an appropriate medical institution. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's lifestyle data and propose an optimal health improvement plan. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple 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 reception 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 reception 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 Generation AI System according to an embodiment of the present invention is an agent-type generation AI service that provides personalized health advice in conjunction with existing health management apps and wearable devices. The Health Generation AI System analyzes the user's lifestyle data, exercise records, dietary records, sleep patterns, etc., and proposes an optimal health improvement plan. Furthermore, the Health Generation AI System also works in conjunction with a medical information database to provide initial diagnoses and recommendations for medical consultation based on symptoms. For example, the Health Generation AI System collects data such as the user's lifestyle data, exercise records, dietary records, and sleep patterns. This data is obtained from existing health management apps and wearable devices. Next, the Health Generation AI System uses AI to analyze the collected data and evaluate the user's health status. For example, it identifies problems such as lack of exercise or irregular eating patterns. Then, based on the analysis results, the Health Generation AI System proposes an optimal health improvement plan to the user. For example, it suggests daily walking and stretching to users who lack exercise, and suggests a balanced meal menu to users with irregular eating patterns. It also suggests improving the sleep environment and relaxation methods to users with disrupted sleep patterns. Furthermore, when a user enters their symptoms, the Health Generation AI system connects with a medical information database to perform an initial diagnosis. For example, if a user enters symptoms such as headache or stomach ache, the Health Generation AI system identifies possible illnesses based on those symptoms and recommends visiting an appropriate medical institution. This allows users to receive appropriate medical care early on. The Health Generation AI system also interacts with users through a chat interface, responding to health consultations and questions. For example, if a user asks, "I've been feeling tired lately, what should I do?", the Health Generation AI system provides appropriate advice based on the user's data. This allows users to easily consult about their health and receive appropriate advice. In this way, the Health Generation AI system supports users' health management by continuously analyzing their health data and providing personalized health advice.Furthermore, by providing initial diagnoses and recommendations for medical consultation based on symptom input, the system enables users to receive appropriate medical care early. This improves users' health and supports disease prevention and early detection. As a result, the Health Generation AI system can continuously monitor users' health status and provide personalized health advice.

[0029] The Health Generation AI system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a reception unit, and a diagnosis unit. The collection unit collects data such as the user's lifestyle data, exercise records, meal records, and sleep patterns. The collection unit acquires data from, for example, existing health management apps or wearable devices. The collection unit can acquire exercise records from, for example, smartwatches or fitness trackers. The collection unit can also acquire meal details from meal recording apps. Furthermore, the collection unit can acquire sleep patterns from sleep trackers. The analysis unit analyzes the data collected by the collection unit and evaluates the user's health status. The analysis unit identifies problems such as lack of exercise or irregular meal patterns. The analysis unit can analyze exercise records and evaluate the user's exercise level. The analysis unit can also analyze meal records and evaluate the user's nutritional balance. Furthermore, the analysis unit can analyze sleep patterns and evaluate the quality of the user's sleep. The proposal unit proposes an optimal health improvement plan to the user based on the health status evaluated by the analysis unit. The suggestion department, for example, suggests daily walking and stretching to users who lack exercise. The suggestion department can suggest, for example, the distance and duration of walking. It can also suggest the type and frequency of stretching. Furthermore, the suggestion department suggests balanced meal plans to users with irregular eating patterns. For example, it can suggest nutritionally balanced meal plans. It can also suggest the timing and quantity of meals. Additionally, the suggestion department suggests improving the sleep environment and relaxation methods to users with disrupted sleep patterns. For example, it can suggest selecting comfortable bedding and adjusting the room temperature. It can also suggest meditation and deep breathing as relaxation methods. The reception department inputs the user's symptoms based on the health improvement plan proposed by the suggestion department. For example, the reception department allows users to input symptoms such as headaches or stomachaches. The reception department allows users to input symptoms via a chat interface, for example. It also allows users to input symptoms using voice input.The diagnostic unit performs an initial diagnosis based on the symptoms entered by the reception unit and recommends that the user visit an appropriate medical institution. The diagnostic unit identifies possible illnesses based on symptoms such as headaches and abdominal pain. For example, based on headache symptoms, the diagnostic unit can identify migraines or tension headaches. The diagnostic unit can also identify gastritis or enteritis based on abdominal pain symptoms. Furthermore, the diagnostic unit recommends that the user visit an appropriate medical institution. For example, in the case of migraines, the diagnostic unit can recommend a visit to a neurologist. The diagnostic unit can also recommend a visit to a gastroenterologist in the case of gastritis. Thus, the Health Generation AI system according to this embodiment can provide personalized health advice by collecting, analyzing, suggesting, and diagnosing the user's lifestyle data.

[0030] The data collection unit collects data such as the user's lifestyle habits, exercise records, meal records, and sleep patterns. For example, the unit acquires data from existing health management apps and wearable devices. Specifically, it can acquire exercise records from smartwatches and fitness trackers. These devices record detailed exercise data such as the user's steps, heart rate, and calories burned in real time and transmit it to the data collection unit via Bluetooth® or Wi-Fi. The data collection unit can also acquire meal details from meal tracking apps. By inputting meal details, data such as calories, nutrients, and meal timing are collected. Furthermore, the data collection unit can acquire sleep patterns from sleep trackers. Sleep trackers record the user's sleep duration, the ratio of deep to light sleep, and the number of times they toss and turn, and transmit this data to the data collection unit. This allows the data collection unit to centrally collect diverse data related to the user's lifestyle habits and understand their overall health status. Additionally, the data collection unit stores this data on a cloud server, making it accessible to the analysis and proposal units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. For example, data collection is tailored to user needs, such as collecting exercise data every minute and meal data after each meal. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes data collected by the data collection unit to assess the user's health status. For example, the analysis unit identifies problems such as lack of exercise or irregular eating patterns. Specifically, it can analyze exercise records to evaluate the user's exercise level. Using AI, it compares the user's exercise data with past data to detect increases or decreases in exercise volume and changes in patterns. It can also analyze eating records to assess the user's nutritional balance. The AI ​​analyzes eating data, calculates calorie intake and nutrient balance, and identifies excess or deficient nutrients. Furthermore, it can analyze sleep patterns to assess the user's sleep quality. The AI ​​analyzes sleep data, evaluates the ratio of deep to light sleep and sleep continuity, and quantifies sleep quality. This allows the analysis unit to quickly and accurately analyze collected data and comprehensively assess the user's health status. Additionally, the analysis unit can utilize past data and statistical information to analyze long-term health trends. For example, it can track changes in the user's exercise habits based on past exercise data to predict future health risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to handle not only real-time health status assessment but also long-term health management and anomaly detection, thereby improving the overall reliability and safety of the system.

[0032] The Proposal Department proposes an optimal health improvement plan to the user based on the health status evaluated by the Analysis Department. For example, for users who are not getting enough exercise, the Proposal Department suggests daily walking and stretching. Specifically, it suggests walking distance and duration, providing an exercise plan that matches the user's lifestyle. It also suggests types and frequencies of stretching to support the user in continuing without difficulty. Furthermore, for users with irregular eating patterns, the Proposal Department suggests a balanced meal plan. Using AI, it analyzes the user's eating data and generates a nutritionally balanced meal plan. For example, it suggests high-protein foods for breakfast and a menu rich in vegetables for lunch. It also suggests the timing and amount of meals to support the user in maintaining a healthy diet. In addition, for users with disrupted sleep patterns, the Proposal Department suggests improvements to the sleep environment and relaxation methods. Specifically, it suggests selecting comfortable bedding and adjusting the room temperature to support the user in getting quality sleep. It also suggests meditation and deep breathing as relaxation methods to help the user reduce stress. In this way, the Proposal Department can provide a personalized health improvement plan tailored to the user's health status and support the user in leading a healthy life.

[0033] The reception department inputs the user's symptoms based on the health improvement plan proposed by the proposal department. The reception department allows users to input symptoms such as headaches or stomachaches. Specifically, users can input symptoms through a chat interface. Users input symptoms in natural language, and AI analyzes the content to extract appropriate information. The reception department can also accept voice input from users. Using speech recognition technology, the system converts the user's voice into text and records the symptoms. This allows the reception department to easily and quickly input symptoms. Furthermore, the reception department can collect additional information about the user's symptoms. For example, inputting detailed information such as the timing and frequency of symptom occurrence and the severity of pain enables a more accurate diagnosis. This allows the reception department to collect detailed information about the user's symptoms and provide it to the diagnosis department.

[0034] The diagnostic department performs an initial diagnosis based on the symptoms entered by the reception department and recommends that the user visit an appropriate medical institution. For example, the diagnostic department identifies possible illnesses based on symptoms such as headaches and abdominal pain. Specifically, it uses AI to analyze the user's symptom data and make a diagnosis by comparing it with past case data. For example, it can identify migraines or tension headaches based on headache symptoms. It can also identify gastritis or enteritis based on abdominal pain symptoms. Furthermore, the diagnostic department recommends that the user visit an appropriate medical institution. The AI ​​selects the most suitable medical institution considering the user's place of residence and the urgency of the symptoms. For example, in the case of a migraine, it can recommend a visit to a neurologist, and in the case of gastritis, it can recommend a visit to a gastroenterologist. The diagnostic department also provides the user with information and precautions to bring when visiting a medical institution. This allows the diagnostic department to support the user in visiting an appropriate medical institution and receiving prompt and appropriate treatment. Furthermore, when notifying the user of the diagnosis results and recommendations for medical visits, the diagnostic department takes measures to protect the user's privacy. For example, the diagnosis results are encrypted and will not be disclosed to third parties without the user's consent. This allows the diagnostic department to provide appropriate medical support while protecting user privacy.

[0035] The data collection unit can acquire data from existing health management apps and wearable devices. For example, the data collection unit can acquire exercise records from smartwatches and fitness trackers. For example, the data collection unit can use the sensors of a smartwatch to record the user's steps and heart rate. The data collection unit can also use data from fitness trackers to record the user's exercise time and calories burned. Furthermore, the data collection unit can acquire meal details from food logging apps. For example, the data collection unit can acquire meal details entered by the user into a food logging app and evaluate the balance of nutrients. The data collection unit can also use data from food logging apps to record the frequency and amount of meals the user eats. Furthermore, the data collection unit can acquire sleep patterns from sleep trackers. For example, the data collection unit can use the sensors of a sleep tracker to record the user's sleep duration and sleep quality. The data collection unit can also use data from sleep trackers to record the user's sleep cycle and wake-up time. In this way, by acquiring data from existing health management apps and wearable devices, user lifestyle data can be efficiently collected. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from a smartwatch or fitness tracker into a generating AI and have the generating AI perform data analysis.

[0036] The analysis unit can identify problems such as lack of exercise and irregular eating patterns. For example, the analysis unit can analyze exercise records and evaluate the user's exercise level. For example, the analysis unit can identify lack of exercise based on the user's step count and exercise time. The analysis unit can also analyze eating records and evaluate the user's nutritional balance. For example, the analysis unit can identify nutrient deficiencies or excesses based on the user's diet. Furthermore, the analysis unit can analyze sleep patterns and evaluate the quality of the user's sleep. For example, the analysis unit can identify sleep deprivation and irregular sleep patterns based on the user's sleep duration and sleep cycle. This allows for an accurate assessment of the user's health status by identifying problems such as lack of exercise and irregular eating patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input exercise records, eating records, and sleep pattern data into a generating AI and have the generating AI perform the data analysis.

[0037] The suggestion unit can make daily walking and stretching suggestions. For example, the suggestion unit can suggest walking distance and duration. For example, the suggestion unit can suggest daily walking distance and duration based on the user's exercise level. The suggestion unit can also suggest the type and frequency of stretching. For example, the suggestion unit can suggest the type and frequency of daily stretching to improve the user's lack of exercise. In this way, by suggesting daily walking and stretching, the user's lack of exercise can be improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's exercise record into a generating AI and have the generating AI execute suggestions to improve the lack of exercise.

[0038] The suggestion unit can propose a balanced meal plan. For example, the suggestion unit can propose a nutritionally balanced meal plan. For example, the suggestion unit can propose a meal plan that considers the balance of nutrients based on the user's meal record. The suggestion unit can also propose the timing and amount of meals. For example, the suggestion unit can analyze the user's eating patterns and propose appropriate timing and amount of meals. In this way, by proposing a balanced meal plan, the user's eating patterns can be improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's meal record into a generating AI and have the generating AI produce a balanced meal plan proposal.

[0039] The suggestion unit can propose improvements to the sleep environment and relaxation methods. For example, the suggestion unit can suggest selecting comfortable bedding and adjusting the room temperature. For example, the suggestion unit can analyze the user's sleep pattern and propose selecting comfortable bedding and adjusting the room temperature. The suggestion unit can also suggest meditation and deep breathing as relaxation methods. For example, the suggestion unit can analyze the user's stress level and suggest meditation and deep breathing as relaxation methods. In this way, by suggesting improvements to the sleep environment and relaxation methods, the user's sleep pattern can be improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's sleep pattern into a generating AI and have the generating AI execute suggestions for improving the sleep environment and relaxation methods.

[0040] The diagnostic unit can identify possible illnesses based on symptoms such as headaches and abdominal pain, and recommend that the user visit an appropriate medical institution. For example, the diagnostic unit can identify migraines or tension headaches based on headache symptoms. For example, the diagnostic unit can analyze the headache symptoms entered by the user and identify migraines or tension headaches. The diagnostic unit can also identify gastritis or enteritis based on abdominal pain symptoms. For example, the diagnostic unit can analyze the abdominal pain symptoms entered by the user and identify gastritis or enteritis. Furthermore, the diagnostic unit recommends that the user visit an appropriate medical institution. For example, in the case of a migraine, the diagnostic unit can recommend that the user visit a neurologist. For example, in the case of gastritis, the diagnostic unit can recommend that the user visit a gastroenterologist. In this way, by identifying possible illnesses based on symptoms such as headaches and abdominal pain and recommending that the user visit an appropriate medical institution, the user can receive appropriate medical care early. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without using AI. For example, the diagnostic unit can input symptoms entered by the user into a generating AI, which can then identify possible illnesses and recommend seeking medical attention.

[0041] The diagnostic unit can interact with users through a chat interface and respond to health consultations and questions. For example, when a user enters a question through the chat interface, the diagnostic unit can provide appropriate advice. For example, if a user asks, "I've been feeling tired lately, what should I do?", the diagnostic unit can provide appropriate advice based on the user's data. The diagnostic unit can also provide appropriate health advice based on symptoms entered by the user. For example, if a user asks, "I've been having persistent headaches, what should I do?", the diagnostic unit can provide appropriate advice based on the headache symptoms. In this way, by interacting with users through a chat interface and responding to health consultations and questions, users can easily consult about their health. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or not using AI. For example, the diagnostic unit can input questions entered by the user through the chat interface into a generating AI and have the generating AI provide appropriate advice.

[0042] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection time based on the time periods when the user frequently collected data in the past. The data collection unit can suggest the optimal collection time based on the user's past data collection history. The data collection unit can suggest the optimal device based on the devices the user has used in the past. The data collection unit can suggest the optimal device based on the user's past data collection history. Furthermore, the data collection unit can suggest the most efficient collection method based on the user's past data collection history. The data collection unit can suggest the most efficient collection method based on the user's past data collection history. This allows the optimal collection method to be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0043] The data collection unit can filter data based on the user's current health and lifestyle. For example, if the user is tired, the data collection unit can limit the data collected to reduce the burden. For example, the data collection unit can analyze the user's health and, if tired, limit the data collected to reduce the burden. The data collection unit can also collect detailed data if the user is in good health. For example, the data collection unit can analyze the user's health and, if good, collect detailed data. Furthermore, the data collection unit can adjust the type of data collected according to the user's lifestyle. For example, the data collection unit can analyze the user's lifestyle and adjust the type of data collected. This improves the accuracy of the collected data by filtering it based on the user's current health and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's health and lifestyle into a generating AI and have the generating AI perform data filtering.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is outdoors, the data collection unit can prioritize the collection of exercise-related data. For example, the data collection unit can analyze the user's geographical location information and prioritize the collection of exercise-related data if the user is outdoors. The data collection unit can also prioritize the collection of lifestyle-related data if the user is at home. For example, the data collection unit can analyze the user's geographical location information and prioritize the collection of lifestyle-related data if the user is at home. Furthermore, if the user is at work, the data collection unit can prioritize the collection of stress-related data. For example, the data collection unit can analyze the user's geographical location information and prioritize the collection of stress-related data if the user is at work. This improves data collection efficiency by prioritizing the collection of highly relevant data while considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0045] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts about exercise on social media, the data collection unit can collect exercise-related data. The data collection unit can analyze a user's social media activity and collect exercise-related data if the user posts about exercise. The data collection unit can also collect diet-related data if a user posts about food. The data collection unit can analyze a user's social media activity and collect diet-related data if the user posts about food. Furthermore, if a user posts about sleep, the data collection unit can collect sleep-related data. The data collection unit can analyze a user's social media activity and collect sleep-related data if the user posts about sleep. This allows for the efficient collection of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into a generating AI and have the generating AI collect the relevant data.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can analyze important data related to the user's health status and provide detailed analysis results. The analysis unit can also perform a simplified analysis on less important data. For example, the analysis unit can analyze less important data related to the user's health status and provide simplified analysis results. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit can evaluate the importance of data related to the user's health status and determine the priority of the analysis. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data related to the user's health status into a generating AI and have the generating AI adjust the level of detail of the analysis based on the importance of the data.

[0047] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an exercise analysis algorithm to exercise data. For example, the analysis unit can analyze a user's exercise record and apply an exercise analysis algorithm. The analysis unit can also apply a diet analysis algorithm to diet data. For example, the analysis unit can analyze a user's diet record and apply a diet analysis algorithm. Furthermore, the analysis unit can apply a sleep analysis algorithm to sleep data. For example, the analysis unit can analyze a user's sleep pattern and apply a sleep analysis algorithm. By applying different analysis algorithms depending on the data category, the accuracy of data analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user exercise record, diet record, and sleep pattern data into a generating AI and have the generating AI execute the application of analysis algorithms according to the data category.

[0048] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can analyze the most recent data regarding the user's health status and provide the analysis results preferentially. The analysis unit can also perform analysis while referring to past data. For example, the analysis unit can analyze past data regarding the user's health status and provide the analysis results while referring to it. Furthermore, the analysis unit can adjust the order of analysis according to the data collection timing. For example, the analysis unit can evaluate the data collection timing regarding the user's health status and adjust the order of analysis. This allows the analysis to prioritize the analysis based on the data collection timing, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data regarding the user's health status into a generating AI and have the generating AI determine the priority of analysis based on the data collection timing.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can analyze highly relevant data related to the user's health status and provide the analysis results preferentially. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit can analyze less relevant data related to the user's health status and provide the analysis results later. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. For example, the analysis unit can evaluate the relevance of data related to the user's health status and adjust the order of analysis. This allows for the prioritization of analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data related to the user's health status into a generating AI and have the generating AI perform the adjustment of the analysis order based on the relevance of the data.

[0050] The proposal unit can adjust the level of detail of its proposals based on the importance of the health improvement plans. For example, the proposal unit can provide detailed proposals for important health improvement plans. For example, the proposal unit can evaluate important health improvement plans related to the user's health status and provide detailed proposals. The proposal unit can also provide simplified proposals for less important health improvement plans. For example, the proposal unit can evaluate less important health improvement plans related to the user's health status and provide simplified proposals. Furthermore, the proposal unit can determine the priority of proposals according to the importance of the health improvement plans. For example, the proposal unit can evaluate the importance of health improvement plans related to the user's health status and determine the priority of proposals. This allows for detailed proposals for important plans by adjusting the level of detail of the proposals based on the importance of the health improvement plans. Some or all of the above processing in the proposal unit may be performed using AI, or not using AI. For example, the proposal unit can input data on the user's health status into a generating AI and have the generating AI adjust the level of detail of the proposals based on the importance of the health improvement plans.

[0051] The suggestion unit can apply different suggestion algorithms depending on the category of the health improvement plan when making a suggestion. For example, the suggestion unit can apply an exercise suggestion algorithm to an exercise improvement plan. For example, the suggestion unit can analyze the user's exercise record and apply the exercise suggestion algorithm. The suggestion unit can also apply a diet suggestion algorithm to a diet improvement plan. For example, the suggestion unit can analyze the user's diet record and apply the diet suggestion algorithm. Furthermore, the suggestion unit can apply a sleep suggestion algorithm to a sleep improvement plan. For example, the suggestion unit can analyze the user's sleep pattern and apply the sleep suggestion algorithm. By applying different suggestion algorithms depending on the category of the health improvement plan, the accuracy of the suggestions is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's exercise record, diet record, and sleep pattern into a generating AI and have the generating AI apply a suggestion algorithm according to the category of the health improvement plan.

[0052] The proposal department can determine the priority of proposals based on the submission timing of health improvement plans. For example, the proposal department can prioritize the most recent health improvement plan. For example, the proposal department can evaluate the most recent health improvement plan related to the user's health status and prioritize its proposal. The proposal department can also make proposals while referring to past health improvement plans. For example, the proposal department can evaluate past health improvement plans related to the user's health status and refer to them when making proposals. Furthermore, the proposal department can adjust the order of proposals according to the submission timing of health improvement plans. For example, the proposal department can evaluate the submission timing of health improvement plans related to the user's health status and adjust the order of proposals. This allows the proposal department to prioritize the most recent plan by determining the priority of proposals based on the submission timing of health improvement plans. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on the user's health status into a generating AI and have the generating AI perform the determination of proposal priority based on the submission timing of health improvement plans.

[0053] The proposal unit can adjust the order of proposals based on the relevance of the health improvement plans when making a proposal. For example, the proposal unit can prioritize proposing health improvement plans that are highly relevant. For example, the proposal unit can evaluate health improvement plans that are highly relevant to the user's health condition and propose them preferentially. The proposal unit can also postpone proposing health improvement plans that are less relevant. For example, the proposal unit can evaluate health improvement plans that are less relevant to the user's health condition and postpone proposing them. Furthermore, the proposal unit can adjust the order of proposals according to the relevance of the health improvement plans. For example, the proposal unit can evaluate the relevance of health improvement plans to the user's health condition and adjust the order of proposals. This allows the proposal unit to prioritize proposing plans that are highly relevant by adjusting the order of proposals based on the relevance of the health improvement plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the user's health condition into a generating AI and have the generating AI perform the adjustment of the order of proposals based on the relevance of the health improvement plans.

[0054] The reception unit can analyze the user's past symptom input history and select the optimal input method. For example, the reception unit can suggest the optimal input method based on symptoms the user has frequently entered in the past. The reception unit can analyze the user's past symptom input history and suggest the optimal input method. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can analyze the user's past symptom input history and suggest the optimal input method. Furthermore, the reception unit can predict and suggest input methods to be used during specific time periods based on the user's past symptom input history. The reception unit can analyze the user's past symptom input history and predict and suggest input methods to be used during specific time periods. This allows the reception unit to select the optimal input method by analyzing the user's past symptom input history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past symptom input history into a generating AI and have the generating AI select the optimal input method.

[0055] The reception unit can filter symptoms based on the user's current health and lifestyle when they are entered. For example, if the user is tired, the reception unit can limit the symptoms that can be entered to reduce the burden. The reception unit can analyze the user's health and, if tired, limit the symptoms that can be entered to reduce the burden. The reception unit can also allow the user to enter detailed symptoms if they are in good health. The reception unit can analyze the user's health and, if good, allow the user to enter detailed symptoms. Furthermore, the reception unit can adjust the types of symptoms entered according to the user's lifestyle. The reception unit can analyze the user's lifestyle and adjust the types of symptoms entered. This improves the accuracy of the symptoms entered by filtering them based on the user's current health and lifestyle. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's health and lifestyle into a generating AI and have the generating AI perform the symptom filtering.

[0056] The reception unit can prioritize the input of highly relevant symptoms by considering the user's geographical location when symptoms are entered. For example, if the user is outdoors, the reception unit can prioritize the input of exercise-related symptoms. The reception unit can analyze the user's geographical location and prioritize the input of exercise-related symptoms if the user is outdoors. The reception unit can also prioritize the input of lifestyle-related symptoms if the user is at home. The reception unit can analyze the user's geographical location and prioritize the input of lifestyle-related symptoms if the user is at home. Furthermore, if the user is at work, the reception unit can prioritize the input of stress-related symptoms. The reception unit can analyze the user's geographical location and prioritize the input of stress-related symptoms if the user is at work. This improves the efficiency of symptom input by prioritizing the input of highly relevant symptoms by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into the generating AI, and have the AI ​​prioritize inputting symptoms that are highly relevant to that information.

[0057] The reception unit can analyze the user's social media activity when symptoms are entered and input relevant symptoms. For example, if the user has posted about exercise on social media, the reception unit can input exercise-related symptoms. The reception unit can analyze the user's social media activity and input exercise-related symptoms if the user has posted about exercise. The reception unit can also input diet-related symptoms if the user has posted about diet. The reception unit can analyze the user's social media activity and input diet-related symptoms if the user has posted about diet. Furthermore, if the user has posted about sleep, the reception unit can input sleep-related symptoms if the user has posted about sleep. The reception unit can analyze the user's social media activity and input sleep-related symptoms if the user has posted about sleep. This allows for efficient input of relevant symptoms by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's social media activity into a generating AI and have the generating AI input the relevant symptoms.

[0058] The diagnostic unit can adjust the level of detail in the diagnosis based on the importance of the symptoms. For example, the diagnostic unit can perform a detailed diagnosis for important symptoms. For example, the diagnostic unit can evaluate important symptoms related to the user's health status and provide a detailed diagnostic result. The diagnostic unit can also perform a simplified diagnosis for less important symptoms. For example, the diagnostic unit can evaluate less important symptoms related to the user's health status and provide a simplified diagnostic result. Furthermore, the diagnostic unit can determine the priority of the diagnosis according to the importance of the symptoms. For example, the diagnostic unit can evaluate the importance of symptoms related to the user's health status and determine the priority of the diagnosis. This allows for a detailed diagnosis of important symptoms by adjusting the level of detail in the diagnosis based on the importance of the symptoms. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input data on the user's health status into a generating AI and have the generating AI perform the adjustment of the level of detail in the diagnosis based on the importance of the symptoms.

[0059] The diagnostic unit can apply different diagnostic algorithms depending on the symptom category during diagnosis. For example, the diagnostic unit can apply a headache diagnostic algorithm for headaches. For example, the diagnostic unit can evaluate the user's headache symptoms and apply the headache diagnostic algorithm. The diagnostic unit can also apply an abdominal pain diagnostic algorithm for abdominal pain. For example, the diagnostic unit can evaluate the user's abdominal pain symptoms and apply the abdominal pain diagnostic algorithm. Furthermore, the diagnostic unit can apply a fever diagnostic algorithm for fever. For example, the diagnostic unit can evaluate the user's fever symptoms and apply the fever diagnostic algorithm. By applying different diagnostic algorithms depending on the symptom category, the accuracy of the diagnosis is improved. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input data on the user's health status into a generating AI and have the generating AI execute the application of a diagnostic algorithm according to the symptom category.

[0060] The diagnostic unit can determine the priority of diagnoses based on when the symptoms were submitted. For example, the diagnostic unit can prioritize diagnosing the most recent symptoms. For example, the diagnostic unit can evaluate the most recent symptoms related to the user's health status and provide diagnostic results with priority. The diagnostic unit can also perform diagnoses while referring to past symptoms. For example, the diagnostic unit can evaluate past symptoms related to the user's health status and provide diagnostic results while referring to them. Furthermore, the diagnostic unit can adjust the order of diagnoses according to when the symptoms were submitted. For example, the diagnostic unit can evaluate when the user's health status symptoms were submitted and adjust the order of diagnoses. This allows for prioritizing diagnoses based on when the symptoms were submitted, thereby prioritizing the most recent symptoms. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or not. For example, the diagnostic unit can input data on the user's health status into a generating AI and have the generating AI perform the determination of diagnostic priorities based on when the symptoms were submitted.

[0061] The diagnostic unit can adjust the order of diagnoses based on the relevance of symptoms during the diagnosis process. For example, the diagnostic unit can prioritize diagnosing highly relevant symptoms. For example, the diagnostic unit can evaluate highly relevant symptoms related to the user's health status and provide diagnostic results preferentially. The diagnostic unit can also postpone diagnosing less relevant symptoms. For example, the diagnostic unit can evaluate less relevant symptoms related to the user's health status and provide diagnostic results later. Furthermore, the diagnostic unit can adjust the order of diagnoses according to the relevance of symptoms. For example, the diagnostic unit can evaluate the relevance of symptoms related to the user's health status and adjust the order of diagnoses. This allows for prioritizing the diagnosis of highly relevant symptoms by adjusting the order of diagnoses based on the relevance of symptoms. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input data on the user's health status into a generating AI and have the generating AI perform the adjustment of the order of diagnoses based on the relevance of symptoms.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The health generation AI system can take into account the user's geographical location and provide advice based on region-specific health risks and environmental factors. For example, if the user lives in a high-altitude area, it can provide advice on health risks specific to high altitudes. If the user lives in an urban area, it can provide advice on environmental pollution and stress specific to urban areas. Furthermore, if the user lives by the sea, it can provide advice based on health risks and environmental factors specific to coastal areas. This allows for the provision of personalized health advice tailored to the user's living environment.

[0064] The AI-generated health advice system can analyze a user's social media activity and provide health advice based on their interests. For example, if a user frequently posts about fitness on social media, it can prioritize providing fitness-related advice. Similarly, if a user frequently posts about diet, it can provide dietary advice. Furthermore, if a user frequently posts about sleep, it can provide sleep-related advice. This allows for the provision of personalized health advice tailored to the user's interests.

[0065] The health generation AI system can analyze a user's past health data and propose a long-term health improvement plan. For example, it can analyze a user's past exercise records and propose a long-term exercise plan. It can also analyze a user's past dietary records and propose a long-term diet improvement plan. Furthermore, it can analyze a user's past sleep patterns and propose a long-term sleep improvement plan. This allows the system to provide a long-term health improvement plan based on the user's past health data.

[0066] The health generation AI system can take into account the user's geographical location and provide advice based on region-specific health risks and environmental factors. For example, if the user lives in a high-altitude area, it can provide advice on health risks specific to high altitudes. If the user lives in an urban area, it can provide advice on environmental pollution and stress specific to urban areas. Furthermore, if the user lives by the sea, it can provide advice based on health risks and environmental factors specific to coastal areas. This allows for the provision of personalized health advice tailored to the user's living environment.

[0067] The AI-generated health advice system can analyze a user's social media activity and provide health advice based on their interests. For example, if a user frequently posts about fitness on social media, it can prioritize providing fitness-related advice. Similarly, if a user frequently posts about diet, it can provide dietary advice. Furthermore, if a user frequently posts about sleep, it can provide sleep-related advice. This allows for the provision of personalized health advice tailored to the user's interests.

[0068] The health generation AI system can analyze a user's past health data and propose a long-term health improvement plan. For example, it can analyze a user's past exercise records and propose a long-term exercise plan. It can also analyze a user's past dietary records and propose a long-term diet improvement plan. Furthermore, it can analyze a user's past sleep patterns and propose a long-term sleep improvement plan. This allows the system to provide a long-term health improvement plan based on the user's past health data.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The data collection unit collects data such as the user's lifestyle, exercise records, meal records, and sleep patterns. The data collection unit can acquire data from existing health management apps or wearable devices, for example. The data collection unit can acquire exercise records from smartwatches and fitness trackers, meal details from meal logging apps, and sleep patterns from sleep trackers. Step 2: The analysis unit analyzes the data collected by the data collection unit and evaluates the user's health status. The analysis unit identifies problems such as lack of exercise and irregular eating patterns, analyzes exercise records to evaluate the user's exercise level, analyzes eating records to evaluate the user's nutritional balance, and analyzes sleep patterns to evaluate the user's sleep quality. Step 3: Based on the health status evaluated by the analysis department, the proposal department proposes an optimal health improvement plan for the user. For users who are not getting enough exercise, the proposal department suggests daily walking and stretching, and proposes the distance and duration of walking, as well as the type and frequency of stretching. For users with irregular eating patterns, it proposes a nutritionally balanced meal menu, meal timing, and portion sizes. For users with disrupted sleep patterns, it suggests selecting comfortable bedding, adjusting the room temperature, and using meditation and deep breathing as relaxation methods. Step 4: The reception desk inputs the user's symptoms based on the health improvement plan proposed by the proposal desk. The reception desk allows users to input symptoms such as headaches or stomachaches, and they can input symptoms using a chat interface or voice input. Step 5: The diagnostic department makes an initial diagnosis based on the symptoms entered by the reception department and recommends that the patient visit an appropriate medical institution. The diagnostic department identifies possible illnesses based on symptoms such as headaches and abdominal pain. For example, based on headache symptoms, it may identify migraines or tension headaches, and based on abdominal pain symptoms, it may identify gastritis or enteritis. Furthermore, the diagnostic department recommends that the patient visit a neurologist in the case of migraines, or a gastroenterologist in the case of gastritis.

[0071] (Example of form 2) The Health Generation AI System according to an embodiment of the present invention is an agent-type generation AI service that provides personalized health advice in conjunction with existing health management apps and wearable devices. The Health Generation AI System analyzes the user's lifestyle data, exercise records, dietary records, sleep patterns, etc., and proposes an optimal health improvement plan. Furthermore, the Health Generation AI System also works in conjunction with a medical information database to provide initial diagnoses and recommendations for medical consultation based on symptoms. For example, the Health Generation AI System collects data such as the user's lifestyle data, exercise records, dietary records, and sleep patterns. This data is obtained from existing health management apps and wearable devices. Next, the Health Generation AI System uses AI to analyze the collected data and evaluate the user's health status. For example, it identifies problems such as lack of exercise or irregular eating patterns. Then, based on the analysis results, the Health Generation AI System proposes an optimal health improvement plan to the user. For example, it suggests daily walking and stretching to users who lack exercise, and suggests a balanced meal menu to users with irregular eating patterns. It also suggests improving the sleep environment and relaxation methods to users with disrupted sleep patterns. Furthermore, when a user enters their symptoms, the Health Generation AI system connects with a medical information database to perform an initial diagnosis. For example, if a user enters symptoms such as headache or stomach ache, the Health Generation AI system identifies possible illnesses based on those symptoms and recommends visiting an appropriate medical institution. This allows users to receive appropriate medical care early on. The Health Generation AI system also interacts with users through a chat interface, responding to health consultations and questions. For example, if a user asks, "I've been feeling tired lately, what should I do?", the Health Generation AI system provides appropriate advice based on the user's data. This allows users to easily consult about their health and receive appropriate advice. In this way, the Health Generation AI system supports users' health management by continuously analyzing their health data and providing personalized health advice.Furthermore, by providing initial diagnoses and recommendations for medical consultation based on symptom input, the system enables users to receive appropriate medical care early. This improves users' health and supports disease prevention and early detection. As a result, the Health Generation AI system can continuously monitor users' health status and provide personalized health advice.

[0072] The Health Generation AI system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a reception unit, and a diagnosis unit. The collection unit collects data such as the user's lifestyle data, exercise records, meal records, and sleep patterns. The collection unit acquires data from, for example, existing health management apps or wearable devices. The collection unit can acquire exercise records from, for example, smartwatches or fitness trackers. The collection unit can also acquire meal details from meal recording apps. Furthermore, the collection unit can acquire sleep patterns from sleep trackers. The analysis unit analyzes the data collected by the collection unit and evaluates the user's health status. The analysis unit identifies problems such as lack of exercise or irregular meal patterns. The analysis unit can analyze exercise records and evaluate the user's exercise level. The analysis unit can also analyze meal records and evaluate the user's nutritional balance. Furthermore, the analysis unit can analyze sleep patterns and evaluate the quality of the user's sleep. The proposal unit proposes an optimal health improvement plan to the user based on the health status evaluated by the analysis unit. The suggestion department, for example, suggests daily walking and stretching to users who lack exercise. The suggestion department can suggest, for example, the distance and duration of walking. It can also suggest the type and frequency of stretching. Furthermore, the suggestion department suggests balanced meal plans to users with irregular eating patterns. For example, it can suggest nutritionally balanced meal plans. It can also suggest the timing and quantity of meals. Additionally, the suggestion department suggests improving the sleep environment and relaxation methods to users with disrupted sleep patterns. For example, it can suggest selecting comfortable bedding and adjusting the room temperature. It can also suggest meditation and deep breathing as relaxation methods. The reception department inputs the user's symptoms based on the health improvement plan proposed by the suggestion department. For example, the reception department allows users to input symptoms such as headaches or stomachaches. The reception department allows users to input symptoms via a chat interface, for example. It also allows users to input symptoms using voice input.The diagnostic unit performs an initial diagnosis based on the symptoms entered by the reception unit and recommends that the user visit an appropriate medical institution. The diagnostic unit identifies possible illnesses based on symptoms such as headaches and abdominal pain. For example, based on headache symptoms, the diagnostic unit can identify migraines or tension headaches. The diagnostic unit can also identify gastritis or enteritis based on abdominal pain symptoms. Furthermore, the diagnostic unit recommends that the user visit an appropriate medical institution. For example, in the case of migraines, the diagnostic unit can recommend a visit to a neurologist. The diagnostic unit can also recommend a visit to a gastroenterologist in the case of gastritis. Thus, the Health Generation AI system according to this embodiment can provide personalized health advice by collecting, analyzing, suggesting, and diagnosing the user's lifestyle data.

[0073] The data collection unit collects data such as the user's lifestyle habits, exercise records, meal records, and sleep patterns. For example, the unit acquires data from existing health management apps and wearable devices. Specifically, it can acquire exercise records from smartwatches and fitness trackers. These devices record detailed exercise data such as the user's steps, heart rate, and calories burned in real time and transmit it to the data collection unit via Bluetooth or Wi-Fi. The data collection unit can also acquire meal details from meal tracking apps. By inputting meal details, data such as calories, nutrients, and meal timing are collected. Furthermore, the data collection unit can acquire sleep patterns from sleep trackers. Sleep trackers record the user's sleep duration, the ratio of deep to light sleep, and the number of times they toss and turn, and transmit this data to the data collection unit. This allows the data collection unit to centrally collect diverse data related to the user's lifestyle habits and understand their overall health status. Additionally, the data collection unit stores this data on a cloud server, making it accessible to the analysis and proposal units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. For example, data collection is tailored to user needs, such as collecting exercise data every minute and meal data after each meal. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0074] The analysis unit analyzes data collected by the data collection unit to assess the user's health status. For example, the analysis unit identifies problems such as lack of exercise or irregular eating patterns. Specifically, it can analyze exercise records to evaluate the user's exercise level. Using AI, it compares the user's exercise data with past data to detect increases or decreases in exercise volume and changes in patterns. It can also analyze eating records to assess the user's nutritional balance. The AI ​​analyzes eating data, calculates calorie intake and nutrient balance, and identifies excess or deficient nutrients. Furthermore, it can analyze sleep patterns to assess the user's sleep quality. The AI ​​analyzes sleep data, evaluates the ratio of deep to light sleep and sleep continuity, and quantifies sleep quality. This allows the analysis unit to quickly and accurately analyze collected data and comprehensively assess the user's health status. Additionally, the analysis unit can utilize past data and statistical information to analyze long-term health trends. For example, it can track changes in the user's exercise habits based on past exercise data to predict future health risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to handle not only real-time health status assessment but also long-term health management and anomaly detection, thereby improving the overall reliability and safety of the system.

[0075] The Proposal Department proposes an optimal health improvement plan to the user based on the health status evaluated by the Analysis Department. For example, for users who are not getting enough exercise, the Proposal Department suggests daily walking and stretching. Specifically, it suggests walking distance and duration, providing an exercise plan that matches the user's lifestyle. It also suggests types and frequencies of stretching to support the user in continuing without difficulty. Furthermore, for users with irregular eating patterns, the Proposal Department suggests a balanced meal plan. Using AI, it analyzes the user's eating data and generates a nutritionally balanced meal plan. For example, it suggests high-protein foods for breakfast and a menu rich in vegetables for lunch. It also suggests the timing and amount of meals to support the user in maintaining a healthy diet. In addition, for users with disrupted sleep patterns, the Proposal Department suggests improvements to the sleep environment and relaxation methods. Specifically, it suggests selecting comfortable bedding and adjusting the room temperature to support the user in getting quality sleep. It also suggests meditation and deep breathing as relaxation methods to help the user reduce stress. In this way, the Proposal Department can provide a personalized health improvement plan tailored to the user's health status and support the user in leading a healthy life.

[0076] The reception department inputs the user's symptoms based on the health improvement plan proposed by the proposal department. The reception department allows users to input symptoms such as headaches or stomachaches. Specifically, users can input symptoms through a chat interface. Users input symptoms in natural language, and AI analyzes the content to extract appropriate information. The reception department can also accept voice input from users. Using speech recognition technology, the system converts the user's voice into text and records the symptoms. This allows the reception department to easily and quickly input symptoms. Furthermore, the reception department can collect additional information about the user's symptoms. For example, inputting detailed information such as the timing and frequency of symptom occurrence and the severity of pain enables a more accurate diagnosis. This allows the reception department to collect detailed information about the user's symptoms and provide it to the diagnosis department.

[0077] The diagnostic department performs an initial diagnosis based on the symptoms entered by the reception department and recommends that the user visit an appropriate medical institution. For example, the diagnostic department identifies possible illnesses based on symptoms such as headaches and abdominal pain. Specifically, it uses AI to analyze the user's symptom data and make a diagnosis by comparing it with past case data. For example, it can identify migraines or tension headaches based on headache symptoms. It can also identify gastritis or enteritis based on abdominal pain symptoms. Furthermore, the diagnostic department recommends that the user visit an appropriate medical institution. The AI ​​selects the most suitable medical institution considering the user's place of residence and the urgency of the symptoms. For example, in the case of a migraine, it can recommend a visit to a neurologist, and in the case of gastritis, it can recommend a visit to a gastroenterologist. The diagnostic department also provides the user with information and precautions to bring when visiting a medical institution. This allows the diagnostic department to support the user in visiting an appropriate medical institution and receiving prompt and appropriate treatment. Furthermore, when notifying the user of the diagnosis results and recommendations for medical visits, the diagnostic department takes measures to protect the user's privacy. For example, the diagnosis results are encrypted and will not be disclosed to third parties without the user's consent. This allows the diagnostic department to provide appropriate medical support while protecting user privacy.

[0078] The data collection unit can acquire data from existing health management apps and wearable devices. For example, the data collection unit can acquire exercise records from smartwatches and fitness trackers. For example, the data collection unit can use the sensors of a smartwatch to record the user's steps and heart rate. The data collection unit can also use data from fitness trackers to record the user's exercise time and calories burned. Furthermore, the data collection unit can acquire meal details from food logging apps. For example, the data collection unit can acquire meal details entered by the user into a food logging app and evaluate the balance of nutrients. The data collection unit can also use data from food logging apps to record the frequency and amount of meals the user eats. Furthermore, the data collection unit can acquire sleep patterns from sleep trackers. For example, the data collection unit can use the sensors of a sleep tracker to record the user's sleep duration and sleep quality. The data collection unit can also use data from sleep trackers to record the user's sleep cycle and wake-up time. In this way, by acquiring data from existing health management apps and wearable devices, user lifestyle data can be efficiently collected. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from a smartwatch or fitness tracker into a generating AI and have the generating AI perform data analysis.

[0079] The analysis unit can identify problems such as lack of exercise and irregular eating patterns. For example, the analysis unit can analyze exercise records and evaluate the user's exercise level. For example, the analysis unit can identify lack of exercise based on the user's step count and exercise time. The analysis unit can also analyze eating records and evaluate the user's nutritional balance. For example, the analysis unit can identify nutrient deficiencies or excesses based on the user's diet. Furthermore, the analysis unit can analyze sleep patterns and evaluate the quality of the user's sleep. For example, the analysis unit can identify sleep deprivation and irregular sleep patterns based on the user's sleep duration and sleep cycle. This allows for an accurate assessment of the user's health status by identifying problems such as lack of exercise and irregular eating patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input exercise records, eating records, and sleep pattern data into a generating AI and have the generating AI perform the data analysis.

[0080] The suggestion unit can make daily walking and stretching suggestions. For example, the suggestion unit can suggest walking distance and duration. For example, the suggestion unit can suggest daily walking distance and duration based on the user's exercise level. The suggestion unit can also suggest the type and frequency of stretching. For example, the suggestion unit can suggest the type and frequency of daily stretching to improve the user's lack of exercise. In this way, by suggesting daily walking and stretching, the user's lack of exercise can be improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's exercise record into a generating AI and have the generating AI execute suggestions to improve the lack of exercise.

[0081] The suggestion unit can propose a balanced meal plan. For example, the suggestion unit can propose a nutritionally balanced meal plan. For example, the suggestion unit can propose a meal plan that considers the balance of nutrients based on the user's meal record. The suggestion unit can also propose the timing and amount of meals. For example, the suggestion unit can analyze the user's eating patterns and propose appropriate timing and amount of meals. In this way, by proposing a balanced meal plan, the user's eating patterns can be improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's meal record into a generating AI and have the generating AI produce a balanced meal plan proposal.

[0082] The suggestion unit can propose improvements to the sleep environment and relaxation methods. For example, the suggestion unit can suggest selecting comfortable bedding and adjusting the room temperature. For example, the suggestion unit can analyze the user's sleep pattern and propose selecting comfortable bedding and adjusting the room temperature. The suggestion unit can also suggest meditation and deep breathing as relaxation methods. For example, the suggestion unit can analyze the user's stress level and suggest meditation and deep breathing as relaxation methods. In this way, by suggesting improvements to the sleep environment and relaxation methods, the user's sleep pattern can be improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's sleep pattern into a generating AI and have the generating AI execute suggestions for improving the sleep environment and relaxation methods.

[0083] The diagnostic unit can identify possible illnesses based on symptoms such as headaches and abdominal pain, and recommend that the user visit an appropriate medical institution. For example, the diagnostic unit can identify migraines or tension headaches based on headache symptoms. For example, the diagnostic unit can analyze the headache symptoms entered by the user and identify migraines or tension headaches. The diagnostic unit can also identify gastritis or enteritis based on abdominal pain symptoms. For example, the diagnostic unit can analyze the abdominal pain symptoms entered by the user and identify gastritis or enteritis. Furthermore, the diagnostic unit recommends that the user visit an appropriate medical institution. For example, in the case of a migraine, the diagnostic unit can recommend that the user visit a neurologist. For example, in the case of gastritis, the diagnostic unit can recommend that the user visit a gastroenterologist. In this way, by identifying possible illnesses based on symptoms such as headaches and abdominal pain and recommending that the user visit an appropriate medical institution, the user can receive appropriate medical care early. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without using AI. For example, the diagnostic unit can input symptoms entered by the user into a generating AI, which can then identify possible illnesses and recommend seeking medical attention.

[0084] The diagnostic unit can interact with users through a chat interface and respond to health consultations and questions. For example, when a user enters a question through the chat interface, the diagnostic unit can provide appropriate advice. For example, if a user asks, "I've been feeling tired lately, what should I do?", the diagnostic unit can provide appropriate advice based on the user's data. The diagnostic unit can also provide appropriate health advice based on symptoms entered by the user. For example, if a user asks, "I've been having persistent headaches, what should I do?", the diagnostic unit can provide appropriate advice based on the headache symptoms. In this way, by interacting with users through a chat interface and responding to health consultations and questions, users can easily consult about their health. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or not using AI. For example, the diagnostic unit can input questions entered by the user through the chat interface into a generating AI and have the generating AI provide appropriate advice.

[0085] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect data during times when the user is relaxed. For example, the data collection unit can capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and collect data during times when the user is relaxed. Furthermore, if the user is tired, the data collection unit can collect data after rest. For example, the data collection unit can record the user's voice, estimate their emotions using voice analysis technology, and collect data after rest. In addition, if the user is active, the data collection unit can collect data after exercise. For example, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and collect data after exercise. This allows for data collection at a more appropriate time by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input user sentiment data into the generating AI and have the generating AI adjust the timing of data collection.

[0086] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection time based on the time periods when the user frequently collected data in the past. The data collection unit can suggest the optimal collection time based on the user's past data collection history. The data collection unit can suggest the optimal device based on the devices the user has used in the past. The data collection unit can suggest the optimal device based on the user's past data collection history. Furthermore, the data collection unit can suggest the most efficient collection method based on the user's past data collection history. The data collection unit can suggest the most efficient collection method based on the user's past data collection history. This allows the optimal collection method to be selected by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0087] The data collection unit can filter data based on the user's current health and lifestyle. For example, if the user is tired, the data collection unit can limit the data collected to reduce the burden. For example, the data collection unit can analyze the user's health and, if tired, limit the data collected to reduce the burden. The data collection unit can also collect detailed data if the user is in good health. For example, the data collection unit can analyze the user's health and, if good, collect detailed data. Furthermore, the data collection unit can adjust the type of data collected according to the user's lifestyle. For example, the data collection unit can analyze the user's lifestyle and adjust the type of data collected. This improves the accuracy of the collected data by filtering it based on the user's current health and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's health and lifestyle into a generating AI and have the generating AI perform data filtering.

[0088] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. For example, the data collection unit can capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and prioritize collecting stress-related data. The data collection unit can also prioritize collecting relaxation-related data if the user is relaxed. For example, the data collection unit can record the user's voice, estimate their emotions using voice analysis technology, and prioritize collecting relaxation-related data. Furthermore, if the user is active, the data collection unit can prioritize collecting exercise-related data. For example, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and prioritize collecting exercise-related data. This allows for the priority of data collection based on the user's emotions, enabling the collection of more important data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit may input user sentiment data into the generation AI and have the generation AI determine the priority of the data.

[0089] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is outdoors, the data collection unit can prioritize the collection of exercise-related data. For example, the data collection unit can analyze the user's geographical location information and prioritize the collection of exercise-related data if the user is outdoors. The data collection unit can also prioritize the collection of lifestyle-related data if the user is at home. For example, the data collection unit can analyze the user's geographical location information and prioritize the collection of lifestyle-related data if the user is at home. Furthermore, if the user is at work, the data collection unit can prioritize the collection of stress-related data. For example, the data collection unit can analyze the user's geographical location information and prioritize the collection of stress-related data if the user is at work. This improves data collection efficiency by prioritizing the collection of highly relevant data while considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0090] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts about exercise on social media, the data collection unit can collect exercise-related data. The data collection unit can analyze a user's social media activity and collect exercise-related data if the user posts about exercise. The data collection unit can also collect diet-related data if a user posts about food. The data collection unit can analyze a user's social media activity and collect diet-related data if the user posts about food. Furthermore, if a user posts about sleep, the data collection unit can collect sleep-related data. The data collection unit can analyze a user's social media activity and collect sleep-related data if the user posts about sleep. This allows for the efficient collection of relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into a generating AI and have the generating AI collect the relevant data.

[0091] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. For example, the analysis unit can capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and provide simple and easy-to-understand analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, the analysis unit can record the user's voice, estimate their emotions using voice analysis technology, and provide detailed analysis results. In addition, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and provide concise analysis results. This allows the analysis unit to provide user-friendly analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit may input user emotion data into the generating AI and have the generating AI adjust the way the analysis is expressed.

[0092] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. For example, the analysis unit can analyze important data related to the user's health status and provide detailed analysis results. The analysis unit can also perform a simplified analysis on less important data. For example, the analysis unit can analyze less important data related to the user's health status and provide simplified analysis results. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit can evaluate the importance of data related to the user's health status and determine the priority of the analysis. This allows for detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data related to the user's health status into a generating AI and have the generating AI adjust the level of detail of the analysis based on the importance of the data.

[0093] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an exercise analysis algorithm to exercise data. For example, the analysis unit can analyze a user's exercise record and apply an exercise analysis algorithm. The analysis unit can also apply a diet analysis algorithm to diet data. For example, the analysis unit can analyze a user's diet record and apply a diet analysis algorithm. Furthermore, the analysis unit can apply a sleep analysis algorithm to sleep data. For example, the analysis unit can analyze a user's sleep pattern and apply a sleep analysis algorithm. By applying different analysis algorithms depending on the data category, the accuracy of data analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user exercise record, diet record, and sleep pattern data into a generating AI and have the generating AI execute the application of analysis algorithms according to the data category.

[0094] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, the analysis unit can capture the user's facial expressions with a camera, estimate the emotions using an emotion estimation algorithm, and provide a short, concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, the analysis unit can record the user's voice, estimate the emotions using voice analysis technology, and provide a detailed analysis result. In addition, if the user is excited, the analysis unit can provide a visually stimulating analysis result. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the emotions using an emotion estimation algorithm, and provide a visually stimulating analysis result. This allows the analysis unit to provide an analysis result of an appropriate length for the user by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the length of the analysis.

[0095] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit can analyze the most recent data regarding the user's health status and provide the analysis results preferentially. The analysis unit can also perform analysis while referring to past data. For example, the analysis unit can analyze past data regarding the user's health status and provide the analysis results while referring to it. Furthermore, the analysis unit can adjust the order of analysis according to the data collection timing. For example, the analysis unit can evaluate the data collection timing regarding the user's health status and adjust the order of analysis. This allows the analysis to prioritize the analysis based on the data collection timing, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data regarding the user's health status into a generating AI and have the generating AI determine the priority of analysis based on the data collection timing.

[0096] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can analyze highly relevant data related to the user's health status and provide the analysis results preferentially. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit can analyze less relevant data related to the user's health status and provide the analysis results later. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. For example, the analysis unit can evaluate the relevance of data related to the user's health status and adjust the order of analysis. This allows for the prioritization of analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data related to the user's health status into a generating AI and have the generating AI perform the adjustment of the analysis order based on the relevance of the data.

[0097] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and easy-to-understand suggestions. For example, it can capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and then provide simple and easy-to-understand suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. For example, it can record the user's voice, estimate their emotions using voice analysis technology, and then provide detailed suggestions. In addition, if the user is in a hurry, the suggestion unit can provide concise suggestions. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and then provide concise suggestions. This allows the suggestion unit to provide suggestions that are easy for the user to understand by adjusting the way it presents suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the proposal section may be performed using AI, or not using AI. For example, the proposal section may input user sentiment data into the generative AI and have the generative AI adjust the way the proposal is expressed.

[0098] The proposal unit can adjust the level of detail of its proposals based on the importance of the health improvement plans. For example, the proposal unit can provide detailed proposals for important health improvement plans. For example, the proposal unit can evaluate important health improvement plans related to the user's health status and provide detailed proposals. The proposal unit can also provide simplified proposals for less important health improvement plans. For example, the proposal unit can evaluate less important health improvement plans related to the user's health status and provide simplified proposals. Furthermore, the proposal unit can determine the priority of proposals according to the importance of the health improvement plans. For example, the proposal unit can evaluate the importance of health improvement plans related to the user's health status and determine the priority of proposals. This allows for detailed proposals for important plans by adjusting the level of detail of the proposals based on the importance of the health improvement plans. Some or all of the above processing in the proposal unit may be performed using AI, or not using AI. For example, the proposal unit can input data on the user's health status into a generating AI and have the generating AI adjust the level of detail of the proposals based on the importance of the health improvement plans.

[0099] The suggestion unit can apply different suggestion algorithms depending on the category of the health improvement plan when making a suggestion. For example, the suggestion unit can apply an exercise suggestion algorithm to an exercise improvement plan. For example, the suggestion unit can analyze the user's exercise record and apply the exercise suggestion algorithm. The suggestion unit can also apply a diet suggestion algorithm to a diet improvement plan. For example, the suggestion unit can analyze the user's diet record and apply the diet suggestion algorithm. Furthermore, the suggestion unit can apply a sleep suggestion algorithm to a sleep improvement plan. For example, the suggestion unit can analyze the user's sleep pattern and apply the sleep suggestion algorithm. By applying different suggestion algorithms depending on the category of the health improvement plan, the accuracy of the suggestions is improved. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's exercise record, diet record, and sleep pattern into a generating AI and have the generating AI apply a suggestion algorithm according to the category of the health improvement plan.

[0100] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide a short, concise suggestion. For example, the suggestion unit can capture the user's facial expression with a camera, estimate the emotions using an emotion estimation algorithm, and provide a short, concise suggestion. Furthermore, if the user is relaxed, the suggestion unit can provide a detailed suggestion. For example, the suggestion unit can record the user's voice, estimate the emotions using voice analysis technology, and provide a detailed suggestion. In addition, if the user is excited, the suggestion unit can provide a visually stimulating suggestion. For example, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the emotions using an emotion estimation algorithm, and provide a visually stimulating suggestion. This allows the suggestion unit to provide suggestions of an appropriate length for the user by adjusting the length of the suggestion based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the proposal unit may be performed using AI, or not using AI. For example, the proposal unit may input user sentiment data into the generation AI and have the generation AI adjust the length of the proposal.

[0101] The proposal department can determine the priority of proposals based on the submission timing of health improvement plans. For example, the proposal department can prioritize the most recent health improvement plan. For example, the proposal department can evaluate the most recent health improvement plan related to the user's health status and prioritize its proposal. The proposal department can also make proposals while referring to past health improvement plans. For example, the proposal department can evaluate past health improvement plans related to the user's health status and refer to them when making proposals. Furthermore, the proposal department can adjust the order of proposals according to the submission timing of health improvement plans. For example, the proposal department can evaluate the submission timing of health improvement plans related to the user's health status and adjust the order of proposals. This allows the proposal department to prioritize the most recent plan by determining the priority of proposals based on the submission timing of health improvement plans. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on the user's health status into a generating AI and have the generating AI perform the determination of proposal priority based on the submission timing of health improvement plans.

[0102] The proposal unit can adjust the order of proposals based on the relevance of the health improvement plans when making a proposal. For example, the proposal unit can prioritize proposing health improvement plans that are highly relevant. For example, the proposal unit can evaluate health improvement plans that are highly relevant to the user's health condition and propose them preferentially. The proposal unit can also postpone proposing health improvement plans that are less relevant. For example, the proposal unit can evaluate health improvement plans that are less relevant to the user's health condition and postpone proposing them. Furthermore, the proposal unit can adjust the order of proposals according to the relevance of the health improvement plans. For example, the proposal unit can evaluate the relevance of health improvement plans to the user's health condition and adjust the order of proposals. This allows the proposal unit to prioritize proposing plans that are highly relevant by adjusting the order of proposals based on the relevance of the health improvement plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the user's health condition into a generating AI and have the generating AI perform the adjustment of the order of proposals based on the relevance of the health improvement plans.

[0103] The reception unit can estimate the user's emotions and adjust the timing of symptom input based on the estimated emotions. For example, if the user is stressed, the reception unit can prompt for symptom input during a relaxed period. For example, the reception unit can capture the user's facial expression with a camera, estimate their emotions using an emotion estimation algorithm, and prompt for symptom input during a relaxed period. The reception unit can also prompt for symptom input after rest if the user is tired. For example, the reception unit can record the user's voice, estimate their emotions using voice analysis technology, and prompt for symptom input after rest. Furthermore, if the user is active, the reception unit can prompt for symptom input after exercise. For example, the reception unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and prompt for symptom input after exercise. This allows for more appropriate symptom input by adjusting the timing of symptom input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the reception unit may be performed using AI, or not using AI. For example, the reception unit may input user emotion data into the generation AI and have the generation AI adjust the timing of symptom input.

[0104] The reception unit can analyze the user's past symptom input history and select the optimal input method. For example, the reception unit can suggest the optimal input method based on symptoms the user has frequently entered in the past. The reception unit can analyze the user's past symptom input history and suggest the optimal input method. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can analyze the user's past symptom input history and suggest the optimal input method. Furthermore, the reception unit can predict and suggest input methods to be used during specific time periods based on the user's past symptom input history. The reception unit can analyze the user's past symptom input history and predict and suggest input methods to be used during specific time periods. This allows the reception unit to select the optimal input method by analyzing the user's past symptom input history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's past symptom input history into a generating AI and have the generating AI select the optimal input method.

[0105] The reception unit can filter symptoms based on the user's current health and lifestyle when they are entered. For example, if the user is tired, the reception unit can limit the symptoms that can be entered to reduce the burden. The reception unit can analyze the user's health and, if tired, limit the symptoms that can be entered to reduce the burden. The reception unit can also allow the user to enter detailed symptoms if they are in good health. The reception unit can analyze the user's health and, if good, allow the user to enter detailed symptoms. Furthermore, the reception unit can adjust the types of symptoms entered according to the user's lifestyle. The reception unit can analyze the user's lifestyle and adjust the types of symptoms entered. This improves the accuracy of the symptoms entered by filtering them based on the user's current health and lifestyle. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's health and lifestyle into a generating AI and have the generating AI perform the symptom filtering.

[0106] The reception unit can estimate the user's emotions and determine the priority of symptoms to be entered based on the estimated emotions. For example, if the user is feeling stressed, the reception unit will prioritize inputting stress-related symptoms. For example, the reception unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and prioritize inputting stress-related symptoms. The reception unit can also prioritize inputting relaxation-related symptoms if the user is relaxed. For example, the reception unit can record the user's voice, estimate the emotion using voice analysis technology, and prioritize inputting relaxation-related symptoms. Furthermore, if the user is active, the reception unit can also prioritize inputting exercise-related symptoms. For example, the reception unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate the emotion using an emotion estimation algorithm, and prioritize inputting exercise-related symptoms. This allows for prioritizing the input of more important symptoms based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the reception unit may be performed using AI, or not using AI. For example, the reception unit may input user emotion data into the generation AI and have the generation AI determine the priority of the symptoms to be entered.

[0107] The reception unit can prioritize the input of highly relevant symptoms by considering the user's geographical location when symptoms are entered. For example, if the user is outdoors, the reception unit can prioritize the input of exercise-related symptoms. The reception unit can analyze the user's geographical location and prioritize the input of exercise-related symptoms if the user is outdoors. The reception unit can also prioritize the input of lifestyle-related symptoms if the user is at home. The reception unit can analyze the user's geographical location and prioritize the input of lifestyle-related symptoms if the user is at home. Furthermore, if the user is at work, the reception unit can prioritize the input of stress-related symptoms. The reception unit can analyze the user's geographical location and prioritize the input of stress-related symptoms if the user is at work. This improves the efficiency of symptom input by prioritizing the input of highly relevant symptoms by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into the generating AI, and have the AI ​​prioritize inputting symptoms that are highly relevant to that information.

[0108] The reception unit can analyze the user's social media activity when symptoms are entered and input relevant symptoms. For example, if the user has posted about exercise on social media, the reception unit can input exercise-related symptoms. The reception unit can analyze the user's social media activity and input exercise-related symptoms if the user has posted about exercise. The reception unit can also input diet-related symptoms if the user has posted about diet. The reception unit can analyze the user's social media activity and input diet-related symptoms if the user has posted about diet. Furthermore, if the user has posted about sleep, the reception unit can input sleep-related symptoms if the user has posted about sleep. The reception unit can analyze the user's social media activity and input sleep-related symptoms if the user has posted about sleep. This allows for efficient input of relevant symptoms by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input data on the user's social media activity into a generating AI and have the generating AI input the relevant symptoms.

[0109] The diagnostic unit can estimate the user's emotions and adjust the way the diagnosis is presented based on the estimated emotions. For example, if the user is stressed, the diagnostic unit can provide a simple and easy-to-understand diagnosis. For example, the diagnostic unit can capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and provide a simple and easy-to-understand diagnosis. The diagnostic unit can also provide a detailed diagnosis if the user is relaxed. For example, the diagnostic unit can record the user's voice, estimate their emotions using voice analysis technology, and provide a detailed diagnosis. Furthermore, if the user is in a hurry, the diagnostic unit can provide a concise diagnosis. For example, the diagnostic unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and provide a concise diagnosis. This allows the diagnostic unit to provide a diagnosis that is easy for the user to understand by adjusting the way the diagnosis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be, but is not limited to, text generating AI (e.g., LLM) or multimodal generating AI. Some or all of the above-described processes in the diagnostic unit may be performed using AI, or not using AI. For example, the diagnostic unit may input user emotion data into the generating AI and have the generating AI adjust the way the diagnosis is expressed.

[0110] The diagnostic unit can adjust the level of detail in the diagnosis based on the importance of the symptoms. For example, the diagnostic unit can perform a detailed diagnosis for important symptoms. For example, the diagnostic unit can evaluate important symptoms related to the user's health status and provide a detailed diagnostic result. The diagnostic unit can also perform a simplified diagnosis for less important symptoms. For example, the diagnostic unit can evaluate less important symptoms related to the user's health status and provide a simplified diagnostic result. Furthermore, the diagnostic unit can determine the priority of the diagnosis according to the importance of the symptoms. For example, the diagnostic unit can evaluate the importance of symptoms related to the user's health status and determine the priority of the diagnosis. This allows for a detailed diagnosis of important symptoms by adjusting the level of detail in the diagnosis based on the importance of the symptoms. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input data on the user's health status into a generating AI and have the generating AI perform the adjustment of the level of detail in the diagnosis based on the importance of the symptoms.

[0111] The diagnostic unit can apply different diagnostic algorithms depending on the symptom category during diagnosis. For example, the diagnostic unit can apply a headache diagnostic algorithm for headaches. For example, the diagnostic unit can evaluate the user's headache symptoms and apply the headache diagnostic algorithm. The diagnostic unit can also apply an abdominal pain diagnostic algorithm for abdominal pain. For example, the diagnostic unit can evaluate the user's abdominal pain symptoms and apply the abdominal pain diagnostic algorithm. Furthermore, the diagnostic unit can apply a fever diagnostic algorithm for fever. For example, the diagnostic unit can evaluate the user's fever symptoms and apply the fever diagnostic algorithm. By applying different diagnostic algorithms depending on the symptom category, the accuracy of the diagnosis is improved. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input data on the user's health status into a generating AI and have the generating AI execute the application of a diagnostic algorithm according to the symptom category.

[0112] The diagnostic unit can estimate the user's emotions and adjust the length of the diagnosis based on the estimated emotions. For example, if the user is in a hurry, the diagnostic unit can provide a short, concise diagnosis. For example, the diagnostic unit can capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and provide a short, concise diagnosis. The diagnostic unit can also provide a detailed diagnosis if the user is relaxed. For example, the diagnostic unit can record the user's voice, estimate their emotions using voice analysis technology, and provide a detailed diagnosis. Furthermore, if the user is excited, the diagnostic unit can provide a visually stimulating diagnosis. For example, the diagnostic unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and provide a visually stimulating diagnosis. This allows the diagnostic unit to provide a diagnosis of an appropriate length for the user by adjusting the length of the diagnosis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the diagnostic unit may be performed using AI, or not using AI. For example, the diagnostic unit may input user emotion data into the generating AI and have the generating AI adjust the length of the diagnosis.

[0113] The diagnostic unit can determine the priority of diagnoses based on when the symptoms were submitted. For example, the diagnostic unit can prioritize diagnosing the most recent symptoms. For example, the diagnostic unit can evaluate the most recent symptoms related to the user's health status and provide diagnostic results with priority. The diagnostic unit can also perform diagnoses while referring to past symptoms. For example, the diagnostic unit can evaluate past symptoms related to the user's health status and provide diagnostic results while referring to them. Furthermore, the diagnostic unit can adjust the order of diagnoses according to when the symptoms were submitted. For example, the diagnostic unit can evaluate when the user's health status symptoms were submitted and adjust the order of diagnoses. This allows for prioritizing diagnoses based on when the symptoms were submitted, thereby prioritizing the most recent symptoms. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or not. For example, the diagnostic unit can input data on the user's health status into a generating AI and have the generating AI perform the determination of diagnostic priorities based on when the symptoms were submitted.

[0114] The diagnostic unit can adjust the order of diagnoses based on the relevance of symptoms during the diagnosis process. For example, the diagnostic unit can prioritize diagnosing highly relevant symptoms. For example, the diagnostic unit can evaluate highly relevant symptoms related to the user's health status and provide diagnostic results preferentially. The diagnostic unit can also postpone diagnosing less relevant symptoms. For example, the diagnostic unit can evaluate less relevant symptoms related to the user's health status and provide diagnostic results later. Furthermore, the diagnostic unit can adjust the order of diagnoses according to the relevance of symptoms. For example, the diagnostic unit can evaluate the relevance of symptoms related to the user's health status and adjust the order of diagnoses. This allows for prioritizing the diagnosis of highly relevant symptoms by adjusting the order of diagnoses based on the relevance of symptoms. Some or all of the above processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input data on the user's health status into a generating AI and have the generating AI perform the adjustment of the order of diagnoses based on the relevance of symptoms.

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

[0116] The health generation AI system can further estimate the user's emotions and adjust the content of health advice based on those emotions. For example, if the user is feeling stressed, it can prioritize providing advice on relaxation methods and stress reduction. If the user is relaxed, it can suggest more proactive health improvement plans. Furthermore, if the user is tired, it can provide advice on rest and recovery. This allows for the provision of personalized health advice tailored to the user's emotions.

[0117] The health generation AI system can take into account the user's geographical location and provide advice based on region-specific health risks and environmental factors. For example, if the user lives in a high-altitude area, it can provide advice on health risks specific to high altitudes. If the user lives in an urban area, it can provide advice on environmental pollution and stress specific to urban areas. Furthermore, if the user lives by the sea, it can provide advice based on health risks and environmental factors specific to coastal areas. This allows for the provision of personalized health advice tailored to the user's living environment.

[0118] The AI-generated health advice system can analyze a user's social media activity and provide health advice based on their interests. For example, if a user frequently posts about fitness on social media, it can prioritize providing fitness-related advice. Similarly, if a user frequently posts about diet, it can provide dietary advice. Furthermore, if a user frequently posts about sleep, it can provide sleep-related advice. This allows for the provision of personalized health advice tailored to the user's interests.

[0119] The health generation AI system can estimate the user's emotions and adjust the timing of health advice based on those emotions. For example, if the user is stressed, it can provide health advice during a time when they are relaxed. If the user is tired, it can provide health advice after rest. Furthermore, if the user is active, it can provide health advice after exercise. This allows for the provision of health advice at the appropriate time according to the user's emotions.

[0120] The health generation AI system can analyze a user's past health data and propose a long-term health improvement plan. For example, it can analyze a user's past exercise records and propose a long-term exercise plan. It can also analyze a user's past dietary records and propose a long-term diet improvement plan. Furthermore, it can analyze a user's past sleep patterns and propose a long-term sleep improvement plan. This allows the system to provide a long-term health improvement plan based on the user's past health data.

[0121] The health generation AI system can estimate a user's emotions and adjust the content of health advice based on those emotions. For example, if a user is feeling stressed, it can prioritize providing advice on relaxation methods and stress reduction. If the user is relaxed, it can suggest more proactive health improvement plans. Furthermore, if the user is tired, it can provide advice on rest and recovery. This allows for the provision of personalized health advice tailored to the user's emotions.

[0122] The health generation AI system can take into account the user's geographical location and provide advice based on region-specific health risks and environmental factors. For example, if the user lives in a high-altitude area, it can provide advice on health risks specific to high altitudes. If the user lives in an urban area, it can provide advice on environmental pollution and stress specific to urban areas. Furthermore, if the user lives by the sea, it can provide advice based on health risks and environmental factors specific to coastal areas. This allows for the provision of personalized health advice tailored to the user's living environment.

[0123] The AI-generated health advice system can analyze a user's social media activity and provide health advice based on their interests. For example, if a user frequently posts about fitness on social media, it can prioritize providing fitness-related advice. Similarly, if a user frequently posts about diet, it can provide dietary advice. Furthermore, if a user frequently posts about sleep, it can provide sleep-related advice. This allows for the provision of personalized health advice tailored to the user's interests.

[0124] The health generation AI system can estimate the user's emotions and adjust the timing of health advice based on those emotions. For example, if the user is stressed, it can provide health advice during a time when they are relaxed. If the user is tired, it can provide health advice after rest. Furthermore, if the user is active, it can provide health advice after exercise. This allows for the provision of health advice at the appropriate time according to the user's emotions.

[0125] The health generation AI system can analyze a user's past health data and propose a long-term health improvement plan. For example, it can analyze a user's past exercise records and propose a long-term exercise plan. It can also analyze a user's past dietary records and propose a long-term diet improvement plan. Furthermore, it can analyze a user's past sleep patterns and propose a long-term sleep improvement plan. This allows the system to provide a long-term health improvement plan based on the user's past health data.

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

[0127] Step 1: The data collection unit collects data such as the user's lifestyle, exercise records, meal records, and sleep patterns. The data collection unit can acquire data from existing health management apps or wearable devices, for example. The data collection unit can acquire exercise records from smartwatches and fitness trackers, meal details from meal logging apps, and sleep patterns from sleep trackers. Step 2: The analysis unit analyzes the data collected by the data collection unit and evaluates the user's health status. The analysis unit identifies problems such as lack of exercise and irregular eating patterns, analyzes exercise records to evaluate the user's exercise level, analyzes eating records to evaluate the user's nutritional balance, and analyzes sleep patterns to evaluate the user's sleep quality. Step 3: Based on the health status evaluated by the analysis department, the proposal department proposes an optimal health improvement plan for the user. For users who are not getting enough exercise, the proposal department suggests daily walking and stretching, and proposes the distance and duration of walking, as well as the type and frequency of stretching. For users with irregular eating patterns, it proposes a nutritionally balanced meal menu, meal timing, and portion sizes. For users with disrupted sleep patterns, it suggests selecting comfortable bedding, adjusting the room temperature, and using meditation and deep breathing as relaxation methods. Step 4: The reception desk inputs the user's symptoms based on the health improvement plan proposed by the proposal desk. The reception desk allows users to input symptoms such as headaches or stomachaches, and they can input symptoms using a chat interface or voice input. Step 5: The diagnostic department makes an initial diagnosis based on the symptoms entered by the reception department and recommends that the patient visit an appropriate medical institution. The diagnostic department identifies possible illnesses based on symptoms such as headaches and abdominal pain. For example, based on headache symptoms, it may identify migraines or tension headaches, and based on abdominal pain symptoms, it may identify gastritis or enteritis. Furthermore, the diagnostic department recommends that the patient visit a neurologist in the case of migraines, or a gastroenterologist in the case of gastritis.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0131] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, reception unit, and diagnostic unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart device 14 and acquires exercise records from a smartwatch or fitness tracker. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to evaluate the user's health status. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a health improvement plan based on the analysis results. The reception unit is implemented by the control unit 46A of the smart device 14 and receives the user's symptoms. The diagnostic unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs an initial diagnosis based on the entered symptoms and recommends visiting an appropriate medical institution. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0133] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

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

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

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0144] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0146] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, reception unit, and diagnostic unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart glasses 214 and acquires exercise records from a smartwatch or fitness tracker. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to evaluate the user's health status. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a health improvement plan based on the analysis results. The reception unit is implemented by the control unit 46A of the smart glasses 214 and the user inputs symptoms. The diagnostic unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs an initial diagnosis based on the input symptoms and recommends visiting an appropriate medical institution. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

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

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

[0162] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, reception unit, and diagnostic unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the headset terminal 314 and acquires exercise records from a smartwatch or fitness tracker. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to evaluate the user's health status. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a health improvement plan based on the analysis results. The reception unit is implemented by the control unit 46A of the headset terminal 314 and the user inputs symptoms. The diagnostic unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs an initial diagnosis based on the input symptoms and recommends visiting an appropriate medical institution. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

[0171] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

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

[0173] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0177] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0179] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, reception unit, and diagnostic unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the robot 414 and acquires exercise records from a smartwatch or fitness tracker. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to evaluate the user's health status. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a health improvement plan based on the analysis results. The reception unit is implemented by the control unit 46A of the robot 414 and receives input from the user regarding their symptoms. The diagnostic unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs an initial diagnosis based on the input symptoms and recommends visiting an appropriate medical institution. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

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

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

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

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

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

[0186] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

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

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

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

[0190] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0191] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0193] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

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

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

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

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

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

[0199] (Note 1) A data collection unit that collects data such as the user's lifestyle, exercise records, diet records, and sleep patterns, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates the user's health status, Based on the health status evaluated by the aforementioned analysis unit, a proposal unit proposes an optimal health improvement plan to the user. Based on the health improvement plan proposed by the aforementioned proposal unit, a reception unit inputs the user's symptoms, The system includes a diagnostic unit that performs an initial diagnosis based on the symptoms entered by the reception unit and recommends that the patient visit an appropriate medical institution. A system characterized by the following features. (Note 2) The aforementioned collection unit is Data is acquired from existing health management apps and wearable devices. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Identify problems such as lack of exercise and irregular eating patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We offer suggestions for daily walking and stretching. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We propose a balanced meal plan. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Suggestions for improving your sleep environment and relaxation methods. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned diagnostic unit, Based on symptoms such as headaches and stomachaches, we identify possible illnesses and recommend seeking appropriate medical attention. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned diagnostic unit, We interact with users through a chat interface and respond to their health concerns and questions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the health improvement plan. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, a different proposal algorithm is applied depending on the category of the health improvement plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of the submission of health improvement plans. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making proposals, adjust the order of suggestions based on their relevance within the health improvement plan. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of symptom input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reception unit is The system analyzes the user's past symptom input history and selects the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reception unit is When entering symptoms, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned reception unit is It estimates the user's emotions and determines the priority of symptoms to input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reception unit is When entering symptoms, the system prioritizes the input of symptoms that are most relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reception unit is When a user enters their symptoms, the system analyzes their social media activity and inputs relevant symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned diagnostic unit, The system estimates the user's emotions and adjusts the way the diagnosis is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned diagnostic unit, During diagnosis, adjust the level of detail in the diagnosis based on the severity of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned diagnostic unit, During diagnosis, different diagnostic algorithms are applied depending on the category of symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned diagnostic unit, It estimates the user's emotions and adjusts the length of the diagnosis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned diagnostic unit, When making a diagnosis, the priority of the diagnosis is determined based on when the symptoms were first presented. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned diagnostic unit, During diagnosis, the order of diagnoses is adjusted based on the relevance of the symptoms. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A data collection unit that collects data such as the user's lifestyle, exercise records, diet records, and sleep patterns, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates the user's health status, Based on the health status evaluated by the aforementioned analysis unit, a proposal unit proposes an optimal health improvement plan to the user. Based on the health improvement plan proposed by the aforementioned proposal unit, a reception unit inputs the user's symptoms, The system includes a diagnostic unit that performs an initial diagnosis based on the symptoms entered by the reception unit and recommends that the patient visit an appropriate medical institution. A system characterized by the following features.

2. The aforementioned collection unit is Data is acquired from existing health management apps and wearable devices. The system according to feature 1.

3. The aforementioned analysis unit, Identify problems such as lack of exercise and irregular eating patterns. The system according to feature 1.

4. The aforementioned proposal section is, We offer suggestions for daily walking and stretching. The system according to feature 1.

5. The aforementioned proposal section is, We propose a balanced meal plan. The system according to feature 1.

6. The aforementioned proposal section is, Suggestions for improving your sleep environment and relaxation methods. The system according to feature 1.

7. The aforementioned diagnostic unit, Based on symptoms such as headaches and stomachaches, we identify possible illnesses and recommend seeking appropriate medical attention. The system according to feature 1.

8. The aforementioned diagnostic unit, We interact with users through a chat interface and respond to their health concerns and questions. The system according to feature 1.

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