system

Abstract

The system according to this embodiment aims to comprehensively manage individual health data and provide personalized advice and virtual consultations with doctors. [Solution] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, a management unit, a consultation unit, and a reporting unit. The collection unit collects health data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides personalized advice based on the analysis results obtained by the analysis unit. The management unit manages chronic diseases, provides medication reminders, and coaches nutrition and fitness. The consultation unit conducts virtual consultations with doctors. The reporting unit provides AI-generated health reports.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, comprehensive management of individual health data and provision of personalized advice and virtual consultations with doctors have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to comprehensively manage individual health data and provide personalized advice and virtual consultations with doctors.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a management unit, a consultation unit, and a reporting unit. The data collection unit collects health data. The analysis unit analyzes the data collected by the data collection unit. The data provision unit provides personalized advice based on the analysis results obtained by the analysis unit. The management unit manages chronic diseases, provides medication reminders, and coaches on nutrition and fitness. The consultation unit conducts virtual consultations with doctors. The reporting unit provides AI-generated health reports. [Effects of the Invention]

[0007] The system according to this embodiment can comprehensively manage individual health data and provide personalized advice and virtual consultations with doctors. [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 receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) A next-generation AI-powered virtual health assistant system according to an embodiment of the present invention is designed to support the journey of health and wellness. This system integrates with wearable devices and smart home systems to monitor real-time metrics such as heart rate, sleep, and activity levels. The system provides personalized health advice and preventative care tailored to individual needs. For example, it manages chronic diseases, provides medication reminders, and offers nutrition and fitness coaching. Furthermore, the system enables seamless virtual consultations with doctors through telehealth integration and clearly indicates progress by providing AI-generated health reports. The system supports users in managing their health and allows them to experience the "magic" of health through secure data management. For example, the system includes a collection unit that collects user health data, and an analysis unit that analyzes the collected data. It also includes a delivery unit that provides personalized advice based on the results, and a management unit that manages chronic diseases, provides medication reminders, and offers nutrition and fitness coaching. Additionally, it includes a consultation unit for telehealth integration and a reporting unit that provides AI-generated health reports. This allows next-generation AI-powered virtual health assistant systems to collect and analyze users' health data, provide personalized advice, manage chronic diseases, set medication reminders, and offer nutrition and fitness coaching. The system can also clearly demonstrate the user's health progress by facilitating virtual consultations with doctors and providing AI-generated health reports.

[0029] The next-generation AI-powered virtual health assistant system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a management unit, a consultation unit, and a reporting unit. The data collection unit collects health data. The data collection unit collects real-time metrics such as heart rate, sleep, and activity level by integrating with, for example, wearable devices and smart home systems. The data collection unit measures heart rate using, for example, a smartwatch or fitness tracker. The data collection unit can also monitor sleep patterns using a smart home system. Furthermore, the data collection unit can also use a fitness tracker to measure activity levels. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data and evaluates the user's health status. For example, the analysis unit analyzes heart rate data and evaluates the user's cardiac health status. The analysis unit can also analyze sleep data and evaluate the user's sleep quality. Furthermore, the analysis unit can analyze activity level data and evaluate the user's exercise habits. The data provision unit provides personalized advice based on the analysis results obtained by the analysis unit. The Service Department provides users with optimal health advice and preventative care. For example, it offers dietary suggestions. It can also recommend exercise. Furthermore, it can suggest lifestyle improvements. The Management Department manages chronic diseases, provides medication reminders, and offers nutrition and fitness coaching. For example, it manages diabetes. It can also manage hypertension. Furthermore, it can manage heart disease. The Consultation Department conducts virtual consultations with doctors through telehealth integration. For example, it uses video calls for consultations with doctors. It can also use chat for consultations. Furthermore, it can use email for consultations. The Reporting Department provides AI-generated health reports, clearly showing the user's health progress. For example, the Reporting Department visualizes data. It can also summarize analysis results. Furthermore, the Reporting Department can generate and provide health reports to users.This enables next-generation AI-powered virtual health assistant systems to provide an integrated system that collects, analyzes, advises, manages, consults on, and generates reports on health data.

[0030] The data collection unit collects health data. For example, it integrates with wearable devices and smart home systems to collect real-time metrics such as heart rate, sleep, and activity levels. Specifically, it measures heart rate using smartwatches and fitness trackers, which are worn on the user's wrist and monitor heart rate 24 hours a day. Heart rate data is transmitted to the data collection unit via Bluetooth® or Wi-Fi and stored in a database in real time. The data collection unit can also monitor sleep patterns using smart home systems. For example, sensors built into smart beds and smart mattresses detect the user's movements and breathing patterns, collecting this data. This allows for a detailed understanding of the user's sleep quality and duration. Furthermore, the data collection unit can use fitness trackers to measure activity levels. Fitness trackers record the user's steps, calories burned, exercise time, etc., and transmit this data to the data collection unit. This allows for an accurate understanding of the user's daily activity level. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and provisioning departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department analyzes the collected data to assess the user's health status. Specifically, it analyzes heart rate data to assess the user's cardiac health. An AI-based analysis algorithm detects heart rate variability and abnormal patterns to assess the risk of heart disease. The analysis department can also analyze sleep data to assess the quality of the user's sleep. The AI ​​analyzes sleep depth and cycles to assess how much deep sleep the user is getting. Furthermore, the analysis department can analyze activity level data to assess the user's exercise habits. The AI ​​analyzes daily exercise volume and calorie expenditure to assess the user's fitness level. This allows the analysis department to quickly and accurately analyze the collected data and comprehensively assess the user's health status. In addition, the analysis department can utilize historical data and statistical information to conduct long-term health trend and risk assessments. For example, based on historical heart rate data, it can track changes in the user's cardiac health and predict future risks. The analysis department can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. 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 reliability and safety of the entire system.

[0032] The service provider offers personalized advice based on the analysis results obtained by the analysis department. For example, the service provider provides users with optimal health advice and preventative care. Specifically, it offers meal suggestions. The AI ​​creates a nutritionally balanced meal plan based on the user's health status and eating history. The service provider can also recommend exercise. The AI ​​suggests exercise programs tailored to the user's fitness level and goals, and provides specific exercises and training menus. Furthermore, the service provider can suggest improvements to lifestyle habits. The AI ​​analyzes the user's lifestyle data and provides advice for stress management and sleep improvement. This allows the service provider to provide personalized advice tailored to the user's health status and support them in leading a healthy life. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, the user provides feedback on the results of following the provided advice, and the AI ​​adjusts the advice based on that data. The service provider can also reliably transmit information using multiple communication methods. For example, important information is reliably delivered not only through smartphone notifications but also through voice calls, SMS, and email. This allows the service provider to provide users with prompt and reliable advice and support their health management.

[0033] The management department handles chronic disease management, medication reminders, and nutrition and fitness coaching. For example, it manages diabetes, specifically providing blood glucose monitoring and insulin administration reminders. AI analyzes the user's blood glucose data and suggests appropriate insulin administration timing. The management department can also manage hypertension, analyzing blood pressure data and providing appropriate medication reminders and lifestyle improvement advice. Furthermore, it can manage heart disease, analyzing heart rate and other health data to assess the risk of heart disease and propose an appropriate management plan. This allows the management department to effectively manage users' chronic diseases and support them in maintaining their health. Additionally, the management department can collect user feedback to continuously improve the accuracy and effectiveness of management plans. For example, users can provide feedback on their results following the provided management plan, and the AI ​​can adjust the plan based on that data. The management department can also reliably transmit information using multiple communication methods, such as smartphone notifications, voice calls, SMS, and email, to ensure important information is delivered reliably. This allows the administration department to provide users with management plans quickly and reliably, supporting them in managing chronic diseases.

[0034] The consultation service will provide virtual consultations with doctors through telehealth integration. For example, the consultation service will use video calls to facilitate consultations with doctors. Specifically, users can use smartphones or tablets to have real-time video calls with doctors to discuss their health status and symptoms. The consultation service will also allow consultations with doctors via chat. Users can send questions to doctors via text messages and receive responses. Furthermore, the consultation service will also allow consultations with doctors via email. Users can send detailed health information and questions via email and receive replies from doctors. This allows the consultation service to provide users with opportunities to consult directly with doctors and support their health management. In addition, the consultation service can collect user feedback and continuously improve the accuracy and effectiveness of its consultations. For example, it can provide feedback on the results of consultations based on the user's provided information, and AI can adjust the consultation content based on that data. The consultation service can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only video calls, chat, and email, but also voice calls and SMS. This allows the consultation service to provide users with quick and reliable consultation opportunities and support their health management.

[0035] The reporting department provides AI-generated health reports that clearly show users' health progress. For example, the reporting department visualizes data. Specifically, it converts collected health data into graphs and charts, allowing users to visually understand their health status. The reporting department can also summarize analysis results. The AI ​​analyzes the collected data, extracts key points and trends, and summarizes them in an easy-to-understand way for users. Furthermore, the reporting department can generate and provide health reports to users. Based on the collected data and analysis results, the AI ​​creates reports detailing the user's health status and progress. This allows the reporting department to provide users with information to understand their own health status and take necessary actions. Additionally, the reporting department can collect user feedback and continuously improve the accuracy and effectiveness of the reports. For example, the AI ​​adjusts the report content based on user feedback on the reports provided. The reporting department can also reliably transmit information using multiple communication methods. For example, reports can be provided not only through smartphone notifications but also via email and cloud storage. This allows the reporting department to provide users with health reports quickly and reliably, supporting their health management.

[0036] The data collection unit can integrate with wearable devices and smart home systems to collect real-time metrics such as heart rate, sleep, and activity level. For example, the data collection unit can measure heart rate using a smartwatch. For example, the data collection unit can measure activity level using a fitness tracker. For example, the data collection unit can monitor sleep patterns using a smart home system. This allows the data collection unit to provide more accurate health data by collecting real-time metrics. 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 acquired from a wearable device into a generating AI and have the generating AI perform data analysis.

[0037] The analysis unit can analyze the collected data and assess the user's health status. For example, the analysis unit can analyze heart rate data to assess the user's cardiac health. For example, the analysis unit can analyze sleep data to assess the user's sleep quality. For example, the analysis unit can analyze activity level data to assess the user's exercise habits. By doing so, the analysis unit can provide appropriate advice by assessing the user's health status. 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 the collected data into a generating AI and have the generating AI perform the data analysis.

[0038] The service provider can provide users with optimal health advice and preventive care based on the analysis results. For example, the service provider can suggest meals. For example, the service provider can also recommend exercise. For example, the service provider can also suggest improvements to lifestyle habits. In this way, the service provider can support health management by providing users with optimal health advice and preventive care. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI generate personalized advice.

[0039] The management department can manage chronic diseases, provide medication reminders, and offer nutrition and fitness coaching. For example, the management department can manage diabetes. For example, the management department can manage hypertension. For example, the management department can manage heart disease. In this way, the management department can support users' health management by managing chronic diseases, providing medication reminders, and offering nutrition and fitness coaching. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input chronic disease management data into a generating AI and have the generating AI generate suggestions for management methods.

[0040] The consultation department can conduct virtual consultations with doctors through telehealth integration. The consultation department can conduct consultations with doctors using, for example, video calls. The consultation department can also conduct consultations with doctors using, for example, chat. The consultation department can also conduct consultations with doctors using, for example, email. In this way, the consultation department can support users' health management by conducting virtual consultations with doctors. Some or all of the above processes in the consultation department may be performed using, for example, AI, or not using AI. For example, the consultation department can input the user's health data into a generating AI and have the generating AI generate the content of the consultation with the doctor.

[0041] The reporting unit can provide AI-generated health reports, clearly showing the user's health progress. The reporting unit can, for example, visualize data. The reporting unit can also, for example, summarize analysis results. The reporting unit can, for example, generate and provide health reports to users. In this way, the reporting unit can clearly show the user's health progress by providing AI-generated health reports. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input collected data into a generating AI and have the generating AI perform the generation of health reports.

[0042] The data collection unit can analyze the user's past health data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on the data the user has frequently collected in the past. For example, the data collection unit can adjust the collection frequency based on the user's past collection history and collect data at the optimal timing. For example, the data collection unit can analyze the user's past collection history and select the most efficient collection method. In this way, the data collection unit can select the optimal collection method by analyzing past 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 past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0043] The data collection unit can filter health data based on the user's current lifestyle and activity level. For example, if the user is exercising, the data collection unit can prioritize collecting exercise data. For example, if the user is resting, the data collection unit can prioritize collecting heart rate and sleep data. For example, if the user is working, the data collection unit can prioritize collecting stress level and concentration data. This allows the data collection unit to collect more relevant data by filtering the data based on the user's lifestyle and activity level. 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 lifestyle data into a generating AI and have the generating AI perform the data filtering.

[0044] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location when collecting health data. For example, if the user is at home, the data collection unit can prioritize the collection of sleep data and heart rate data. For example, if the user is at the gym, the data collection unit can prioritize the collection of exercise data and activity level data. For example, if the user is out, the data collection unit can prioritize the collection of step count data and distance traveled data. In this way, the data collection unit can collect more relevant data by collecting data based on the user's geographical location. 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 data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0045] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user is experiencing stress on social media, the data collection unit may prioritize collecting stress level and heart rate data. For example, if a user is relaxing on social media, the data collection unit may prioritize collecting sleep data and activity level data. For example, if a user is posting about exercise on social media, the data collection unit may prioritize collecting exercise data and activity level data. In this way, the data collection unit can collect 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 the user's social media activity data 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 health data during the analysis. For example, the analysis unit can analyze high-importance data (heart rate, blood pressure, etc.) in detail. The analysis unit can also analyze low-importance data (steps, distance traveled, etc.) in a simplified manner. The analysis unit can also adjust the frequency of analysis according to importance. This enables efficient data analysis by allowing the analysis unit to adjust the level of detail of the analysis based on the importance of the health 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 the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can apply a sleep stage analysis algorithm to sleep data. For example, the analysis unit can apply an exercise intensity analysis algorithm to activity level data. This allows the analysis unit to perform more accurate data analysis by applying different analysis algorithms depending on the category of health 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 the categories of health data into a generating AI and have the generating AI select the analysis algorithm to apply.

[0048] The analysis unit can determine the priority of analysis based on when the health data was collected. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit may also analyze current data while referring to past data. For example, the analysis unit may focus on analyzing data collected during a specific period. This enables efficient data analysis by allowing the analysis unit to determine the priority of analysis based on when the health data was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may input collection time data into a generating AI and have the generating AI determine the priority of analysis.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the health data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit can adjust the order of analysis according to the relevance of the data. This enables efficient data analysis by allowing the analysis unit to adjust the order of analysis based on the relevance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0050] The service provider can adjust the level of detail of advice based on the importance of the health data when providing advice. For example, the service provider can provide detailed advice based on high-importance data. For example, the service provider can also provide simplified advice based on low-importance data. The service provider can also adjust the frequency of advice according to its importance. This enables the service provider to provide advice efficiently by adjusting the level of detail of advice based on the importance of the health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0051] The service provider can apply different advice algorithms depending on the category of health data when providing advice. For example, the service provider can apply a heart rate variability analysis algorithm to advice based on heart rate data. For example, the service provider can also apply a sleep stage analysis algorithm to advice based on sleep data. For example, the service provider can also apply an exercise intensity analysis algorithm to advice based on activity level data. By applying different advice algorithms depending on the category of health data, the service provider can provide more accurate advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the category of health data into a generating AI and have the generating AI select the advice algorithm to apply.

[0052] The service provider can prioritize advice based on the timing of health data collection when providing advice. For example, the service provider may prioritize advice based on recently collected data. The service provider may also provide advice based on current data, while referring to past data. The service provider may also focus on providing advice based on data collected during a specific period. This enables the service provider to provide advice efficiently by prioritizing advice based on the timing of health data collection. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input collection timing data into a generating AI and have the generating AI determine the priority of advice.

[0053] The service provider can adjust the order of advice based on the relevance of health data when providing advice. For example, the service provider may prioritize advice based on highly relevant data. For example, the service provider may postpone advice based on less relevant data. For example, the service provider may adjust the order of advice according to the relevance of the data. This enables the service provider to provide advice efficiently by adjusting the order of advice based on the relevance of health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0054] The management department can analyze the user's past health data during management to select the optimal management method. For example, the management department can propose the optimal management method based on the user's past health data. The management department can also adjust the management method based on the user's past data and select the optimal method. For example, the management department can analyze the user's past data and select the most effective management method. In this way, the management department can select the optimal management method by analyzing the user's past health data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past health data into a generating AI and have the generating AI select the optimal management method.

[0055] The management unit can customize management methods based on the user's current living situation during management. For example, if the user is at work, the management unit can suggest stretches and relaxation methods that can be done during work breaks. If the user is at home, the management unit can also suggest health management methods that can be done at home. If the user is traveling, the management unit can also suggest health management methods that can be performed at the travel destination. This allows the management unit to provide more appropriate management by customizing management methods based on the user's current living situation. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input current living situation data into a generating AI and have the generating AI perform the customization of management methods.

[0056] The management department can select the optimal management method based on the user's geographical location information during management. For example, if the user is at home, the management department can suggest health management methods that can be done at home. For example, if the user is at a gym, the management department can also suggest health management methods that can be done at the gym. For example, if the user is out, the management department can also suggest health management methods that can be performed while out. This allows the management department to provide more appropriate management by selecting the optimal management method based on the user's geographical location information. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input geographical location data into a generating AI and have the generating AI select the optimal management method.

[0057] The management department can analyze users' social media activity and propose management measures during management. For example, if a user is experiencing stress on social media, the management department can propose stress management methods. For example, if a user is relaxing on social media, the management department can also propose ways to maintain that relaxation. For example, if a user is posting about exercise on social media, the management department can also propose exercise management methods. In this way, the management department can propose more appropriate management measures by analyzing users' social media activity. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input social media activity data into a generating AI and have the generating AI execute the proposal of management measures.

[0058] The consultation unit can select the optimal consultation method by referring to the user's past health data during a consultation. For example, the consultation unit can propose the optimal consultation method based on the user's past health data. The consultation unit can also adjust the consultation method based on the user's past data and select the optimal method. For example, the consultation unit can analyze the user's past data and select the most effective consultation method. In this way, the consultation unit can select the optimal consultation method by referring to the user's past health data. Some or all of the above processes in the consultation unit may be performed using AI, for example, or without AI. For example, the consultation unit can input past health data into a generating AI and have the generating AI select the optimal consultation method.

[0059] The consultation unit can customize the consultation content based on the user's current health condition during the consultation. For example, if the user is tired, the consultation unit may suggest consultation content related to fatigue recovery. For example, if the user is stressed, the consultation unit may also suggest consultation content related to stress management. For example, if the user is healthy, the consultation unit may also suggest consultation content related to maintaining health. In this way, the consultation unit can provide more appropriate consultation by customizing the consultation content based on the user's current health condition. Some or all of the above processing in the consultation unit may be performed using AI, for example, or without AI. For example, the consultation unit can input current health condition data into a generating AI and have the generating AI perform the customization of the consultation content.

[0060] The consultation unit can select the most suitable consultation method based on the user's geographical location information during a consultation. For example, if the user is at home, the consultation unit can suggest a consultation method that can be done at home. For example, if the user is at a gym, the consultation unit can suggest a consultation method that can be done at the gym. For example, if the user is out, the consultation unit can suggest a consultation method that can be done while out. In this way, the consultation unit can provide more appropriate consultations by selecting the most suitable consultation method based on the user's geographical location information. Some or all of the above processing in the consultation unit may be performed using AI, for example, or without AI. For example, the consultation unit can input geographical location data into a generating AI and have the generating AI select the most suitable consultation method.

[0061] The consultation department can analyze a user's social media activity and suggest consultation topics during a consultation. For example, if a user is experiencing stress on social media, the consultation department can suggest consultation topics related to stress management. For example, if a user is relaxing on social media, the consultation department can also suggest consultation topics to maintain that relaxation. For example, if a user is posting about exercise on social media, the consultation department can also suggest consultation topics related to exercise. In this way, the consultation department can suggest more appropriate consultation topics by analyzing the user's social media activity. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input social media activity data into a generating AI and have the generating AI generate consultation topic suggestions.

[0062] The reporting unit can adjust the level of detail in reports based on the importance of the health data during report generation. For example, the reporting unit can report highly important data (heart rate, blood pressure, etc.) in detail. It can also report less important data (steps, distance traveled, etc.) in a simplified manner. The reporting unit can also adjust the frequency of reports according to their importance. This allows the reporting unit to efficiently generate reports by adjusting the level of detail based on the importance of the health data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the importance of the health data into a generation AI and have the generation AI perform the adjustment of the level of detail in the reports.

[0063] The reporting unit can apply different report generation algorithms depending on the category of health data when generating reports. For example, the reporting unit can apply a heart rate variability analysis algorithm to heart rate data. For example, the reporting unit can apply a sleep stage analysis algorithm to sleep data. For example, the reporting unit can apply an exercise intensity analysis algorithm to activity level data. This allows the reporting unit to generate more accurate reports by applying different report generation algorithms depending on the category of health data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the categories of health data into a generation AI and have the generation AI select the report generation algorithm to apply.

[0064] The reporting unit can prioritize reports based on when health data was collected during report generation. For example, the reporting unit may prioritize reports based on recently collected data. The reporting unit may also provide reports based on current data, while referring to historical data. The reporting unit may also focus on providing reports based on data collected during a specific period. This enables efficient report generation by prioritizing reports based on when health data was collected. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input collection timing data into a generation AI and have the generation AI determine the report prioritization.

[0065] The reporting unit can adjust the order of reports based on the relevance of health data when generating reports. For example, the reporting unit may prioritize reports based on highly relevant data. For example, the reporting unit may postpone reports based on less relevant data. For example, the reporting unit can adjust the order of reports according to the relevance of the data. This enables efficient report generation by adjusting the order of reports based on the relevance of health data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the relevance of the data into a generation AI and have the generation AI perform the adjustment of the report order.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] Next-generation AI-powered virtual health assistant systems can analyze a user's past health data and provide optimal health advice. For example, they can suggest optimal health advice based on data the user has frequently collected in the past. They can also adjust the collection frequency based on the user's past collection history and collect data at the optimal time. Furthermore, they can analyze the user's past collection history and select the most efficient collection method. As a result, the system can provide optimal health advice by analyzing past collection history. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0068] Next-generation AI-powered virtual health assistant systems can provide health advice based on the user's geographical location. For example, if the user is at home, it can suggest health management methods that can be done at home. If the user is at a gym, it can suggest health management methods that can be done at the gym. Furthermore, if the user is out, it can suggest health management methods that can be performed while out. This allows the system to provide optimal health advice based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI provide optimal health advice.

[0069] A next-generation AI-powered virtual health assistant system can analyze a user's social media activity and provide relevant health advice. For example, if a user is experiencing stress on social media, it can provide advice on stress management. If a user is relaxing on social media, it can also provide advice on maintaining that relaxation. Furthermore, if a user posts about exercise on social media, it can provide advice on exercise. In this way, the system can provide relevant health advice by analyzing the user's social media activity. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI perform the task of providing relevant health advice.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The data collection unit collects health data. The data collection unit integrates with, for example, wearable devices and smart home systems to collect real-time metrics such as heart rate, sleep, and activity level. The data collection unit measures heart rate using, for example, a smartwatch or fitness tracker. The data collection unit can also monitor sleep patterns using a smart home system. Furthermore, the data collection unit can use a fitness tracker to measure activity levels. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to assess the user's health status. For example, the analysis unit analyzes heart rate data to assess the user's cardiac health. The analysis unit can also analyze sleep data to assess the user's sleep quality. Furthermore, the analysis unit can analyze activity level data to assess the user's exercise habits. Step 3: The service provider provides personalized advice based on the analysis results obtained by the analysis provider. For example, the service provider provides the user with optimal health advice and preventive care. For example, the service provider makes dietary suggestions. The service provider can also recommend exercise. Furthermore, the service provider can also suggest improvements to lifestyle habits. Step 4: The administration department manages chronic diseases, provides medication reminders, and offers nutrition and fitness coaching. For example, the administration department manages diabetes. It can also manage hypertension. Furthermore, it can also manage heart disease. Step 5: The consultation department conducts virtual consultations with doctors through telehealth integration. The consultation department can, for example, conduct consultations with doctors using video calls. The consultation department can also conduct consultations with doctors using chat. Furthermore, the consultation department can also conduct consultations with doctors using email. Step 6: The reporting unit provides AI-generated health reports, clearly showing the user's health progress. The reporting unit can, for example, visualize data. It can also summarize the analysis results. Furthermore, the reporting unit can generate and provide health reports to the user.

[0072] (Example of form 2) A next-generation AI-powered virtual health assistant system according to an embodiment of the present invention is designed to support the journey of health and wellness. This system integrates with wearable devices and smart home systems to monitor real-time metrics such as heart rate, sleep, and activity levels. The system provides personalized health advice and preventative care tailored to individual needs. For example, it manages chronic diseases, provides medication reminders, and offers nutrition and fitness coaching. Furthermore, the system enables seamless virtual consultations with doctors through telehealth integration and clearly indicates progress by providing AI-generated health reports. The system supports users in managing their health and allows them to experience the "magic" of health through secure data management. For example, the system includes a collection unit that collects user health data, and an analysis unit that analyzes the collected data. It also includes a delivery unit that provides personalized advice based on the results, and a management unit that manages chronic diseases, provides medication reminders, and offers nutrition and fitness coaching. Additionally, it includes a consultation unit for telehealth integration and a reporting unit that provides AI-generated health reports. This allows next-generation AI-powered virtual health assistant systems to collect and analyze users' health data, provide personalized advice, manage chronic diseases, set medication reminders, and offer nutrition and fitness coaching. The system can also clearly demonstrate the user's health progress by facilitating virtual consultations with doctors and providing AI-generated health reports.

[0073] The next-generation AI-powered virtual health assistant system according to this embodiment comprises a data collection unit, an analysis unit, a data provision unit, a management unit, a consultation unit, and a reporting unit. The data collection unit collects health data. The data collection unit collects real-time metrics such as heart rate, sleep, and activity level by integrating with, for example, wearable devices and smart home systems. The data collection unit measures heart rate using, for example, a smartwatch or fitness tracker. The data collection unit can also monitor sleep patterns using a smart home system. Furthermore, the data collection unit can also use a fitness tracker to measure activity levels. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data and evaluates the user's health status. For example, the analysis unit analyzes heart rate data and evaluates the user's cardiac health status. The analysis unit can also analyze sleep data and evaluate the user's sleep quality. Furthermore, the analysis unit can analyze activity level data and evaluate the user's exercise habits. The data provision unit provides personalized advice based on the analysis results obtained by the analysis unit. The Service Department provides users with optimal health advice and preventative care. For example, it offers dietary suggestions. It can also recommend exercise. Furthermore, it can suggest lifestyle improvements. The Management Department manages chronic diseases, provides medication reminders, and offers nutrition and fitness coaching. For example, it manages diabetes. It can also manage hypertension. Furthermore, it can manage heart disease. The Consultation Department conducts virtual consultations with doctors through telehealth integration. For example, it uses video calls for consultations with doctors. It can also use chat for consultations. Furthermore, it can use email for consultations. The Reporting Department provides AI-generated health reports, clearly showing the user's health progress. For example, the Reporting Department visualizes data. It can also summarize analysis results. Furthermore, the Reporting Department can generate and provide health reports to users.This enables next-generation AI-powered virtual health assistant systems to provide an integrated system that collects, analyzes, advises, manages, consults on, and generates reports on health data.

[0074] The data collection unit collects health data. For example, it integrates with wearable devices and smart home systems to collect real-time metrics such as heart rate, sleep, and activity levels. Specifically, it measures heart rate using smartwatches and fitness trackers, which are worn on the user's wrist and monitor heart rate 24 hours a day. Heart rate data is transmitted to the data collection unit via Bluetooth or Wi-Fi and stored in a database in real time. The data collection unit can also monitor sleep patterns using smart home systems. For example, sensors built into smart beds and smart mattresses detect the user's movements and breathing patterns, collecting this data. This allows for a detailed understanding of the user's sleep quality and duration. Furthermore, the data collection unit can use fitness trackers to measure activity levels. Fitness trackers record the user's steps, calories burned, exercise time, etc., and transmit this data to the data collection unit. This allows for an accurate understanding of the user's daily activity level. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and provisioning departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0075] The analysis department analyzes the data collected by the data collection department. For example, the analysis department analyzes the collected data to assess the user's health status. Specifically, it analyzes heart rate data to assess the user's cardiac health. An AI-based analysis algorithm detects heart rate variability and abnormal patterns to assess the risk of heart disease. The analysis department can also analyze sleep data to assess the quality of the user's sleep. The AI ​​analyzes sleep depth and cycles to assess how much deep sleep the user is getting. Furthermore, the analysis department can analyze activity level data to assess the user's exercise habits. The AI ​​analyzes daily exercise volume and calorie expenditure to assess the user's fitness level. This allows the analysis department to quickly and accurately analyze the collected data and comprehensively assess the user's health status. In addition, the analysis department can utilize historical data and statistical information to conduct long-term health trend and risk assessments. For example, based on historical heart rate data, it can track changes in the user's cardiac health and predict future risks. The analysis department can also use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. 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 reliability and safety of the entire system.

[0076] The service provider offers personalized advice based on the analysis results obtained by the analysis department. For example, the service provider provides users with optimal health advice and preventative care. Specifically, it offers meal suggestions. The AI ​​creates a nutritionally balanced meal plan based on the user's health status and eating history. The service provider can also recommend exercise. The AI ​​suggests exercise programs tailored to the user's fitness level and goals, and provides specific exercises and training menus. Furthermore, the service provider can suggest improvements to lifestyle habits. The AI ​​analyzes the user's lifestyle data and provides advice for stress management and sleep improvement. This allows the service provider to provide personalized advice tailored to the user's health status and support them in leading a healthy life. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, the user provides feedback on the results of following the provided advice, and the AI ​​adjusts the advice based on that data. The service provider can also reliably transmit information using multiple communication methods. For example, important information is reliably delivered not only through smartphone notifications but also through voice calls, SMS, and email. This allows the service provider to provide users with prompt and reliable advice and support their health management.

[0077] The management department handles chronic disease management, medication reminders, and nutrition and fitness coaching. For example, it manages diabetes, specifically providing blood glucose monitoring and insulin administration reminders. AI analyzes the user's blood glucose data and suggests appropriate insulin administration timing. The management department can also manage hypertension, analyzing blood pressure data and providing appropriate medication reminders and lifestyle improvement advice. Furthermore, it can manage heart disease, analyzing heart rate and other health data to assess the risk of heart disease and propose an appropriate management plan. This allows the management department to effectively manage users' chronic diseases and support them in maintaining their health. Additionally, the management department can collect user feedback to continuously improve the accuracy and effectiveness of management plans. For example, users can provide feedback on their results following the provided management plan, and the AI ​​can adjust the plan based on that data. The management department can also reliably transmit information using multiple communication methods, such as smartphone notifications, voice calls, SMS, and email, to ensure important information is delivered reliably. This allows the administration department to provide users with management plans quickly and reliably, supporting them in managing chronic diseases.

[0078] The consultation service will provide virtual consultations with doctors through telehealth integration. For example, the consultation service will use video calls to facilitate consultations with doctors. Specifically, users can use smartphones or tablets to have real-time video calls with doctors to discuss their health status and symptoms. The consultation service will also allow consultations with doctors via chat. Users can send questions to doctors via text messages and receive responses. Furthermore, the consultation service will also allow consultations with doctors via email. Users can send detailed health information and questions via email and receive replies from doctors. This allows the consultation service to provide users with opportunities to consult directly with doctors and support their health management. In addition, the consultation service can collect user feedback and continuously improve the accuracy and effectiveness of its consultations. For example, it can provide feedback on the results of consultations based on the user's provided information, and AI can adjust the consultation content based on that data. The consultation service can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only video calls, chat, and email, but also voice calls and SMS. This allows the consultation service to provide users with quick and reliable consultation opportunities and support their health management.

[0079] The reporting department provides AI-generated health reports that clearly show users' health progress. For example, the reporting department visualizes data. Specifically, it converts collected health data into graphs and charts, allowing users to visually understand their health status. The reporting department can also summarize analysis results. The AI ​​analyzes the collected data, extracts key points and trends, and summarizes them in an easy-to-understand way for users. Furthermore, the reporting department can generate and provide health reports to users. Based on the collected data and analysis results, the AI ​​creates reports detailing the user's health status and progress. This allows the reporting department to provide users with information to understand their own health status and take necessary actions. Additionally, the reporting department can collect user feedback and continuously improve the accuracy and effectiveness of the reports. For example, the AI ​​adjusts the report content based on user feedback on the reports provided. The reporting department can also reliably transmit information using multiple communication methods. For example, reports can be provided not only through smartphone notifications but also via email and cloud storage. This allows the reporting department to provide users with health reports quickly and reliably, supporting their health management.

[0080] The data collection unit can integrate with wearable devices and smart home systems to collect real-time metrics such as heart rate, sleep, and activity level. For example, the data collection unit can measure heart rate using a smartwatch. For example, the data collection unit can measure activity level using a fitness tracker. For example, the data collection unit can monitor sleep patterns using a smart home system. This allows the data collection unit to provide more accurate health data by collecting real-time metrics. 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 acquired from a wearable device into a generating AI and have the generating AI perform data analysis.

[0081] The analysis unit can analyze the collected data and assess the user's health status. For example, the analysis unit can analyze heart rate data to assess the user's cardiac health. For example, the analysis unit can analyze sleep data to assess the user's sleep quality. For example, the analysis unit can analyze activity level data to assess the user's exercise habits. By doing so, the analysis unit can provide appropriate advice by assessing the user's health status. 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 the collected data into a generating AI and have the generating AI perform the data analysis.

[0082] The service provider can provide users with optimal health advice and preventive care based on the analysis results. For example, the service provider can suggest meals. For example, the service provider can also recommend exercise. For example, the service provider can also suggest improvements to lifestyle habits. In this way, the service provider can support health management by providing users with optimal health advice and preventive care. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI generate personalized advice.

[0083] The management department can manage chronic diseases, provide medication reminders, and offer nutrition and fitness coaching. For example, the management department can manage diabetes. For example, the management department can manage hypertension. For example, the management department can manage heart disease. In this way, the management department can support users' health management by managing chronic diseases, providing medication reminders, and offering nutrition and fitness coaching. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input chronic disease management data into a generating AI and have the generating AI generate suggestions for management methods.

[0084] The consultation department can conduct virtual consultations with doctors through telehealth integration. The consultation department can conduct consultations with doctors using, for example, video calls. The consultation department can also conduct consultations with doctors using, for example, chat. The consultation department can also conduct consultations with doctors using, for example, email. In this way, the consultation department can support users' health management by conducting virtual consultations with doctors. Some or all of the above processes in the consultation department may be performed using, for example, AI, or not using AI. For example, the consultation department can input the user's health data into a generating AI and have the generating AI generate the content of the consultation with the doctor.

[0085] The reporting unit can provide AI-generated health reports, clearly showing the user's health progress. The reporting unit can, for example, visualize data. The reporting unit can also, for example, summarize analysis results. The reporting unit can, for example, generate and provide health reports to users. In this way, the reporting unit can clearly show the user's health progress by providing AI-generated health reports. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input collected data into a generating AI and have the generating AI perform the generation of health reports.

[0086] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection frequency and collect data when the user is relaxed. For example, if the user is relaxed, the data collection unit can increase the collection frequency and collect more detailed data. For example, if the user is in a hurry, the data collection unit can shorten the collection timing and collect data quickly. This allows the data collection unit to collect more appropriate data by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0087] The data collection unit can analyze the user's past health data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on the data the user has frequently collected in the past. For example, the data collection unit can adjust the collection frequency based on the user's past collection history and collect data at the optimal timing. For example, the data collection unit can analyze the user's past collection history and select the most efficient collection method. In this way, the data collection unit can select the optimal collection method by analyzing past 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 past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0088] The data collection unit can filter health data based on the user's current lifestyle and activity level. For example, if the user is exercising, the data collection unit can prioritize collecting exercise data. For example, if the user is resting, the data collection unit can prioritize collecting heart rate and sleep data. For example, if the user is working, the data collection unit can prioritize collecting stress level and concentration data. This allows the data collection unit to collect more relevant data by filtering the data based on the user's lifestyle and activity level. 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 lifestyle data into a generating AI and have the generating AI perform the data filtering.

[0089] 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 may prioritize collecting stress level and heart rate data. For example, if the user is relaxed, the data collection unit may prioritize collecting sleep data and activity level data. For example, if the user is in a hurry, the data collection unit may prioritize collecting exercise data and activity level data. In this way, the data collection unit can prioritize collecting more important data by determining the priority of data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of data priority.

[0090] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location when collecting health data. For example, if the user is at home, the data collection unit can prioritize the collection of sleep data and heart rate data. For example, if the user is at the gym, the data collection unit can prioritize the collection of exercise data and activity level data. For example, if the user is out, the data collection unit can prioritize the collection of step count data and distance traveled data. In this way, the data collection unit can collect more relevant data by collecting data based on the user's geographical location. 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 data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0091] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user is experiencing stress on social media, the data collection unit may prioritize collecting stress level and heart rate data. For example, if a user is relaxing on social media, the data collection unit may prioritize collecting sleep data and activity level data. For example, if a user is posting about exercise on social media, the data collection unit may prioritize collecting exercise data and activity level data. In this way, the data collection unit can collect 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 the user's social media activity data into a generating AI and have the generating AI collect the relevant data.

[0092] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit may focus on analyzing stress level and heart rate data. For example, if the user is relaxed, the analysis unit may focus on analyzing sleep data and activity level data. For example, if the user is in a hurry, the analysis unit may focus on analyzing exercise data and activity level data. This allows the analysis unit to perform more appropriate data analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the data analysis method.

[0093] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can analyze high-importance data (heart rate, blood pressure, etc.) in detail. The analysis unit can also analyze low-importance data (steps, distance traveled, etc.) in a simplified manner. The analysis unit can also adjust the frequency of analysis according to importance. This enables efficient data analysis by allowing the analysis unit to adjust the level of detail of the analysis based on the importance of the health 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 the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0094] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. For example, the analysis unit can apply a sleep stage analysis algorithm to sleep data. For example, the analysis unit can apply an exercise intensity analysis algorithm to activity level data. This allows the analysis unit to perform more accurate data analysis by applying different analysis algorithms depending on the category of health 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 the categories of health data into a generating AI and have the generating AI select the analysis algorithm to apply.

[0095] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. This allows the analysis unit to display more appropriate analysis results by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0096] The analysis unit can determine the priority of analysis based on when the health data was collected. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit may also analyze current data while referring to past data. For example, the analysis unit may focus on analyzing data collected during a specific period. This enables efficient data analysis by allowing the analysis unit to determine the priority of analysis based on when the health data was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit may input collection time data into a generating AI and have the generating AI determine the priority of analysis.

[0097] The analysis unit can adjust the order of analysis based on the relevance of the health data during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit can adjust the order of analysis according to the relevance of the data. This enables efficient data analysis by allowing the analysis unit to adjust the order of analysis based on the relevance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0098] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is stressed, the service provider can provide relaxing advice. For example, if the user is relaxed, the service provider can also provide detailed advice. For example, if the user is in a hurry, the service provider can provide concise advice. In this way, the service provider can provide more appropriate advice by adjusting the way advice is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way advice is expressed.

[0099] The service provider can adjust the level of detail of advice based on the importance of the health data when providing advice. For example, the service provider can provide detailed advice based on high-importance data. For example, the service provider can also provide simplified advice based on low-importance data. The service provider can also adjust the frequency of advice according to its importance. This enables the service provider to provide advice efficiently by adjusting the level of detail of advice based on the importance of the health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0100] The service provider can apply different advice algorithms depending on the category of health data when providing advice. For example, the service provider can apply a heart rate variability analysis algorithm to advice based on heart rate data. For example, the service provider can also apply a sleep stage analysis algorithm to advice based on sleep data. For example, the service provider can also apply an exercise intensity analysis algorithm to advice based on activity level data. By applying different advice algorithms depending on the category of health data, the service provider can provide more accurate advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the category of health data into a generating AI and have the generating AI select the advice algorithm to apply.

[0101] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is stressed, the service provider can provide short, concise advice. If the user is relaxed, the service provider can also provide detailed advice. If the user is in a hurry, the service provider can also provide brief advice. By adjusting the length of the advice based on the user's emotions, the service provider can provide more appropriate advice. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the length of the advice.

[0102] The service provider can prioritize advice based on the timing of health data collection when providing advice. For example, the service provider may prioritize advice based on recently collected data. The service provider may also provide advice based on current data, while referring to past data. The service provider may also focus on providing advice based on data collected during a specific period. This enables the service provider to provide advice efficiently by prioritizing advice based on the timing of health data collection. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input collection timing data into a generating AI and have the generating AI determine the priority of advice.

[0103] The service provider can adjust the order of advice based on the relevance of health data when providing advice. For example, the service provider may prioritize advice based on highly relevant data. For example, the service provider may postpone advice based on less relevant data. For example, the service provider may adjust the order of advice according to the relevance of the data. This enables the service provider to provide advice efficiently by adjusting the order of advice based on the relevance of health data. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of advice.

[0104] The management unit can estimate the user's emotions and adjust management methods based on the estimated emotions. For example, if the user is stressed, the management unit can suggest stress management methods. For example, if the user is relaxed, the management unit can also suggest methods to maintain that relaxation. For example, if the user is in a hurry, the management unit can also suggest management methods that can be implemented quickly. This allows the management unit to provide more appropriate management by adjusting management methods based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI adjust management methods.

[0105] The management department can analyze the user's past health data during management to select the optimal management method. For example, the management department can propose the optimal management method based on the user's past health data. The management department can also adjust the management method based on the user's past data and select the optimal method. For example, the management department can analyze the user's past data and select the most effective management method. In this way, the management department can select the optimal management method by analyzing the user's past health data. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input past health data into a generating AI and have the generating AI select the optimal management method.

[0106] The management unit can customize management methods based on the user's current living situation during management. For example, if the user is at work, the management unit can suggest stretches and relaxation methods that can be done during work breaks. If the user is at home, the management unit can also suggest health management methods that can be done at home. If the user is traveling, the management unit can also suggest health management methods that can be performed at the travel destination. This allows the management unit to provide more appropriate management by customizing management methods based on the user's current living situation. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input current living situation data into a generating AI and have the generating AI perform the customization of management methods.

[0107] The management unit can estimate the user's emotions and determine management priorities based on the estimated emotions. For example, if the user is stressed, the management unit will prioritize stress management. For example, if the user is relaxed, the management unit may also prioritize management to maintain relaxation. For example, if the user is in a hurry, the management unit may also prioritize management that can be performed quickly. This allows the management unit to provide more appropriate management by determining management priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user emotion data into a generative AI and have the generative AI determine the management priorities.

[0108] The management department can select the optimal management method based on the user's geographical location information during management. For example, if the user is at home, the management department can suggest health management methods that can be done at home. For example, if the user is at a gym, the management department can also suggest health management methods that can be done at the gym. For example, if the user is out, the management department can also suggest health management methods that can be performed while out. This allows the management department to provide more appropriate management by selecting the optimal management method based on the user's geographical location information. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input geographical location data into a generating AI and have the generating AI select the optimal management method.

[0109] The management department can analyze users' social media activity and propose management measures during management. For example, if a user is experiencing stress on social media, the management department can propose stress management methods. For example, if a user is relaxing on social media, the management department can also propose ways to maintain that relaxation. For example, if a user is posting about exercise on social media, the management department can also propose exercise management methods. In this way, the management department can propose more appropriate management measures by analyzing users' social media activity. Some or all of the above processing in the management department may be performed using AI, for example, or without AI. For example, the management department can input social media activity data into a generating AI and have the generating AI execute the proposal of management measures.

[0110] The consultation unit can estimate the user's emotions and adjust the consultation method based on the estimated emotions. For example, if the user is feeling stressed, the consultation unit can suggest a relaxing consultation method. For example, if the user is relaxed, the consultation unit can also suggest a detailed consultation method. For example, if the user is in a hurry, the consultation unit can also suggest a concise consultation method. In this way, the consultation unit can provide more appropriate consultation by adjusting the consultation method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consultation unit may be performed using AI, for example, or not using AI. For example, the consultation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the consultation method.

[0111] The consultation unit can select the optimal consultation method by referring to the user's past health data during a consultation. For example, the consultation unit can propose the optimal consultation method based on the user's past health data. The consultation unit can also adjust the consultation method based on the user's past data and select the optimal method. For example, the consultation unit can analyze the user's past data and select the most effective consultation method. In this way, the consultation unit can select the optimal consultation method by referring to the user's past health data. Some or all of the above processes in the consultation unit may be performed using AI, for example, or without AI. For example, the consultation unit can input past health data into a generating AI and have the generating AI select the optimal consultation method.

[0112] The consultation unit can customize the consultation content based on the user's current health condition during the consultation. For example, if the user is tired, the consultation unit may suggest consultation content related to fatigue recovery. For example, if the user is stressed, the consultation unit may also suggest consultation content related to stress management. For example, if the user is healthy, the consultation unit may also suggest consultation content related to maintaining health. In this way, the consultation unit can provide more appropriate consultation by customizing the consultation content based on the user's current health condition. Some or all of the above processing in the consultation unit may be performed using AI, for example, or without AI. For example, the consultation unit can input current health condition data into a generating AI and have the generating AI perform the customization of the consultation content.

[0113] The consultation unit can estimate the user's emotions and determine the priority of consultations based on the estimated emotions. For example, if the user is feeling stressed, the consultation unit will prioritize consultations related to stress management. For example, if the user is relaxed, the consultation unit may also prioritize consultations to maintain relaxation. For example, if the user is in a hurry, the consultation unit may also prioritize consultations that can be performed quickly. This allows the consultation unit to provide more appropriate consultations by determining the priority of consultations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consultation unit may be performed using AI, for example, or not using AI. For example, the consultation unit can input user emotion data into a generative AI and have the generative AI determine the priority of consultations.

[0114] The consultation unit can select the most suitable consultation method based on the user's geographical location information during a consultation. For example, if the user is at home, the consultation unit can suggest a consultation method that can be done at home. For example, if the user is at a gym, the consultation unit can suggest a consultation method that can be done at the gym. For example, if the user is out, the consultation unit can suggest a consultation method that can be done while out. In this way, the consultation unit can provide more appropriate consultations by selecting the most suitable consultation method based on the user's geographical location information. Some or all of the above processing in the consultation unit may be performed using AI, for example, or without AI. For example, the consultation unit can input geographical location data into a generating AI and have the generating AI select the most suitable consultation method.

[0115] The consultation department can analyze a user's social media activity and suggest consultation topics during a consultation. For example, if a user is experiencing stress on social media, the consultation department can suggest consultation topics related to stress management. For example, if a user is relaxing on social media, the consultation department can also suggest consultation topics to maintain that relaxation. For example, if a user is posting about exercise on social media, the consultation department can also suggest consultation topics related to exercise. In this way, the consultation department can suggest more appropriate consultation topics by analyzing the user's social media activity. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input social media activity data into a generating AI and have the generating AI generate consultation topic suggestions.

[0116] The reporting unit can estimate the user's emotions and adjust how the report is displayed based on the estimated emotions. For example, if the user is stressed, the reporting unit can provide a simple and easy-to-read display. If the user is relaxed, the reporting unit can also provide a display that includes detailed information. If the user is in a hurry, the reporting unit can also provide a display that gets straight to the point. In this way, the reporting unit can provide a more appropriate report by adjusting the display based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI adjust how the report is displayed.

[0117] The reporting unit can adjust the level of detail in reports based on the importance of the health data during report generation. For example, the reporting unit can report highly important data (heart rate, blood pressure, etc.) in detail. It can also report less important data (steps, distance traveled, etc.) in a simplified manner. The reporting unit can also adjust the frequency of reports according to their importance. This allows the reporting unit to efficiently generate reports by adjusting the level of detail based on the importance of the health data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the importance of the health data into a generation AI and have the generation AI perform the adjustment of the level of detail in the reports.

[0118] The reporting unit can apply different report generation algorithms depending on the category of health data when generating reports. For example, the reporting unit can apply a heart rate variability analysis algorithm to heart rate data. For example, the reporting unit can apply a sleep stage analysis algorithm to sleep data. For example, the reporting unit can apply an exercise intensity analysis algorithm to activity level data. This allows the reporting unit to generate more accurate reports by applying different report generation algorithms depending on the category of health data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the categories of health data into a generation AI and have the generation AI select the report generation algorithm to apply.

[0119] The reporting unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is stressed, the reporting unit can provide a short, concise report. If the user is relaxed, the reporting unit can also provide a detailed report. If the user is in a hurry, the reporting unit can provide a brief report. In this way, the reporting unit can provide a more appropriate report by adjusting the length of the report based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI adjust the length of the report.

[0120] The reporting unit can prioritize reports based on when health data was collected during report generation. For example, the reporting unit may prioritize reports based on recently collected data. The reporting unit may also provide reports based on current data, while referring to historical data. The reporting unit may also focus on providing reports based on data collected during a specific period. This enables efficient report generation by prioritizing reports based on when health data was collected. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input collection timing data into a generation AI and have the generation AI determine the report prioritization.

[0121] The reporting unit can adjust the order of reports based on the relevance of health data when generating reports. For example, the reporting unit may prioritize reports based on highly relevant data. For example, the reporting unit may postpone reports based on less relevant data. For example, the reporting unit can adjust the order of reports according to the relevance of the data. This enables efficient report generation by adjusting the order of reports based on the relevance of health data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input the relevance of the data into a generation AI and have the generation AI perform the adjustment of the report order.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] Next-generation AI-powered virtual health assistant systems can estimate a user's emotions and adjust health advice based on those emotions. For example, if a user is stressed, it can provide relaxing advice. If the user is relaxed, it can provide more detailed health advice. Furthermore, if the user is in a hurry, it can provide concise advice. In this way, the system can support more appropriate health management by adjusting the advice based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into the generative AI and have the generative AI adjust the advice content.

[0124] Next-generation AI-powered virtual health assistant systems can analyze a user's past health data and provide optimal health advice. For example, they can suggest optimal health advice based on data the user has frequently collected in the past. They can also adjust the collection frequency based on the user's past collection history and collect data at the optimal time. Furthermore, they can analyze the user's past collection history and select the most efficient collection method. As a result, the system can provide optimal health advice by analyzing past collection history. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input past collection history data into a generating AI and have the generating AI select the optimal collection method.

[0125] Next-generation AI-powered virtual health assistant systems can provide health advice based on the user's geographical location. For example, if the user is at home, it can suggest health management methods that can be done at home. If the user is at a gym, it can suggest health management methods that can be done at the gym. Furthermore, if the user is out, it can suggest health management methods that can be performed while out. This allows the system to provide optimal health advice based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI provide optimal health advice.

[0126] A next-generation AI-powered virtual health assistant system can analyze a user's social media activity and provide relevant health advice. For example, if a user is experiencing stress on social media, it can provide advice on stress management. If a user is relaxing on social media, it can also provide advice on maintaining that relaxation. Furthermore, if a user posts about exercise on social media, it can provide advice on exercise. In this way, the system can provide relevant health advice by analyzing the user's social media activity. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI perform the task of providing relevant health advice.

[0127] Next-generation AI-powered virtual health assistant systems can estimate a user's emotions and adjust the timing of health data collection based on those emotions. For example, if a user is stressed, the collection frequency can be reduced, and data can be collected when the user is relaxed. If the user is relaxed, the collection frequency can be increased, and more detailed data can be collected. Furthermore, if the user is in a hurry, the collection timing can be shortened, and data can be collected quickly. This allows the system to collect more appropriate data by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the collection timing.

[0128] Next-generation AI-powered virtual health assistant systems can estimate a user's emotions and prioritize health data based on those emotions. For example, if a user is stressed, stress level and heart rate data can be prioritized for collection. If a user is relaxed, sleep data and activity level data can also be prioritized for collection. Furthermore, if a user is in a hurry, exercise data and activity level data can also be prioritized for collection. This allows the system to prioritize the collection of more important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, such as, but not limited to, text generation AI or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into a generative AI and have the generative AI determine the data prioritization.

[0129] Next-generation AI-powered virtual health assistant systems can estimate a user's emotions and adjust how health data is analyzed based on those emotions. For example, if a user is stressed, the system can focus on analyzing stress levels and heart rate data. If a user is relaxed, it can focus on analyzing sleep data and activity level data. Furthermore, if a user is in a hurry, it can focus on analyzing exercise data and activity level data. This allows the system to perform more appropriate data analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, such as, but not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the data analysis method.

[0130] Next-generation AI-powered virtual health assistant systems can estimate a user's emotions and adjust the way health advice is presented based on those emotions. For example, if a user is stressed, it can provide relaxing advice. If the user is relaxed, it can provide more detailed advice. Furthermore, if the user is in a hurry, it can provide concise advice. This allows the system to provide more appropriate advice by adjusting the way it is presented based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other things. Generative AI includes, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the processing described above in the delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into the generative AI and have the generative AI adjust the way the advice is presented.

[0131] Next-generation AI-powered virtual health assistant systems can estimate a user's emotions and adjust the length of health advice based on those emotions. For example, if a user is stressed, the system can provide short, concise advice. If the user is relaxed, it can provide more detailed advice. Furthermore, if the user is in a hurry, it can provide brief advice. This allows the system to provide more appropriate advice by adjusting the length of advice based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, such as, but not limited to, text-generating AI or multimodal-generating AI. Some or all of the processing described above in the delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into the generative AI and have the generative AI adjust the length of the advice.

[0132] Next-generation AI-powered virtual health assistant systems can estimate a user's emotions and adjust how health data is collected based on those emotions. For example, if a user is stressed, stress level and heart rate data can be prioritized. If a user is relaxed, sleep data and activity level data can be prioritized. Furthermore, if a user is in a hurry, exercise data and activity level data can be prioritized. This allows the system to prioritize the collection of more important data by adjusting how data is collected based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, such as, but not limited to, text generation AI or multimodal generation AI. Some or all of the processing described above in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into a generative AI and have the generative AI adjust how data is collected.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The data collection unit collects health data. The data collection unit integrates with, for example, wearable devices and smart home systems to collect real-time metrics such as heart rate, sleep, and activity level. The data collection unit measures heart rate using, for example, a smartwatch or fitness tracker. The data collection unit can also monitor sleep patterns using a smart home system. Furthermore, the data collection unit can use a fitness tracker to measure activity levels. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to assess the user's health status. For example, the analysis unit analyzes heart rate data to assess the user's cardiac health. The analysis unit can also analyze sleep data to assess the user's sleep quality. Furthermore, the analysis unit can analyze activity level data to assess the user's exercise habits. Step 3: The service provider provides personalized advice based on the analysis results obtained by the analysis provider. For example, the service provider provides the user with optimal health advice and preventive care. For example, the service provider makes dietary suggestions. The service provider can also recommend exercise. Furthermore, the service provider can also suggest improvements to lifestyle habits. Step 4: The administration department manages chronic diseases, provides medication reminders, and offers nutrition and fitness coaching. For example, the administration department manages diabetes. It can also manage hypertension. Furthermore, it can also manage heart disease. Step 5: The consultation department conducts virtual consultations with doctors through telehealth integration. The consultation department can, for example, conduct consultations with doctors using video calls. The consultation department can also conduct consultations with doctors using chat. Furthermore, the consultation department can also conduct consultations with doctors using email. Step 6: The reporting unit provides AI-generated health reports, clearly showing the user's health progress. The reporting unit can, for example, visualize data. It can also summarize the analysis results. Furthermore, the reporting unit can generate and provide health reports to the user.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, management unit, consultation unit, and reporting unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects health data using the computer 36 and camera 42 of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The provision unit generates personalized advice using the specific processing unit 290 of the data processing unit 12 and provides it to the user through the output device 40 of the smart device 14. The management unit manages chronic diseases and provides medication reminders using the specific processing unit 290 of the data processing unit 12 and notifies the user through the output device 40 of the smart device 14. The consultation unit enables virtual consultations with a doctor using the communication I / F 44 of the smart device 14. The reporting unit displays a health report generated by the specific processing unit 290 of the data processing unit 12 on the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, management unit, consultation unit, and reporting unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects health data using the camera 42 and microphone 238 of the smart glasses 214 and analyzes it using the identification processing unit 290 of the data processing unit 12. The provision unit generates personalized advice using the identification processing unit 290 of the data processing unit 12 and provides it to the user through the speaker 240 of the smart glasses 214. The management unit manages chronic diseases and provides medication reminders using the identification processing unit 290 of the data processing unit 12 and notifies the user through the speaker 240 of the smart glasses 214. The consultation unit enables virtual consultations with a doctor using the communication I / F 44 of the smart glasses 214. The reporting unit displays the health report generated by the identification processing unit 290 of the data processing unit 12 on the display of the smart glasses 214. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, management unit, consultation unit, and reporting unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects health data using the camera 42 and microphone 238 of the headset terminal 314 and analyzes it using the identification processing unit 290 of the data processing unit 12. The provision unit generates personalized advice using the identification processing unit 290 of the data processing unit 12 and provides it to the user through the speaker 240 of the headset terminal 314. The management unit manages chronic diseases and provides medication reminders using the identification processing unit 290 of the data processing unit 12 and notifies the user through the speaker 240 of the headset terminal 314. The consultation unit enables virtual consultations with a doctor using the communication I / F 44 of the headset terminal 314. The reporting unit displays the health report generated by the identification processing unit 290 of the data processing unit 12 on the display 343 of the headset terminal 314. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, management unit, consultation unit, and reporting unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects health data using the camera 42 and microphone 238 of the robot 414 and analyzes it using the identification processing unit 290 of the data processing unit 12. The provision unit generates personalized advice using the identification processing unit 290 of the data processing unit 12 and provides it to the user through the speaker 240 of the robot 414. The management unit manages chronic diseases and provides medication reminders using the identification processing unit 290 of the data processing unit 12 and notifies the user through the speaker 240 of the robot 414. The consultation unit enables virtual consultations with a doctor using the communication I / F 44 of the robot 414. The reporting unit displays health reports generated by the identification processing unit 290 of the data processing unit 12 on the display of the robot 414. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] (Note 1) A data collection unit that collects health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provisioning unit that provides personalized advice based on the analysis results obtained by the aforementioned analysis unit, The administrative department handles the management of chronic diseases, medication reminders, and nutrition and fitness coaching. The consultation department provides virtual consultations with doctors, It includes a reporting section that provides AI-generated health reports. A system characterized by the following features. (Note 2) The aforementioned collection unit is It integrates with wearable devices and smart home systems to collect real-time metrics such as heart rate, sleep, and activity levels. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed to assess the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Based on the analysis results, we provide users with optimal health advice and preventative care. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, We provide management of chronic diseases, medication reminders, and coaching on nutrition and fitness. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned consultation department, Telehealth integration enables virtual consultations with doctors. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned report section is, We provide AI-generated health reports that clearly show the user's health progress. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past health data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current lifestyle and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned collection unit is When collecting health data, the system prioritizes collecting highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting health data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is We estimate user sentiment and adjust the data analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing advice, we prioritize the advice based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing advice, we adjust the order of advice based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, We estimate the user's emotions and adjust management methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, During management, the system analyzes the user's past health data to select the optimal management method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, During management, the management methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, It estimates user sentiment and determines management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, During management, the optimal management method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned management department, During management, we analyze users' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned consultation department, It estimates the user's emotions and adjusts the consultation method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned consultation department, During the consultation, the system will refer to the user's past health data to select the most appropriate consultation method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned consultation department, During the consultation, the content of the consultation will be customized based on the user's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned consultation department, It estimates the user's emotions and determines the priority of consultations based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned consultation department, During a consultation, the most suitable consultation method is selected based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned consultation department, During the consultation, we analyze the user's social media activity and propose consultation topics. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned report section is, It estimates user sentiment and adjusts how reports are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned report section is, When generating reports, adjust the level of detail in the report based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned report section is, When generating reports, different report generation algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned report section is, It estimates the user's sentiment and adjusts the length of the report based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned report section is, When generating reports, prioritize reports based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned report section is, When generating reports, the order of reports is adjusted based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0207] 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 health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A provisioning unit that provides personalized advice based on the analysis results obtained by the aforementioned analysis unit, The administrative department handles the management of chronic diseases, medication reminders, and nutrition and fitness coaching. The consultation department provides virtual consultations with doctors, It includes a reporting unit that provides AI-generated health reports. A system characterized by the following features.

2. The aforementioned collection unit is It integrates with wearable devices and smart home systems to collect real-time metrics such as heart rate, sleep, and activity levels. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed to assess the user's health status. The system according to feature 1.

4. The aforementioned supply unit is, Based on the analysis results, we provide users with optimal health advice and preventative care. The system according to feature 1.

5. The aforementioned management department, We provide management of chronic diseases, medication reminders, and coaching on nutrition and fitness. The system according to feature 1.

6. The aforementioned consultation department, Telehealth integration enables virtual consultations with doctors. The system according to feature 1.

7. The aforementioned report section is, We provide AI-generated health reports that clearly show the user's health progress. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system according to feature 1.

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