system

Abstract

The system according to this embodiment aims to manage daily vital data and activity data, detect changes in health status early, and provide appropriate advice. [Solution] The system according to the embodiment comprises a collection unit, a transmission unit, an analysis unit, and an advice unit. The collection unit collects data from a smartphone and vital devices. The transmission unit transmits the data collected by the collection unit to the cloud. The analysis unit analyzes the data transmitted by the transmission unit. The advice unit provides advice based on the results of the analysis performed by the analysis unit.

Description

Technical Field

[0004] ,

[0006] , , , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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] ​​​​​​​​​​​​The system according to this embodiment comprises a data collection unit, a data transmission unit, an analysis unit, and an advice unit. The data collection unit collects data from smartphones and vital devices. The data transmission unit transmits the data collected by the data collection unit to the cloud. The data analysis unit analyzes the data transmitted by the data transmission unit. The advice unit provides advice based on the results of the analysis performed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can manage daily vital data and activity data, detect changes in health status early, and provide appropriate advice. [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 applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health management system according to an embodiment of the present invention is a system that aggregates data collected from smartphones and vital devices in the cloud, and uses AI to analyze that data and check the user's health status. This health management system collects daily vital data and activity data (such as steps) from smartphones and vital devices, sends the collected data to the cloud, and the AI ​​analyzes that data. Based on the analysis results, an agent (AI doctor) residing in a dedicated smartphone app provides advice to the user. This advice is intended to prevent illness and frailty, with a particular emphasis on preventing frailty. Since reduced outings and social isolation are major factors in preventing frailty, the AI ​​doctor aims to improve the situation by encouraging user awareness and increasing motivation. It should be noted that the AI ​​doctor does not perform medical procedures, but only provides advice based on the data. For example, daily vital data and activity data are collected from smartphones and vital devices. In this case, data such as heart rate, blood pressure, body temperature, and steps are collected. For example, if the user is wearing a smartwatch, heart rate and step count data are collected from that smartwatch. Next, the collected data is sent to the cloud. In the cloud, the collected data is centrally managed, and AI analyzes it. Based on the collected data, the AI ​​checks the user's health status and checks for any abnormalities. For example, abnormalities may be detected if the heart rate is higher than normal or if the number of steps has decreased sharply. Based on the analysis results, an agent (AI doctor) residing in a dedicated smartphone app provides advice to the user. For example, if the heart rate is high, it may suggest ways to relax, or if the number of steps has decreased, it may recommend going for a walk. In this way, the AI ​​doctor constantly monitors the user's health status and provides appropriate advice to prevent illness and frailty. In particular, since reduced outdoor activity and social isolation are major factors in preventing frailty, the AI ​​doctor encourages user awareness and increases motivation to improve the situation.For example, if a user has not left their home for an extended period, the system will explain the importance of going out and propose a concrete action plan to encourage them to do so. In this way, the system aims to raise the user's awareness of their own health and encourage them to take proactive action. It should be noted that the AI ​​doctor does not perform medical procedures; it only provides advice based on data. If medical treatment is necessary, the system will prompt the user to consult a specialist physician. In this manner, a system that comprehensively supports the user's health is realized. As a result, the health management system can provide comprehensive support for the user's health.

[0029] The health management system according to this embodiment comprises a data collection unit, a data transmission unit, an analysis unit, and an advice unit. The data collection unit collects data from smartphones and vital devices. The data collection unit collects data such as heart rate, blood pressure, body temperature, and steps taken. The data collection unit can collect heart rate and steps taken from a smartwatch, for example. The data collection unit can also collect blood pressure data from a blood pressure monitor. Furthermore, the data collection unit can also collect body temperature data from a thermometer. For example, the data collection unit collects heart rate data from a user wearing a smartwatch. The data collection unit can also collect the user's blood pressure data using a blood pressure monitor. The data collection unit can also collect the user's body temperature data using a thermometer. The data transmission unit transmits the data collected by the data collection unit to the cloud. The data transmission unit can transmit the collected data to the cloud, for example. The data transmission unit can transmit the data to the cloud using Wi-Fi, for example. The data transmission unit can also transmit the data to the cloud using mobile data communication. Furthermore, the data transmission unit can also transmit the data to the cloud using Bluetooth®. For example, the transmission unit sends collected data to the cloud using Wi-Fi. The transmission unit can also send collected data to the cloud using mobile data communication. The transmission unit can also send collected data to the cloud using Bluetooth. The analysis unit analyzes the data transmitted by the transmission unit. For example, the analysis unit checks the user's health status based on the collected data and confirms that there are no abnormalities. For example, the analysis unit can analyze heart rate data to confirm that there are no abnormalities. The analysis unit can also analyze blood pressure data to confirm that there are no abnormalities. Furthermore, the analysis unit can analyze body temperature data to confirm that there are no abnormalities. For example, the analysis unit can analyze heart rate data to confirm that there are no abnormalities. The analysis unit can also analyze blood pressure data to confirm that there are no abnormalities. The analysis unit can also analyze body temperature data to confirm that there are no abnormalities. The advice unit provides advice based on the results analyzed by the analysis unit. For example, the advice unit suggests ways for the user to relax based on the analysis results.The advice unit can, for example, suggest deep breathing. It can also suggest meditation. Furthermore, it can suggest stretching. For example, the advice unit can suggest deep breathing. The advice unit can also suggest meditation. The advice unit can also suggest stretching. In this way, the health management system according to the embodiment can comprehensively support the user's health status.

[0030] The data collection unit collects data from smartphones and vital devices. For example, it collects data such as heart rate, blood pressure, body temperature, and steps. Specifically, it can collect heart rate and step count data from smartwatches. Smartwatches are worn on the user's wrist and use built-in sensors to measure heart rate in real time and count steps. This data is transmitted to a smartphone via Bluetooth and acquired by the data collection unit. The data collection unit can also collect blood pressure data from blood pressure monitors. Blood pressure monitors are worn on the user's arm, and the measured blood pressure data is transmitted to a smartphone via Wi-Fi or Bluetooth. Furthermore, the data collection unit can collect body temperature data from thermometers. Thermometers are inserted into the user's mouth or armpit, and the measured body temperature data is similarly transmitted to a smartphone. For example, the data collection unit collects heart rate data from a user wearing a smartwatch. The data collection unit can also collect the user's blood pressure data using a blood pressure monitor. The data collection unit can also collect the user's body temperature data using a thermometer. This allows the data collection unit to centrally collect diverse data on users' health status and monitor their health in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and advisory units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The transmitting unit sends the data collected by the collecting unit to the cloud. Specifically, the transmitting unit can send data to the cloud using Wi-Fi. Wi-Fi provides high-speed and stable communication, making it suitable for rapidly transmitting large amounts of data. The transmitting unit can also send data to the cloud using mobile data communication. Mobile data communication allows data to be transmitted even in locations where Wi-Fi is unavailable, ensuring uninterrupted data transmission even when the user is on the move. Furthermore, the transmitting unit can also send data to the cloud using Bluetooth. Bluetooth is suitable for short-range data communication and is used when transmitting data from smartphones or vital devices to the cloud. For example, the transmitting unit sends data collected using Wi-Fi to the cloud. The transmitting unit can also send data collected using mobile data communication to the cloud. The transmitting unit can also send data collected using Bluetooth to the cloud. This allows the transmitting unit to quickly and reliably transmit collected data to the cloud, enabling the analysis and advisory units to access the data in real time. Furthermore, the transmitting unit can optimize communication efficiency by adjusting the frequency and priority of data transmission, ensuring that important data is transmitted quickly. This allows the transmission unit to streamline data communication across the entire system and provide a foundation for monitoring the user's health status in real time.

[0032] The analysis unit analyzes the data transmitted by the transmission unit. For example, the analysis unit checks the user's health status based on the collected data and confirms that there are no abnormalities. Specifically, it can analyze heart rate data to check for abnormalities. Heart rate data is processed by an AI-based analysis algorithm, and if a value outside the normal range is detected, it is flagged as abnormal. The analysis unit can also analyze blood pressure data to check for abnormalities. Similarly, blood pressure data is processed by an AI-based analysis algorithm, and if a value outside the normal range is detected, it is flagged as abnormal. Furthermore, the analysis unit can analyze body temperature data to check for abnormalities. Similarly, body temperature data is processed by an AI-based analysis algorithm, and if a value outside the normal range is detected, it is flagged as abnormal. For example, the analysis unit can analyze heart rate data to check for abnormalities. The analysis unit can also analyze blood pressure data to check for abnormalities. The analysis unit can also analyze body temperature data to check for abnormalities. This allows the analysis unit to quickly and accurately analyze the collected data and understand the user's health status in real time. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term health trends. For example, it can analyze the user's heart rate variability patterns based on past heart rate data to predict future risks. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analysis unit can not only monitor health status in real time but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0033] The advice unit provides advice based on the results analyzed by the analysis unit. For example, the advice unit can suggest ways for users to relax based on the analysis results. Specifically, it can suggest deep breathing, which has the effect of reducing stress and stabilizing heart rate. The advice unit can also suggest meditation, which has the effect of calming the mind and promoting mental relaxation. Furthermore, the advice unit can suggest stretching, which has the effect of relieving muscle tension and promoting blood circulation. For example, the advice unit can suggest deep breathing, meditation, and stretching. This allows the advice unit to comprehensively support the user's health. In addition, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of its advice. For example, it can review and improve the advice based on feedback from users who have received advice. The advice unit can also provide individualized advice tailored to the user's lifestyle and health condition. For example, it can suggest daily exercise to users who are sedentary and suggest relaxation methods to users who are under a lot of stress. This allows the advice unit to provide users with appropriate advice tailored to their individual needs and support the improvement of their health.

[0034] The data collection unit can collect data such as heart rate, blood pressure, body temperature, and steps. For example, the data collection unit can collect heart rate data. For example, the data collection unit can collect heart rate data using an optical heart rate sensor. The data collection unit can also collect blood pressure data. For example, the data collection unit can collect blood pressure data using a pressure sensor. Furthermore, the data collection unit can also collect body temperature data. For example, the data collection unit can collect body temperature data using a temperature sensor. The data collection unit can also collect steps. For example, the data collection unit can collect steps using an accelerometer. In this way, the data collection unit can gain a detailed understanding of the user's health status by collecting data such as heart rate, blood pressure, body temperature, and steps. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input heart rate data acquired by an optical heart rate sensor into a generating AI and have the generating AI perform analysis of the heart rate data.

[0035] The transmitting unit can send the collected data to the cloud. The transmitting unit can, for example, send the collected data to the cloud. The transmitting unit can, for example, send data to the cloud using Wi-Fi. The transmitting unit can also send data to the cloud using mobile data communication. Furthermore, the transmitting unit can also send data to the cloud using Bluetooth. For example, the transmitting unit sends the collected data to the cloud using Wi-Fi. The transmitting unit can also send the collected data to the cloud using mobile data communication. The transmitting unit can also send the collected data to the cloud using Bluetooth. This allows the transmitting unit to centrally manage the data by sending the collected data to the cloud. The cloud includes, but is not limited to, services such as AWS, Google Cloud, and Microsoft Azure. Some or all of the processing described above in the transmitting unit may be performed using, for example, AI, or not using AI. For example, when the transmitting unit sends the collected data to the cloud using Wi-Fi, it can have a generating AI perform optimization of the transmitted data.

[0036] The analysis unit can check the user's health status based on the collected data and confirm whether there are any abnormalities. For example, the analysis unit can analyze heart rate data to check for abnormalities. For example, the analysis unit can detect an abnormality if the heart rate is higher than normal. The analysis unit can also analyze blood pressure data to check for abnormalities. For example, the analysis unit can detect an abnormality if the blood pressure is higher than normal. Furthermore, the analysis unit can also analyze body temperature data to check for abnormalities. For example, the analysis unit can detect an abnormality if the body temperature is higher than normal. In this way, the analysis unit can check the user's health status based on the collected data and confirm whether there are any abnormalities, enabling early detection of abnormalities. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input the collected heart rate data into a generation AI and have the generation AI perform the analysis of the heart rate data.

[0037] The advice unit can suggest methods for relaxation to the user based on the analysis results. For example, the advice unit may suggest deep breathing. For example, the advice unit may explain that deep breathing has a relaxing effect. The advice unit may also suggest meditation. For example, the advice unit may explain that meditation has a calming effect on the mind. Furthermore, the advice unit may also suggest stretching. For example, the advice unit may explain that stretching relieves muscle tension and has a relaxing effect. In this way, the advice unit can reduce the user's stress by suggesting methods for relaxation based on the analysis results. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit can input the analysis results into a generative AI and have the generative AI execute the suggestion of relaxation methods.

[0038] The advice unit can recommend that the user go for a walk based on the analysis results. For example, the advice unit may suggest going for a walk. For example, the advice unit may explain that going for a walk can have a refreshing effect on the mind and body. The advice unit may also suggest specific recommended walking times and distances. For example, the advice unit may suggest a 30-minute walk. Furthermore, the advice unit may suggest points to be aware of when walking. For example, the advice unit may suggest wearing appropriate shoes and staying hydrated. In this way, the advice unit can increase the user's activity level by recommending that they go for a walk based on the analysis results. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the advice unit may input the analysis results into a generative AI and have the generative AI execute the walking suggestion.

[0039] The advice unit can propose action plans to encourage user awareness and increase motivation. For example, the advice unit can explain to the user the importance of going outside. For example, the advice unit can explain that going outside improves physical and mental health. The advice unit can also propose specific action plans. For example, the advice unit can suggest taking a walk three times a week. Furthermore, the advice unit can suggest ways to increase the user's motivation. For example, the advice unit can suggest setting goals and recording the degree of their achievement. In this way, the advice unit can raise the user's health awareness by encouraging user awareness and proposing action plans to increase motivation. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the advice unit can input the user's health data into a generative AI and have the generative AI execute the action plan proposal.

[0040] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can analyze the user's past heart rate data and collect data during times when the heart rate is stable. For example, the data collection unit can analyze the user's past step count data and collect data during times when the step count is high. The data collection unit can also analyze the user's past body temperature data and collect data during times when the body temperature is stable. For example, the data collection unit can analyze the user's past heart rate data and collect data during times when the heart rate is stable. For example, the data collection unit can analyze the user's past step count data and collect data during times when the step count is high. For example, the data collection unit can analyze the user's past body temperature data and collect data during times when the body temperature is stable. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past health data. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without using generative AI. For example, the data collection unit can input the user's past health data into a generating AI, which can then select the optimal data collection method.

[0041] The data collection unit can filter data based on the user's current activity status and environment during data collection. 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 body temperature data. Also, if the user is outdoors, the data collection unit can prioritize collecting step count data. 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 body temperature data. For example, if the user is outdoors, the data collection unit can prioritize collecting step count data. This allows the data collection unit to collect more relevant data by filtering the data based on the user's current activity status and environment. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's current activity status and environment data into a generative AI and have the generative AI perform data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is at home, the data collection unit can prioritize the collection of data related to the indoor environment. For example, if the user is out, the data collection unit can prioritize the collection of data related to the external environment. Furthermore, if the user is at work, the data collection unit can prioritize the collection of data related to the work environment. In this way, the data collection unit can collect more relevant data by considering the user's geographical location information during data collection. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI and have the generative AI perform the priority collection of highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, if the user posts about exercise on social media, the data collection unit will prioritize collecting exercise data. For example, if the user posts about health on social media, the data collection unit will prioritize collecting health data. The data collection unit can also prioritize collecting stress-related data if the user posts about stress on social media. For example, if the user posts about exercise on social media, the data collection unit will prioritize collecting exercise data. For example, if the user posts about health on social media, the data collection unit will prioritize collecting health data. For example, if the user posts about stress on social media, the data collection unit will prioritize collecting stress-related data. In this way, the data collection unit can collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI and have the generative AI collect the relevant data.

[0044] The transmitting unit can determine the transmission priority based on the importance of the data when transmitting data. For example, if heart rate data shows an abnormal value, the transmitting unit will transmit it with the highest priority. For example, if step count data is decreasing rapidly, the transmitting unit can transmit it with priority. Also, if body temperature data is within the normal range, the transmitting unit can transmit it later. For example, if heart rate data shows an abnormal value, the transmitting unit will transmit it with the highest priority. For example, if step count data is decreasing rapidly, the transmitting unit can transmit it with priority. For example, if body temperature data is within the normal range, the transmitting unit can transmit it later. In this way, the transmitting unit can transmit important data with priority by determining the transmission priority based on the importance of the data. Some or all of the above processing in the transmitting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the transmitting unit can input the importance of the data into a generative AI and have the generative AI perform the determination of the transmission priority.

[0045] The transmitting unit can apply different transmission protocols depending on the type of data when transmitting data. For example, the transmitting unit can apply a real-time transmission protocol to heart rate data. For example, the transmitting unit can apply a protocol to periodically batch transmission of step count data. The transmitting unit can also apply a protocol to transmit body temperature data all at once at the end of the day. This allows the transmitting unit to efficiently transmit data by applying different transmission protocols depending on the type of data. Some or all of the above processing in the transmitting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the transmitting unit can input the type of data into a generative AI and have the generative AI apply the transmission protocol.

[0046] The transmission unit can select the optimal transmission method when transmitting data, taking into account the user's network status. For example, if the user is connected via Wi-Fi, the transmission unit can transmit large amounts of data. For example, if the user is connected via mobile data, the transmission unit can compress the data before transmission. Furthermore, if the user is offline, the transmission unit can transmit the data as soon as the network connection is restored. The transmission unit can transmit large amounts of data when the user is connected via Wi-Fi. For example, if the user is connected via mobile data, the transmission unit can compress the data before transmission. For example, if the user is offline, the transmission unit can transmit the data as soon as the network connection is restored. This allows the transmission unit to efficiently transmit data by selecting the optimal transmission method, taking into account the user's network status. Some or all of the above processing in the transmission unit may be performed using, for example, a generative AI, or without a generative AI. For example, the transmission unit can input the user's network status data into a generative AI and have the generative AI select the optimal transmission method.

[0047] The transmitting unit can adjust the transmission order based on the relationships between data when transmitting data. For example, if heart rate data and body temperature data are related, the transmitting unit can transmit them simultaneously. For example, if step count data and activity data are related, the transmitting unit can transmit them simultaneously. Also, if body temperature data and sleep data are related, the transmitting unit can transmit them simultaneously. For example, if heart rate data and body temperature data are related, the transmitting unit can transmit them simultaneously. For example, if step count data and activity data are related, the transmitting unit can transmit them simultaneously. For example, if body temperature data and sleep data are related, the transmitting unit can transmit them simultaneously. This allows the transmitting unit to efficiently transmit data by adjusting the transmission order based on the relationships between data. Some or all of the above processing in the transmitting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the transmitting unit can input the relationships between data into a generative AI and have the generative AI adjust the transmission order.

[0048] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data during the analysis. For example, the analysis unit can analyze by considering the correlation between heart rate data and body temperature data. For example, the analysis unit can analyze by considering the correlation between step count data and activity data. Furthermore, the analysis unit can also analyze by considering the correlation between body temperature data and sleep data. For example, the analysis unit can analyze by considering the correlation between heart rate data and body temperature data. For example, the analysis unit can analyze by considering the correlation between step count data and activity data. For example, the analysis unit can analyze by considering the correlation between body temperature data and sleep data. In this way, the analysis unit improves the accuracy of its analysis by considering the interrelationships between data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the interrelationships of data into a generative AI and have the generative AI perform the improvement of analysis accuracy.

[0049] The analysis unit can perform analysis while considering the attribute information of the data submitter. For example, the analysis unit can perform analysis while considering the user's age. For example, the analysis unit can perform analysis while considering the user's gender. Furthermore, the analysis unit can also perform analysis while considering the user's lifestyle. For example, the analysis unit can perform analysis while considering the user's age. For example, the analysis unit can perform analysis while considering the user's gender. For example, the analysis unit can perform analysis while considering the user's lifestyle. This allows the analysis unit to perform more personalized analysis by considering the attribute information of the data submitter. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's attribute information into a generating AI and have the generating AI perform the analysis.

[0050] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can perform analysis while considering the climate of the user's place of residence. For example, the analysis unit can perform analysis while considering the environment of the user's activity area. Furthermore, the analysis unit can also perform analysis while considering the health risks of the user's travel destination. For example, the analysis unit can perform analysis while considering the climate of the user's place of residence. For example, the analysis unit can perform analysis while considering the environment of the user's activity area. For example, the analysis unit can perform analysis while considering the health risks of the user's travel destination. This allows the analysis unit to perform more appropriate analysis by considering the geographical distribution of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the geographical distribution of the data into a generative AI and have the generative AI perform the analysis.

[0051] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the data during the analysis. For example, the analysis unit can perform the analysis by referring to the latest medical papers. For example, the analysis unit can perform the analysis by referring to past research data. The analysis unit can also perform the analysis by referring to relevant health guidelines. For example, the analysis unit can perform the analysis by referring to the latest medical papers. For example, the analysis unit can perform the analysis by referring to past research data. For example, the analysis unit can perform the analysis by referring to relevant health guidelines. In this way, the analysis unit improves the accuracy of its analysis by referring to relevant literature on the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0052] The advice unit can adjust the level of detail of its advice based on the importance of the analysis results. For example, the advice unit can provide detailed advice for important analysis results, concise advice for minor analysis results, and advice of moderate detail for moderate analysis results. The advice unit can provide detailed advice for important analysis results, concise advice for minor analysis results, and advice of moderate detail for moderate analysis results. By adjusting the level of detail of the advice unit based on the importance of the analysis results, it can provide more appropriate advice. Some or all of the above processing in the advice unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the advice unit can input the importance of the analysis results into the generating AI and have the generating AI adjust the level of detail of the advice.

[0053] The advice unit can apply different advice algorithms depending on the user's health condition when providing advice. For example, if the user is healthy, the advice unit can provide preventative advice. For example, if the user has a minor health problem, the advice unit can suggest solutions. The advice unit can also recommend that the user see a specialist if they have a serious health problem. The advice unit can provide preventative advice if the user is healthy. For example, if the user has a minor health problem, the advice unit can suggest solutions. For example, if the user has a serious health problem, the advice unit can recommend that the user see a specialist. This allows the advice unit to provide more appropriate advice by applying different advice algorithms depending on the user's health condition. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit can input the user's health condition data into a generative AI and have the generative AI apply the advice algorithm.

[0054] The advice unit can determine the priority of advice based on the timing of the submission of analysis results when providing advice. For example, the advice unit can prioritize the most important advice based on the most recent analysis results. For example, the advice unit can provide ongoing advice based on past analysis results. The advice unit can also provide preventative advice based on future predictions. For example, the advice unit can prioritize the most important advice based on the most recent analysis results. For example, the advice unit can provide ongoing advice based on past analysis results. For example, the advice unit can provide preventative advice based on future predictions. This allows the advice unit to provide more appropriate advice by determining the priority of advice based on the timing of the submission of analysis results. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the advice unit can input the data on the timing of the submission of analysis results into a generative AI and have the generative AI determine the priority of advice.

[0055] The advice unit can adjust the order of advice based on the relevance of the analysis results when providing advice. For example, if heart rate data and body temperature data are related, the advice unit can provide advice simultaneously. For example, if step count data and activity data are related, the advice unit can provide advice simultaneously. Furthermore, if body temperature data and sleep data are related, the advice unit can provide advice simultaneously. For example, if heart rate data and body temperature data are related, the advice unit can provide advice simultaneously. For example, if step count data and activity data are related, the advice unit can provide advice simultaneously. For example, if body temperature data and sleep data are related, the advice unit can provide advice simultaneously. This allows the advice unit to provide more appropriate advice by adjusting the order of advice based on the relevance of the analysis results. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the advice unit can input the relevance data of the analysis results into a generative AI and have the generative AI adjust the order of advice.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The health management system can also include a dietary analysis unit that collects and analyzes the user's dietary data. For example, the dietary analysis unit can analyze photos of meals taken by the user with their smartphone to estimate the nutrients and calories consumed. It can also evaluate the balance of nutrients based on the meal details entered by the user. Furthermore, the dietary analysis unit can analyze the user's dietary history and provide suggestions for dietary improvements tailored to their health condition. This allows the health management system to provide more comprehensive health management by analyzing the user's dietary data.

[0058] The health management system can also include a sleep analysis unit that collects and analyzes the user's sleep data. For example, the sleep analysis unit collects sleep data from a smartwatch worn by the user and evaluates the quality and duration of sleep. The sleep analysis unit can also analyze the user's sleep patterns and suggest areas for improvement. Furthermore, based on the user's sleep data, the sleep analysis unit can suggest an appropriate sleep environment. This allows the health management system to provide more comprehensive health management by analyzing the user's sleep data.

[0059] The health management system can also include an exercise analysis unit that collects and analyzes the user's exercise data. The exercise analysis unit can, for example, analyze the type and intensity of exercise performed by the user and evaluate the effectiveness of the exercise. It can also, for example, analyze the user's exercise history and propose an appropriate exercise plan. Furthermore, the exercise analysis unit can provide exercise advice tailored to the user's health condition. This allows the health management system to provide more comprehensive health management by analyzing the user's exercise data.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects data from smartphones and vital devices. For example, the data collection unit collects data such as heart rate, blood pressure, body temperature, and steps taken. Specifically, it collects heart rate and step count data from smartwatches, blood pressure data from blood pressure monitors, and body temperature data from thermometers. Step 2: The transmitting unit sends the data collected by the collecting unit to the cloud. For example, the transmitting unit can send data to the cloud using Wi-Fi, mobile data communication, or Bluetooth. Step 3: The analysis unit analyzes the data transmitted by the transmission unit. For example, the analysis unit analyzes heart rate data, blood pressure data, and body temperature data to check the user's health status and confirm that there are no abnormalities. Step 4: The advice unit provides advice based on the results analyzed by the analysis unit. For example, the advice unit suggests methods for relaxation to the user based on the analysis results, such as deep breathing, meditation, and stretching.

[0062] (Example of form 2) The health management system according to an embodiment of the present invention is a system that aggregates data collected from smartphones and vital devices in the cloud, and uses AI to analyze that data and check the user's health status. This health management system collects daily vital data and activity data (such as steps) from smartphones and vital devices, sends the collected data to the cloud, and the AI ​​analyzes that data. Based on the analysis results, an agent (AI doctor) residing in a dedicated smartphone app provides advice to the user. This advice is intended to prevent illness and frailty, with a particular emphasis on preventing frailty. Since reduced outings and social isolation are major factors in preventing frailty, the AI ​​doctor aims to improve the situation by encouraging user awareness and increasing motivation. It should be noted that the AI ​​doctor does not perform medical procedures, but only provides advice based on the data. For example, daily vital data and activity data are collected from smartphones and vital devices. In this case, data such as heart rate, blood pressure, body temperature, and steps are collected. For example, if the user is wearing a smartwatch, heart rate and step count data are collected from that smartwatch. Next, the collected data is sent to the cloud. In the cloud, the collected data is centrally managed, and AI analyzes it. Based on the collected data, the AI ​​checks the user's health status and checks for any abnormalities. For example, abnormalities may be detected if the heart rate is higher than normal or if the number of steps has decreased sharply. Based on the analysis results, an agent (AI doctor) residing in a dedicated smartphone app provides advice to the user. For example, if the heart rate is high, it may suggest ways to relax, or if the number of steps has decreased, it may recommend going for a walk. In this way, the AI ​​doctor constantly monitors the user's health status and provides appropriate advice to prevent illness and frailty. In particular, since reduced outdoor activity and social isolation are major factors in preventing frailty, the AI ​​doctor encourages user awareness and increases motivation to improve the situation.For example, if a user has not left their home for an extended period, the system will explain the importance of going out and propose a concrete action plan to encourage them to do so. In this way, the system aims to raise the user's awareness of their own health and encourage them to take proactive action. It should be noted that the AI ​​doctor does not perform medical procedures; it only provides advice based on data. If medical treatment is necessary, the system will prompt the user to consult a specialist physician. In this manner, a system that comprehensively supports the user's health is realized. As a result, the health management system can provide comprehensive support for the user's health.

[0063] The health management system according to this embodiment comprises a data collection unit, a data transmission unit, an analysis unit, and an advice unit. The data collection unit collects data from smartphones and vital devices. The data collection unit collects data such as heart rate, blood pressure, body temperature, and steps taken. The data collection unit can collect heart rate and steps taken from a smartwatch, for example. The data collection unit can also collect blood pressure data from a blood pressure monitor. Furthermore, the data collection unit can also collect body temperature data from a thermometer. For example, the data collection unit collects heart rate data from a user wearing a smartwatch. The data collection unit can also collect the user's blood pressure data using a blood pressure monitor. The data collection unit can also collect the user's body temperature data using a thermometer. The data transmission unit transmits the data collected by the data collection unit to the cloud. The data transmission unit can transmit the collected data to the cloud, for example. The data transmission unit can transmit the data to the cloud using Wi-Fi, for example. The data transmission unit can also transmit the data to the cloud using mobile data communication. Furthermore, the data transmission unit can also transmit the data to the cloud using Bluetooth. For example, the transmission unit sends collected data to the cloud using Wi-Fi. The transmission unit can also send collected data to the cloud using mobile data communication. The transmission unit can also send collected data to the cloud using Bluetooth. The analysis unit analyzes the data transmitted by the transmission unit. For example, the analysis unit checks the user's health status based on the collected data and confirms that there are no abnormalities. For example, the analysis unit can analyze heart rate data to confirm that there are no abnormalities. The analysis unit can also analyze blood pressure data to confirm that there are no abnormalities. Furthermore, the analysis unit can analyze body temperature data to confirm that there are no abnormalities. For example, the analysis unit can analyze heart rate data to confirm that there are no abnormalities. The analysis unit can also analyze blood pressure data to confirm that there are no abnormalities. The analysis unit can also analyze body temperature data to confirm that there are no abnormalities. The advice unit provides advice based on the results analyzed by the analysis unit. For example, the advice unit suggests ways for the user to relax based on the analysis results.The advice unit can, for example, suggest deep breathing. It can also suggest meditation. Furthermore, it can suggest stretching. For example, the advice unit can suggest deep breathing. The advice unit can also suggest meditation. The advice unit can also suggest stretching. In this way, the health management system according to the embodiment can comprehensively support the user's health status.

[0064] The data collection unit collects data from smartphones and vital devices. For example, it collects data such as heart rate, blood pressure, body temperature, and steps. Specifically, it can collect heart rate and step count data from smartwatches. Smartwatches are worn on the user's wrist and use built-in sensors to measure heart rate in real time and count steps. This data is transmitted to a smartphone via Bluetooth and acquired by the data collection unit. The data collection unit can also collect blood pressure data from blood pressure monitors. Blood pressure monitors are worn on the user's arm, and the measured blood pressure data is transmitted to a smartphone via Wi-Fi or Bluetooth. Furthermore, the data collection unit can collect body temperature data from thermometers. Thermometers are inserted into the user's mouth or armpit, and the measured body temperature data is similarly transmitted to a smartphone. For example, the data collection unit collects heart rate data from a user wearing a smartwatch. The data collection unit can also collect the user's blood pressure data using a blood pressure monitor. The data collection unit can also collect the user's body temperature data using a thermometer. This allows the data collection unit to centrally collect diverse data on users' health status and monitor their health in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and advisory units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0065] The transmitting unit sends the data collected by the collecting unit to the cloud. Specifically, the transmitting unit can send data to the cloud using Wi-Fi. Wi-Fi provides high-speed and stable communication, making it suitable for rapidly transmitting large amounts of data. The transmitting unit can also send data to the cloud using mobile data communication. Mobile data communication allows data to be transmitted even in locations where Wi-Fi is unavailable, ensuring uninterrupted data transmission even when the user is on the move. Furthermore, the transmitting unit can also send data to the cloud using Bluetooth. Bluetooth is suitable for short-range data communication and is used when transmitting data from smartphones or vital devices to the cloud. For example, the transmitting unit sends data collected using Wi-Fi to the cloud. The transmitting unit can also send data collected using mobile data communication to the cloud. The transmitting unit can also send data collected using Bluetooth to the cloud. This allows the transmitting unit to quickly and reliably transmit collected data to the cloud, enabling the analysis and advisory units to access the data in real time. Furthermore, the transmitting unit can optimize communication efficiency by adjusting the frequency and priority of data transmission, ensuring that important data is transmitted quickly. This allows the transmission unit to streamline data communication across the entire system and provide a foundation for monitoring the user's health status in real time.

[0066] The analysis unit analyzes the data transmitted by the transmission unit. For example, the analysis unit checks the user's health status based on the collected data and confirms that there are no abnormalities. Specifically, it can analyze heart rate data to check for abnormalities. Heart rate data is processed by an AI-based analysis algorithm, and if a value outside the normal range is detected, it is flagged as abnormal. The analysis unit can also analyze blood pressure data to check for abnormalities. Similarly, blood pressure data is processed by an AI-based analysis algorithm, and if a value outside the normal range is detected, it is flagged as abnormal. Furthermore, the analysis unit can analyze body temperature data to check for abnormalities. Similarly, body temperature data is processed by an AI-based analysis algorithm, and if a value outside the normal range is detected, it is flagged as abnormal. For example, the analysis unit can analyze heart rate data to check for abnormalities. The analysis unit can also analyze blood pressure data to check for abnormalities. The analysis unit can also analyze body temperature data to check for abnormalities. This allows the analysis unit to quickly and accurately analyze the collected data and understand the user's health status in real time. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term health trends. For example, it can analyze the user's heart rate variability patterns based on past heart rate data to predict future risks. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. As a result, the analysis unit can not only monitor health status in real time but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0067] The advice unit provides advice based on the results analyzed by the analysis unit. For example, the advice unit can suggest ways for users to relax based on the analysis results. Specifically, it can suggest deep breathing, which has the effect of reducing stress and stabilizing heart rate. The advice unit can also suggest meditation, which has the effect of calming the mind and promoting mental relaxation. Furthermore, the advice unit can suggest stretching, which has the effect of relieving muscle tension and promoting blood circulation. For example, the advice unit can suggest deep breathing, meditation, and stretching. This allows the advice unit to comprehensively support the user's health. In addition, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of its advice. For example, it can review and improve the advice based on feedback from users who have received advice. The advice unit can also provide individualized advice tailored to the user's lifestyle and health condition. For example, it can suggest daily exercise to users who are sedentary and suggest relaxation methods to users who are under a lot of stress. This allows the advice unit to provide users with appropriate advice tailored to their individual needs and support the improvement of their health.

[0068] The data collection unit can collect data such as heart rate, blood pressure, body temperature, and steps. For example, the data collection unit can collect heart rate data. For example, the data collection unit can collect heart rate data using an optical heart rate sensor. The data collection unit can also collect blood pressure data. For example, the data collection unit can collect blood pressure data using a pressure sensor. Furthermore, the data collection unit can also collect body temperature data. For example, the data collection unit can collect body temperature data using a temperature sensor. The data collection unit can also collect steps. For example, the data collection unit can collect steps using an accelerometer. In this way, the data collection unit can gain a detailed understanding of the user's health status by collecting data such as heart rate, blood pressure, body temperature, and steps. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input heart rate data acquired by an optical heart rate sensor into a generating AI and have the generating AI perform analysis of the heart rate data.

[0069] The transmitting unit can send the collected data to the cloud. The transmitting unit can, for example, send the collected data to the cloud. The transmitting unit can, for example, send data to the cloud using Wi-Fi. The transmitting unit can also send data to the cloud using mobile data communication. Furthermore, the transmitting unit can also send data to the cloud using Bluetooth. For example, the transmitting unit sends the collected data to the cloud using Wi-Fi. The transmitting unit can also send the collected data to the cloud using mobile data communication. The transmitting unit can also send the collected data to the cloud using Bluetooth. This allows the transmitting unit to centrally manage the data by sending the collected data to the cloud. The cloud includes, but is not limited to, services such as AWS, Google Cloud, and Microsoft Azure. Some or all of the processing described above in the transmitting unit may be performed using, for example, AI, or not using AI. For example, when the transmitting unit sends the collected data to the cloud using Wi-Fi, it can have a generating AI perform optimization of the transmitted data.

[0070] The analysis unit can check the user's health status based on the collected data and confirm whether there are any abnormalities. For example, the analysis unit can analyze heart rate data to check for abnormalities. For example, the analysis unit can detect an abnormality if the heart rate is higher than normal. The analysis unit can also analyze blood pressure data to check for abnormalities. For example, the analysis unit can detect an abnormality if the blood pressure is higher than normal. Furthermore, the analysis unit can also analyze body temperature data to check for abnormalities. For example, the analysis unit can detect an abnormality if the body temperature is higher than normal. In this way, the analysis unit can check the user's health status based on the collected data and confirm whether there are any abnormalities, enabling early detection of abnormalities. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input the collected heart rate data into a generation AI and have the generation AI perform the analysis of the heart rate data.

[0071] The advice unit can suggest methods for relaxation to the user based on the analysis results. For example, the advice unit may suggest deep breathing. For example, the advice unit may explain that deep breathing has a relaxing effect. The advice unit may also suggest meditation. For example, the advice unit may explain that meditation has a calming effect on the mind. Furthermore, the advice unit may also suggest stretching. For example, the advice unit may explain that stretching relieves muscle tension and has a relaxing effect. In this way, the advice unit can reduce the user's stress by suggesting methods for relaxation based on the analysis results. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit can input the analysis results into a generative AI and have the generative AI execute the suggestion of relaxation methods.

[0072] The advice unit can recommend that the user go for a walk based on the analysis results. For example, the advice unit may suggest going for a walk. For example, the advice unit may explain that going for a walk can have a refreshing effect on the mind and body. The advice unit may also suggest specific recommended walking times and distances. For example, the advice unit may suggest a 30-minute walk. Furthermore, the advice unit may suggest points to be aware of when walking. For example, the advice unit may suggest wearing appropriate shoes and staying hydrated. In this way, the advice unit can increase the user's activity level by recommending that they go for a walk based on the analysis results. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the advice unit may input the analysis results into a generative AI and have the generative AI execute the walking suggestion.

[0073] The advice unit can propose action plans to encourage user awareness and increase motivation. For example, the advice unit can explain to the user the importance of going outside. For example, the advice unit can explain that going outside improves physical and mental health. The advice unit can also propose specific action plans. For example, the advice unit can suggest taking a walk three times a week. Furthermore, the advice unit can suggest ways to increase the user's motivation. For example, the advice unit can suggest setting goals and recording the degree of their achievement. In this way, the advice unit can raise the user's health awareness by encouraging user awareness and proposing action plans to increase motivation. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the advice unit can input the user's health data into a generative AI and have the generative AI execute the action plan proposal.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect data during times when the user is relaxed. For example, if the user is active, the data collection unit can prioritize data collection after exercise. Also, if the user is tired, the data collection unit can collect data during rest periods. For example, if the user is stressed, the data collection unit can collect data during times when the user is relaxed. For example, if the user is active, the data collection unit can prioritize data collection after exercise. For example, if the user is tired, the data collection unit can collect data during rest periods. This allows the data collection unit to collect data at more appropriate times by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of data collection.

[0075] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can analyze the user's past heart rate data and collect data during times when the heart rate is stable. For example, the data collection unit can analyze the user's past step count data and collect data during times when the step count is high. The data collection unit can also analyze the user's past body temperature data and collect data during times when the body temperature is stable. For example, the data collection unit can analyze the user's past heart rate data and collect data during times when the heart rate is stable. For example, the data collection unit can analyze the user's past step count data and collect data during times when the step count is high. For example, the data collection unit can analyze the user's past body temperature data and collect data during times when the body temperature is stable. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past health data. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without using generative AI. For example, the data collection unit can input the user's past health data into a generating AI, which can then select the optimal data collection method.

[0076] The data collection unit can filter data based on the user's current activity status and environment during data collection. 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 body temperature data. Also, if the user is outdoors, the data collection unit can prioritize collecting step count data. 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 body temperature data. For example, if the user is outdoors, the data collection unit can prioritize collecting step count data. This allows the data collection unit to collect more relevant data by filtering the data based on the user's current activity status and environment. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's current activity status and environment data into a generative AI and have the generative AI perform data filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit may prioritize collecting heart rate data. For example, if the user is relaxed, the data collection unit may prioritize collecting body temperature data. The data collection unit may also prioritize collecting step count data if the user is active. For example, if the user is stressed, the data collection unit may prioritize collecting heart rate data. For example, if the user is relaxed, the data collection unit may prioritize collecting body temperature data. For example, if the user is active, the data collection unit may also prioritize collecting step count data. This allows the data collection unit to prioritize collecting more important data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI determine the priority of the data.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is at home, the data collection unit can prioritize the collection of data related to the indoor environment. For example, if the user is out, the data collection unit can prioritize the collection of data related to the external environment. Furthermore, if the user is at work, the data collection unit can prioritize the collection of data related to the work environment. In this way, the data collection unit can collect more relevant data by considering the user's geographical location information during data collection. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI and have the generative AI perform the priority collection of highly relevant data.

[0079] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, if the user posts about exercise on social media, the data collection unit will prioritize collecting exercise data. For example, if the user posts about health on social media, the data collection unit will prioritize collecting health data. The data collection unit can also prioritize collecting stress-related data if the user posts about stress on social media. For example, if the user posts about exercise on social media, the data collection unit will prioritize collecting exercise data. For example, if the user posts about health on social media, the data collection unit will prioritize collecting health data. For example, if the user posts about stress on social media, the data collection unit will prioritize collecting stress-related data. In this way, the data collection unit can collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI and have the generative AI collect the relevant data.

[0080] The transmitting unit can estimate the user's emotions and adjust the timing of data transmission based on the estimated emotions. For example, if the user is relaxed, the transmitting unit can transmit data immediately. For example, if the user is stressed, the transmitting unit can postpone data transmission. Also, if the user is active, the transmitting unit can transmit data after the activity is completed. For example, if the user is relaxed, the transmitting unit can transmit data immediately. For example, if the user is stressed, the transmitting unit can postpone data transmission. For example, if the user is active, the transmitting unit can transmit data after the activity is completed. This allows the transmitting unit to transmit data at a more appropriate time by adjusting the timing of data transmission based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 transmitting unit may be performed using AI, for example, or without AI. For example, the transmission unit can input user emotion data into a generating AI and have the generating AI adjust the timing of data transmission.

[0081] The transmitting unit can determine the transmission priority based on the importance of the data when transmitting data. For example, if heart rate data shows an abnormal value, the transmitting unit will transmit it with the highest priority. For example, if step count data is decreasing rapidly, the transmitting unit can transmit it with priority. Also, if body temperature data is within the normal range, the transmitting unit can transmit it later. For example, if heart rate data shows an abnormal value, the transmitting unit will transmit it with the highest priority. For example, if step count data is decreasing rapidly, the transmitting unit can transmit it with priority. For example, if body temperature data is within the normal range, the transmitting unit can transmit it later. In this way, the transmitting unit can transmit important data with priority by determining the transmission priority based on the importance of the data. Some or all of the above processing in the transmitting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the transmitting unit can input the importance of the data into a generative AI and have the generative AI perform the determination of the transmission priority.

[0082] The transmitting unit can apply different transmission protocols depending on the type of data when transmitting data. For example, the transmitting unit can apply a real-time transmission protocol to heart rate data. For example, the transmitting unit can apply a protocol to periodically batch transmission of step count data. The transmitting unit can also apply a protocol to transmit body temperature data all at once at the end of the day. This allows the transmitting unit to efficiently transmit data by applying different transmission protocols depending on the type of data. Some or all of the above processing in the transmitting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the transmitting unit can input the type of data into a generative AI and have the generative AI apply the transmission protocol.

[0083] The transmitting unit can estimate the user's emotions and adjust the amount of data it transmits based on the estimated emotions. For example, if the user is stressed, the transmitting unit can transmit only essential data. For example, if the user is relaxed, the transmitting unit can transmit detailed data. The transmitting unit can also transmit only the minimum necessary data if the user is active. For example, if the user is stressed, the transmitting unit can transmit only essential data. For example, if the user is relaxed, the transmitting unit can transmit detailed data. For example, if the user is active, the transmitting unit can transmit only the minimum necessary data. This allows the transmitting unit to transmit a more appropriate amount of data by adjusting the amount of data it transmits based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 transmitting unit may be performed using AI, for example, or without AI. For example, the transmission unit can input user emotion data into a generating AI and have the generating AI adjust the amount of data to be transmitted.

[0084] The transmission unit can select the optimal transmission method when transmitting data, taking into account the user's network status. For example, if the user is connected via Wi-Fi, the transmission unit can transmit large amounts of data. For example, if the user is connected via mobile data, the transmission unit can compress the data before transmission. Furthermore, if the user is offline, the transmission unit can transmit the data as soon as the network connection is restored. The transmission unit can transmit large amounts of data when the user is connected via Wi-Fi. For example, if the user is connected via mobile data, the transmission unit can compress the data before transmission. For example, if the user is offline, the transmission unit can transmit the data as soon as the network connection is restored. This allows the transmission unit to efficiently transmit data by selecting the optimal transmission method, taking into account the user's network status. Some or all of the above processing in the transmission unit may be performed using, for example, a generative AI, or without a generative AI. For example, the transmission unit can input the user's network status data into a generative AI and have the generative AI select the optimal transmission method.

[0085] The transmitting unit can adjust the transmission order based on the relationships between data when transmitting data. For example, if heart rate data and body temperature data are related, the transmitting unit can transmit them simultaneously. For example, if step count data and activity data are related, the transmitting unit can transmit them simultaneously. Also, if body temperature data and sleep data are related, the transmitting unit can transmit them simultaneously. For example, if heart rate data and body temperature data are related, the transmitting unit can transmit them simultaneously. For example, if step count data and activity data are related, the transmitting unit can transmit them simultaneously. For example, if body temperature data and sleep data are related, the transmitting unit can transmit them simultaneously. This allows the transmitting unit to efficiently transmit data by adjusting the transmission order based on the relationships between data. Some or all of the above processing in the transmitting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the transmitting unit can input the relationships between data into a generative AI and have the generative AI adjust the transmission order.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, if the user is stressed, the analysis unit will prioritize stress-related data in its analysis. For example, if the user is relaxed, the analysis unit can analyze overall health data in a balanced manner. The analysis unit can also prioritize activity data in its analysis if the user is active. For example, if the user is stressed, the analysis unit will prioritize stress-related data in its analysis. For example, if the user is relaxed, the analysis unit can analyze overall health data in a balanced manner. For example, if the user is active, the analysis unit can also prioritize activity data in its analysis. This allows the analysis unit to perform more appropriate analysis by adjusting the analysis criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the criteria for analysis.

[0087] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data during the analysis. For example, the analysis unit can analyze by considering the correlation between heart rate data and body temperature data. For example, the analysis unit can analyze by considering the correlation between step count data and activity data. Furthermore, the analysis unit can also analyze by considering the correlation between body temperature data and sleep data. For example, the analysis unit can analyze by considering the correlation between heart rate data and body temperature data. For example, the analysis unit can analyze by considering the correlation between step count data and activity data. For example, the analysis unit can analyze by considering the correlation between body temperature data and sleep data. In this way, the analysis unit improves the accuracy of its analysis by considering the interrelationships between data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the interrelationships of data into a generative AI and have the generative AI perform the improvement of analysis accuracy.

[0088] The analysis unit can perform analysis while considering the attribute information of the data submitter. For example, the analysis unit can perform analysis while considering the user's age. For example, the analysis unit can perform analysis while considering the user's gender. Furthermore, the analysis unit can also perform analysis while considering the user's lifestyle. For example, the analysis unit can perform analysis while considering the user's age. For example, the analysis unit can perform analysis while considering the user's gender. For example, the analysis unit can perform analysis while considering the user's lifestyle. This allows the analysis unit to perform more personalized analysis by considering the attribute information of the data submitter. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the user's attribute information into a generating AI and have the generating AI perform the analysis.

[0089] 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 provide a display method that includes detailed information. The analysis unit can also provide a concise display method if the user is in a hurry. 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 provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a concise display method. This allows the analysis unit to provide a more appropriate display by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the display method.

[0090] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can perform analysis while considering the climate of the user's place of residence. For example, the analysis unit can perform analysis while considering the environment of the user's activity area. Furthermore, the analysis unit can also perform analysis while considering the health risks of the user's travel destination. For example, the analysis unit can perform analysis while considering the climate of the user's place of residence. For example, the analysis unit can perform analysis while considering the environment of the user's activity area. For example, the analysis unit can perform analysis while considering the health risks of the user's travel destination. This allows the analysis unit to perform more appropriate analysis by considering the geographical distribution of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the geographical distribution of the data into a generative AI and have the generative AI perform the analysis.

[0091] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the data during the analysis. For example, the analysis unit can perform the analysis by referring to the latest medical papers. For example, the analysis unit can perform the analysis by referring to past research data. The analysis unit can also perform the analysis by referring to relevant health guidelines. For example, the analysis unit can perform the analysis by referring to the latest medical papers. For example, the analysis unit can perform the analysis by referring to past research data. For example, the analysis unit can perform the analysis by referring to relevant health guidelines. In this way, the analysis unit improves the accuracy of its analysis by referring to relevant literature on the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0092] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on the estimated emotions. For example, if the user is nervous, the advice unit can give advice in a calm tone. For example, if the user is relaxed, the advice unit can give advice in a cheerful tone. The advice unit can also give concise and quick advice if the user is in a hurry. For example, if the user is nervous, the advice unit can give advice in a calm tone. For example, if the user is relaxed, the advice unit can give advice in a cheerful tone. For example, if the user is in a hurry, the advice unit can give concise and quick advice. This allows the advice unit to provide more appropriate advice by adjusting the way it expresses advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input user emotion data into a generating AI and have the AI ​​adjust how the advice is expressed.

[0093] The advice unit can adjust the level of detail of its advice based on the importance of the analysis results. For example, the advice unit can provide detailed advice for important analysis results, concise advice for minor analysis results, and advice of moderate detail for moderate analysis results. The advice unit can provide detailed advice for important analysis results, concise advice for minor analysis results, and advice of moderate detail for moderate analysis results. By adjusting the level of detail of the advice unit based on the importance of the analysis results, it can provide more appropriate advice. Some or all of the above processing in the advice unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the advice unit can input the importance of the analysis results into the generating AI and have the generating AI adjust the level of detail of the advice.

[0094] The advice unit can apply different advice algorithms depending on the user's health condition when providing advice. For example, if the user is healthy, the advice unit can provide preventative advice. For example, if the user has a minor health problem, the advice unit can suggest solutions. The advice unit can also recommend that the user see a specialist if they have a serious health problem. The advice unit can provide preventative advice if the user is healthy. For example, if the user has a minor health problem, the advice unit can suggest solutions. For example, if the user has a serious health problem, the advice unit can recommend that the user see a specialist. This allows the advice unit to provide more appropriate advice by applying different advice algorithms depending on the user's health condition. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit can input the user's health condition data into a generative AI and have the generative AI apply the advice algorithm.

[0095] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the advice unit will provide short, to-the-point advice. For example, if the user is relaxed, the advice unit can provide longer advice that includes detailed explanations. Furthermore, if the user is excited, the advice unit can provide advice with visually stimulating effects. For example, if the user is in a hurry, the advice unit will provide short, to-the-point advice. For example, if the user is relaxed, the advice unit can provide longer advice that includes detailed explanations. For example, if the user is excited, the advice unit can provide advice with visually stimulating effects. This allows the advice unit to provide more appropriate advice by adjusting the length of the advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input user emotion data into a generating AI and have the AI ​​adjust the length of the advice.

[0096] The advice unit can determine the priority of advice based on the timing of the submission of analysis results when providing advice. For example, the advice unit can prioritize the most important advice based on the most recent analysis results. For example, the advice unit can provide ongoing advice based on past analysis results. The advice unit can also provide preventative advice based on future predictions. For example, the advice unit can prioritize the most important advice based on the most recent analysis results. For example, the advice unit can provide ongoing advice based on past analysis results. For example, the advice unit can provide preventative advice based on future predictions. This allows the advice unit to provide more appropriate advice by determining the priority of advice based on the timing of the submission of analysis results. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the advice unit can input the data on the timing of the submission of analysis results into a generative AI and have the generative AI determine the priority of advice.

[0097] The advice unit can adjust the order of advice based on the relevance of the analysis results when providing advice. For example, if heart rate data and body temperature data are related, the advice unit can provide advice simultaneously. For example, if step count data and activity data are related, the advice unit can provide advice simultaneously. Furthermore, if body temperature data and sleep data are related, the advice unit can provide advice simultaneously. For example, if heart rate data and body temperature data are related, the advice unit can provide advice simultaneously. For example, if step count data and activity data are related, the advice unit can provide advice simultaneously. For example, if body temperature data and sleep data are related, the advice unit can provide advice simultaneously. This allows the advice unit to provide more appropriate advice by adjusting the order of advice based on the relevance of the analysis results. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the advice unit can input the relevance data of the analysis results into a generative AI and have the generative AI adjust the order of advice.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The health management system can also include a dietary analysis unit that collects and analyzes the user's dietary data. For example, the dietary analysis unit can analyze photos of meals taken by the user with their smartphone to estimate the nutrients and calories consumed. It can also evaluate the balance of nutrients based on the meal details entered by the user. Furthermore, the dietary analysis unit can analyze the user's dietary history and provide suggestions for dietary improvements tailored to their health condition. This allows the health management system to provide more comprehensive health management by analyzing the user's dietary data.

[0100] The health management system can also include a sleep analysis unit that collects and analyzes the user's sleep data. For example, the sleep analysis unit collects sleep data from a smartwatch worn by the user and evaluates the quality and duration of sleep. The sleep analysis unit can also analyze the user's sleep patterns and suggest areas for improvement. Furthermore, based on the user's sleep data, the sleep analysis unit can suggest an appropriate sleep environment. This allows the health management system to provide more comprehensive health management by analyzing the user's sleep data.

[0101] The health management system can also include a stress analysis unit that evaluates the user's stress level. For example, the stress analysis unit can analyze the user's heart rate variability data to estimate the stress level. It can also combine the user's activity data and sleep data to identify the causes of stress. Furthermore, the stress analysis unit can suggest relaxation methods tailored to the user's stress level. This allows the health management system to provide more comprehensive health management by analyzing the user's stress level.

[0102] The health management system can also include an exercise analysis unit that collects and analyzes the user's exercise data. The exercise analysis unit can, for example, analyze the type and intensity of exercise performed by the user and evaluate the effectiveness of the exercise. It can also, for example, analyze the user's exercise history and propose an appropriate exercise plan. Furthermore, the exercise analysis unit can provide exercise advice tailored to the user's health condition. This allows the health management system to provide more comprehensive health management by analyzing the user's exercise data.

[0103] The health management system can further estimate the user's emotions and adjust the content of the advice based on those emotions. For example, if the user is feeling stressed, it can suggest ways to relax. If the user is relaxed, it can provide advice on maintaining good health. Furthermore, if the user is active, it can provide advice on how to enhance the effects of exercise. In this way, the health management system can provide more appropriate advice by adjusting the content of the advice based on the user's emotions.

[0104] The health management system can further estimate the user's emotions and adjust the data collection frequency based on those estimates. For example, if the user is stressed, the data collection frequency can be reduced. If the user is relaxed, the data collection frequency can be increased. Similarly, if the user is active, the exercise data collection frequency can be increased. This allows the health management system to collect more appropriate data by adjusting the data collection frequency based on the user's emotions.

[0105] The health management system can further estimate the user's emotions and adjust the data analysis method based on those estimated emotions. For example, if the user is stressed, stress-related data can be given more weight in the analysis. If the user is relaxed, overall health data can be analyzed in a balanced manner. Also, if the user is active, activity data can be given more weight in the analysis. In this way, the health management system can perform more appropriate analysis by adjusting the data analysis method based on the user's emotions.

[0106] The health management system can further estimate the user's emotions and adjust the timing of advice based on those emotions. For example, if the user is feeling stressed, advice can be given during times when they are relaxed. If the user is relaxed, advice can be given immediately. Also, if the user is active, advice can be given after exercise. In this way, the health management system can provide more appropriate advice by adjusting the timing of advice based on the user's emotions.

[0107] The health management system can further estimate the user's emotions and customize the advice based on those emotions. For example, if the user is stressed, it can suggest specific ways to relax. If the user is relaxed, it can suggest specific ways to maintain their health. Furthermore, if the user is active, it can suggest specific ways to enhance the effects of exercise. This allows the health management system to provide more appropriate advice by customizing it based on the user's emotions.

[0108] The health management system can further estimate the user's emotions and adjust the way advice is delivered based on those emotions. For example, if the user is nervous, advice can be given in a calm tone. If the user is relaxed, advice can be given in a cheerful tone. Also, if the user is in a hurry, concise and quick advice can be given. In this way, the health management system can provide more appropriate advice by adjusting the way advice is delivered based on the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects data from smartphones and vital devices. For example, the data collection unit collects data such as heart rate, blood pressure, body temperature, and steps taken. Specifically, it collects heart rate and step count data from smartwatches, blood pressure data from blood pressure monitors, and body temperature data from thermometers. Step 2: The transmitting unit sends the data collected by the collecting unit to the cloud. For example, the transmitting unit can send data to the cloud using Wi-Fi, mobile data communication, or Bluetooth. Step 3: The analysis unit analyzes the data transmitted by the transmission unit. For example, the analysis unit analyzes heart rate data, blood pressure data, and body temperature data to check the user's health status and confirm that there are no abnormalities. Step 4: The advice unit provides advice based on the results analyzed by the analysis unit. For example, the advice unit suggests methods for relaxation to the user based on the analysis results, such as deep breathing, meditation, and stretching.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] For example, each of the multiple elements, including the data collection unit, transmission unit, analysis unit, and advice unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data from the computer 36 and vital devices of the smart device 14, and the transmission unit transmits the data to the cloud using the communication I / F 44 of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and the advice unit is implemented by the control unit 46A 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 various modifications are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] For example, each of the multiple elements, including the data collection unit, transmission unit, analysis unit, and advice unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data from the computer 36 of the smart glasses 214 or from vital devices, and the transmission unit transmits the data to the cloud using the communication I / F 44 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and the advice unit is implemented by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] For example, each of the multiple elements, including the data collection unit, transmission unit, analysis unit, and advice unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data from the computer 36 of the headset terminal 314 and vital devices, and the transmission unit transmits the data to the cloud using the communication I / F 44 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and the advice unit is implemented by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

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

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

[0158] In 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.

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

[0160] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0162] The data processing system 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.

[0163] For example, each of the multiple elements, including the data collection unit, transmission unit, analysis unit, and advice unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data from the robot 414's computer 36 and vital devices, and the transmission unit sends the data to the cloud using the robot 414's communication I / F 44. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and the advice unit is implemented by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A data collection unit that collects data from smartphones and vital devices, A transmission unit that transmits the data collected by the aforementioned collection unit to the cloud, An analysis unit that analyzes the data transmitted by the transmission unit, The system includes an advice unit that provides advice based on the results of the analysis performed by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects data such as heart rate, blood pressure, body temperature, and steps taken. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned transmitting unit Send the collected data to the cloud. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Based on the collected data, the system checks the user's health status and confirms that there are no abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned advice section, Based on the analysis results, we propose methods to help users relax. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice section, Based on the analysis results, we recommend that the user go for a walk. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned advice section, We propose action plans to encourage user awareness and increase motivation. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activity status and environment. 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 data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned transmitting unit It estimates the user's emotions and adjusts the timing of data transmission based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned transmitting unit When sending data, the transmission priority is determined based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned transmitting unit When transmitting data, different transmission protocols are applied depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned transmitting unit It estimates the user's emotions and adjusts the amount of data sent based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned transmitting unit When transmitting data, the system selects the optimal transmission method considering the user's network conditions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned transmitting unit When sending data, the order of transmission is adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, consider the interrelationships between data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, the attribute information of the data submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, When performing analysis, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advice section, It estimates the user's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned advice section, When providing advice, we prioritize the advice based on when the analysis results are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned advice section, When providing advice, adjust the order of advice based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data from smartphones and vital devices, A transmission unit that transmits the data collected by the aforementioned collection unit to the cloud, An analysis unit that analyzes the data transmitted by the transmission unit, The system includes an advice unit that provides advice based on the results of the analysis performed by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects data such as heart rate, blood pressure, body temperature, and steps taken. The system according to feature 1.

3. The aforementioned transmitting unit Send the collected data to the cloud. The system according to feature 1.

4. The aforementioned analysis unit, Based on the collected data, the system checks the user's health status and confirms that there are no abnormalities. The system according to feature 1.

5. The aforementioned advice section, Based on the analysis results, we propose methods to help users relax. The system according to feature 1.

6. The aforementioned advice section, Based on the analysis results, we recommend that the user go for a walk. The system according to feature 1.

7. The aforementioned advice section, We propose action plans to encourage user awareness and increase motivation. The system according to feature 1.

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

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