system

Abstract

The system according to the embodiment aims to grasp the health condition of elderly people at an early stage and provide appropriate assistance. [Solution] A system according to an embodiment includes a biosensor, a generation AI, a proposal unit, an execution unit, and a notification unit. The biosensor measures vital signs. The generation AI analyzes the data measured by the biosensor. The proposal unit proposes assistance based on the results of the analysis by the generation AI. The execution unit executes the assistance proposed by the proposal unit. The notification unit sends a notification regarding the user's health condition if an abnormal value is detected by the generation AI.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to grasp the health status of elderly people early and provide appropriate assistance.

[0005] The system according to the embodiment aims to grasp the health condition of elderly people at an early stage and provide appropriate assistance. [Means for solving the problem]

[0006] The system according to the embodiment includes a biosensor, a generation AI, a proposal unit, an execution unit, and a notification unit. The biosensor measures vital signs. The generation AI analyzes the data measured by the biosensor. The proposal unit proposes assistance based on the results of the analysis by the generation AI. The execution unit executes the assistance proposed by the proposal unit. The notification unit sends a notification regarding the user's health condition when an abnormal value is detected by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the health condition of elderly people at an early stage and provide appropriate assistance. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A health monitoring system according to an embodiment of the present invention is a device incorporating vital sign (pulse, blood pressure, heart rate, and body temperature) biosensors into wireless earphones with a sound-collecting function that eliminates hearing loss, in order to address health issues in an aging society. This device monitors the user's health status in real time, and a generation AI analyzes the measured values. Based on the analysis results, the generation AI proposes optimal assistance (diet, exercise, sleep, and brain training) to promote healthy life expectancy. Furthermore, if the measured values ​​indicate abnormalities, a notification is sent to pre-registered relatives, etc., enabling early detection and early prevention. Specifically, the system consists of the following steps: First, the user puts on the wireless earphones. The earphones are equipped with built-in biosensors that measure the user's pulse, blood pressure, heart rate, and body temperature. These sensors measure the user's vital signs in real time and collect data. The collected data is then sent to the generation AI. The generation AI analyzes the data and evaluates the user's health status. For example, it checks whether the pulse and blood pressure are within normal ranges, whether the heart rate is stable, and whether the body temperature is abnormally high. Based on the analysis results, the generation AI proposes optimal assistance to the user. For example, it may suggest a nutritionally balanced meal menu as dietary advice or an appropriate exercise plan as exercise advice. It may also provide advice on improving sleep quality and suggest brain training. Furthermore, if the measurement values ​​indicate abnormalities, the generation AI will send a notification to pre-registered relatives. For example, if the pulse rate is abnormally high or blood pressure rises sharply, a notification will be sent to relatives, enabling early detection and early prevention. This device monitors the health status of elderly people in real time and provides appropriate assistance, promoting healthy life expectancy. In addition, if abnormal values ​​are detected, a notification will be sent to relatives, enabling early detection and early prevention. This allows the health monitoring system to monitor the user's health status in real time and provide appropriate assistance. In addition, if abnormal values ​​are detected, a notification will be sent to relatives, enabling early detection and early prevention.

[0029] A health monitoring system according to an embodiment includes a biosensor, a generation AI, a suggestion unit, an execution unit, and a notification unit. The biosensor measures a user's vital signs. Examples of vital signs include, but are not limited to, pulse, blood pressure, heart rate, and body temperature. The biosensor measures pulse using a pulse sensor, for example. The biosensor can also measure blood pressure using a blood pressure sensor. The biosensor can also measure heart rate using a heart rate sensor. The biosensor can also measure body temperature using a body temperature sensor. For example, the biosensor can use a photoelectric pulse wave sensor as the pulse sensor. The photoelectric pulse wave sensor is a sensor that measures pulse using light, and measures pulse by irradiating light onto the skin and detecting changes in reflected light. For example, a cuff-type blood pressure monitor can be used as the blood pressure sensor. The cuff-type blood pressure monitor is a device that measures blood pressure using a cuff wrapped around the arm, and measures blood pressure by changing the pressure in the cuff. For example, an electrocardiogram sensor can be used as the heart rate sensor. An electrocardiogram sensor is a sensor that detects the electrical activity of the heart and can measure heart rate. An infrared thermometer, for example, can be used as a body temperature sensor. An infrared thermometer is a device that measures body temperature using infrared rays and measures body temperature by detecting the surface temperature of the skin. The generation AI analyzes the measured data and evaluates the user's health condition. The generation AI can analyze the data using, for example, a machine learning algorithm. For example, the generation AI can analyze pulse data and evaluate whether the pulse is within a normal range. The generation AI can also analyze blood pressure data and evaluate whether the blood pressure is within a normal range. The generation AI can also analyze heart rate data and evaluate whether the heart rate is stable. The generation AI can also analyze body temperature data and evaluate whether the body temperature is abnormally high. For example, the generation AI can input pulse data and analyze the pulse data using a machine learning model that outputs whether the pulse is within a normal range. Similarly, blood pressure data can be analyzed using a machine learning model that outputs whether the blood pressure is within a normal range. Similarly, heart rate data can be analyzed using a machine learning model that outputs whether the heart rate is stable.Similarly, body temperature data can be analyzed using a machine learning model that outputs whether the body temperature is abnormally high. The suggestion unit proposes assistance based on the results of the analysis by the generation AI. For example, the suggestion unit can propose a nutritionally balanced meal menu as a meal assistance. The suggestion unit can also propose an appropriate exercise plan as an exercise assistance. The suggestion unit can also propose advice for improving sleep quality as a sleep assistance. The suggestion unit can also suggest brain training as a brain training assistance. For example, the suggestion unit can propose an appropriate exercise plan based on pulse data analyzed by the generation AI. When the generation AI analyzes the pulse data and the pulse is within a normal range, the suggestion unit can propose an appropriate exercise plan. When the generation AI analyzes blood pressure data and the blood pressure is within a normal range, the suggestion unit can propose an appropriate meal menu. When the generation AI analyzes heart rate data and the heart rate is stable, the suggestion unit can propose appropriate sleep advice. When the generation AI analyzes body temperature data and the body temperature is not abnormally high, the suggestion unit can propose an appropriate brain training. The execution unit executes the assistance proposed by the suggestion unit. The execution unit can, for example, execute a proposed meal menu. The execution unit can also execute a proposed exercise plan. The execution unit can also execute proposed sleep advice. The execution unit can also execute proposed brain training. For example, the execution unit can generate an ingredient shopping list to execute the proposed meal menu. The execution unit can set an exercise schedule to execute the proposed exercise plan. The execution unit can provide advice on improving a sleep environment to execute the proposed sleep advice. The execution unit can also set a brain training schedule to execute the proposed brain training. The notification unit sends a notification regarding the user's health condition when an abnormal value is detected by the generation AI. For example, the notification unit can send a notification to relatives, etc. when the pulse rate is abnormally high.The notification unit can also send a notification to relatives, etc. if the blood pressure suddenly rises. The notification unit can also send a notification to relatives, etc. if the heart rate is unstable. The notification unit can also send a notification to relatives, etc. if the body temperature is abnormally high. For example, the notification unit can analyze pulse data by the generation AI and send a notification to relatives, etc. if the pulse rate is abnormally high. The notification unit can analyze blood pressure data by the generation AI and send a notification to relatives, etc. if the blood pressure suddenly rises. The notification unit can analyze heart rate data by the generation AI and send a notification to relatives, etc. if the heart rate is unstable. The notification unit can analyze body temperature data by the generation AI and send a notification to relatives, etc. if the body temperature is abnormally high. This allows the health monitoring system according to the embodiment to monitor the user's health condition in real time and provide appropriate assistance. Furthermore, by sending a notification to relatives, etc. if an abnormal value is detected, early detection and early prevention can be achieved.

[0030] The biosensor can measure pulse, blood pressure, heart rate, and body temperature. The biosensor measures pulse using, for example, a pulse sensor. The pulse sensor can be, for example, a photoelectric pulse wave sensor. The photoelectric pulse wave sensor is a sensor that measures pulse using light, irradiating light onto the skin and measuring pulse by detecting changes in the reflected light. The biosensor can also measure blood pressure using a blood pressure sensor. The blood pressure sensor can be, for example, a cuff-type blood pressure monitor. The cuff-type blood pressure monitor is a device that measures blood pressure using a cuff wrapped around the arm and measures blood pressure by changing the pressure in the cuff. The biosensor can also measure heart rate using a heart rate sensor. The heart rate sensor can be, for example, an electrocardiogram sensor. The electrocardiogram sensor is a sensor that detects the electrical activity of the heart and can measure heart rate. The biosensor can also measure body temperature using a body temperature sensor. The body temperature sensor can be, for example, an infrared thermometer. An infrared thermometer is a device that uses infrared rays to measure body temperature by detecting the surface temperature of the skin. This allows for more detailed understanding of a person's health condition by measuring multiple vital signs. Some or all of the above-described processing in the biosensor may be performed using, for example, AI, or may be performed without using AI. For example, the biosensor may input data acquired by a pulse sensor into a generation AI, which then analyzes the pulse data.

[0031] The generating AI can analyze the measured data and evaluate the user's health condition. The generating AI can analyze the data using, for example, a machine learning algorithm. For example, the generating AI can analyze pulse data and evaluate whether the pulse is within a normal range. The generating AI can also analyze blood pressure data and evaluate whether the blood pressure is within a normal range. The generating AI can also analyze heart rate data and evaluate whether the heart rate is stable. The generating AI can also analyze body temperature data and evaluate whether the body temperature is abnormally high. For example, the generating AI can analyze the pulse data using a machine learning model that inputs pulse data and outputs whether the pulse is within a normal range. Similarly, the generating AI can analyze blood pressure data using a machine learning model that outputs whether the blood pressure is within a normal range. Similarly, the heart rate data can be analyzed using a machine learning model that outputs whether the heart rate is stable. Similarly, the body temperature data can be analyzed using a machine learning model that outputs whether the body temperature is abnormally high. This allows the generating AI to accurately evaluate the user's health condition through data analysis. Some or all of the above-mentioned processing in the generating AI may be performed, for example, using AI or without AI. For example, the generation AI can analyze pulse data using an AI model that takes pulse data as input and outputs whether the pulse is within the normal range.

[0032] The suggestion unit can suggest assistance for diet, exercise, sleep, and brain training. The suggestion unit suggests assistance based on the results of analysis by the generation AI. For example, as a dietary assistance, the suggestion unit can suggest a nutritionally balanced meal menu. As an exercise assistance, the suggestion unit can suggest an appropriate exercise plan. As a sleep assistance, the suggestion unit can suggest advice for improving sleep quality. As a brain training assistance, the suggestion unit can suggest brain training. For example, the suggestion unit can suggest an appropriate exercise plan based on pulse data analyzed by the generation AI. When the generation AI analyzes pulse data and the pulse is within a normal range, the suggestion unit can suggest an appropriate exercise plan. When the generation AI analyzes blood pressure data and the blood pressure is within a normal range, the suggestion unit can suggest an appropriate meal menu. When the generation AI analyzes heart rate data and the heart rate is stable, the suggestion unit can suggest appropriate sleep advice. When the generation AI analyzes body temperature data and the body temperature is not abnormally high, the suggestion unit can suggest an appropriate brain training. This allows for the promotion of a healthy lifespan by proposing various types of assistance according to the user's health condition. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may suggest assistance using an AI model that receives data analyzed by the generation AI as input and outputs appropriate assistance.

[0033] The execution unit can execute the proposed assistance. The execution unit executes the assistance proposed by the suggestion unit. The execution unit can, for example, execute a proposed meal menu. The execution unit can also execute a proposed exercise plan. The execution unit can also execute proposed sleep advice. The execution unit can also execute proposed brain training. For example, the execution unit can generate an ingredient shopping list to execute the proposed meal menu. The execution unit can set an exercise schedule to execute the proposed exercise plan. The execution unit can provide advice on improving a sleep environment to execute the proposed sleep advice. The execution unit can also set a brain training schedule to execute the proposed brain training. In this way, by executing the proposed assistance, the user's health condition can be improved. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can execute the assistance using an AI model that executes the assistance, using the assistance proposed by the suggestion unit as input.

[0034] The notification unit can send a notification to relatives or designated contacts when an abnormal value is detected. The notification unit can send a notification regarding the user's health condition when an abnormal value is detected by the generation AI. For example, the notification unit can send a notification to relatives, etc. when the pulse rate is abnormally high. The notification unit can also send a notification to relatives, etc. when the blood pressure rises suddenly. The notification unit can also send a notification to relatives, etc. when the heart rate is unstable. The notification unit can also send a notification to relatives, etc. when the body temperature is abnormally high. For example, the notification unit can send a notification to relatives, etc. when the pulse rate data is analyzed by the generation AI and the pulse rate is abnormally high. The notification unit can send a notification to relatives, etc. when the blood pressure data is analyzed by the generation AI and the blood pressure rises suddenly. The notification unit can send a notification to relatives, etc. when the heart rate data is analyzed by the generation AI and the heart rate is unstable. The notification unit can send a notification to relatives, etc. when the body temperature data is analyzed by the generation AI and the body temperature is abnormally high. This allows for early detection and prevention by sending a notification to relatives and others when an abnormal value is detected. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may send a notification using an AI model that receives data analyzed by the generation AI as input and outputs a notification.

[0035] The health monitoring system further includes a biosensor that combines multiple sensors to acquire data in order to improve measurement accuracy. The biosensor combines multiple sensors to acquire data in order to improve measurement accuracy. For example, a biosensor can combine a pulse sensor and a heart rate sensor to measure a more accurate heart rate. A biosensor can also combine a blood pressure sensor and a body temperature sensor to analyze the relationship between blood pressure fluctuations and body temperature. The biosensor can also improve measurement accuracy by placing multiple pulse sensors at different locations and averaging the data. For example, a biosensor can use a photoelectric pulse wave sensor as the pulse sensor. A photoelectric pulse wave sensor is a sensor that measures pulse using light, measuring pulse by irradiating light onto the skin and detecting changes in reflected light. For example, an electrocardiogram sensor can be used as the heart rate sensor. An electrocardiogram sensor is a sensor that detects the electrical activity of the heart and can measure heart rate. For example, a cuff-type blood pressure monitor can be used as the blood pressure sensor. A cuff-type blood pressure monitor is a device that measures blood pressure using a cuff wrapped around the arm and measures blood pressure by changing the pressure in the cuff. An infrared thermometer, for example, can be used as a body temperature sensor. An infrared thermometer is a device that measures body temperature using infrared rays, and measures body temperature by detecting the surface temperature of the skin. This allows for the combination of multiple sensors to improve measurement accuracy. Some or all of the above-mentioned processing in the biosensor may be performed using, for example, AI, or may be performed without using AI. For example, the biosensor can input data acquired by multiple sensors into a generation AI, which then analyzes the data.

[0036] The health monitoring system further includes a biosensor that dynamically adjusts the measurement data according to the user's activity level. The biosensor dynamically adjusts the measurement data according to the user's activity level. For example, when the user is exercising, the biosensor can update the measurement data in real time and provide data according to the exercise intensity. When the user is resting, the biosensor can also reduce the frequency of measurement data updates to reduce battery consumption. When the user is sleeping, the biosensor can also adjust the measurement data according to each sleep stage to evaluate the sleep quality. For example, the biosensor can use an activity level recognition algorithm to evaluate the user's activity level. The activity level recognition algorithm can evaluate the activity level by analyzing, for example, the user's number of steps, exercise intensity, and activity time. This allows for more accurate data to be provided by adjusting the measurement data according to the user's activity level. Some or all of the above-described processing in the biosensor may be performed using, for example, AI, or without AI. For example, the biosensor can adjust the measurement data using an AI model that adjusts the measurement data using activity level data evaluated by the activity level recognition algorithm as input.

[0037] The health monitoring system further includes a biosensor that links measurement data with the user's geographical location information and considers environmental factors such as temperature, humidity, and air pressure. The biosensor links measurement data with the user's geographical location information and considers environmental factors such as temperature, humidity, and air pressure. For example, if the user is at high altitude, the biosensor can analyze heart rate data taking into account oxygen concentration. Furthermore, if the user is in a cold region, the biosensor can prioritize measuring body temperature data. Furthermore, if the user is in an urban area, the biosensor can evaluate stress levels taking into account noise levels. For example, the biosensor can use GPS data to obtain the user's geographical location information. GPS data is data for identifying the user's current location and provides geographical location information. This enables data analysis according to environmental factors by considering the user's geographical location information. Some or all of the above-described processing in the biosensor may be performed using, for example, AI, or without AI. For example, the biosensor can analyze measurement data using an AI model that uses GPS data as input and considers environmental factors.

[0038] The health monitoring system further includes a biosensor that compares the measurement data with the user's past health history to detect abnormalities. The biosensor compares the measurement data with the user's past health history to detect abnormalities. For example, the biosensor can detect abnormal fluctuations by comparing the measurement data with the user's past heart rate data. The biosensor can also detect sudden increases or decreases by comparing the measurement data with the user's past blood pressure data. The biosensor can also detect abnormally high or low temperatures by comparing the measurement data with the user's past body temperature data. For example, the biosensor can use past vital sign data to obtain the user's past health history. The past vital sign data indicates the user's past health condition and serves as a reference for detecting abnormalities. This allows for early detection of abnormalities by comparing the measurement data with the past health history. Some or all of the above-described processing in the biosensor may be performed using, for example, AI, or without AI. For example, the biosensor can input past vital sign data and use an AI model to detect abnormalities.

[0039] The health monitoring system further includes a generation AI that adds a user's lifestyle data to the data to be analyzed to improve analysis accuracy. The generation AI adds the user's lifestyle data to the data to be analyzed to improve analysis accuracy. For example, the generation AI can add the user's food record and perform a health assessment that takes nutritional balance into consideration. The generation AI can also add the user's exercise record and perform a health assessment that takes exercise habits into consideration. The generation AI can also add the user's sleep record and perform a health assessment that takes sleep quality into consideration. For example, the generation AI can use food records, exercise records, sleep records, etc. to obtain the user's lifestyle data. By adding the lifestyle data, analysis accuracy is improved. Some or all of the above-described processing in the generation AI may be performed using, for example, AI, or may be performed without using AI. For example, the generation AI can input lifestyle data and analyze the data using an AI model that improves analysis accuracy.

[0040] The health monitoring system further includes a generation AI that adds the user's genetic information to the data to be analyzed and performs a personalized health assessment. The generation AI adds the user's genetic information to the data to be analyzed and performs a personalized health assessment. For example, the generation AI can evaluate the risk of a specific disease based on the user's genetic information. The generation AI can also propose an optimal diet plan based on the user's genetic information. The generation AI can also propose an appropriate exercise plan based on the user's genetic information. For example, the generation AI can use genetic test results, family history, etc. to obtain the user's genetic information. This enables a personalized health assessment by adding the genetic information. Some or all of the above-described processing in the generation AI may be performed using, for example, AI, or may be performed without using AI. For example, the generation AI can analyze data using an AI model that uses genetic information as input and performs a personalized health assessment.

[0041] The health monitoring system further includes a generation AI that adds a user's dietary record to the data to be analyzed and evaluates the user's nutritional status. The generation AI adds the user's dietary record to the data to be analyzed and evaluates the user's nutritional status. For example, the generation AI can evaluate nutritional balance based on the user's dietary record. The generation AI can also detect deficiencies of specific nutrients based on the user's dietary record. The generation AI can also suggest dietary improvements based on the user's dietary record. For example, the generation AI can use the type of food, calorie intake, nutrients, etc. to acquire the user's dietary record. This makes it possible to evaluate the nutritional status by adding the dietary record. Some or all of the above-described processing in the generation AI may be performed using, for example, AI, or may be performed without using AI. For example, the generation AI can input dietary records and analyze data using an AI model that evaluates nutritional status.

[0042] The health monitoring system further includes a generation AI that adds the user's exercise record to the data to be analyzed and evaluates the fitness level. The generation AI adds the user's exercise record to the data to be analyzed and evaluates the fitness level. For example, the generation AI can evaluate the user's exercise habits based on the user's exercise record. The generation AI can also evaluate the appropriateness of exercise intensity based on the user's exercise record. The generation AI can also suggest exercise improvements based on the user's exercise record. For example, the generation AI can use the type of exercise, exercise time, calories burned, etc. to obtain the user's exercise record. This makes it possible to evaluate the fitness level by adding the exercise record. Some or all of the above-mentioned processing in the generation AI may be performed using, for example, AI, or may be performed without using AI. For example, the generation AI can input the exercise record and analyze the data using an AI model that evaluates the fitness level.

[0043] The health monitoring system further includes a suggestion unit that reflects the user's past assistance history in the proposed assistance content. The suggestion unit reflects the user's past assistance history in the proposed assistance content. For example, the suggestion unit can suggest a new meal plan based on a meal plan previously performed by the user. The suggestion unit can also suggest a new exercise plan based on an exercise plan previously performed by the user. The suggestion unit can also suggest a new sleep improvement plan based on a sleep improvement plan previously performed by the user. For example, the suggestion unit can use past suggestions, execution results, feedback, etc. to acquire the user's past assistance history. This allows for more personalized assistance to be provided by reflecting the past assistance history. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can customize the suggested assistance content using an AI model that inputs the past assistance history and reflects the assistance content.

[0044] The health monitoring system further includes a suggestion unit that reflects the user's current health condition in real time in the assistance content to be proposed. The suggestion unit reflects the user's current health condition in real time in the assistance content to be proposed. For example, the suggestion unit can suggest an appropriate exercise plan based on the user's current pulse rate. The suggestion unit can also suggest an appropriate meal plan based on the user's current blood pressure. The suggestion unit can also suggest an appropriate rest plan based on the user's current body temperature. For example, the suggestion unit can use real-time vital sign data to acquire the user's current health condition. This allows for more appropriate assistance to be provided by reflecting the current health condition in real time. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can customize the assistance content using an AI model that inputs real-time vital sign data and reflects the assistance content.

[0045] The health monitoring system further includes a suggestion unit that provides region-specific health advice by taking into account the user's geographical location information when proposing assistance. The suggestion unit provides region-specific health advice by taking into account the user's geographical location information when proposing assistance. For example, if the user is in a high altitude, the suggestion unit can provide high altitude-specific health advice. Furthermore, if the user is in a cold region, the suggestion unit can provide cold region-specific health advice. Furthermore, if the user is in an urban area, the suggestion unit can provide urban region-specific health advice. For example, the suggestion unit can use GPS data to acquire the user's geographical location information. The GPS data is data for identifying the user's current location and provides geographical location information. This allows region-specific health advice to be provided by taking the geographical location information into account. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can customize the suggestion content using an AI model that uses GPS data as input and provides region-specific health advice.

[0046] The health monitoring system further includes a suggestion unit that analyzes the user's social media activity to provide relevant advice for the assistance content to be proposed. The suggestion unit analyzes the user's social media activity to provide relevant advice for the assistance content to be proposed. For example, the suggestion unit can provide dietary advice based on the meal content shared by the user on social media. The suggestion unit can also provide exercise advice based on the exercise content shared by the user on social media. The suggestion unit can also provide sleep advice based on the sleep patterns shared by the user on social media. For example, the suggestion unit can use the content of posts, activity frequency, number of followers, etc. to analyze the user's social media activity. This allows for more relevant advice to be provided by analyzing the social media activity. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can customize the suggestion content using an AI model that inputs social media activity data and provides relevant advice.

[0047] The health monitoring system further includes an execution unit that reflects the user's past execution history in the assistance content to be executed. The execution unit reflects the user's past execution history in the assistance content to be executed. For example, the execution unit can execute a new meal plan based on a meal plan previously executed by the user. The execution unit can also execute a new exercise plan based on an exercise plan previously executed by the user. The execution unit can also execute a new sleep improvement plan based on a sleep improvement plan previously executed by the user. For example, the execution unit can use past execution content, execution results, feedback, etc. to acquire the user's past execution history. This allows for more personalized assistance to be provided by reflecting the past execution history. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without AI. For example, the execution unit can customize the assistance content using an AI model that inputs the past execution history and reflects the assistance content.

[0048] The health monitoring system further includes an execution unit that reflects the user's current activity level in real time in the assistance content to be performed. The execution unit reflects the user's current activity level in real time in the assistance content to be performed. For example, when the user is exercising, the execution unit can provide assistance in real time according to the exercise intensity. Furthermore, when the user is resting, the execution unit can provide assistance in real time to help the user relax. Furthermore, when the user is working, the execution unit can provide assistance in real time to help the user concentrate. For example, the execution unit can use real-time activity data to obtain the user's current activity level. This allows for more appropriate assistance to be provided by reflecting the current activity level in real time. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without AI. For example, the execution unit can customize the assistance content using an AI model that inputs real-time activity data and reflects the assistance content.

[0049] The health monitoring system further includes an execution unit that provides an optimal execution method for the assistance content to be performed, taking into account the user's geographical location information. The execution unit provides an optimal execution method for the assistance content to be performed, taking into account the user's geographical location information. For example, when the user is at high altitude, the execution unit can execute health assistance specific to high altitudes. Furthermore, when the user is in a cold region, the execution unit can execute health assistance specific to cold regions. Furthermore, when the user is in an urban area, the execution unit can execute health assistance specific to urban areas. For example, the execution unit can use GPS data to acquire the user's geographical location information. GPS data is data for identifying the user's current location and provides geographical location information. This allows the optimal execution method to be provided by taking the geographical location information into account. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can customize the execution content using an AI model that uses GPS data as input and provides an optimal execution method.

[0050] The health monitoring system further includes an execution unit that analyzes the user's social media activity and provides a relevant execution method for the assistance content to be performed. The execution unit analyzes the user's social media activity and provides a relevant execution method for the assistance content to be performed. For example, the execution unit can perform meal assistance based on meal details shared by the user on social media. The execution unit can also perform exercise assistance based on exercise details shared by the user on social media. The execution unit can also perform sleep assistance based on sleep patterns shared by the user on social media. For example, the execution unit can use posted content, activity frequency, number of followers, etc. to analyze the user's social media activity. This allows for the provision of more relevant execution methods by analyzing social media activity. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without AI. For example, the execution unit can customize the execution content using an AI model that inputs social media activity data and provides a relevant execution method.

[0051] The health monitoring system further includes a notification unit that reflects the user's past health history in the content of the notification to be sent. The notification unit reflects the user's past health history in the content of the notification to be sent. For example, the notification unit can send a notification when an abnormality is detected based on the user's past heart rate data. The notification unit can also send a notification when an abnormality is detected based on the user's past blood pressure data. The notification unit can also send a notification when an abnormality is detected based on the user's past body temperature data. For example, the notification unit can use past vital sign data to obtain the user's past health history. This allows for more appropriate notifications to be provided by reflecting the past health history. Some or all of the above-described processing by the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can customize the content of the notification using an AI model that inputs past vital sign data and reflects the notification content.

[0052] The health monitoring system further includes a notification unit that reflects the user's current health condition in real time in the content of the notification to be sent. The notification unit reflects the user's current health condition in real time in the content of the notification to be sent. For example, the notification unit can transmit a notification when an abnormality is detected based on the user's current pulse rate. The notification unit can also transmit a notification when an abnormality is detected based on the user's current blood pressure. The notification unit can also transmit a notification when an abnormality is detected based on the user's current body temperature. For example, the notification unit can use real-time vital sign data to acquire the user's current health condition. This allows for more appropriate notifications to be provided by reflecting the current health condition in real time. Some or all of the above-described processing in the notification unit may be performed, for example, using AI or without AI. For example, the notification unit can customize the content of the notification using an AI model that inputs real-time vital sign data and reflects the content of the notification.

[0053] The health monitoring system further includes a notification unit that provides an optimal notification method by taking into account the user's geographical location information when sending notification content. The notification unit provides an optimal notification method by taking into account the user's geographical location information when sending notification content. For example, if the user is at high altitude, the notification unit can send a high-altitude specific health notification. Furthermore, if the user is in a cold region, the notification unit can send a cold-region specific health notification. Furthermore, if the user is in an urban area, the notification unit can send an urban-region specific health notification. For example, the notification unit can use GPS data to obtain the user's geographical location information. The GPS data is data for identifying the user's current location and provides geographical location information. This allows the optimal notification method to be provided by taking the geographical location information into account. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can customize notification content using an AI model that uses GPS data as input and provides an optimal notification method.

[0054] The health monitoring system further includes a notification unit that analyzes the user's social media activity and provides a relevant notification method for the notification content to be sent. The notification unit analyzes the user's social media activity and provides a relevant notification method for the notification content to be sent. For example, the notification unit can send a meal notification based on the meal content shared by the user on social media. The notification unit can also send an exercise notification based on the exercise content shared by the user on social media. The notification unit can also send a sleep notification based on the sleep pattern shared by the user on social media. For example, the notification unit can use the content of posts, activity frequency, number of followers, etc. to analyze the user's social media activity. This allows for more relevant notifications to be provided by analyzing the social media activity. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can customize the notification content using an AI model that inputs social media activity data and provides a relevant notification method.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The health monitoring system can evaluate a user's activity level in real time and provide assistance according to the activity level. For example, if the user is exercising, it can provide advice on diet and hydration according to the exercise intensity. If the user is resting, it can provide advice on creating a relaxing environment. Furthermore, if the user is working, it can provide assistance to improve concentration. In this way, it is possible to provide appropriate assistance according to the user's activity level.

[0057] The health monitoring system can provide region-specific health advice by taking into account the user's geographical location information. For example, if the user is in a high altitude, health advice specific to high altitudes can be provided. If the user is in a cold region, health advice specific to cold regions can be provided. Furthermore, if the user is in an urban area, health advice specific to urban areas can be provided. This makes it possible to provide appropriate assistance that takes into account the user's geographical location information.

[0058] Health monitoring systems can detect abnormalities early on based on a user's past health history. For example, they can compare the user's past heart rate data to detect abnormal fluctuations. They can also compare the user's past blood pressure data to detect sudden increases or decreases. They can also compare the user's past body temperature data to detect abnormally high or low temperatures. By comparing with past health history, abnormalities can be detected early on.

[0059] Health monitoring systems can improve the accuracy of analysis by adding user lifestyle habit data to the analysis. For example, by adding the user's dietary records, it is possible to perform health evaluations that take nutritional balance into account. In addition, by adding the user's exercise records, it is possible to perform health evaluations that take exercise habits into account. Furthermore, by adding the user's sleep records, it is possible to perform health evaluations that take sleep quality into account. In this way, adding lifestyle habit data improves the accuracy of analysis.

[0060] Health monitoring systems can perform personalized health assessments by adding a user's genetic information to the analysis. For example, the risk of a particular disease can be assessed based on the user's genetic information. Optimal diet plans can also be proposed based on the user's genetic information. Furthermore, appropriate exercise plans can also be proposed based on the user's genetic information. Thus, adding genetic information makes personalized health assessments possible.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The biosensor measures the user's vital signs. Vital signs include pulse, blood pressure, heart rate, body temperature, etc. For example, a photoelectric pulse wave sensor can be used as the pulse sensor, a cuff-type blood pressure monitor can be used as the blood pressure sensor, an electrocardiogram sensor can be used as the heart rate sensor, and an infrared thermometer can be used as the body temperature sensor. Step 2: The Generative AI analyzes the measured data and evaluates the user's health condition. The Generative AI uses machine learning algorithms to analyze the data and evaluate whether the pulse, blood pressure, heart rate, and body temperature are within normal ranges. Step 3: The suggestion unit proposes assistance based on the results analyzed by the generative AI. The suggestion unit can suggest assistance for diet, exercise, sleep, and brain training. Step 4: The execution unit executes the assistance proposed by the suggestion unit. The execution unit executes the meal menu, the exercise plan, the sleep advice, and the brain training. Step 5: If the generation AI detects abnormal values, the notification unit sends a notification about the user's health condition. If the notification unit detects abnormal values ​​for pulse, blood pressure, heart rate, or body temperature, it sends a notification to relatives, etc.

[0063] (Example 2) A health monitoring system according to an embodiment of the present invention is a device incorporating vital sign (pulse, blood pressure, heart rate, and body temperature) biosensors into wireless earphones with a sound-collecting function that eliminates hearing loss, in order to address health issues in an aging society. This device monitors the user's health status in real time, and a generation AI analyzes the measured values. Based on the analysis results, the generation AI proposes optimal assistance (diet, exercise, sleep, and brain training) to promote healthy life expectancy. Furthermore, if the measured values ​​indicate abnormalities, a notification is sent to pre-registered relatives, etc., enabling early detection and early prevention. Specifically, the system consists of the following steps: First, the user puts on the wireless earphones. The earphones are equipped with built-in biosensors that measure the user's pulse, blood pressure, heart rate, and body temperature. These sensors measure the user's vital signs in real time and collect data. The collected data is then sent to the generation AI. The generation AI analyzes the data and evaluates the user's health status. For example, it checks whether the pulse and blood pressure are within normal ranges, whether the heart rate is stable, and whether the body temperature is abnormally high. Based on the analysis results, the generation AI proposes optimal assistance to the user. For example, it may suggest a nutritionally balanced meal menu as dietary advice or an appropriate exercise plan as exercise advice. It may also provide advice on improving sleep quality and suggest brain training. Furthermore, if the measurement values ​​indicate abnormalities, the generation AI will send a notification to pre-registered relatives. For example, if the pulse rate is abnormally high or blood pressure rises sharply, a notification will be sent to relatives, enabling early detection and early prevention. This device monitors the health status of elderly people in real time and provides appropriate assistance, promoting healthy life expectancy. In addition, if abnormal values ​​are detected, a notification will be sent to relatives, enabling early detection and early prevention. This allows the health monitoring system to monitor the user's health status in real time and provide appropriate assistance. In addition, if abnormal values ​​are detected, a notification will be sent to relatives, enabling early detection and early prevention.

[0064] A health monitoring system according to an embodiment includes a biosensor, a generation AI, a suggestion unit, an execution unit, and a notification unit. The biosensor measures a user's vital signs. Examples of vital signs include, but are not limited to, pulse, blood pressure, heart rate, and body temperature. The biosensor measures pulse using a pulse sensor, for example. The biosensor can also measure blood pressure using a blood pressure sensor. The biosensor can also measure heart rate using a heart rate sensor. The biosensor can also measure body temperature using a body temperature sensor. For example, the biosensor can use a photoelectric pulse wave sensor as the pulse sensor. The photoelectric pulse wave sensor is a sensor that measures pulse using light, and measures pulse by irradiating light onto the skin and detecting changes in reflected light. For example, a cuff-type blood pressure monitor can be used as the blood pressure sensor. The cuff-type blood pressure monitor is a device that measures blood pressure using a cuff wrapped around the arm, and measures blood pressure by changing the pressure in the cuff. For example, an electrocardiogram sensor can be used as the heart rate sensor. An electrocardiogram sensor is a sensor that detects the electrical activity of the heart and can measure heart rate. An infrared thermometer, for example, can be used as a body temperature sensor. An infrared thermometer is a device that measures body temperature using infrared rays and measures body temperature by detecting the surface temperature of the skin. The generation AI analyzes the measured data and evaluates the user's health condition. The generation AI can analyze the data using, for example, a machine learning algorithm. For example, the generation AI can analyze pulse data and evaluate whether the pulse is within a normal range. The generation AI can also analyze blood pressure data and evaluate whether the blood pressure is within a normal range. The generation AI can also analyze heart rate data and evaluate whether the heart rate is stable. The generation AI can also analyze body temperature data and evaluate whether the body temperature is abnormally high. For example, the generation AI can input pulse data and analyze the pulse data using a machine learning model that outputs whether the pulse is within a normal range. Similarly, blood pressure data can be analyzed using a machine learning model that outputs whether the blood pressure is within a normal range. Similarly, heart rate data can be analyzed using a machine learning model that outputs whether the heart rate is stable.Similarly, body temperature data can be analyzed using a machine learning model that outputs whether the body temperature is abnormally high. The suggestion unit proposes assistance based on the results of the analysis by the generation AI. For example, the suggestion unit can propose a nutritionally balanced meal menu as a meal assistance. The suggestion unit can also propose an appropriate exercise plan as an exercise assistance. The suggestion unit can also propose advice for improving sleep quality as a sleep assistance. The suggestion unit can also suggest brain training as a brain training assistance. For example, the suggestion unit can propose an appropriate exercise plan based on pulse data analyzed by the generation AI. When the generation AI analyzes the pulse data and the pulse is within a normal range, the suggestion unit can propose an appropriate exercise plan. When the generation AI analyzes blood pressure data and the blood pressure is within a normal range, the suggestion unit can propose an appropriate meal menu. When the generation AI analyzes heart rate data and the heart rate is stable, the suggestion unit can propose appropriate sleep advice. When the generation AI analyzes body temperature data and the body temperature is not abnormally high, the suggestion unit can propose an appropriate brain training. The execution unit executes the assistance proposed by the suggestion unit. The execution unit can, for example, execute a proposed meal menu. The execution unit can also execute a proposed exercise plan. The execution unit can also execute proposed sleep advice. The execution unit can also execute proposed brain training. For example, the execution unit can generate an ingredient shopping list to execute the proposed meal menu. The execution unit can set an exercise schedule to execute the proposed exercise plan. The execution unit can provide advice on improving a sleep environment to execute the proposed sleep advice. The execution unit can also set a brain training schedule to execute the proposed brain training. The notification unit sends a notification regarding the user's health condition when an abnormal value is detected by the generation AI. For example, the notification unit can send a notification to relatives, etc. when the pulse rate is abnormally high.The notification unit can also send a notification to relatives, etc. if the blood pressure suddenly rises. The notification unit can also send a notification to relatives, etc. if the heart rate is unstable. The notification unit can also send a notification to relatives, etc. if the body temperature is abnormally high. For example, the notification unit can analyze pulse data by the generation AI and send a notification to relatives, etc. if the pulse rate is abnormally high. The notification unit can analyze blood pressure data by the generation AI and send a notification to relatives, etc. if the blood pressure suddenly rises. The notification unit can analyze heart rate data by the generation AI and send a notification to relatives, etc. if the heart rate is unstable. The notification unit can analyze body temperature data by the generation AI and send a notification to relatives, etc. if the body temperature is abnormally high. This allows the health monitoring system according to the embodiment to monitor the user's health condition in real time and provide appropriate assistance. Furthermore, by sending a notification to relatives, etc. if an abnormal value is detected, early detection and early prevention can be achieved.

[0065] The biosensor can measure pulse, blood pressure, heart rate, and body temperature. The biosensor measures pulse using, for example, a pulse sensor. The pulse sensor can be, for example, a photoelectric pulse wave sensor. The photoelectric pulse wave sensor is a sensor that measures pulse using light, irradiating light onto the skin and measuring pulse by detecting changes in the reflected light. The biosensor can also measure blood pressure using a blood pressure sensor. The blood pressure sensor can be, for example, a cuff-type blood pressure monitor. The cuff-type blood pressure monitor is a device that measures blood pressure using a cuff wrapped around the arm and measures blood pressure by changing the pressure in the cuff. The biosensor can also measure heart rate using a heart rate sensor. The heart rate sensor can be, for example, an electrocardiogram sensor. The electrocardiogram sensor is a sensor that detects the electrical activity of the heart and can measure heart rate. The biosensor can also measure body temperature using a body temperature sensor. The body temperature sensor can be, for example, an infrared thermometer. An infrared thermometer is a device that uses infrared rays to measure body temperature by detecting the surface temperature of the skin. This allows for more detailed understanding of a person's health condition by measuring multiple vital signs. Some or all of the above-described processing in the biosensor may be performed using, for example, AI, or may be performed without using AI. For example, the biosensor may input data acquired by a pulse sensor into a generation AI, which then analyzes the pulse data.

[0066] The generating AI can analyze the measured data and evaluate the user's health condition. The generating AI can analyze the data using, for example, a machine learning algorithm. For example, the generating AI can analyze pulse data and evaluate whether the pulse is within a normal range. The generating AI can also analyze blood pressure data and evaluate whether the blood pressure is within a normal range. The generating AI can also analyze heart rate data and evaluate whether the heart rate is stable. The generating AI can also analyze body temperature data and evaluate whether the body temperature is abnormally high. For example, the generating AI can analyze the pulse data using a machine learning model that inputs pulse data and outputs whether the pulse is within a normal range. Similarly, the generating AI can analyze blood pressure data using a machine learning model that outputs whether the blood pressure is within a normal range. Similarly, the heart rate data can be analyzed using a machine learning model that outputs whether the heart rate is stable. Similarly, the body temperature data can be analyzed using a machine learning model that outputs whether the body temperature is abnormally high. This allows the generating AI to accurately evaluate the user's health condition through data analysis. Some or all of the above-mentioned processing in the generating AI may be performed, for example, using AI or without AI. For example, the generation AI can analyze pulse data using an AI model that takes pulse data as input and outputs whether the pulse is within the normal range.

[0067] The suggestion unit can suggest assistance for diet, exercise, sleep, and brain training. The suggestion unit suggests assistance based on the results of analysis by the generation AI. For example, as a dietary assistance, the suggestion unit can suggest a nutritionally balanced meal menu. As an exercise assistance, the suggestion unit can suggest an appropriate exercise plan. As a sleep assistance, the suggestion unit can suggest advice for improving sleep quality. As a brain training assistance, the suggestion unit can suggest brain training. For example, the suggestion unit can suggest an appropriate exercise plan based on pulse data analyzed by the generation AI. When the generation AI analyzes pulse data and the pulse is within a normal range, the suggestion unit can suggest an appropriate exercise plan. When the generation AI analyzes blood pressure data and the blood pressure is within a normal range, the suggestion unit can suggest an appropriate meal menu. When the generation AI analyzes heart rate data and the heart rate is stable, the suggestion unit can suggest appropriate sleep advice. When the generation AI analyzes body temperature data and the body temperature is not abnormally high, the suggestion unit can suggest an appropriate brain training. This allows for the promotion of a healthy lifespan by proposing various types of assistance according to the user's health condition. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may suggest assistance using an AI model that receives data analyzed by the generation AI as input and outputs appropriate assistance.

[0068] The execution unit can execute the proposed assistance. The execution unit executes the assistance proposed by the suggestion unit. The execution unit can, for example, execute a proposed meal menu. The execution unit can also execute a proposed exercise plan. The execution unit can also execute proposed sleep advice. The execution unit can also execute proposed brain training. For example, the execution unit can generate an ingredient shopping list to execute the proposed meal menu. The execution unit can set an exercise schedule to execute the proposed exercise plan. The execution unit can provide advice on improving a sleep environment to execute the proposed sleep advice. The execution unit can also set a brain training schedule to execute the proposed brain training. In this way, by executing the proposed assistance, the user's health condition can be improved. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can execute the assistance using an AI model that executes the assistance, using the assistance proposed by the suggestion unit as input.

[0069] The notification unit can send a notification to relatives or designated contacts when an abnormal value is detected. The notification unit can send a notification regarding the user's health condition when an abnormal value is detected by the generation AI. For example, the notification unit can send a notification to relatives, etc. when the pulse rate is abnormally high. The notification unit can also send a notification to relatives, etc. when the blood pressure rises suddenly. The notification unit can also send a notification to relatives, etc. when the heart rate is unstable. The notification unit can also send a notification to relatives, etc. when the body temperature is abnormally high. For example, the notification unit can send a notification to relatives, etc. when the pulse rate data is analyzed by the generation AI and the pulse rate is abnormally high. The notification unit can send a notification to relatives, etc. when the blood pressure data is analyzed by the generation AI and the blood pressure rises suddenly. The notification unit can send a notification to relatives, etc. when the heart rate data is analyzed by the generation AI and the heart rate is unstable. The notification unit can send a notification to relatives, etc. when the body temperature data is analyzed by the generation AI and the body temperature is abnormally high. This allows for early detection and prevention by sending a notification to relatives and others when an abnormal value is detected. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may send a notification using an AI model that receives data analyzed by the generation AI as input and outputs a notification.

[0070] The health monitoring system further includes a biosensor that estimates a user's emotions and adjusts the frequency of vital sign measurements based on the estimated user emotions. The biosensor estimates a user's emotions and adjusts the frequency of vital sign measurements based on the estimated user emotions. For example, when a user is stressed, the biosensor can increase the measurement frequency to collect more detailed data. Furthermore, when a user is relaxed, the biosensor can decrease the measurement frequency to reduce the burden on the device. Furthermore, when a user is exercising, the biosensor can increase the measurement frequency to monitor the user's health in real time. For example, the biosensor can use an emotion recognition algorithm to estimate the user's emotions. The emotion recognition algorithm can estimate emotions by analyzing, for example, the user's facial expressions, voice, heart rate data, etc. This allows for more appropriate data collection by adjusting the measurement frequency according to the user's emotions. Some or all of the above-described processing in the biosensor may be performed using, for example, AI, or without AI. For example, the biosensor can adjust the measurement frequency using an AI model that adjusts the measurement frequency using emotion data estimated by the emotion recognition algorithm as input.

[0071] The health monitoring system further includes a biosensor that combines multiple sensors to acquire data in order to improve measurement accuracy. The biosensor combines multiple sensors to acquire data in order to improve measurement accuracy. For example, a biosensor can combine a pulse sensor and a heart rate sensor to measure a more accurate heart rate. A biosensor can also combine a blood pressure sensor and a body temperature sensor to analyze the relationship between blood pressure fluctuations and body temperature. The biosensor can also improve measurement accuracy by placing multiple pulse sensors at different locations and averaging the data. For example, a biosensor can use a photoelectric pulse wave sensor as the pulse sensor. A photoelectric pulse wave sensor is a sensor that measures pulse using light, measuring pulse by irradiating light onto the skin and detecting changes in reflected light. For example, an electrocardiogram sensor can be used as the heart rate sensor. An electrocardiogram sensor is a sensor that detects the electrical activity of the heart and can measure heart rate. For example, a cuff-type blood pressure monitor can be used as the blood pressure sensor. A cuff-type blood pressure monitor is a device that measures blood pressure using a cuff wrapped around the arm and measures blood pressure by changing the pressure in the cuff. An infrared thermometer, for example, can be used as a body temperature sensor. An infrared thermometer is a device that measures body temperature using infrared rays, and measures body temperature by detecting the surface temperature of the skin. This allows for the combination of multiple sensors to improve measurement accuracy. Some or all of the above-mentioned processing in the biosensor may be performed using, for example, AI, or may be performed without using AI. For example, the biosensor can input data acquired by multiple sensors into a generation AI, which then analyzes the data.

[0072] The health monitoring system further includes a biosensor that dynamically adjusts the measurement data according to the user's activity level. The biosensor dynamically adjusts the measurement data according to the user's activity level. For example, when the user is exercising, the biosensor can update the measurement data in real time and provide data according to the exercise intensity. When the user is resting, the biosensor can also reduce the frequency of measurement data updates to reduce battery consumption. When the user is sleeping, the biosensor can also adjust the measurement data according to each sleep stage to evaluate the sleep quality. For example, the biosensor can use an activity level recognition algorithm to evaluate the user's activity level. The activity level recognition algorithm can evaluate the activity level by analyzing, for example, the user's number of steps, exercise intensity, and activity time. This allows for more accurate data to be provided by adjusting the measurement data according to the user's activity level. Some or all of the above-described processing in the biosensor may be performed using, for example, AI, or without AI. For example, the biosensor can adjust the measurement data using an AI model that adjusts the measurement data using activity level data evaluated by the activity level recognition algorithm as input.

[0073] The health monitoring system further includes a biosensor that estimates a user's emotions and prioritizes the measurement data based on the estimated user emotions. The biosensor estimates the user's emotions and prioritizes the measurement data based on the estimated user emotions. For example, the biosensor can prioritize measuring heart rate and blood pressure data when the user is stressed. The biosensor can also prioritize measuring body temperature and pulse data when the user is relaxed. The biosensor can also prioritize measuring heart rate and pulse data when the user is exercising. For example, the biosensor can use an emotion recognition algorithm to estimate the user's emotions. The emotion recognition algorithm can estimate emotions by analyzing, for example, the user's facial expressions, voice, heart rate data, etc. This allows the measurement data to be prioritized according to the user's emotions, thereby enabling important data to be acquired preferentially. Some or all of the above-described processing in the biosensor may be performed using, for example, AI, or without AI. For example, the biosensor can input emotion data estimated by the emotion recognition algorithm and prioritize the measurement data using an AI model that prioritizes the measurement data.

[0074] The health monitoring system further includes a biosensor that links measurement data with the user's geographical location information and considers environmental factors such as temperature, humidity, and air pressure. The biosensor links measurement data with the user's geographical location information and considers environmental factors such as temperature, humidity, and air pressure. For example, if the user is at high altitude, the biosensor can analyze heart rate data taking into account oxygen concentration. Furthermore, if the user is in a cold region, the biosensor can prioritize measuring body temperature data. Furthermore, if the user is in an urban area, the biosensor can evaluate stress levels taking into account noise levels. For example, the biosensor can use GPS data to obtain the user's geographical location information. GPS data is data for identifying the user's current location and provides geographical location information. This enables data analysis according to environmental factors by considering the user's geographical location information. Some or all of the above-described processing in the biosensor may be performed using, for example, AI, or without AI. For example, the biosensor can analyze measurement data using an AI model that uses GPS data as input and considers environmental factors.

[0075] The health monitoring system further includes a biosensor that compares the measurement data with the user's past health history to detect abnormalities. The biosensor compares the measurement data with the user's past health history to detect abnormalities. For example, the biosensor can detect abnormal fluctuations by comparing the measurement data with the user's past heart rate data. The biosensor can also detect sudden increases or decreases by comparing the measurement data with the user's past blood pressure data. The biosensor can also detect abnormally high or low temperatures by comparing the measurement data with the user's past body temperature data. For example, the biosensor can use past vital sign data to obtain the user's past health history. The past vital sign data indicates the user's past health condition and serves as a reference for detecting abnormalities. This allows for early detection of abnormalities by comparing the measurement data with the past health history. Some or all of the above-described processing in the biosensor may be performed using, for example, AI, or without AI. For example, the biosensor can input past vital sign data and use an AI model to detect abnormalities.

[0076] The health monitoring system further includes a generation AI that estimates a user's emotions and adjusts an analysis algorithm based on the estimated user emotions. The generation AI estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can focus its analysis on stress-related data. Furthermore, if the user is relaxed, the generation AI can use an algorithm that evaluates the user's overall health status. Furthermore, if the user is exercising, the generation AI can use an analysis algorithm based on the exercise intensity. For example, the generation AI can use an emotion recognition algorithm to estimate the user's emotions. The emotion recognition algorithm can estimate emotions by analyzing, for example, the user's facial expressions, voice, heart rate data, etc. This allows for more appropriate analysis results to be provided by adjusting the analysis algorithm based on the user's emotions. Some or all of the above-described processing in the generation AI may be performed using, for example, AI, or may be performed without AI. For example, the generation AI can adjust the analysis algorithm using an AI model that adjusts the analysis algorithm using emotion data estimated by the emotion recognition algorithm as input.

[0077] The health monitoring system further includes a generation AI that adds a user's lifestyle data to the data to be analyzed to improve analysis accuracy. The generation AI adds the user's lifestyle data to the data to be analyzed to improve analysis accuracy. For example, the generation AI can add the user's food record and perform a health assessment that takes nutritional balance into consideration. The generation AI can also add the user's exercise record and perform a health assessment that takes exercise habits into consideration. The generation AI can also add the user's sleep record and perform a health assessment that takes sleep quality into consideration. For example, the generation AI can use food records, exercise records, sleep records, etc. to obtain the user's lifestyle data. By adding the lifestyle data, analysis accuracy is improved. Some or all of the above-described processing in the generation AI may be performed using, for example, AI, or may be performed without using AI. For example, the generation AI can input lifestyle data and analyze the data using an AI model that improves analysis accuracy.

[0078] The health monitoring system further includes a generation AI that adds the user's genetic information to the data to be analyzed and performs a personalized health assessment. The generation AI adds the user's genetic information to the data to be analyzed and performs a personalized health assessment. For example, the generation AI can evaluate the risk of a specific disease based on the user's genetic information. The generation AI can also propose an optimal diet plan based on the user's genetic information. The generation AI can also propose an appropriate exercise plan based on the user's genetic information. For example, the generation AI can use genetic test results, family history, etc. to obtain the user's genetic information. This enables a personalized health assessment by adding the genetic information. Some or all of the above-described processing in the generation AI may be performed using, for example, AI, or may be performed without using AI. For example, the generation AI can analyze data using an AI model that uses genetic information as input and performs a personalized health assessment.

[0079] The health monitoring system further includes a generation AI that estimates a user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. The generation AI estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the generation AI can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generation AI can provide a display method that focuses on the main points. For example, the generation AI can use an emotion recognition algorithm to estimate the user's emotions. The emotion recognition algorithm can estimate emotions by analyzing, for example, the user's facial expressions, voice, heart rate data, etc. This enables more appropriate information to be provided by adjusting the display method according to the user's emotions. Some or all of the above-described processing in the generation AI may be performed using, for example, AI, or may be performed without AI. For example, the generation AI can adjust the display method of the analysis results using an AI model that adjusts the display method using emotion data estimated by the emotion recognition algorithm as input.

[0080] The health monitoring system further includes a generation AI that adds a user's dietary record to the data to be analyzed and evaluates the user's nutritional status. The generation AI adds the user's dietary record to the data to be analyzed and evaluates the user's nutritional status. For example, the generation AI can evaluate nutritional balance based on the user's dietary record. The generation AI can also detect deficiencies of specific nutrients based on the user's dietary record. The generation AI can also suggest dietary improvements based on the user's dietary record. For example, the generation AI can use the type of food, calorie intake, nutrients, etc. to acquire the user's dietary record. This makes it possible to evaluate the nutritional status by adding the dietary record. Some or all of the above-described processing in the generation AI may be performed using, for example, AI, or may be performed without using AI. For example, the generation AI can input dietary records and analyze data using an AI model that evaluates nutritional status.

[0081] The health monitoring system further includes a generation AI that adds the user's exercise record to the data to be analyzed and evaluates the fitness level. The generation AI adds the user's exercise record to the data to be analyzed and evaluates the fitness level. For example, the generation AI can evaluate the user's exercise habits based on the user's exercise record. The generation AI can also evaluate the appropriateness of exercise intensity based on the user's exercise record. The generation AI can also suggest exercise improvements based on the user's exercise record. For example, the generation AI can use the type of exercise, exercise time, calories burned, etc. to obtain the user's exercise record. This makes it possible to evaluate the fitness level by adding the exercise record. Some or all of the above-mentioned processing in the generation AI may be performed using, for example, AI, or may be performed without using AI. For example, the generation AI can input the exercise record and analyze the data using an AI model that evaluates the fitness level.

[0082] The health monitoring system further includes a suggestion unit that estimates the user's emotions and customizes the suggestions based on the estimated user emotions. The suggestion unit estimates the user's emotions and customizes the suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can suggest relaxing meals and exercises. Furthermore, if the user is relaxed, the suggestion unit can suggest healthy meals and exercises. Furthermore, if the user is exercising, the suggestion unit can suggest meals and rests suitable for post-exercise recovery. For example, the suggestion unit can use an emotion recognition algorithm to estimate the user's emotions. The emotion recognition algorithm can estimate emotions by analyzing, for example, the user's facial expressions, voice, heart rate data, etc. This allows the suggestions to be customized according to the user's emotions, thereby providing more appropriate assistance. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can customize the suggestions using an AI model that uses emotion data estimated by the emotion recognition algorithm as input.

[0083] The health monitoring system further includes a suggestion unit that reflects the user's past assistance history in the proposed assistance content. The suggestion unit reflects the user's past assistance history in the proposed assistance content. For example, the suggestion unit can suggest a new meal plan based on a meal plan previously performed by the user. The suggestion unit can also suggest a new exercise plan based on an exercise plan previously performed by the user. The suggestion unit can also suggest a new sleep improvement plan based on a sleep improvement plan previously performed by the user. For example, the suggestion unit can use past suggestions, execution results, feedback, etc. to acquire the user's past assistance history. This allows for more personalized assistance to be provided by reflecting the past assistance history. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can customize the suggested assistance content using an AI model that inputs the past assistance history and reflects the assistance content.

[0084] The health monitoring system further includes a suggestion unit that reflects the user's current health condition in real time in the assistance content to be proposed. The suggestion unit reflects the user's current health condition in real time in the assistance content to be proposed. For example, the suggestion unit can suggest an appropriate exercise plan based on the user's current pulse rate. The suggestion unit can also suggest an appropriate meal plan based on the user's current blood pressure. The suggestion unit can also suggest an appropriate rest plan based on the user's current body temperature. For example, the suggestion unit can use real-time vital sign data to acquire the user's current health condition. This allows for more appropriate assistance to be provided by reflecting the current health condition in real time. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can customize the assistance content using an AI model that inputs real-time vital sign data and reflects the assistance content.

[0085] The health monitoring system further includes a suggestion unit that estimates the user's emotions and prioritizes suggestions based on the estimated user emotions. The suggestion unit estimates the user's emotions and prioritizes the suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggestions for relaxation. Furthermore, if the user is relaxed, the suggestion unit can prioritize suggestions for maintaining health. Furthermore, if the user is exercising, the suggestion unit can prioritize suggestions for post-exercise recovery. For example, the suggestion unit can use an emotion recognition algorithm to estimate the user's emotions. The emotion recognition algorithm can estimate emotions by analyzing, for example, the user's facial expressions, voice, heart rate data, etc. This allows the system to prioritize suggestions based on the user's emotions, thereby providing more appropriate assistance. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can customize the content of suggestions using an AI model that receives emotion data estimated by the emotion recognition algorithm and prioritizes suggestions.

[0086] The health monitoring system further includes a suggestion unit that provides region-specific health advice by taking into account the user's geographical location information when proposing assistance. The suggestion unit provides region-specific health advice by taking into account the user's geographical location information when proposing assistance. For example, if the user is in a high altitude, the suggestion unit can provide high altitude-specific health advice. Furthermore, if the user is in a cold region, the suggestion unit can provide cold region-specific health advice. Furthermore, if the user is in an urban area, the suggestion unit can provide urban region-specific health advice. For example, the suggestion unit can use GPS data to acquire the user's geographical location information. The GPS data is data for identifying the user's current location and provides geographical location information. This allows region-specific health advice to be provided by taking the geographical location information into account. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can customize the suggestion content using an AI model that uses GPS data as input and provides region-specific health advice.

[0087] The health monitoring system further includes a suggestion unit that analyzes the user's social media activity to provide relevant advice for the assistance content to be proposed. The suggestion unit analyzes the user's social media activity to provide relevant advice for the assistance content to be proposed. For example, the suggestion unit can provide dietary advice based on the meal content shared by the user on social media. The suggestion unit can also provide exercise advice based on the exercise content shared by the user on social media. The suggestion unit can also provide sleep advice based on the sleep patterns shared by the user on social media. For example, the suggestion unit can use the content of posts, activity frequency, number of followers, etc. to analyze the user's social media activity. This allows for more relevant advice to be provided by analyzing the social media activity. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can customize the suggestion content using an AI model that inputs social media activity data and provides relevant advice.

[0088] The health monitoring system further includes an execution unit that estimates the user's emotion and adjusts the timing of assistance execution based on the estimated user's emotion. The execution unit estimates the user's emotion and adjusts the timing of assistance execution based on the estimated user's emotion. For example, if the user is feeling stressed, the execution unit can execute assistance at a timing that allows the user to relax. Furthermore, if the user is relaxed, the execution unit can execute assistance at a timing that is convenient for maintaining health. Furthermore, if the user is exercising, the execution unit can execute assistance at a timing that is appropriate for post-exercise recovery. For example, the execution unit can use an emotion recognition algorithm to estimate the user's emotion. The emotion recognition algorithm can estimate emotions by analyzing, for example, the user's facial expressions, voice, heart rate data, etc. This allows for more effective assistance to be provided by adjusting the execution timing according to the user's emotion. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without AI. For example, the execution unit can adjust the execution timing of assistance using an AI model that receives emotion data estimated by the emotion recognition algorithm as input and adjusts the execution timing.

[0089] The health monitoring system further includes an execution unit that reflects the user's past execution history in the assistance content to be executed. The execution unit reflects the user's past execution history in the assistance content to be executed. For example, the execution unit can execute a new meal plan based on a meal plan previously executed by the user. The execution unit can also execute a new exercise plan based on an exercise plan previously executed by the user. The execution unit can also execute a new sleep improvement plan based on a sleep improvement plan previously executed by the user. For example, the execution unit can use past execution content, execution results, feedback, etc. to acquire the user's past execution history. This allows for more personalized assistance to be provided by reflecting the past execution history. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without AI. For example, the execution unit can customize the assistance content using an AI model that inputs the past execution history and reflects the assistance content.

[0090] The health monitoring system further includes an execution unit that reflects the user's current activity level in real time in the assistance content to be performed. The execution unit reflects the user's current activity level in real time in the assistance content to be performed. For example, when the user is exercising, the execution unit can provide assistance in real time according to the exercise intensity. Furthermore, when the user is resting, the execution unit can provide assistance in real time to help the user relax. Furthermore, when the user is working, the execution unit can provide assistance in real time to help the user concentrate. For example, the execution unit can use real-time activity data to obtain the user's current activity level. This allows for more appropriate assistance to be provided by reflecting the current activity level in real time. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without AI. For example, the execution unit can customize the assistance content using an AI model that inputs real-time activity data and reflects the assistance content.

[0091] The health monitoring system further includes an execution unit that estimates the user's emotions and adjusts the execution method of the assistance based on the estimated user emotions. The execution unit estimates the user's emotions and adjusts the execution method of the assistance based on the estimated user emotions. For example, if the user is feeling stressed, the execution unit can execute assistance in a way that helps the user relax. Furthermore, if the user is relaxed, the execution unit can execute assistance in a way that helps the user maintain health. Furthermore, if the user is exercising, the execution unit can execute assistance in a way that is suitable for post-exercise recovery. For example, the execution unit can use an emotion recognition algorithm to estimate the user's emotions. The emotion recognition algorithm can estimate emotions by analyzing, for example, the user's facial expressions, voice, heart rate data, etc. This allows for more effective assistance to be provided by adjusting the execution method according to the user's emotions. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without AI. For example, the execution unit can adjust the execution method of the assistance using an AI model that inputs emotion data estimated by the emotion recognition algorithm and adjusts the execution method.

[0092] The health monitoring system further includes an execution unit that provides an optimal execution method for the assistance content to be performed, taking into account the user's geographical location information. The execution unit provides an optimal execution method for the assistance content to be performed, taking into account the user's geographical location information. For example, when the user is at high altitude, the execution unit can execute health assistance specific to high altitudes. Furthermore, when the user is in a cold region, the execution unit can execute health assistance specific to cold regions. Furthermore, when the user is in an urban area, the execution unit can execute health assistance specific to urban areas. For example, the execution unit can use GPS data to acquire the user's geographical location information. GPS data is data for identifying the user's current location and provides geographical location information. This allows the optimal execution method to be provided by taking the geographical location information into account. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can customize the execution content using an AI model that uses GPS data as input and provides an optimal execution method.

[0093] The health monitoring system further includes an execution unit that analyzes the user's social media activity and provides a relevant execution method for the assistance content to be performed. The execution unit analyzes the user's social media activity and provides a relevant execution method for the assistance content to be performed. For example, the execution unit can perform meal assistance based on meal details shared by the user on social media. The execution unit can also perform exercise assistance based on exercise details shared by the user on social media. The execution unit can also perform sleep assistance based on sleep patterns shared by the user on social media. For example, the execution unit can use posted content, activity frequency, number of followers, etc. to analyze the user's social media activity. This allows for the provision of more relevant execution methods by analyzing social media activity. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without AI. For example, the execution unit can customize the execution content using an AI model that inputs social media activity data and provides a relevant execution method.

[0094] The health monitoring system further includes a notification unit that estimates a user's emotion and customizes notification content based on the estimated user's emotion. The notification unit estimates the user's emotion and customizes notification content based on the estimated user's emotion. For example, if the user is feeling stressed, the notification unit can send a notification with content to help the user relax. Furthermore, if the user is relaxed, the notification unit can send a notification with content to maintain health. Furthermore, if the user is exercising, the notification unit can send a notification with content related to post-exercise recovery. For example, the notification unit can use an emotion recognition algorithm to estimate the user's emotion. The emotion recognition algorithm can estimate the user's emotion by analyzing, for example, the user's facial expression, voice, heart rate data, etc. This allows for customizing the notification content according to the user's emotion, thereby providing more appropriate notifications. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can customize the notification content using an AI model that uses emotion data estimated by the emotion recognition algorithm as input and customizes the notification content.

[0095] The health monitoring system further includes a notification unit that reflects the user's past health history in the content of the notification to be sent. The notification unit reflects the user's past health history in the content of the notification to be sent. For example, the notification unit can send a notification when an abnormality is detected based on the user's past heart rate data. The notification unit can also send a notification when an abnormality is detected based on the user's past blood pressure data. The notification unit can also send a notification when an abnormality is detected based on the user's past body temperature data. For example, the notification unit can use past vital sign data to obtain the user's past health history. This allows for more appropriate notifications to be provided by reflecting the past health history. Some or all of the above-described processing by the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can customize the content of the notification using an AI model that inputs past vital sign data and reflects the notification content.

[0096] The health monitoring system further includes a notification unit that reflects the user's current health condition in real time in the content of the notification to be sent. The notification unit reflects the user's current health condition in real time in the content of the notification to be sent. For example, the notification unit can transmit a notification when an abnormality is detected based on the user's current pulse rate. The notification unit can also transmit a notification when an abnormality is detected based on the user's current blood pressure. The notification unit can also transmit a notification when an abnormality is detected based on the user's current body temperature. For example, the notification unit can use real-time vital sign data to acquire the user's current health condition. This allows for more appropriate notifications to be provided by reflecting the current health condition in real time. Some or all of the above-described processing in the notification unit may be performed, for example, using AI or without AI. For example, the notification unit can customize the content of the notification using an AI model that inputs real-time vital sign data and reflects the content of the notification.

[0097] The health monitoring system further includes a notification unit that estimates a user's emotion and determines the priority of notifications based on the estimated user's emotion. The notification unit estimates the user's emotion and determines the priority of notifications based on the estimated user's emotion. For example, if the user is feeling stressed, the notification unit can prioritize notifications about relaxation. Furthermore, if the user is relaxed, the notification unit can prioritize notifications about health maintenance. Furthermore, if the user is exercising, the notification unit can prioritize notifications about post-exercise recovery. For example, the notification unit can use an emotion recognition algorithm to estimate the user's emotion. The emotion recognition algorithm can estimate emotions by analyzing, for example, the user's facial expressions, voice, heart rate data, etc. This allows for more appropriate notifications to be provided by determining the priority of notifications based on the user's emotion. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can customize notification content using an AI model that receives emotion data estimated by the emotion recognition algorithm and determines the priority of notifications.

[0098] The health monitoring system further includes a notification unit that provides an optimal notification method by taking into account the user's geographical location information when sending notification content. The notification unit provides an optimal notification method by taking into account the user's geographical location information when sending notification content. For example, if the user is at high altitude, the notification unit can send a high-altitude specific health notification. Furthermore, if the user is in a cold region, the notification unit can send a cold-region specific health notification. Furthermore, if the user is in an urban area, the notification unit can send an urban-region specific health notification. For example, the notification unit can use GPS data to obtain the user's geographical location information. The GPS data is data for identifying the user's current location and provides geographical location information. This allows the optimal notification method to be provided by taking the geographical location information into account. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can customize notification content using an AI model that uses GPS data as input and provides an optimal notification method.

[0099] The health monitoring system further includes a notification unit that analyzes the user's social media activity and provides a relevant notification method for the notification content to be sent. The notification unit analyzes the user's social media activity and provides a relevant notification method for the notification content to be sent. For example, the notification unit can send a meal notification based on the meal content shared by the user on social media. The notification unit can also send an exercise notification based on the exercise content shared by the user on social media. The notification unit can also send a sleep notification based on the sleep pattern shared by the user on social media. For example, the notification unit can use the content of posts, activity frequency, number of followers, etc. to analyze the user's social media activity. This allows for more relevant notifications to be provided by analyzing the social media activity. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without AI. For example, the notification unit can customize the notification content using an AI model that inputs social media activity data and provides a relevant notification method. === Hard Collateral 1-1 === Each of the multiple elements including the biosensor, generation AI, suggestion unit, execution unit, and notification unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the biosensor is built into the smart device 14 and measures the user's vital signs. The generation AI is realized by the specific processing unit 290 of the data processing device 12 and analyzes the measurement data. The suggestion unit suggests an assistance by the control unit 46A of the smart device 14 based on the analysis result of the generation AI. The execution unit executes the proposed assistance by the control unit 46A of the smart device 14. The notification unit sends a notification to relatives, etc. by the specific processing unit 290 of the data processing device 12 when an abnormal value is detected. === Hard Collateral 1-2 === Each of the multiple elements including the biosensor, generation AI, suggestion unit, execution unit, and notification unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the biosensor is built into the smart glasses 214 and measures the user's vital signs. The generation AI is realized by the specific processing unit 290 of the data processing device 12 and analyzes the measurement data. The suggestion unit suggests an assistance by the control unit 46A of the smart glasses 214 based on the analysis result of the generation AI. The execution unit executes the proposed assistance by the control unit 46A of the smart glasses 214. The notification unit sends a notification to relatives, etc. by the specific processing unit 290 of the data processing device 12 when an abnormal value is detected. === Hard Collateral 1-3 === Each of the multiple elements including the biosensor, generation AI, suggestion unit, execution unit, and notification unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the biosensor is built into the headset type terminal 314 and measures the user's vital signs. The generation AI is realized by the specific processing unit 290 of the data processing device 12 and analyzes the measurement data. The suggestion unit suggests an assistance by the control unit 46A of the headset type terminal 314 based on the analysis results of the generation AI. The execution unit executes the proposed assistance by the control unit 46A of the headset type terminal 314. When an abnormal value is detected, the notification unit sends a notification to relatives, etc. by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the biosensor, generation AI, suggestion unit, execution unit, and notification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the biosensor is built into the robot 414 and measures the user's vital signs. The generation AI is realized by the specific processing unit 290 of the data processing device 12 and analyzes the measurement data. The suggestion unit proposes an assistance by the control unit 46A of the robot 414 based on the analysis results of the generation AI. The execution unit executes the proposed assistance by the control unit 46A of the robot 414. The notification unit sends a notification to relatives, etc. by the specific processing unit 290 of the data processing device 12 when an abnormal value is detected.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The health monitoring system can estimate a user's emotions and evaluate the user's stress level based on the estimated emotions. For example, an emotion estimation algorithm can be used to analyze the user's facial expressions and voice data to quantify the stress level. Furthermore, if the stress level is high, relaxing music or a meditation guide can be provided. If the stress level is low, assistance to improve concentration can be provided. This makes it possible to provide appropriate assistance according to the user's emotions.

[0102] The health monitoring system can evaluate a user's activity level in real time and provide assistance according to the activity level. For example, if the user is exercising, it can provide advice on diet and hydration according to the exercise intensity. If the user is resting, it can provide advice on creating a relaxing environment. Furthermore, if the user is working, it can provide assistance to improve concentration. In this way, it is possible to provide appropriate assistance according to the user's activity level.

[0103] The health monitoring system can estimate the user's emotions and customize the content of notifications based on the estimated emotions. For example, if the user is feeling stressed, a notification with content to help them relax can be sent. If the user is relaxed, a notification with content to help them maintain their health can be sent. Furthermore, if the user is exercising, a notification with content related to post-exercise recovery can be sent. This makes it possible to provide appropriate notifications according to the user's emotions.

[0104] The health monitoring system can provide region-specific health advice by taking into account the user's geographical location information. For example, if the user is in a high altitude, health advice specific to high altitudes can be provided. If the user is in a cold region, health advice specific to cold regions can be provided. Furthermore, if the user is in an urban area, health advice specific to urban areas can be provided. This makes it possible to provide appropriate assistance that takes into account the user's geographical location information.

[0105] The health monitoring system can estimate the user's emotions and adjust the timing of assistance based on the estimated emotions. For example, if the user is feeling stressed, assistance can be provided at a time when the user can relax. Also, if the user is relaxed, assistance can be provided at a time that is convenient for maintaining health. Furthermore, if the user is exercising, assistance can be provided at a time that is appropriate for post-exercise recovery. This makes it possible to provide assistance at appropriate times according to the user's emotions.

[0106] Health monitoring systems can detect abnormalities early on based on a user's past health history. For example, they can compare the user's past heart rate data to detect abnormal fluctuations. They can also compare the user's past blood pressure data to detect sudden increases or decreases. They can also compare the user's past body temperature data to detect abnormally high or low temperatures. By comparing with past health history, abnormalities can be detected early on.

[0107] The health monitoring system can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is feeling stressed, the analysis can emphasize stress-related data. If the user is relaxed, an algorithm that evaluates the user's overall health can be used. Furthermore, if the user is exercising, an analysis algorithm according to the exercise intensity can be used. This makes it possible to provide appropriate analysis results according to the user's emotions.

[0108] Health monitoring systems can improve the accuracy of analysis by adding user lifestyle habit data to the analysis. For example, by adding the user's dietary records, it is possible to perform health evaluations that take nutritional balance into account. In addition, by adding the user's exercise records, it is possible to perform health evaluations that take exercise habits into account. Furthermore, by adding the user's sleep records, it is possible to perform health evaluations that take sleep quality into account. In this way, adding lifestyle habit data improves the accuracy of analysis.

[0109] The health monitoring system can estimate the user's emotions and customize the suggestions based on the estimated emotions. For example, if the user is feeling stressed, it can suggest meals and exercises that will help them relax. If the user is relaxed, it can also suggest meals and exercises that will help them maintain their health. Furthermore, if the user is exercising, it can suggest meals and rests that are suitable for post-exercise recovery. This makes it possible to provide appropriate assistance according to the user's emotions.

[0110] Health monitoring systems can perform personalized health assessments by adding a user's genetic information to the analysis. For example, the risk of a particular disease can be assessed based on the user's genetic information. Optimal diet plans can also be proposed based on the user's genetic information. Furthermore, appropriate exercise plans can also be proposed based on the user's genetic information. Thus, adding genetic information makes personalized health assessments possible.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The biosensor measures the user's vital signs. Vital signs include pulse, blood pressure, heart rate, body temperature, etc. For example, a photoelectric pulse wave sensor can be used as the pulse sensor, a cuff-type blood pressure monitor can be used as the blood pressure sensor, an electrocardiogram sensor can be used as the heart rate sensor, and an infrared thermometer can be used as the body temperature sensor. Step 2: The Generative AI analyzes the measured data and evaluates the user's health condition. The Generative AI uses machine learning algorithms to analyze the data and evaluate whether the pulse, blood pressure, heart rate, and body temperature are within normal ranges. Step 3: The suggestion unit proposes assistance based on the results analyzed by the generative AI. The suggestion unit can suggest assistance for diet, exercise, sleep, and brain training. Step 4: The execution unit executes the assistance proposed by the suggestion unit. The execution unit executes the meal menu, the exercise plan, the sleep advice, and the brain training. Step 5: If the generation AI detects abnormal values, the notification unit sends a notification about the user's health condition. If the notification unit detects abnormal values ​​for pulse, blood pressure, heart rate, or body temperature, it sends a notification to relatives, etc.

[0113] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a 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.

[0151] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0156] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] 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.

[0176] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a biosensor that measures vital signs; A generation AI that analyzes data measured by the biosensor; A suggestion unit that proposes assistance based on the results of analysis by the generation AI; an execution unit that executes the assistance proposed by the proposal unit; a notification unit that sends a notification regarding the user's health condition when an abnormal value is detected by the generation AI. A system characterized by:

2. The biosensor includes: Measure pulse, blood pressure, heart rate, and temperature 2. The system of claim 1.

3. The generated AI is Analyze the measured data and evaluate the user's health condition 2. The system of claim 1.

4. The proposal unit Suggestions for diet, exercise, sleep, and brain training 2. The system of claim 1.

5. The execution unit: Execute the suggested assist 2. The system of claim 1.

6. The notification unit Send notifications to relatives or designated contacts when abnormal values ​​are detected 2. The system of claim 1.

7. The biosensor includes: Estimate the user's emotions and adjust the frequency of vital sign measurements based on the estimated user emotions.

2. The system of claim 1.

8. The biosensor includes: To improve measurement accuracy, data is acquired by combining multiple sensors.

2. The system of claim 1.

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