System

Abstract

To provide a system according to an embodiment which can provide advice of improving life habits on the basis of health data of individuals.SOLUTION: The system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a measurement unit. The collection unit collects health data of a user. The analysis unit analyzes data collected by the collection unit. The provision unit provides advice on the basis of the result of the analysis obtained by the analysis unit. The measurement unit automatically measures the ventral girth of a user.SELECTED DRAWING: Figure 1

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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not provide sufficient advice on improving lifestyle habits based on individual health data, and there is room for improvement.

[0005] The system according to the embodiment aims to provide advice on improving lifestyle habits based on individual health data. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a measurement unit. The collection unit collects health data of a user. The analysis unit analyzes the data collected by the collection unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. The measurement unit automatically measures the waist circumference of the user. Effect of the Invention

[0007] The system according to the embodiment can provide advice on improving lifestyle habits based on individual health data. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The health management system according to the embodiment of the present invention is a system that provides advice on lifestyle improvement actions (diet, exercise) tailored to each individual based on the individual's health condition, medical history, and medication status. This system collects health data of the user, analyzes the collected data, and provides advice based on the analysis results. It also has a function of automatically measuring the user's waist circumference using an IoT belt. For example, the user's health data includes medical history, medication status, and daily health condition. These data are collected by a sensor or manual input. Next, the collected data is analyzed by AI, and the user's health condition is evaluated. For example, the AI ​​analyzes the data using machine learning or deep learning to evaluate the risk of lifestyle-related diseases. Based on the analysis results, appropriate advice on lifestyle improvement actions is provided to the user. For example, an appropriate meal menu and exercise plan are proposed according to the user's health condition. Furthermore, the IoT belt measures the user's waist circumference in real time and transmits the data to the system. This allows the user to continuously monitor changes in the user's waist circumference. For example, if the user is on a diet, the change in waist circumference can be grasped in real time. This allows the user to confirm the results of their efforts and maintain their motivation. This system is intended to be adopted by companies to protect the health of their employees, and will extend the healthy lifespan of employees with metabolic syndrome, those at risk of metabolic syndrome, and those at risk of lifestyle-related diseases, and contribute to lifestyle prevention. This will contribute to health management that protects the health of working employees. As a result, the health management system can provide advice on lifestyle improvement actions tailored to each individual, and extend the healthy lifespan of users.

[0029] The health management system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a measurement unit. The collection unit collects health data of a user. The health data of the user includes, for example, blood pressure, heart rate, weight, and the like, but is not limited to such examples. The collection unit collects these data using, for example, a sensor. The collection unit can also collect data by manual input. For example, the collection unit can collect data input by the user himself. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit analyzes the data using a machine learning algorithm to evaluate the health status of the user. The provision unit provides advice based on the analysis result obtained by the analysis unit. The advice includes, for example, dietary suggestions and exercise suggestions, but is not limited to such examples. For example, the provision unit proposes an appropriate diet menu and exercise plan according to the health status of the user. The measurement unit automatically measures the waist circumference of the user. The measurement is performed using, for example, sensor technology, but is not limited to such examples. For example, the measurement unit measures the waist circumference of the user in real time using an IoT belt and transmits the data to the system, which enables the health management system according to the embodiment to efficiently collect, analyze, provide advice, and measure the health data of the user.

[0030] The collection unit collects the health data of the user. The health data of the user includes, for example, blood pressure, heart rate, weight, etc., but is not limited to such examples. The collection unit collects these data using, for example, a sensor. Specifically, devices such as a blood pressure monitor, a heart rate monitor, and a weight scale are attached to the user's body, and data obtained from these devices is transmitted to the collection unit through wireless communication. This allows the user to naturally provide health data in daily life. The collection unit can also collect data by manual input. For example, the user can collect data that he or she inputs by using a smartphone or tablet. This allows the user to record his or her health condition in detail and provide it to the collection unit. Furthermore, the collection unit can monitor the user's health data on a 24-hour basis by utilizing the latest technology such as wearable devices and smart watches. This allows the collection unit to grasp the user's health condition in real time and respond immediately if an abnormality is detected. For example, if the heart rate rises suddenly or the blood pressure shows an abnormal value, the collection unit can automatically issue an alert to alert the user. This allows the collection unit to efficiently and accurately collect the user's health data and improve the reliability of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis or a machine learning algorithm, but is not limited to such examples. Specifically, the average value and standard deviation of the data can be calculated using statistical analysis, and the health condition of the user can be quantitatively evaluated. In addition, a machine learning algorithm can be used to learn patterns from past data and predict future health risks. For example, the analysis unit analyzes data using a machine learning algorithm to evaluate the health status of the user. This makes it possible to detect changes in the user's health condition early and take appropriate measures. Furthermore, the analysis unit can use an anomaly detection algorithm to detect data patterns that are different from normal and find abnormalities early. For example, it can detect abnormal fluctuations in heart rate or blood pressure and issue a warning to the user. This allows the analysis unit to monitor the user's health condition in real time and respond quickly when an abnormality occurs. In addition, the analysis unit visualizes the data to allow the user to intuitively understand his or her health condition. For example, the user's health data can be visually displayed using graphs and charts, allowing the user to grasp changes in the health condition at a glance. This allows the analysis unit to efficiently analyze the user's health data and provide the user with useful information.

[0032] The providing unit provides advice based on the analysis results obtained by the analyzing unit. The advice includes, for example, meal suggestions and exercise suggestions, but is not limited to such examples. Specifically, based on the analysis results, a meal menu and an exercise plan according to the user's health condition can be proposed. For example, if the user's blood pressure is high, a low-salt meal menu can be proposed, and if the heart rate is low, aerobic exercise can be recommended. Furthermore, the providing unit can provide customized advice according to the user's lifestyle and preferences. For example, if the user likes a specific ingredient, a healthy recipe using that ingredient can be proposed. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, the user can feed back the results of the proposed exercise plan and adjust the next advice based on the results. This allows the providing unit to provide individualized advice to the user and maximize the effect of health management. Furthermore, the providing unit can diversify the method of providing advice and provide information in a form that is easy for the user to use. For example, the advice can be notified through a smartphone app, or information can be provided using email or SMS. This allows the providing unit to quickly and reliably provide appropriate advice to the user and support health management.

[0033] The measurement unit automatically measures the waist circumference of the user. The measurement is performed, for example, using sensor technology, but is not limited to such an example. Specifically, the IoT belt is used to measure the waist circumference of the user in real time, and the data is transmitted to the system. The IoT belt is worn on the user's abdomen, and the built-in sensor detects the change in waist circumference with high accuracy. This allows the user to naturally provide waist circumference data in daily life. Furthermore, the measurement unit collects the measurement data in real time, and can comprehensively evaluate the user's health condition in cooperation with the analysis unit and the provision unit. For example, if the waist circumference data increases suddenly, the analysis unit can analyze the cause, and the provision unit can propose appropriate measures. In addition, the measurement unit can monitor the waist circumference data of the user over a long period of time and grasp the trend of the health condition. This allows the user to track changes in his or her health condition over the long term and take appropriate measures. Furthermore, the measurement unit can integrate with other health data to perform a more accurate health evaluation. For example, the waist circumference data and weight data can be combined to estimate the user's body fat percentage and evaluate health risks. This enables the measurement unit to efficiently collect health data of the user and improve the performance of the entire system.

[0034] The collection unit can collect data on the user's medical history, medication status, and daily health condition. The collection unit collects, for example, the user's medical history, such as past medical history and surgery history. The collection unit can also collect the user's current medication status and medication status, such as past medication history. Furthermore, the collection unit can collect the user's daily health condition, such as changes in daily physical condition and the presence or absence of symptoms. This allows the collection unit to collect detailed health data of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's health data into AI and optimize the timing and method for the AI ​​to collect data.

[0035] The analysis unit can analyze the collected data using machine learning or deep learning. The analysis unit, for example, analyzes the data using a machine learning algorithm. For example, the analysis unit analyzes the data using a machine learning algorithm such as linear regression or a decision tree to evaluate the user's health status. The analysis unit can also analyze the data using a deep learning algorithm. For example, the analysis unit analyzes the data using a deep learning algorithm such as a neural network or a convolutional neural network to evaluate the user's health risk. Thereby, the analysis unit can improve the accuracy of the analysis by using machine learning or deep learning. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by inputting the collected data into AI and having the AI ​​analyze the data.

[0036] The providing unit can suggest calorie restriction and nutritional balance. The providing unit suggests calorie restriction according to, for example, the health condition of the user. For example, the providing unit sets an upper limit on daily calorie intake and suggests an appropriate meal menu to the user. The providing unit can also suggest nutritional balance. For example, the providing unit suggests a balanced meal menu to the user based on the recommended intake amount of each nutrient. In this way, the providing unit can provide specific dietary advice to the user and support health improvement. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's health data into AI, and the AI ​​can suggest calorie restriction and nutritional balance.

[0037] The measurement unit can measure the waist circumference of the user using sensor technology. The measurement unit can measure the waist circumference of the user using, for example, an optical sensor. For example, the measurement unit can measure the waist circumference of the user in real time using an optical sensor and transmit the data to the system. The measurement unit can also measure the waist circumference of the user using a pressure sensor. For example, the measurement unit can measure the waist circumference of the user using a pressure sensor and transmit the data to the system. As a result, the measurement unit can improve the measurement accuracy of the waist circumference by using sensor technology. Part or all of the above-mentioned processing in the measurement unit can be performed using, for example, AI, or can be performed without using AI. For example, the measurement unit can improve the measurement accuracy by inputting data acquired by the sensor into AI, and the AI ​​analyzes the data.

[0038] The collection unit can collect vital data such as blood pressure, blood glucose level, and heart rate. The collection unit, for example, collects blood pressure data using a sensor that measures blood pressure. For example, the collection unit measures the user's blood pressure using a blood pressure meter and transmits the data to the system. The collection unit can also collect blood glucose level data using a sensor that measures blood glucose level. For example, the collection unit measures the user's blood glucose level using a blood glucose meter and transmits the data to the system. Furthermore, the collection unit can also collect heart rate data using a sensor that measures heart rate. For example, the collection unit measures the user's heart rate using a heart rate meter and transmits the data to the system. In this way, the collection unit can grasp the user's health condition in more detail by collecting the vital data. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the vital data to AI, and the AI ​​can analyze the data to evaluate the user's health condition.

[0039] The providing unit can propose a meal menu and an exercise plan according to the health condition of the user. The providing unit, for example, proposes an appropriate meal menu according to the health condition of the user. For example, the providing unit proposes a meal menu that takes into account the balance of calories and nutrients based on the health data of the user. The providing unit can also propose an appropriate exercise plan according to the health condition of the user. For example, the providing unit proposes an exercise plan that takes into account the type, frequency, and intensity of exercise based on the health data of the user. This allows the providing unit to provide the user with lifestyle improvement advice that is individually appropriate for the user and support health improvement. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's health data into AI, and the AI ​​can propose a meal menu and an exercise plan.

[0040] The measurement unit can monitor the change in the waist circumference of the user in real time. The measurement unit, for example, uses an IoT belt to measure the waist circumference of the user in real time and transmits the data to the system. For example, the measurement unit continuously measures the waist circumference of the user wearing the IoT belt and transmits the data to the system in real time. The measurement unit can also monitor the change in the waist circumference of the user based on the measurement data. For example, the measurement unit displays the change in the waist circumference of the user as a graph or chart and provides it in a visually easy-to-understand format. Thereby, the measurement unit can support the user's health management by grasping the change in the waist circumference of the user in real time. A part or all of the above-mentioned processing in the measurement unit may be performed, for example, using AI or may be performed without using AI. For example, the measurement unit can monitor the change in the waist circumference by inputting the data acquired by the IoT belt into AI and having the AI ​​analyze the data.

[0041] The provision unit can provide a specific operation method when a company introduces it as an employee health management program. The provision unit, for example, provides specific procedures for a company to effectively operate an employee health management program. For example, the provision unit explains in detail the implementation procedure and evaluation method of the program, and supports the company to operate the program smoothly. The provision unit can also provide guidelines for the company to collect and analyze employee health data and provide appropriate advice. For example, the provision unit provides specific instructions on the data collection method, analysis method, and advice provision method. In this way, the provision unit can support the company to effectively operate the employee health management program. Part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input operation data of the company's health management program into AI, and the AI ​​can suggest the optimal operation method.

[0042] The providing unit can provide specific data on the extent to which the risk of lifestyle-related diseases is reduced. The providing unit provides specific data on the extent to which the risk of lifestyle-related diseases is reduced, for example, based on the health data of the user. For example, the providing unit indicates the effect of reducing the risk of lifestyle-related diseases using statistical data or experimental data. In this way, the providing unit can specifically indicate the effect of reducing the risk of lifestyle-related diseases to the user, thereby improving the motivation of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the health data of the user into AI, which analyzes the effect of reducing the risk of lifestyle-related diseases, and provide specific data.

[0043] The collection unit can analyze the user's past health data and select the optimal collection method. The collection unit uses AI, for example, to analyze the user's past health data. For example, the collection unit determines the optimal collection frequency based on data provided by the user in the past. The collection unit can also select a method of collection for a specific time period from the user's past health data. Furthermore, the collection unit can analyze the user's past data and suggest the most effective collection method. As a result, the collection unit can improve the efficiency of data collection by selecting the optimal collection method based on the past data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past health data into AI, which can then suggest the optimal collection method.

[0044] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting health data. The collection unit uses AI, for example, to grasp the user's current living situation and areas of interest. For example, if the user is on a diet, the collection unit prioritizes collecting data related to meals. In addition, if the user is interested in exercise, the collection unit can also focus on collecting exercise data. Furthermore, the collection unit can filter and collect highly relevant data according to the user's living situation. This enables the collection unit to collect data according to the user's living situation and areas of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's living situation and areas of interest into AI, and the AI ​​can filter and collect the data.

[0045] When collecting health data, the collection unit can preferentially collect highly relevant data based on the geographical location information of the user. The collection unit uses AI, for example, to collect data taking into account the geographical location information of the user. For example, if the user lives in a high altitude, the collection unit collects high altitude specific health data. In addition, if the user lives in an urban area, the collection unit can also collect urban area specific health data. Furthermore, the collection unit can preferentially collect highly relevant data based on the geographical location information of the user. Thereby, the collection unit can improve the accuracy of the data by collecting highly relevant data based on the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI, and the AI ​​can preferentially collect highly relevant data.

[0046] The collection unit can analyze the user's social media activity and collect related data when collecting health data. The collection unit, for example, uses AI to analyze the user's social media activity. For example, the collection unit collects health information shared by the user on social media. The collection unit can also identify areas of interest related to health from the user's social media activity and collect related data. Furthermore, the collection unit can analyze the user's social media activity and use it to collect health data. This allows the collection unit to collect data according to the user's areas of interest by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI, which can collect related data.

[0047] The analysis unit can improve the accuracy of the analysis by referring to the user's past health data during the analysis. The analysis unit uses AI, for example, to refer to the user's past health data. For example, the analysis unit improves the accuracy of the analysis based on the user's past health data. The analysis unit can also improve the reliability of the analysis result by referring to the user's past data. Furthermore, the analysis unit can improve the accuracy of the analysis by utilizing the user's past health data. As a result, the analysis unit can improve the accuracy of the analysis by referring to the past data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by inputting the user's past health data into AI and having the AI ​​analyze the data.

[0048] The analysis unit can perform the analysis while taking into consideration the user's lifestyle and dietary history. The analysis unit uses AI, for example, to take into consideration the user's lifestyle and dietary history. For example, the analysis unit performs the analysis while taking into consideration nutritional balance based on the user's dietary history. The analysis unit can also take into consideration the user's lifestyle and reflect it in the analysis result. Furthermore, the analysis unit can perform the analysis while comprehensively taking into consideration the user's dietary history and lifestyle. This allows the analysis unit to perform more accurate analysis by taking into consideration the lifestyle and dietary history. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by inputting the user's lifestyle and dietary history into AI, and the AI ​​analyzes the data.

[0049] The analysis unit can perform the analysis while taking into account the geographical location information of the user. The analysis unit uses AI, for example, to take into account the geographical location information of the user. For example, if the user lives at high altitude, the analysis unit performs the analysis while taking into account health risks specific to high altitude. In addition, if the user lives in an urban area, the analysis unit can also perform the analysis while taking into account health risks specific to urban areas. Furthermore, the analysis unit can also perform the analysis while taking into account health risks based on the geographical location information of the user. This allows the analysis unit to perform more accurate analysis by taking into account the geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by inputting the geographical location information of the user into AI and having the AI ​​analyze the data.

[0050] During the analysis, the analysis unit can analyze the user's social media activity and use related data for the analysis. The analysis unit uses AI, for example, to analyze the user's social media activity. For example, the analysis unit uses health information shared by the user on social media for the analysis. The analysis unit can also identify areas of interest related to health from the user's social media activity and use related data for the analysis. Furthermore, the analysis unit can analyze the user's social media activity and use it for analyzing health data. This allows the analysis unit to analyze social media activity according to the user's areas of interest. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into AI, which can analyze the data.

[0051] The providing unit can adjust the level of detail of the advice based on the user's health data when providing the advice. The providing unit uses AI, for example, to adjust the level of detail of the advice based on the user's health data. For example, the providing unit provides detailed advice based on the user's health data. The providing unit can also provide concise advice based on the user's health data. Furthermore, the providing unit can adjust the level of detail of the advice according to the user's health data. As a result, the providing unit can provide advice suitable for the user by adjusting the level of detail of the advice based on the health data. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's health data into AI, and the AI ​​can optimize the level of detail of the advice.

[0052] The providing unit can provide advice taking into consideration the user's lifestyle habits and dietary history when providing advice. The providing unit uses AI, for example, to take into consideration the user's lifestyle habits and dietary history. For example, the providing unit provides advice on nutritional balance based on the user's dietary history. The providing unit can also provide appropriate advice by taking into consideration the user's lifestyle habits. Furthermore, the providing unit can provide advice by comprehensively taking into consideration the user's dietary history and lifestyle habits. As a result, the providing unit can provide more appropriate advice by taking into consideration the lifestyle habits and dietary history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's lifestyle habits and dietary history into AI, and the AI ​​can optimize the advice.

[0053] The providing unit can provide highly relevant advice by considering the geographical location information of the user when providing advice. The providing unit uses AI, for example, to consider the geographical location information of the user. For example, if the user lives in a high altitude, the providing unit provides high altitude specific health advice. In addition, if the user lives in an urban area, the providing unit can also provide urban area specific health advice. Furthermore, the providing unit can also provide highly relevant advice based on the geographical location information of the user. As a result, the providing unit can provide highly relevant advice based on the geographical location information, thereby enabling advice suitable for the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI, and the AI ​​can provide highly relevant advice.

[0054] The providing unit can analyze the user's social media activity and provide related advice when providing advice. The providing unit uses AI, for example, to analyze the user's social media activity. For example, the providing unit provides advice based on health information shared by the user on social media. The providing unit can also identify areas of interest related to health from the user's social media activity and provide related advice. Furthermore, the providing unit can analyze the user's social media activity and utilize it for health advice. As a result, the providing unit can provide advice according to the user's areas of interest by analyzing the social media activity. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI, which can provide related advice.

[0055] The measurement unit can improve the accuracy of the measurement by referring to the user's past measurement data at the time of measurement. The measurement unit uses AI, for example, to refer to the user's past measurement data. For example, the measurement unit improves the accuracy of the measurement based on the user's past measurement data. The measurement unit can also improve the reliability of the measurement result by referring to the user's past data. Furthermore, the measurement unit can improve the accuracy of the measurement by utilizing the user's past measurement data. Thereby, the measurement unit can improve the accuracy of the measurement by referring to the past data. A part or all of the above-mentioned processing in the measurement unit may be performed, for example, using AI, or may be performed without using AI. For example, the measurement unit can improve the accuracy of the measurement by inputting the user's past measurement data into AI, and the AI ​​analyzes the data.

[0056] The measurement unit can perform the measurement while taking into consideration the user's lifestyle and dietary history. The measurement unit uses AI, for example, to take into consideration the user's lifestyle and dietary history. For example, the measurement unit performs the measurement while taking into consideration nutritional balance based on the user's dietary history. The measurement unit can also take into consideration the user's lifestyle and reflect it in the measurement result. Furthermore, the measurement unit can perform the measurement while comprehensively taking into consideration the user's dietary history and lifestyle. As a result, the measurement unit can perform more accurate measurement by taking into consideration the lifestyle and dietary history. Part or all of the above-mentioned processing in the measurement unit may be performed, for example, using AI, or may be performed without using AI. For example, the measurement unit can improve the accuracy of the measurement by inputting the user's lifestyle and dietary history into AI, and the AI ​​analyzes the data.

[0057] The measurement unit can perform the measurement while taking into account the geographical location information of the user. The measurement unit uses AI, for example, to take into account the geographical location information of the user. For example, when the user lives at high altitude, the measurement unit performs the measurement while taking into account the health risks specific to the high altitude. In addition, when the user lives in an urban area, the measurement unit can also perform the measurement while taking into account the health risks specific to the urban area. Furthermore, the measurement unit can also perform the measurement while taking into account the health risks based on the geographical location information of the user. As a result, the measurement unit can perform a more accurate measurement by taking into account the geographical location information. A part or all of the above-mentioned processing in the measurement unit may be performed, for example, using AI, or may be performed without using AI. For example, the measurement unit can improve the accuracy of the measurement by inputting the geographical location information of the user into AI, and the AI ​​analyzes the data.

[0058] The measurement unit can analyze the user's social media activity at the time of measurement and use the related data for the measurement. The measurement unit uses AI, for example, to analyze the user's social media activity. For example, the measurement unit uses health information shared by the user on social media for the measurement. The measurement unit can also identify areas of interest related to health from the user's social media activity and use them for the measurement. Furthermore, the measurement unit can analyze the user's social media activity and use them for measuring health data. As a result, the measurement unit can perform measurement according to the user's areas of interest by analyzing the social media activity. A part or all of the above-mentioned processing in the measurement unit may be performed using AI, for example, or may be performed without using AI. For example, the measurement unit can improve the accuracy of the measurement by inputting the user's social media activity data into AI, and the AI ​​analyzes the data.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The health management system can further collect and analyze the user's sleep data. For example, the collection unit can use a wearable device to monitor the user's sleep pattern and sleep quality. The analysis unit can analyze the collected sleep data and evaluate the user's sleep quality. The provision unit can provide the user with advice on improving sleep based on the analysis result. For example, appropriate bedtimes and wake-up times can be suggested based on the user's sleep pattern. In addition, the system can provide advice on relaxation methods and environmental adjustments to improve the user's sleep quality. In this way, the health management system can improve the user's sleep quality and improve overall health.

[0061] The health management system can further monitor and analyze the user's stress level. For example, the collection unit can collect the stress level using a sensor that measures the user's heart rate variability and electrodermal activity. The analysis unit can analyze the collected data and evaluate the user's stress level. The provision unit can provide the user with stress management advice based on the analysis result. For example, relaxation techniques and exercise plans for stress reduction can be suggested depending on the user's stress level. In addition, if the user's stress level is high, professional counseling can be recommended. In this way, the health management system can manage the user's stress level and improve the overall health condition.

[0062] The health management system can further analyze the user's meal photos and evaluate the nutritional balance. For example, the collection unit can collect photos of meals taken by the user. The analysis unit can use image analysis technology to evaluate the contents and nutrients of the meals. The provision unit can provide the user with advice on nutritional balance based on the analysis results. For example, based on the user's meal photos, it can point out excesses and deficiencies in calorie intake and nutrients and suggest a balanced meal menu. It can also suggest ingredients and recipes for the user to ingest specific nutrients. In this way, the health management system can grasp the details of the user's meal contents and improve nutritional balance.

[0063] The health management system can further collect and analyze the user's exercise data. For example, the collection unit can use a wearable device to monitor the user's amount of exercise and type of exercise. The analysis unit can analyze the collected exercise data and evaluate the user's exercise habits. The provision unit can provide the user with advice on an exercise plan based on the analysis results. For example, it can suggest an appropriate type and frequency of exercise based on the user's exercise data. It can also provide a specific training plan according to the user's exercise goal. In this way, the health management system can improve the user's exercise habits and improve overall health.

[0064] The health management system can further monitor and analyze the user's water intake. For example, the collection unit can use a smart bottle to record the amount of water consumed by the user. The analysis unit can analyze the collected water intake data and evaluate the user's water intake status. The provision unit can provide the user with appropriate water intake advice based on the analysis result. For example, the system can suggest the amount of water required per day based on the user's water intake. It can also send reminders during times when the user is prone to dehydration. In this way, the health management system can manage the user's water intake status and improve the overall health status.

[0065] The process flow of the first embodiment will be briefly described below.

[0066] Step 1: The collection unit collects health data of the user. The health data of the user includes, but is not limited to, blood pressure, heart rate, weight, and the like. The collection unit collects these data using, for example, a sensor. The collection unit can also collect data by manual input. For example, the collection unit can collect data entered by the user himself. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit analyzes the data using a machine learning algorithm to evaluate the user's health condition. Step 3: The providing unit provides advice based on the analysis results obtained by the analyzing unit. The advice includes, but is not limited to, dietary suggestions and exercise suggestions. For example, the providing unit suggests an appropriate diet menu and exercise plan according to the user's health condition. Step 4: The measurement unit automatically measures the waist circumference of the user. The measurement is performed, for example, using sensor technology, but is not limited to such an example. For example, the measurement unit measures the waist circumference of the user in real time using an IoT belt and transmits the data to the system.

[0067] (Example 2) The health management system according to the embodiment of the present invention is a system that provides advice on lifestyle improvement actions (diet, exercise) tailored to each individual based on the individual's health condition, medical history, and medication status. This system collects health data of the user, analyzes the collected data, and provides advice based on the analysis results. It also has a function of automatically measuring the user's waist circumference using an IoT belt. For example, the user's health data includes medical history, medication status, and daily health condition. These data are collected by a sensor or manual input. Next, the collected data is analyzed by AI, and the user's health condition is evaluated. For example, the AI ​​analyzes the data using machine learning or deep learning to evaluate the risk of lifestyle-related diseases. Based on the analysis results, appropriate advice on lifestyle improvement actions is provided to the user. For example, an appropriate meal menu and exercise plan are proposed according to the user's health condition. Furthermore, the IoT belt measures the user's waist circumference in real time and transmits the data to the system. This allows the user to continuously monitor changes in the user's waist circumference. For example, if the user is on a diet, the change in waist circumference can be grasped in real time. This allows the user to confirm the results of their efforts and maintain their motivation. This system is intended to be adopted by companies to protect the health of their employees, and will extend the healthy lifespan of employees with metabolic syndrome, those at risk of metabolic syndrome, and those at risk of lifestyle-related diseases, and contribute to lifestyle prevention. This will contribute to health management that protects the health of working employees. As a result, the health management system can provide advice on lifestyle improvement actions tailored to each individual, and extend the healthy lifespan of users.

[0068] The health management system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a measurement unit. The collection unit collects health data of a user. The health data of the user includes, for example, blood pressure, heart rate, weight, and the like, but is not limited to such examples. The collection unit collects these data using, for example, a sensor. The collection unit can also collect data by manual input. For example, the collection unit can collect data input by the user himself. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit analyzes the data using a machine learning algorithm to evaluate the health status of the user. The provision unit provides advice based on the analysis result obtained by the analysis unit. The advice includes, for example, dietary suggestions and exercise suggestions, but is not limited to such examples. For example, the provision unit proposes an appropriate diet menu and exercise plan according to the health status of the user. The measurement unit automatically measures the waist circumference of the user. The measurement is performed using, for example, sensor technology, but is not limited to such examples. For example, the measurement unit measures the waist circumference of the user in real time using an IoT belt and transmits the data to the system, which enables the health management system according to the embodiment to efficiently collect, analyze, provide advice, and measure the health data of the user.

[0069] The collection unit collects the health data of the user. The health data of the user includes, for example, blood pressure, heart rate, weight, etc., but is not limited to such examples. The collection unit collects these data using, for example, a sensor. Specifically, devices such as a blood pressure monitor, a heart rate monitor, and a weight scale are attached to the user's body, and data obtained from these devices is transmitted to the collection unit through wireless communication. This allows the user to naturally provide health data in daily life. The collection unit can also collect data by manual input. For example, the user can collect data that he or she inputs by using a smartphone or tablet. This allows the user to record his or her health condition in detail and provide it to the collection unit. Furthermore, the collection unit can monitor the user's health data on a 24-hour basis by utilizing the latest technology such as wearable devices and smart watches. This allows the collection unit to grasp the user's health condition in real time and respond immediately if an abnormality is detected. For example, if the heart rate rises suddenly or the blood pressure shows an abnormal value, the collection unit can automatically issue an alert to alert the user. This allows the collection unit to efficiently and accurately collect the user's health data and improve the reliability of the entire system.

[0070] The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis or a machine learning algorithm, but is not limited to such examples. Specifically, the average value and standard deviation of the data can be calculated using statistical analysis, and the health condition of the user can be quantitatively evaluated. In addition, a machine learning algorithm can be used to learn patterns from past data and predict future health risks. For example, the analysis unit analyzes data using a machine learning algorithm to evaluate the health status of the user. This makes it possible to detect changes in the user's health condition early and take appropriate measures. Furthermore, the analysis unit can use an anomaly detection algorithm to detect data patterns that are different from normal and find abnormalities early. For example, it can detect abnormal fluctuations in heart rate or blood pressure and issue a warning to the user. This allows the analysis unit to monitor the user's health condition in real time and respond quickly when an abnormality occurs. In addition, the analysis unit visualizes the data to allow the user to intuitively understand his or her health condition. For example, the user's health data can be visually displayed using graphs and charts, allowing the user to grasp changes in the health condition at a glance. This allows the analysis unit to efficiently analyze the user's health data and provide the user with useful information.

[0071] The providing unit provides advice based on the analysis results obtained by the analyzing unit. The advice includes, for example, meal suggestions and exercise suggestions, but is not limited to such examples. Specifically, based on the analysis results, a meal menu and an exercise plan according to the user's health condition can be proposed. For example, if the user's blood pressure is high, a low-salt meal menu can be proposed, and if the heart rate is low, aerobic exercise can be recommended. Furthermore, the providing unit can provide customized advice according to the user's lifestyle and preferences. For example, if the user likes a specific ingredient, a healthy recipe using that ingredient can be proposed. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, the user can feed back the results of the proposed exercise plan and adjust the next advice based on the results. This allows the providing unit to provide individualized advice to the user and maximize the effect of health management. Furthermore, the providing unit can diversify the method of providing advice and provide information in a form that is easy for the user to use. For example, the advice can be notified through a smartphone app, or information can be provided using email or SMS. This allows the providing unit to quickly and reliably provide appropriate advice to the user and support health management.

[0072] The measurement unit automatically measures the waist circumference of the user. The measurement is performed, for example, using sensor technology, but is not limited to such an example. Specifically, the IoT belt is used to measure the waist circumference of the user in real time, and the data is transmitted to the system. The IoT belt is worn on the user's abdomen, and the built-in sensor detects the change in waist circumference with high accuracy. This allows the user to naturally provide waist circumference data in daily life. Furthermore, the measurement unit collects the measurement data in real time, and can comprehensively evaluate the user's health condition in cooperation with the analysis unit and the provision unit. For example, if the waist circumference data increases suddenly, the analysis unit can analyze the cause, and the provision unit can propose appropriate measures. In addition, the measurement unit can monitor the waist circumference data of the user over a long period of time and grasp the trend of the health condition. This allows the user to track changes in his or her health condition over the long term and take appropriate measures. Furthermore, the measurement unit can integrate with other health data to perform a more accurate health evaluation. For example, the waist circumference data and weight data can be combined to estimate the user's body fat percentage and evaluate health risks. This enables the measurement unit to efficiently collect health data of the user and improve the performance of the entire system.

[0073] The collection unit can collect data on the user's medical history, medication status, and daily health condition. The collection unit collects, for example, the user's medical history, such as past medical history and surgery history. The collection unit can also collect the user's current medication status and medication status, such as past medication history. Furthermore, the collection unit can collect the user's daily health condition, such as changes in daily physical condition and the presence or absence of symptoms. This allows the collection unit to collect detailed health data of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's health data into AI and optimize the timing and method for the AI ​​to collect data.

[0074] The analysis unit can analyze the collected data using machine learning or deep learning. The analysis unit, for example, analyzes the data using a machine learning algorithm. For example, the analysis unit analyzes the data using a machine learning algorithm such as linear regression or a decision tree to evaluate the user's health status. The analysis unit can also analyze the data using a deep learning algorithm. For example, the analysis unit analyzes the data using a deep learning algorithm such as a neural network or a convolutional neural network to evaluate the user's health risk. Thereby, the analysis unit can improve the accuracy of the analysis by using machine learning or deep learning. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by inputting the collected data into AI and having the AI ​​analyze the data.

[0075] The providing unit can suggest calorie restriction and nutritional balance. The providing unit suggests calorie restriction according to, for example, the health condition of the user. For example, the providing unit sets an upper limit on daily calorie intake and suggests an appropriate meal menu to the user. The providing unit can also suggest nutritional balance. For example, the providing unit suggests a balanced meal menu to the user based on the recommended intake amount of each nutrient. In this way, the providing unit can provide specific dietary advice to the user and support health improvement. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's health data into AI, and the AI ​​can suggest calorie restriction and nutritional balance.

[0076] The measurement unit can measure the waist circumference of the user using sensor technology. The measurement unit can measure the waist circumference of the user using, for example, an optical sensor. For example, the measurement unit can measure the waist circumference of the user in real time using an optical sensor and transmit the data to the system. The measurement unit can also measure the waist circumference of the user using a pressure sensor. For example, the measurement unit can measure the waist circumference of the user using a pressure sensor and transmit the data to the system. As a result, the measurement unit can improve the measurement accuracy of the waist circumference by using sensor technology. Part or all of the above-mentioned processing in the measurement unit can be performed using, for example, AI, or can be performed without using AI. For example, the measurement unit can improve the measurement accuracy by inputting data acquired by the sensor into AI, and the AI ​​analyzes the data.

[0077] The collection unit can collect vital data such as blood pressure, blood glucose level, and heart rate. The collection unit, for example, collects blood pressure data using a sensor that measures blood pressure. For example, the collection unit measures the user's blood pressure using a blood pressure meter and transmits the data to the system. The collection unit can also collect blood glucose level data using a sensor that measures blood glucose level. For example, the collection unit measures the user's blood glucose level using a blood glucose meter and transmits the data to the system. Furthermore, the collection unit can also collect heart rate data using a sensor that measures heart rate. For example, the collection unit measures the user's heart rate using a heart rate meter and transmits the data to the system. In this way, the collection unit can grasp the user's health condition in more detail by collecting the vital data. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the vital data to AI, and the AI ​​can analyze the data to evaluate the user's health condition.

[0078] The providing unit can propose a meal menu and an exercise plan according to the health condition of the user. The providing unit, for example, proposes an appropriate meal menu according to the health condition of the user. For example, the providing unit proposes a meal menu that takes into account the balance of calories and nutrients based on the health data of the user. The providing unit can also propose an appropriate exercise plan according to the health condition of the user. For example, the providing unit proposes an exercise plan that takes into account the type, frequency, and intensity of exercise based on the health data of the user. This allows the providing unit to provide the user with lifestyle improvement advice that is individually appropriate for the user and support health improvement. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's health data into AI, and the AI ​​can propose a meal menu and an exercise plan.

[0079] The measurement unit can monitor the change in the waist circumference of the user in real time. The measurement unit, for example, uses an IoT belt to measure the waist circumference of the user in real time and transmits the data to the system. For example, the measurement unit continuously measures the waist circumference of the user wearing the IoT belt and transmits the data to the system in real time. The measurement unit can also monitor the change in the waist circumference of the user based on the measurement data. For example, the measurement unit displays the change in the waist circumference of the user as a graph or chart and provides it in a visually easy-to-understand format. Thereby, the measurement unit can support the user's health management by grasping the change in the waist circumference of the user in real time. A part or all of the above-mentioned processing in the measurement unit may be performed, for example, using AI or may be performed without using AI. For example, the measurement unit can monitor the change in the waist circumference by inputting the data acquired by the IoT belt into AI and having the AI ​​analyze the data.

[0080] The provision unit can provide a specific operation method when a company introduces it as an employee health management program. The provision unit, for example, provides specific procedures for a company to effectively operate an employee health management program. For example, the provision unit explains in detail the implementation procedure and evaluation method of the program, and supports the company to operate the program smoothly. The provision unit can also provide guidelines for the company to collect and analyze employee health data and provide appropriate advice. For example, the provision unit provides specific instructions on the data collection method, analysis method, and advice provision method. In this way, the provision unit can support the company to effectively operate the employee health management program. Part or all of the above-mentioned processing in the provision unit may be performed, for example, using AI, or may be performed without using AI. For example, the provision unit can input operation data of the company's health management program into AI, and the AI ​​can suggest the optimal operation method.

[0081] The providing unit can provide specific data on the extent to which the risk of lifestyle-related diseases is reduced. The providing unit provides specific data on the extent to which the risk of lifestyle-related diseases is reduced, for example, based on the health data of the user. For example, the providing unit indicates the effect of reducing the risk of lifestyle-related diseases using statistical data or experimental data. In this way, the providing unit can specifically indicate the effect of reducing the risk of lifestyle-related diseases to the user, thereby improving the motivation of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the health data of the user into AI, which analyzes the effect of reducing the risk of lifestyle-related diseases, and provide specific data.

[0082] The collection unit can estimate the user's emotion and adjust the timing of collecting health data based on the estimated user's emotion. The collection unit, for example, uses an emotion estimation algorithm to estimate the user's emotion. For example, the collection unit analyzes the user's facial expression, voice, and text data to calculate an emotion score. Next, the collection unit adjusts the timing of collecting health data based on the estimated user's emotion. For example, when the user is stressed, the health data is collected during a relaxed time period. When the user is relaxed, detailed health data can also be collected. Furthermore, when the user is in a hurry, simplified health data can also be collected. This allows the collection unit to adjust the collection timing according to the user's emotion, thereby enabling more appropriate data collection. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotional data into the generation AI, which can then optimize the collection timing.

[0083] The collection unit can analyze the user's past health data and select the optimal collection method. The collection unit uses AI, for example, to analyze the user's past health data. For example, the collection unit determines the optimal collection frequency based on data provided by the user in the past. The collection unit can also select a method of collection for a specific time period from the user's past health data. Furthermore, the collection unit can analyze the user's past data and suggest the most effective collection method. As a result, the collection unit can improve the efficiency of data collection by selecting the optimal collection method based on the past data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past health data into AI, which can then suggest the optimal collection method.

[0084] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting health data. The collection unit uses AI, for example, to grasp the user's current living situation and areas of interest. For example, if the user is on a diet, the collection unit prioritizes collecting data related to meals. In addition, if the user is interested in exercise, the collection unit can also focus on collecting exercise data. Furthermore, the collection unit can filter and collect highly relevant data according to the user's living situation. This enables the collection unit to collect data according to the user's living situation and areas of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's living situation and areas of interest into AI, and the AI ​​can filter and collect the data.

[0085] The collection unit can estimate the user's emotion and determine the priority of the health data to be collected based on the estimated user's emotion. The collection unit, for example, uses an emotion estimation algorithm to estimate the user's emotion. For example, the collection unit analyzes the user's facial expression, voice, and text data to calculate an emotion score. Next, the collection unit determines the priority of the health data to be collected based on the estimated user's emotion. For example, when the user is stressed, stress-related data is preferentially collected. In addition, when the user is relaxed, detailed health data can be preferentially collected. Furthermore, when the user is in a hurry, simplified health data can be preferentially collected. In this way, the collection unit can preferentially collect more important data by determining the priority of data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotional data into the generation AI, which can then optimize the priority of the data.

[0086] When collecting health data, the collection unit can preferentially collect highly relevant data based on the geographical location information of the user. The collection unit uses AI, for example, to collect data taking into account the geographical location information of the user. For example, if the user lives in a high altitude, the collection unit collects high altitude specific health data. In addition, if the user lives in an urban area, the collection unit can also collect urban area specific health data. Furthermore, the collection unit can preferentially collect highly relevant data based on the geographical location information of the user. Thereby, the collection unit can improve the accuracy of the data by collecting highly relevant data based on the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI, and the AI ​​can preferentially collect highly relevant data.

[0087] The collection unit can analyze the user's social media activity and collect related data when collecting health data. The collection unit, for example, uses AI to analyze the user's social media activity. For example, the collection unit collects health information shared by the user on social media. The collection unit can also identify areas of interest related to health from the user's social media activity and collect related data. Furthermore, the collection unit can analyze the user's social media activity and use it to collect health data. This allows the collection unit to collect data according to the user's areas of interest by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI, which can collect related data.

[0088] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. The analysis unit uses an emotion estimation algorithm, for example, to estimate the user's emotion. For example, the analysis unit analyzes the user's facial expression, voice, and text data to calculate an emotion score. Next, the analysis unit adjusts the analysis algorithm based on the estimated user's emotion. For example, when the user is stressed, the analysis can emphasize stress-related data. In addition, when the user is relaxed, the analysis can emphasize detailed data. Furthermore, when the user is in a hurry, the analysis can emphasize simplified data. Thereby, the analysis unit can improve the accuracy of the analysis by adjusting the analysis algorithm according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit inputs the user's emotion data into the generation AI, and the generation AI can optimize the analysis algorithm.

[0089] The analysis unit can improve the accuracy of the analysis by referring to the user's past health data during the analysis. The analysis unit uses AI, for example, to refer to the user's past health data. For example, the analysis unit improves the accuracy of the analysis based on the user's past health data. The analysis unit can also improve the reliability of the analysis result by referring to the user's past data. Furthermore, the analysis unit can improve the accuracy of the analysis by utilizing the user's past health data. As a result, the analysis unit can improve the accuracy of the analysis by referring to the past data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by inputting the user's past health data into AI and having the AI ​​analyze the data.

[0090] The analysis unit can perform the analysis while taking into consideration the user's lifestyle and dietary history. The analysis unit uses AI, for example, to take into consideration the user's lifestyle and dietary history. For example, the analysis unit performs the analysis while taking into consideration nutritional balance based on the user's dietary history. The analysis unit can also take into consideration the user's lifestyle and reflect it in the analysis result. Furthermore, the analysis unit can perform the analysis while comprehensively taking into consideration the user's dietary history and lifestyle. This allows the analysis unit to perform more accurate analysis by taking into consideration the lifestyle and dietary history. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by inputting the user's lifestyle and dietary history into AI, and the AI ​​analyzes the data.

[0091] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit uses an emotion estimation algorithm, for example, to estimate the user's emotion. For example, the analysis unit analyzes the user's facial expression, voice, and text data, and calculates an emotion score. Next, the analysis unit adjusts the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, a simple and highly visible display method is provided. Also, when the user is relaxed, a display method including detailed information can be provided. Furthermore, when the user is in a hurry, a display method that focuses on the main points can be provided. This makes it easier for the analysis unit to understand the analysis result by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input a user's emotional data into the generation AI, which can then optimize the display method.

[0092] The analysis unit can perform the analysis while taking into account the geographical location information of the user. The analysis unit uses AI, for example, to take into account the geographical location information of the user. For example, if the user lives at high altitude, the analysis unit performs the analysis while taking into account health risks specific to high altitude. In addition, if the user lives in an urban area, the analysis unit can also perform the analysis while taking into account health risks specific to urban areas. Furthermore, the analysis unit can also perform the analysis while taking into account health risks based on the geographical location information of the user. This allows the analysis unit to perform more accurate analysis by taking into account the geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can improve the accuracy of the analysis by inputting the geographical location information of the user into AI and having the AI ​​analyze the data.

[0093] During the analysis, the analysis unit can analyze the user's social media activity and use related data for the analysis. The analysis unit uses AI, for example, to analyze the user's social media activity. For example, the analysis unit uses health information shared by the user on social media for the analysis. The analysis unit can also identify areas of interest related to health from the user's social media activity and use related data for the analysis. Furthermore, the analysis unit can analyze the user's social media activity and use it for analyzing health data. This allows the analysis unit to analyze social media activity according to the user's areas of interest. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into AI, which can analyze the data.

[0094] The providing unit can estimate the user's emotion and adjust the way of expressing advice based on the estimated user's emotion. The providing unit, for example, uses an emotion estimation algorithm to estimate the user's emotion. For example, the providing unit analyzes the user's facial expression, voice, and text data to calculate an emotion score. Next, the providing unit adjusts the way of expressing advice based on the estimated user's emotion. For example, if the user is stressed, the providing unit provides advice in gentle words. In addition, if the user is relaxed, detailed advice can be provided. Furthermore, if the user is in a hurry, concise advice can be provided. This enables the providing unit to adjust the way of expressing advice according to the user's emotion, thereby enabling more effective advice. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit inputs the user's emotion data into the generation AI, and the generation AI can optimize the way of expressing advice.

[0095] The providing unit can adjust the level of detail of the advice based on the user's health data when providing the advice. The providing unit uses AI, for example, to adjust the level of detail of the advice based on the user's health data. For example, the providing unit provides detailed advice based on the user's health data. The providing unit can also provide concise advice based on the user's health data. Furthermore, the providing unit can adjust the level of detail of the advice according to the user's health data. As a result, the providing unit can provide advice suitable for the user by adjusting the level of detail of the advice based on the health data. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's health data into AI, and the AI ​​can optimize the level of detail of the advice.

[0096] The providing unit can provide advice taking into consideration the user's lifestyle habits and dietary history when providing advice. The providing unit uses AI, for example, to take into consideration the user's lifestyle habits and dietary history. For example, the providing unit provides advice on nutritional balance based on the user's dietary history. The providing unit can also provide appropriate advice by taking into consideration the user's lifestyle habits. Furthermore, the providing unit can provide advice by comprehensively taking into consideration the user's dietary history and lifestyle habits. As a result, the providing unit can provide more appropriate advice by taking into consideration the lifestyle habits and dietary history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's lifestyle habits and dietary history into AI, and the AI ​​can optimize the advice.

[0097] The providing unit can estimate the user's emotion and determine the priority of advice based on the estimated user's emotion. The providing unit uses an emotion estimation algorithm, for example, to estimate the user's emotion. For example, the providing unit analyzes the user's facial expression, voice, and text data to calculate an emotion score. Next, the providing unit determines the priority of advice based on the estimated user's emotion. For example, if the user is stressed, the providing unit prioritizes advice for stress reduction. Also, if the user is relaxed, detailed advice can be prioritized. Furthermore, if the user is in a hurry, concise advice can be prioritized. In this way, the providing unit can provide more effective advice by determining the priority of advice according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotional data to the generating AI, which can then optimize the priority of advice.

[0098] The providing unit can provide highly relevant advice by considering the geographical location information of the user when providing advice. The providing unit uses AI, for example, to consider the geographical location information of the user. For example, if the user lives in a high altitude, the providing unit provides high altitude specific health advice. In addition, if the user lives in an urban area, the providing unit can also provide urban area specific health advice. Furthermore, the providing unit can also provide highly relevant advice based on the geographical location information of the user. As a result, the providing unit can provide highly relevant advice based on the geographical location information, thereby enabling advice suitable for the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI, and the AI ​​can provide highly relevant advice.

[0099] The providing unit can analyze the user's social media activity and provide related advice when providing advice. The providing unit uses AI, for example, to analyze the user's social media activity. For example, the providing unit provides advice based on health information shared by the user on social media. The providing unit can also identify areas of interest related to health from the user's social media activity and provide related advice. Furthermore, the providing unit can analyze the user's social media activity and utilize it for health advice. As a result, the providing unit can provide advice according to the user's areas of interest by analyzing the social media activity. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI, which can provide related advice.

[0100] The measurement unit can estimate the user's emotion and adjust the timing of the measurement based on the estimated user's emotion. The measurement unit uses an emotion estimation algorithm, for example, to estimate the user's emotion. For example, the measurement unit analyzes the user's facial expression, voice, and text data to calculate an emotion score. Next, the measurement unit adjusts the timing of the measurement based on the estimated user's emotion. For example, if the user is stressed, the measurement is performed during a relaxed time period. In addition, if the user is relaxed, detailed measurement can be performed. Furthermore, if the user is in a hurry, simplified measurement can be performed. This allows the measurement unit to adjust the measurement timing according to the user's emotion, thereby enabling more appropriate measurement. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the measurement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the measurement unit inputs the user's emotion data into the generation AI, and the generation AI can optimize the measurement timing.

[0101] The measurement unit can improve the accuracy of the measurement by referring to the user's past measurement data at the time of measurement. The measurement unit uses AI, for example, to refer to the user's past measurement data. For example, the measurement unit improves the accuracy of the measurement based on the user's past measurement data. The measurement unit can also improve the reliability of the measurement result by referring to the user's past data. Furthermore, the measurement unit can improve the accuracy of the measurement by utilizing the user's past measurement data. Thereby, the measurement unit can improve the accuracy of the measurement by referring to the past data. A part or all of the above-mentioned processing in the measurement unit may be performed, for example, using AI, or may be performed without using AI. For example, the measurement unit can improve the accuracy of the measurement by inputting the user's past measurement data into AI, and the AI ​​analyzes the data.

[0102] The measurement unit can perform the measurement while taking into consideration the user's lifestyle and dietary history. The measurement unit uses AI, for example, to take into consideration the user's lifestyle and dietary history. For example, the measurement unit performs the measurement while taking into consideration nutritional balance based on the user's dietary history. The measurement unit can also take into consideration the user's lifestyle and reflect it in the measurement result. Furthermore, the measurement unit can perform the measurement while comprehensively taking into consideration the user's dietary history and lifestyle. As a result, the measurement unit can perform more accurate measurement by taking into consideration the lifestyle and dietary history. Part or all of the above-mentioned processing in the measurement unit may be performed, for example, using AI, or may be performed without using AI. For example, the measurement unit can improve the accuracy of the measurement by inputting the user's lifestyle and dietary history into AI, and the AI ​​analyzes the data.

[0103] The measurement unit can estimate the user's emotion and adjust the display method of the measurement result based on the estimated user's emotion. The measurement unit uses an emotion estimation algorithm, for example, to estimate the user's emotion. For example, the measurement unit analyzes the user's facial expression, voice, and text data to calculate an emotion score. Next, the measurement unit adjusts the display method of the measurement result based on the estimated user's emotion. For example, when the user is nervous, a simple and highly visible display method is provided. Also, when the user is relaxed, a display method including detailed information can be provided. Furthermore, when the user is in a hurry, a display method that focuses on the main points can be provided. This makes it easier for the measurement unit to understand the measurement result by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the measurement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the measurement unit can input a user's emotional data into the generation AI, which can then optimize the display method.

[0104] The measurement unit can perform the measurement while taking into account the geographical location information of the user. The measurement unit uses AI, for example, to take into account the geographical location information of the user. For example, when the user lives at high altitude, the measurement unit performs the measurement while taking into account the health risks specific to the high altitude. In addition, when the user lives in an urban area, the measurement unit can also perform the measurement while taking into account the health risks specific to the urban area. Furthermore, the measurement unit can also perform the measurement while taking into account the health risks based on the geographical location information of the user. As a result, the measurement unit can perform a more accurate measurement by taking into account the geographical location information. A part or all of the above-mentioned processing in the measurement unit may be performed, for example, using AI, or may be performed without using AI. For example, the measurement unit can improve the accuracy of the measurement by inputting the geographical location information of the user into AI, and the AI ​​analyzes the data.

[0105] The measurement unit can analyze the user's social media activity at the time of measurement and use the related data for the measurement. The measurement unit uses AI, for example, to analyze the user's social media activity. For example, the measurement unit uses health information shared by the user on social media for the measurement. The measurement unit can also identify areas of interest related to health from the user's social media activity and use them for the measurement. Furthermore, the measurement unit can analyze the user's social media activity and use them for measuring health data. As a result, the measurement unit can perform measurement according to the user's areas of interest by analyzing the social media activity. A part or all of the above-mentioned processing in the measurement unit may be performed using AI, for example, or may be performed without using AI. For example, the measurement unit can improve the accuracy of the measurement by inputting the user's social media activity data into AI, and the AI ​​analyzes the data.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The health management system can further collect and analyze the user's sleep data. For example, the collection unit can use a wearable device to monitor the user's sleep pattern and sleep quality. The analysis unit can analyze the collected sleep data and evaluate the user's sleep quality. The provision unit can provide the user with advice on improving sleep based on the analysis result. For example, appropriate bedtimes and wake-up times can be suggested based on the user's sleep pattern. In addition, the system can provide advice on relaxation methods and environmental adjustments to improve the user's sleep quality. In this way, the health management system can improve the user's sleep quality and improve overall health.

[0108] The health management system can further monitor and analyze the user's stress level. For example, the collection unit can collect the stress level using a sensor that measures the user's heart rate variability and electrodermal activity. The analysis unit can analyze the collected data and evaluate the user's stress level. The provision unit can provide the user with stress management advice based on the analysis result. For example, relaxation techniques and exercise plans for stress reduction can be suggested depending on the user's stress level. In addition, if the user's stress level is high, professional counseling can be recommended. In this way, the health management system can manage the user's stress level and improve the overall health condition.

[0109] The health management system can further analyze the user's meal photos and evaluate the nutritional balance. For example, the collection unit can collect photos of meals taken by the user. The analysis unit can use image analysis technology to evaluate the contents and nutrients of the meals. The provision unit can provide the user with advice on nutritional balance based on the analysis results. For example, based on the user's meal photos, it can point out excesses and deficiencies in calorie intake and nutrients and suggest a balanced meal menu. It can also suggest ingredients and recipes for the user to ingest specific nutrients. In this way, the health management system can grasp the details of the user's meal contents and improve nutritional balance.

[0110] The health management system can further collect and analyze the user's exercise data. For example, the collection unit can use a wearable device to monitor the user's amount of exercise and type of exercise. The analysis unit can analyze the collected exercise data and evaluate the user's exercise habits. The provision unit can provide the user with advice on an exercise plan based on the analysis results. For example, it can suggest an appropriate type and frequency of exercise based on the user's exercise data. It can also provide a specific training plan according to the user's exercise goal. In this way, the health management system can improve the user's exercise habits and improve overall health.

[0111] The health management system can further monitor and analyze the user's water intake. For example, the collection unit can use a smart bottle to record the amount of water consumed by the user. The analysis unit can analyze the collected water intake data and evaluate the user's water intake status. The provision unit can provide the user with appropriate water intake advice based on the analysis result. For example, the system can suggest the amount of water required per day based on the user's water intake. It can also send reminders during times when the user is prone to dehydration. In this way, the health management system can manage the user's water intake status and improve the overall health status.

[0112] The health management system can estimate the user's emotions and adjust the content of the health advice based on the estimated emotions. For example, the providing unit can use an emotion estimation algorithm to estimate the user's emotions. If the user is feeling stressed, advice to relax can be provided preferentially. Also, if the user is relaxed, more detailed health advice can be provided. Furthermore, if the user is in a hurry, concise advice that gets to the point can be provided. In this way, the health management system can provide appropriate advice according to the user's emotions and support the user's health improvement.

[0113] The health management system can estimate the user's emotions and adjust the exercise plan based on the estimated emotions. For example, the providing unit can use an emotion estimation algorithm to estimate the user's emotions. If the user is feeling stressed, it can suggest an exercise that has a relaxing effect. Also, if the user is relaxed, it can suggest an exercise with a higher intensity. Furthermore, if the user is in a hurry, it can suggest an effective exercise in a short time. In this way, the health management system can provide an exercise plan according to the user's emotions and support the user's health improvement.

[0114] The health management system can estimate the user's emotions and adjust the content of the dietary advice based on the estimated emotions. For example, the providing unit can use an emotion estimation algorithm to estimate the user's emotions. If the user is feeling stressed, a meal containing ingredients that have a relaxing effect can be suggested. Also, if the user is relaxed, a nutritionally balanced meal can be suggested. Furthermore, if the user is in a hurry, a meal that is easy to prepare can be suggested. In this way, the health management system can provide dietary advice according to the user's emotions and support the user's health improvement.

[0115] The health management system can estimate the user's emotions and adjust the frequency of collecting health data based on the estimated emotions. For example, the collection unit can use an emotion estimation algorithm to estimate the user's emotions. When the user is feeling stressed, the frequency of data collection can be reduced to reduce the burden on the user. When the user is relaxed, detailed data can be collected. Furthermore, when the user is in a hurry, simplified data can be collected. In this way, the health management system can collect data according to the user's emotions and collect appropriate data while reducing the burden on the user.

[0116] The health management system can estimate the user's emotion and adjust the analysis method of the health data based on the estimated emotion. For example, the analysis unit can use an emotion estimation algorithm to estimate the user's emotion. If the user is feeling stressed, the analysis can emphasize stress-related data. If the user is relaxed, the analysis can emphasize detailed data. Furthermore, if the user is in a hurry, the analysis can emphasize simplified data. This allows the health management system to perform analysis according to the user's emotion and provide more appropriate analysis results.

[0117] The process flow of the second embodiment will be briefly described below.

[0118] Step 1: The collection unit collects health data of the user. The health data of the user includes, but is not limited to, blood pressure, heart rate, weight, and the like. The collection unit collects these data using, for example, a sensor. The collection unit can also collect data by manual input. For example, the collection unit can collect data entered by the user himself. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit analyzes the data using a machine learning algorithm to evaluate the user's health condition. Step 3: The providing unit provides advice based on the analysis results obtained by the analyzing unit. The advice includes, but is not limited to, dietary suggestions and exercise suggestions. For example, the providing unit suggests an appropriate diet menu and exercise plan according to the user's health condition. Step 4: The measurement unit automatically measures the waist circumference of the user. The measurement is performed, for example, using sensor technology, but is not limited to such an example. For example, the measurement unit measures the waist circumference of the user in real time using an IoT belt and transmits the data to the system.

[0119] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0120] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0121] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0122] For example, the collection unit can collect health data of the user using a sensor or a manual input device of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice based on the analysis results. The measurement unit measures the waist circumference of the user in real time using a sensor of the smart device 14 and transmits the data to the system. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0126] 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.

[0127] 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 the voice according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0129] 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 performed in a secure state.

[0130] Fig. 4 shows an example of 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.

[0131] 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.

[0132] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0135] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0136] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0137] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0138] For example, the collection unit can collect health data of the user using a sensor of the smart glasses 214 or a manual input device. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides advice based on the analysis results. The measurement unit measures the waist circumference of the user in real time using the sensor of the smart glasses 214 and transmits the data to the system. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0142] 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.

[0143] 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 the voice according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0145] 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 performed in a secure state.

[0146] 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.

[0147] 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.

[0148] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0149] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0150] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0151] 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 voice indicating a user input for 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.

[0152] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0153] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0154] For example, the collection unit can collect health data of the user using a sensor or a manual input device of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides advice based on the analysis results. The measurement unit measures the waist circumference of the user in real time using a sensor of the headset type terminal 314, and transmits the data to the system. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0158] 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.

[0159] 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 the voice according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0161] 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 performed in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0163] 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.

[0164] 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.

[0165] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0166] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0167] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0168] 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 a voice indicating a user input for 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.

[0169] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0170] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0171] For example, the collection unit can collect health data of the user using a sensor or a manual input device of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The provision unit is realized by the specific processing unit 290 of the data processing device 12, and provides advice based on the analysis results. The measurement unit measures the waist circumference of the user in real time using a sensor of the robot 414, and transmits the data to the system. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0173] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0175] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0177] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0179] 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.

[0180] 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 Universal Serial Bus (USB) 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.

[0181] In addition, 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 upon request from the data processing device 12.

[0182] 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.

[0183] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0184] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0185] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0186] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0188] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0190] (Appendix 1) A collection unit that collects health data of a user; an analysis unit that analyzes the data collected by the collection unit; a providing unit that provides advice based on the analysis result obtained by the analyzing unit; A measuring unit that automatically measures the waist circumference of the user. A system characterized by: (Appendix 2) The collecting unit includes: Collect data on users' medical history, medication status, and daily health conditions 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze the collected data using machine learning or deep learning 2. The system of claim 1, (Appendix 4) The providing unit is Suggestions for calorie restriction and nutritional balance 2. The system of claim 1, (Appendix 5) The measurement unit includes: Measure the user's waist circumference using sensor technology 2. The system of claim 1, (Appendix 6) The collecting unit includes: Collect vital data on blood pressure, blood sugar and heart rate 2. The system of claim 1, (Appendix 7) The providing unit is Suggesting meal menus and exercise plans based on the user's health status 2. The system of claim 1, (Appendix 8) The measurement unit includes: Monitor changes in the user's waist circumference in real time 2. The system of claim 1, (Appendix 9) The providing unit is Provide specific operational methods for companies to introduce as employee health management programs 2. The system of claim 1, (Appendix 10) The providing unit is Provide concrete data on the extent to which the risk of lifestyle-related diseases is reduced 2. The system of claim 1, (Appendix 11) The collecting unit includes: To estimate a user's emotion and adjust the timing of collection of health data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 12) The collecting unit includes: Analyze the user's past health data and select the optimal collection method 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting health data, it will be filtered based on the user's current life situation and areas of interest. 2. The system of claim 1, (Appendix 14) The collecting unit includes: Estimate a user's emotions and prioritize health data to be collected based on the estimated user's emotions. 2. The system of claim 1, (Appendix 15) The collecting unit includes: When collecting health data, prioritize collection of relevant data based on the user's geographic location 2. The system of claim 1, (Appendix 16) The collecting unit includes: When collecting health data, analyze users’ social media activity to collect relevant data 2. The system of claim 1, (Appendix 17) The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, the user's past health data is referenced to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 19) The analysis unit is When analyzing, the user's lifestyle and dietary history are taken into consideration. 2. The system of claim 1, (Appendix 20) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 21) The analysis unit is When analyzing, the geographic location of the user is taken into account. 2. The system of claim 1, (Appendix 22) The analysis unit is During analysis, we analyze users' social media activities and use related data for analysis. 2. The system of claim 1, (Appendix 23) The providing unit is Inferring the user's emotions and adjusting the way advice is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 24) The providing unit is When providing advice, adjust the level of detail based on the user's health data 2. The system of claim 1, (Appendix 25) The providing unit is When providing advice, the system takes into account the user's lifestyle habits and dietary history. 2. The system of claim 1, (Appendix 26) The providing unit is Estimate the user's emotions and prioritize advice based on the estimated user emotions. 2. The system of claim 1, (Appendix 27) The providing unit is When providing advice, the system takes into account the user's geographic location to provide more relevant advice. 2. The system of claim 1, (Appendix 28) The providing unit is When providing advice, analyze the user's social media activity to provide relevant advice 2. The system of claim 1, (Appendix 29) The measurement unit includes: A user's emotion is estimated, and the timing of measurement is adjusted based on the estimated user's emotion. 2. The system of claim 1, (Appendix 30) The measurement unit includes: When measuring, the accuracy of the measurement can be improved by referring to the user's past measurement data. 2. The system of claim 1, (Appendix 31) The measurement unit includes: When taking measurements, the user's lifestyle and dietary history are taken into consideration. 2. The system of claim 1, (Appendix 32) The measurement unit includes: Inferring user emotion and adjusting the display method of measurement results based on the estimated user emotion 2. The system of claim 1, (Appendix 33) The measurement unit includes: When measuring, the geographic location information of the user is taken into account. 2. The system of claim 1, (Appendix 34) The measurement unit includes: When measuring, analyze users' social media activities and use relevant data for measurement. 2. The system of claim 1, [Explanation of symbols]

[0191] 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 collection unit that collects health data of a user; an analysis unit that analyzes the data collected by the collection unit; a providing unit that provides advice based on the analysis result obtained by the analyzing unit; A measuring unit that automatically measures the waist circumference of the user. A system characterized by:

2. The collecting unit includes: Collecting data on the user's medical history, medication status, and daily health condition 2. The system of claim 1 .

3. The analysis unit is Analyze the collected data using machine learning or deep learning 2. The system of claim 1 .

4. The providing unit is Suggestions for calorie restriction and nutritional balance 2. The system of claim 1 .

5. The measurement unit includes: Measuring the waist circumference of the user using sensor technology 2. The system of claim 1 .

6. The collecting unit includes: Collect vital data on blood pressure, blood sugar and heart rate 2. The system of claim 1 .

7. The providing unit is Proposing meal menus and exercise plans according to the user's health condition 2. The system of claim 1 .

8. The measurement unit includes: Monitoring changes in the user's waist circumference in real time.

2. The system of claim 1 .

9. The providing unit is Provide specific operational methods for companies to introduce as employee health management programs 2. The system of claim 1 .

10. The providing unit is Provide concrete data on the extent to which the risk of lifestyle-related diseases is reduced 2. The system of claim 1 .

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