system

Abstract

The system according to this embodiment aims to efficiently manage daily health data and provide health advice. [Solution] The system according to the embodiment comprises a measurement unit, an authentication unit, an analysis unit, and an advice unit. The measurement unit measures body fat and weight. The authentication unit performs personal authentication using a camera. The analysis unit analyzes the data collected by the measurement unit and the authentication unit. The advice unit provides health advice based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to manage daily health data.

[0005] The system according to the embodiment aims to efficiently manage daily health data and provide health advice.

Means for Solving the Problems

[0006] The system according to the embodiment includes a measurement unit, an authentication unit, an analysis unit, and an advice unit. The measurement unit measures body fat and body weight. The authentication unit performs personal authentication by a camera. The analysis unit analyzes the data collected by the measurement unit and the authentication unit. The advice unit provides health advice based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage daily health data and provide health advice. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health management system according to an embodiment of the present invention is a system that manages daily health data using a toilet. This health management system collects user health data using a body fat and weight measurement function and a personal authentication function using a camera installed in the toilet. Next, an AI analyzes the collected data and monitors the user's health status. Furthermore, the AI ​​provides health advice based on the analysis results. This mechanism improves the accuracy of health management and provides daily health advice. For example, the health management system measures the user's body fat and weight using the body fat and weight measurement function installed in the toilet. For example, simply sitting on the toilet seat automatically measures body fat and weight. This data becomes important information for understanding the user's health status. Next, the health management system identifies the user using a personal authentication function using a camera installed in the toilet. For example, the camera performs facial recognition when the user enters the toilet to identify the user. This ensures that the collected data is accurately linked to the individual. The collected data is analyzed by an AI. The AI ​​analyzes data such as fluctuations in body fat and weight, frequency of toilet use, and intervals of use to monitor the user's health status. For example, if body fat increases or the interval between toilet visits shortens, the AI ​​detects changes in health status based on this data. Furthermore, the AI ​​provides health advice based on the analysis results. For instance, if body fat increases, it provides advice on diet and exercise. If the interval between toilet visits shortens, it provides advice on adjusting fluid intake or recommending a doctor's visit. In this way, users can manage their health on a daily basis. This system improves the accuracy of health management and provides daily health advice. Users can understand their health status and receive appropriate advice simply by using the toilet. This is expected to help prevent lifestyle-related diseases, enable personalized health management, and contribute to maintaining good health. As a result, the health management system can efficiently collect and analyze users' health data and provide appropriate health advice.

[0029] The health management system according to this embodiment comprises a measurement unit, an authentication unit, an analysis unit, and an advice unit. The measurement unit measures body fat and weight. The measurement unit measures body fat using, for example, bioimpedance. The measurement unit can also measure body fat using dual-energy X-ray absorptiometry. Furthermore, the measurement unit can measure weight using a weighing scale. For example, the measurement unit measures weight using a weighing scale built into a toilet seat. The authentication unit performs personal authentication using a camera. The authentication unit authenticates the user using, for example, facial recognition technology. Furthermore, the authentication unit can authenticate the user using fingerprint authentication technology. Furthermore, the authentication unit can authenticate the user using iris authentication technology. For example, the authentication unit performs facial authentication using a camera installed in a toilet to identify the user. The analysis unit analyzes the data collected by the measurement unit and the authentication unit. The analysis unit performs, for example, statistical analysis of the data. Furthermore, the analysis unit can also analyze the data using machine learning algorithms. Furthermore, the analysis unit can also perform trend analysis of the data. For example, the analysis unit analyzes fluctuations in body fat and weight to monitor the user's health status. The advice unit provides health advice based on the analysis results obtained by the analysis unit. The advice unit can, for example, provide dietary guidance. The advice unit can also provide exercise guidance. Furthermore, the advice unit can also suggest improvements to lifestyle habits. For example, if body fat is increasing, the advice unit will provide advice regarding diet and exercise. As a result, the health management system according to this embodiment can efficiently collect and analyze the user's health data and provide appropriate health advice.

[0030] The measurement unit measures body fat and weight. For example, it can measure body fat using bioimpedance. Bioimpedance is a method of calculating body fat percentage by passing a weak electric current through the body and measuring its electrical resistance. This method is non-invasive and allows for quick measurements, making it suitable for daily health management. The measurement unit can also measure body fat using dual-energy X-ray absorptiometry. Dual-energy X-ray absorptiometry accurately measures body fat by irradiating the body with X-rays of different energies and utilizing the difference in their absorption rates. This method is highly accurate and widely used in medical institutions. Furthermore, the measurement unit can measure weight using a scale. For example, it can measure weight using a scale built into a toilet seat. A scale built into a toilet seat allows users to measure their weight naturally while using the toilet, making it very convenient for daily weight management. This allows the measurement unit to accurately and efficiently measure the user's body fat and weight, providing the data necessary for health management. Additionally, the measurement unit has a function to automatically record this measurement data and transmit it to a cloud server. This allows users to check their health data anytime, anywhere, enabling long-term health management.

[0031] The authentication unit performs personal authentication using a camera. For example, the authentication unit can authenticate users using facial recognition technology. Facial recognition technology identifies individuals by analyzing facial images captured by a camera and comparing them with pre-registered facial data. This technology offers high accuracy and rapid authentication, allowing users to authenticate naturally without any special actions. The authentication unit can also authenticate users using fingerprint authentication technology. Fingerprint authentication technology identifies individuals by analyzing the characteristic points of fingerprints and comparing them with pre-registered fingerprint data. This technology offers high security and reliability and is often used in situations where security is particularly important. Furthermore, the authentication unit can also authenticate users using iris recognition technology. Iris recognition technology identifies individuals by analyzing the pattern of the iris and comparing it with pre-registered iris data. This technology offers extremely high accuracy and can be combined with other authentication methods to further enhance security. For example, the authentication unit can use a camera installed in a restroom to perform facial recognition and identify users. This allows the authentication unit to protect users' personal information and ensure accurate management of health data. Furthermore, the authentication unit has a function to encrypt and store authentication data, preventing unauthorized access by third parties. This allows users to use the system with peace of mind.

[0032] The analysis unit analyzes the data collected by the measurement unit and the authentication unit. For example, the analysis unit performs statistical analysis of the data. Statistical analysis is a method of understanding data trends and distributions by calculating statistical indicators such as the mean and standard deviation based on the collected data. This allows for a quantitative evaluation of the user's health status. The analysis unit can also analyze data using machine learning algorithms. Machine learning algorithms are methods of making future predictions and detecting anomalies by learning from large amounts of data and finding patterns and regularities. For example, the analysis unit can learn the user's body fat and weight fluctuation data and predict future health risks. Furthermore, the analysis unit can also perform trend analysis of the data. Trend analysis is a method of understanding long-term trends and periodicities by analyzing data fluctuations over time. This allows for long-term monitoring of changes in the user's health status and early detection of anomalies. For example, the analysis unit analyzes fluctuations in body fat and weight to monitor the user's health status. This allows the analysis unit to analyze the user's health data from multiple perspectives and provide an accurate health assessment. Furthermore, the analysis unit includes a function to visualize the analysis results, making them intuitively understandable to the user. This allows users to easily grasp their own health status and take appropriate measures.

[0033] The advice unit provides health advice based on the analysis results obtained by the analysis unit. For example, the advice unit provides dietary guidance. Dietary guidance involves proposing a meal plan tailored to the user's health condition and goals, supporting a nutritionally balanced diet. For example, for a user with increased body fat, it would propose a meal plan that involves calorie restriction or reducing fat intake. The advice unit can also provide exercise guidance. Exercise guidance involves proposing an exercise plan tailored to the user's physical fitness and health condition, supporting appropriate exercise habits. For example, for a user with increased weight, it would propose an exercise plan that combines aerobic exercise and strength training. Furthermore, the advice unit can also offer suggestions for improving lifestyle habits. Suggestions for improving lifestyle habits support comprehensive health management, including the user's daily rhythm and stress management. For example, for a user who is experiencing persistent sleep deprivation, it would provide advice on improving sleep quality. In this way, the advice unit can provide specific and practical advice tailored to the user's health condition, supporting health improvement. Furthermore, the advice unit has a function to collect user feedback and continuously improve the accuracy and effectiveness of the advice provided. This allows the advisory unit to respond flexibly to user needs, enabling more effective health management.

[0034] The health management system includes a usage analysis unit that analyzes the frequency and interval of toilet use. The usage analysis unit can analyze the frequency and interval of toilet use. For example, the usage analysis unit can count the number of times the toilet is used. The usage analysis unit can also measure the time interval between toilet uses. Furthermore, the usage analysis unit can analyze toilet use patterns. For example, the usage analysis unit counts the number of times the toilet is used on a daily basis and analyzes the frequency of use. The usage analysis unit measures the time interval between toilet uses and analyzes the interval of use. The usage analysis unit analyzes toilet use patterns and detects changes in usage frequency and interval. As a result, changes in health status can be detected by analyzing the frequency and interval of toilet use. Some or all of the above processing in the usage analysis unit may be performed using AI, for example, or without AI. For example, the usage analysis unit can input data on the number of times the toilet is used and the interval of use into a generating AI and have the generating AI perform the analysis of usage frequency and interval.

[0035] The usage analysis unit can analyze the frequency and interval of toilet use and detect changes in health status. For example, the usage analysis unit can analyze increases or decreases in the number of times the toilet is used. It can also analyze changes in the interval of toilet use. Furthermore, it can analyze changes in toilet use patterns. For example, the usage analysis unit detects a change in health status if the number of times the toilet is used increases. The usage analysis unit detects a change in health status if the interval of toilet use becomes shorter. The usage analysis unit detects a change in health status if the toilet use pattern changes. In this way, changes in health status can be detected by analyzing the frequency and interval of toilet use. Some or all of the above processing in the usage analysis unit may be performed using AI, for example, or without AI. For example, the usage analysis unit can input data on the number of times the toilet is used and the interval of use into a generating AI and have the generating AI perform the detection of changes in health status.

[0036] The advice unit can provide advice on diet and exercise based on the analysis results. For example, the advice unit can provide advice on meal menus. It can also provide advice on the type and frequency of exercise. Furthermore, it can provide advice on improving lifestyle habits. For example, if body fat is increasing, the advice unit will advise reviewing meal menus. If lack of exercise is the cause, the advice unit will advise increasing the type and frequency of exercise. If lifestyle improvements are necessary, the advice unit will provide advice indicating specific areas for improvement. This enables the provision of appropriate diet and exercise advice based on the analysis results. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the analysis results into a generating AI and have the generating AI generate advice on diet and exercise.

[0037] The advice unit can provide advice on adjusting fluid intake or recommending a doctor's consultation based on the analysis results. For example, the advice unit can provide advice indicating a guideline for fluid intake. It can also provide advice on the timing of fluid intake. Furthermore, the advice unit can provide advice recommending a doctor's consultation if specific symptoms are observed. For example, the advice unit may advise increasing fluid intake if body fat is increasing. The advice unit may advise adjusting the timing of fluid intake if the interval between toilet visits is becoming shorter. The advice unit may advise seeking a doctor's consultation if specific symptoms are observed. This enables the provision of appropriate advice on adjusting fluid intake or recommending a doctor's consultation based on the analysis results. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the analysis results into a generating AI and have the generating AI generate advice recommending adjustment of fluid intake or a doctor's consultation.

[0038] The measurement unit can analyze the user's past measurement data and select the optimal measurement method. For example, the measurement unit can select the most accurate measurement method from the user's past measurement data to improve measurement accuracy. The measurement unit can also adjust the measurement frequency based on the user's past measurement data and perform measurements at appropriate times. Furthermore, the measurement unit can analyze the user's past measurement data and customize and individually optimize the measurement method. For example, the measurement unit can select the most accurate measurement method from the user's past measurement data to improve measurement accuracy. The measurement unit can adjust the measurement frequency based on the user's past measurement data and perform measurements at appropriate times. The measurement unit analyzes the user's past measurement data and customizes and individually optimizes the measurement method. As a result, by analyzing the user's past measurement data, the optimal measurement method can be selected and measurement accuracy can be improved. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's past measurement data into a generating AI and have the generating AI select the optimal measurement method.

[0039] The measurement unit can customize measurement items based on the user's current health status and lifestyle during measurement. For example, the measurement unit can add or remove necessary measurement items considering the user's current health status. It can also adjust measurement items based on the user's lifestyle to collect more appropriate data. Furthermore, the measurement unit can prioritize measurement items according to the user's health status and lifestyle, prioritizing the collection of important data. For example, the measurement unit can add or remove necessary measurement items considering the user's current health status. The measurement unit adjusts measurement items based on the user's lifestyle to collect more appropriate data. The measurement unit prioritizes measurement items according to the user's health status and lifestyle, prioritizing the collection of important data. This allows for the collection of more appropriate data by customizing measurement items based on the user's current health status and lifestyle. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input data on the user's health status and lifestyle into a generating AI and have the generating AI perform the customization of measurement items.

[0040] The measurement unit can prioritize acquiring highly relevant data by considering the user's geographical location information during measurement. For example, if the user is at high altitude, the measurement unit will perform measurements that take into account atmospheric pressure and oxygen concentration. Furthermore, if the user is in an urban area, the measurement unit can perform measurements that take into account the effects of environmental pollution. Additionally, if the user is traveling, the measurement unit can perform measurements that are appropriate to the local climate and environment. This allows the measurement unit to prioritize acquiring highly relevant data by considering the user's geographical location information. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant data.

[0041] The measurement unit can analyze the user's social media activity during measurement and acquire relevant health data. For example, the measurement unit can acquire information about diet and exercise from the user's social media posts and reflect it in the measurement data. The measurement unit can also estimate the stress level from the user's social media activity and reflect it in the measurement data. Furthermore, the measurement unit can analyze the sleep pattern from the user's social media activity and reflect it in the measurement data. For example, the measurement unit can acquire information about diet and exercise from the user's social media posts and reflect it in the measurement data. The measurement unit can estimate the stress level from the user's social media activity and reflect it in the measurement data. The measurement unit can analyze the sleep pattern from the user's social media activity and reflect it in the measurement data. In this way, by analyzing the user's social media activity, relevant health data can be acquired and reflected in the measurement data. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's social media activity data into a generating AI and have the generating AI acquire the relevant health data.

[0042] The authentication unit can analyze the user's past authentication history and select the optimal authentication method during authentication. For example, the authentication unit can select the authentication method with the highest success rate based on the user's past authentication history. The authentication unit can also customize and individually optimize authentication methods based on the user's past authentication history. Furthermore, the authentication unit can analyze the user's past authentication history and set priorities for authentication methods. For example, the authentication unit can select the authentication method with the highest success rate based on the user's past authentication history. The authentication unit can customize and individually optimize authentication methods based on the user's past authentication history. The authentication unit analyzes the user's past authentication history and sets priorities for authentication methods. This allows the authentication unit to select the optimal authentication method and improve authentication accuracy by analyzing the user's past authentication history. Some or all of the above processes in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input the user's past authentication history data into a generating AI and have the generating AI select the optimal authentication method.

[0043] The authentication unit can customize authentication items based on the user's current lifestyle and health status during authentication. For example, the authentication unit can consider the user's current health status and add or remove necessary authentication items. The authentication unit can also adjust authentication items based on the user's lifestyle to perform more appropriate authentication. Furthermore, the authentication unit can set priorities for authentication items according to the user's health and lifestyle, and authenticate important items first. For example, the authentication unit can consider the user's current health status and add or remove necessary authentication items. The authentication unit can adjust authentication items based on the user's lifestyle to perform more appropriate authentication. The authentication unit can set priorities for authentication items according to the user's health and lifestyle, and authenticate important items first. This allows for more appropriate authentication by customizing authentication items based on the user's current lifestyle and health status. Some or all of the above processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input data on the user's lifestyle and health status into a generating AI and have the generating AI perform the customization of authentication items.

[0044] The authentication unit can prioritize the acquisition of highly relevant authentication data by considering the user's geographical location information during authentication. For example, if the user is at high altitude, the authentication unit will perform authentication considering atmospheric pressure and oxygen concentration. Furthermore, if the user is in an urban area, the authentication unit can perform authentication considering the effects of environmental pollution. Additionally, if the user is traveling, the authentication unit can perform authentication according to the local climate and environment. This allows the authentication unit to prioritize the acquisition of highly relevant authentication data by considering the user's geographical location information. Some or all of the above processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant authentication data.

[0045] The authentication unit can analyze the user's social media activity during authentication and obtain relevant authentication data. For example, the authentication unit can obtain information about the user's lifestyle and health status from the user's social media posts and reflect it in the authentication data. The authentication unit can also estimate the user's stress level from the user's social media activity and reflect it in the authentication data. Furthermore, the authentication unit can analyze the user's sleep patterns from the user's social media activity and reflect it in the authentication data. For example, the authentication unit can obtain information about the user's lifestyle and health status from the user's social media posts and reflect it in the authentication data. The authentication unit can estimate the stress level from the user's social media activity and reflect it in the authentication data. The authentication unit can analyze the user's sleep patterns from the user's social media activity and reflect it in the authentication data. In this way, by analyzing the user's social media activity, relevant authentication data can be obtained and authentication accuracy can be improved. Some or all of the above processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant authentication data.

[0046] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can optimize the analysis algorithm based on past analysis data to improve accuracy. The analysis unit can also adjust the parameters of the analysis algorithm by referring to past analysis data. Furthermore, the analysis unit can analyze past analysis data to identify areas for improvement in the analysis algorithm. For example, the analysis unit optimizes the analysis algorithm based on past analysis data to improve accuracy. The analysis unit adjusts the parameters of the analysis algorithm by referring to past analysis data. The analysis unit analyzes past analysis data to identify areas for improvement in the analysis algorithm. In this way, by referring to past analysis data, the analysis algorithm can be optimized and accuracy improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0047] The analysis unit can customize analysis items based on the user's health status and lifestyle during analysis. For example, the analysis unit can add or remove necessary analysis items considering the user's health status. It can also adjust analysis items based on the user's lifestyle to provide more appropriate data. Furthermore, the analysis unit can set priorities for analysis items according to the user's health status and lifestyle, prioritizing the analysis of important data. For example, the analysis unit can add or remove necessary analysis items considering the user's health status. The analysis unit can adjust analysis items based on the user's lifestyle to provide more appropriate data. The analysis unit can set priorities for analysis items according to the user's health status and lifestyle, prioritizing the analysis of important data. This allows for the provision of more appropriate data by customizing analysis items based on the user's health status and lifestyle. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's health status and lifestyle into a generating AI and have the generating AI perform the customization of analysis items.

[0048] The analysis unit can prioritize the acquisition of highly relevant analysis data by considering the user's geographical location information during analysis. For example, if the user is at high altitude, the analysis unit will perform analysis that takes into account atmospheric pressure and oxygen concentration. Furthermore, if the user is in an urban area, the analysis unit can perform analysis that takes into account the effects of environmental pollution. Additionally, if the user is traveling, the analysis unit can perform analysis that adapts to the local climate and environment. This allows the analysis unit to prioritize the acquisition of highly relevant analysis data by considering the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant analysis data.

[0049] The analysis unit can analyze the user's social media activity and acquire relevant analytical data during analysis. For example, the analysis unit can acquire information on lifestyle habits and health status from the user's social media posts and reflect it in the analytical data. The analysis unit can also estimate the stress level from the user's social media activity and reflect it in the analytical data. Furthermore, the analysis unit can analyze the sleep pattern from the user's social media activity and reflect it in the analytical data. For example, the analysis unit can acquire information on lifestyle habits and health status from the user's social media posts and reflect it in the analytical data. The analysis unit can estimate the stress level from the user's social media activity and reflect it in the analytical data. The analysis unit can analyze the sleep pattern from the user's social media activity and reflect it in the analytical data. In this way, by analyzing the user's social media activity, relevant analytical data can be acquired and the accuracy of the analysis can be improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI acquire the relevant analytical data.

[0050] The advice unit can adjust the level of detail of its advice based on the importance of the analysis results. For example, the advice unit can provide detailed advice and specific action guidelines based on important analysis results. It can also provide concise advice and basic guidelines based on less important analysis results. Furthermore, the advice unit can adjust the level of detail of its advice in stages according to the importance of the analysis results to provide appropriate information. For example, the advice unit can provide detailed advice and specific action guidelines based on important analysis results. It can provide concise advice and basic guidelines based on less important analysis results. It can adjust the level of detail of its advice in stages according to the importance of the analysis results to provide appropriate information. In this way, by adjusting the level of detail of the advice based on the importance of the analysis results, appropriate information can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the importance data of the analysis results into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0051] The advice unit can customize the advice it provides based on the user's health condition and lifestyle. For example, the advice unit can provide advice on appropriate diet and exercise, taking into account the user's health condition. It can also provide actionable advice, suggesting specific areas for improvement based on the user's lifestyle. Furthermore, the advice unit can individually customize the advice based on the user's health condition and lifestyle to provide optimal guidance. For example, the advice unit can provide advice on appropriate diet and exercise, taking into account the user's health condition. The advice unit can provide actionable advice, suggesting specific areas for improvement based on the user's lifestyle. The advice unit can individually customize the advice based on the user's health condition and lifestyle to provide optimal guidance. This allows for the provision of more appropriate advice by customizing the advice based on the user's health condition and lifestyle. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input data on the user's health condition and lifestyle into a generating AI and have the generating AI customize the advice.

[0052] The advice unit can determine the priority of advice based on the timing of the analysis results submission. For example, if important analysis results are submitted, the advice unit will provide priority advice and respond quickly. Conversely, if less important analysis results are submitted, the advice unit may postpone providing advice. Furthermore, the advice unit can adjust the priority of advice in stages according to the timing of the analysis results submission, providing information at the appropriate time. For example, if important analysis results are submitted, the advice unit will provide priority advice and respond quickly. If less important analysis results are submitted, the advice unit will postpone providing advice. The advice unit can adjust the priority of advice in stages according to the timing of the analysis results submission, providing information at the appropriate time. This allows the advice unit to provide information at the appropriate time by determining the priority of advice based on the timing of the analysis results submission. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the data on the timing of the analysis result submission into a generating AI and have the generating AI determine the priority of advice.

[0053] The advice unit can adjust the advice content by referring to the user's relevant market data when providing advice. For example, the advice unit can refer to market data related to the user's health status and provide advice based on the latest information. The advice unit can also refer to market data related to the user's lifestyle and suggest specific areas for improvement. Furthermore, the advice unit can customize the advice content based on relevant market data according to the user's health status and lifestyle. For example, the advice unit can refer to market data related to the user's health status and provide advice based on the latest information. The advice unit can refer to market data related to the user's lifestyle and suggest specific areas for improvement. The advice unit customizes the advice content based on relevant market data according to the user's health status and lifestyle. This allows the advice unit to provide advice based on the latest information by referring to the user's relevant market data. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's relevant market data into a generating AI and have the generating AI adjust the advice content.

[0054] The usage analysis unit can optimize the analysis algorithm by referring to past usage data during usage analysis. For example, the usage analysis unit can optimize the analysis algorithm based on past usage data to improve accuracy. The usage analysis unit can also adjust the parameters of the analysis algorithm by referring to past usage data. Furthermore, the usage analysis unit can analyze past usage data to identify areas for improvement in the analysis algorithm. For example, the usage analysis unit optimizes the analysis algorithm based on past usage data to improve accuracy. The usage analysis unit adjusts the parameters of the analysis algorithm by referring to past usage data. The usage analysis unit analyzes past usage data to identify areas for improvement in the analysis algorithm. In this way, by referring to past usage data, the analysis algorithm can be optimized and accuracy improved. Some or all of the above processes in the usage analysis unit may be performed using AI, for example, or without AI. For example, the usage analysis unit can input past usage data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0055] The usage analysis unit can prioritize the acquisition of highly relevant usage data by considering the user's geographical location information during usage analysis. For example, if the user is at high altitude, the usage analysis unit will perform usage analysis that takes into account atmospheric pressure and oxygen concentration. Furthermore, if the user is in an urban area, the usage analysis unit can perform usage analysis that takes into account the effects of environmental pollution. Additionally, if the user is traveling, the usage analysis unit can perform usage analysis that adapts to the local climate and environment. This allows for the priority acquisition of highly relevant usage data by considering the user's geographical location information. Some or all of the above-described processes in the usage analysis unit may be performed using AI, for example, or without AI. For example, the usage analysis unit can input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant usage data.

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

[0057] A health management system can include a sleep analysis unit that acquires and analyzes the user's sleep data. The sleep analysis unit can, for example, monitor the user's sleep duration and quality. It can also analyze the user's sleep patterns and identify areas for improvement. Furthermore, based on the user's sleep data, the sleep analysis unit can provide appropriate sleep advice. For example, if the user's sleep duration is short, it might advise extending their sleep time. If the user's sleep quality is poor, it might suggest improvements to the sleep environment. The sleep analysis unit analyzes the user's sleep patterns and provides advice to maintain a proper sleep rhythm. This allows for the provision of more appropriate sleep advice by analyzing the user's sleep data.

[0058] A health management system can include a dietary analysis unit that acquires and analyzes the user's dietary data. The dietary analysis unit can, for example, monitor the user's diet and calorie intake. It can also analyze the user's eating patterns and identify areas for improvement. Furthermore, based on the user's dietary data, the dietary analysis unit can provide appropriate dietary advice. For example, if the user's calorie intake is high, it may advise calorie restriction. If the user's diet is unbalanced, it may suggest a balanced diet. The dietary analysis unit analyzes the user's eating patterns and provides advice to maintain a proper eating rhythm. In this way, by analyzing the user's dietary data, more appropriate dietary advice can be provided.

[0059] A health management system can include an exercise analysis unit that acquires and analyzes the user's exercise data. The exercise analysis unit can, for example, monitor the user's exercise volume and type. It can also analyze the user's exercise patterns and identify areas for improvement. Furthermore, based on the user's exercise data, the exercise analysis unit can provide appropriate exercise advice. For example, if the user's exercise volume is low, it will advise increasing it. If the user's exercise patterns are unbalanced, it will suggest a more balanced exercise routine. The exercise analysis unit analyzes the user's exercise patterns and provides advice to maintain an appropriate exercise rhythm. In this way, by analyzing the user's exercise data, more appropriate exercise advice can be provided.

[0060] A health management system can include a stress analysis unit that monitors and analyzes the user's stress level. The stress analysis unit can, for example, monitor the user's heart rate and blood pressure. It can also analyze the user's stress patterns and identify areas for stress reduction. Furthermore, based on the user's stress data, the stress analysis unit can provide appropriate stress management advice. For example, if the user's heart rate is high, it might advise on relaxation techniques. If the user's blood pressure is high, it might suggest ways to reduce stress. The stress analysis unit analyzes the user's stress patterns and provides advice to maintain an appropriate stress management rhythm. This allows for the provision of more appropriate stress management advice by analyzing the user's stress data.

[0061] A health management system can include a hydration analysis unit that acquires and analyzes the user's hydration data. The hydration analysis unit can, for example, monitor the user's hydration volume and timing. It can also analyze the user's hydration patterns and identify areas for improvement. Furthermore, based on the user's hydration data, the hydration analysis unit can provide appropriate hydration advice. For example, if the user's hydration is low, it might advise increasing their intake. If the user's hydration timing is irregular, it might suggest appropriate timing for hydration. The hydration analysis unit analyzes the user's hydration patterns and provides advice to maintain a proper hydration rhythm. This allows for the provision of more appropriate hydration advice by analyzing the user's hydration data.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The measurement unit measures body fat and weight. The measurement unit can measure body fat using methods such as bioimpedance or dual-energy X-ray absorptiometry. Alternatively, weight can be measured using a scale, such as a scale built into a toilet seat. Step 2: The authentication unit performs personal authentication using a camera. The authentication unit can authenticate users using, for example, facial recognition technology, fingerprint recognition technology, or iris recognition technology. For example, it can use a camera installed in a restroom to perform facial recognition and identify the user. Step 3: The analysis unit analyzes the data collected by the measurement unit and the authentication unit. The analysis unit can perform statistical analysis of the data, analysis using machine learning algorithms, and trend analysis of the data, for example. For example, it can analyze fluctuations in body fat and weight to monitor the user's health status. Step 4: The advice unit provides health advice based on the analysis results obtained by the analysis unit. The advice unit can, for example, provide dietary guidance, exercise guidance, and suggestions for improving lifestyle habits. For example, if body fat is increasing, it will provide advice on diet and exercise.

[0064] (Example of form 2) The health management system according to an embodiment of the present invention is a system that manages daily health data using a toilet. This health management system collects user health data using a body fat and weight measurement function and a personal authentication function using a camera installed in the toilet. Next, an AI analyzes the collected data and monitors the user's health status. Furthermore, the AI ​​provides health advice based on the analysis results. This mechanism improves the accuracy of health management and provides daily health advice. For example, the health management system measures the user's body fat and weight using the body fat and weight measurement function installed in the toilet. For example, simply sitting on the toilet seat automatically measures body fat and weight. This data becomes important information for understanding the user's health status. Next, the health management system identifies the user using a personal authentication function using a camera installed in the toilet. For example, the camera performs facial recognition when the user enters the toilet to identify the user. This ensures that the collected data is accurately linked to the individual. The collected data is analyzed by an AI. The AI ​​analyzes data such as fluctuations in body fat and weight, frequency of toilet use, and intervals of use to monitor the user's health status. For example, if body fat increases or the interval between toilet visits shortens, the AI ​​detects changes in health status based on this data. Furthermore, the AI ​​provides health advice based on the analysis results. For instance, if body fat increases, it provides advice on diet and exercise. If the interval between toilet visits shortens, it provides advice on adjusting fluid intake or recommending a doctor's visit. In this way, users can manage their health on a daily basis. This system improves the accuracy of health management and provides daily health advice. Users can understand their health status and receive appropriate advice simply by using the toilet. This is expected to help prevent lifestyle-related diseases, enable personalized health management, and contribute to maintaining good health. As a result, the health management system can efficiently collect and analyze users' health data and provide appropriate health advice.

[0065] The health management system according to this embodiment comprises a measurement unit, an authentication unit, an analysis unit, and an advice unit. The measurement unit measures body fat and weight. The measurement unit measures body fat using, for example, bioimpedance. The measurement unit can also measure body fat using dual-energy X-ray absorptiometry. Furthermore, the measurement unit can measure weight using a weighing scale. For example, the measurement unit measures weight using a weighing scale built into a toilet seat. The authentication unit performs personal authentication using a camera. The authentication unit authenticates the user using, for example, facial recognition technology. Furthermore, the authentication unit can authenticate the user using fingerprint authentication technology. Furthermore, the authentication unit can authenticate the user using iris authentication technology. For example, the authentication unit performs facial authentication using a camera installed in a toilet to identify the user. The analysis unit analyzes the data collected by the measurement unit and the authentication unit. The analysis unit performs, for example, statistical analysis of the data. Furthermore, the analysis unit can also analyze the data using machine learning algorithms. Furthermore, the analysis unit can also perform trend analysis of the data. For example, the analysis unit analyzes fluctuations in body fat and weight to monitor the user's health status. The advice unit provides health advice based on the analysis results obtained by the analysis unit. The advice unit can, for example, provide dietary guidance. The advice unit can also provide exercise guidance. Furthermore, the advice unit can also suggest improvements to lifestyle habits. For example, if body fat is increasing, the advice unit will provide advice regarding diet and exercise. As a result, the health management system according to this embodiment can efficiently collect and analyze the user's health data and provide appropriate health advice.

[0066] The measurement unit measures body fat and weight. For example, it can measure body fat using bioimpedance. Bioimpedance is a method of calculating body fat percentage by passing a weak electric current through the body and measuring its electrical resistance. This method is non-invasive and allows for quick measurements, making it suitable for daily health management. The measurement unit can also measure body fat using dual-energy X-ray absorptiometry. Dual-energy X-ray absorptiometry accurately measures body fat by irradiating the body with X-rays of different energies and utilizing the difference in their absorption rates. This method is highly accurate and widely used in medical institutions. Furthermore, the measurement unit can measure weight using a scale. For example, it can measure weight using a scale built into a toilet seat. A scale built into a toilet seat allows users to measure their weight naturally while using the toilet, making it very convenient for daily weight management. This allows the measurement unit to accurately and efficiently measure the user's body fat and weight, providing the data necessary for health management. Additionally, the measurement unit has a function to automatically record this measurement data and transmit it to a cloud server. This allows users to check their health data anytime, anywhere, enabling long-term health management.

[0067] The authentication unit performs personal authentication using a camera. For example, the authentication unit can authenticate users using facial recognition technology. Facial recognition technology identifies individuals by analyzing facial images captured by a camera and comparing them with pre-registered facial data. This technology offers high accuracy and rapid authentication, allowing users to authenticate naturally without any special actions. The authentication unit can also authenticate users using fingerprint authentication technology. Fingerprint authentication technology identifies individuals by analyzing the characteristic points of fingerprints and comparing them with pre-registered fingerprint data. This technology offers high security and reliability and is often used in situations where security is particularly important. Furthermore, the authentication unit can also authenticate users using iris recognition technology. Iris recognition technology identifies individuals by analyzing the pattern of the iris and comparing it with pre-registered iris data. This technology offers extremely high accuracy and can be combined with other authentication methods to further enhance security. For example, the authentication unit can use a camera installed in a restroom to perform facial recognition and identify users. This allows the authentication unit to protect users' personal information and ensure accurate management of health data. Furthermore, the authentication unit has a function to encrypt and store authentication data, preventing unauthorized access by third parties. This allows users to use the system with peace of mind.

[0068] The analysis unit analyzes the data collected by the measurement unit and the authentication unit. For example, the analysis unit performs statistical analysis of the data. Statistical analysis is a method of understanding data trends and distributions by calculating statistical indicators such as the mean and standard deviation based on the collected data. This allows for a quantitative evaluation of the user's health status. The analysis unit can also analyze data using machine learning algorithms. Machine learning algorithms are methods of making future predictions and detecting anomalies by learning from large amounts of data and finding patterns and regularities. For example, the analysis unit can learn the user's body fat and weight fluctuation data and predict future health risks. Furthermore, the analysis unit can also perform trend analysis of the data. Trend analysis is a method of understanding long-term trends and periodicities by analyzing data fluctuations over time. This allows for long-term monitoring of changes in the user's health status and early detection of anomalies. For example, the analysis unit analyzes fluctuations in body fat and weight to monitor the user's health status. This allows the analysis unit to analyze the user's health data from multiple perspectives and provide an accurate health assessment. Furthermore, the analysis unit includes a function to visualize the analysis results, making them intuitively understandable to the user. This allows users to easily grasp their own health status and take appropriate measures.

[0069] The advice unit provides health advice based on the analysis results obtained by the analysis unit. For example, the advice unit provides dietary guidance. Dietary guidance involves proposing a meal plan tailored to the user's health condition and goals, supporting a nutritionally balanced diet. For example, for a user with increased body fat, it would propose a meal plan that involves calorie restriction or reducing fat intake. The advice unit can also provide exercise guidance. Exercise guidance involves proposing an exercise plan tailored to the user's physical fitness and health condition, supporting appropriate exercise habits. For example, for a user with increased weight, it would propose an exercise plan that combines aerobic exercise and strength training. Furthermore, the advice unit can also offer suggestions for improving lifestyle habits. Suggestions for improving lifestyle habits support comprehensive health management, including the user's daily rhythm and stress management. For example, for a user who is experiencing persistent sleep deprivation, it would provide advice on improving sleep quality. In this way, the advice unit can provide specific and practical advice tailored to the user's health condition, supporting health improvement. Furthermore, the advice unit has a function to collect user feedback and continuously improve the accuracy and effectiveness of the advice provided. This allows the advisory unit to respond flexibly to user needs, enabling more effective health management.

[0070] The health management system includes a usage analysis unit that analyzes the frequency and interval of toilet use. The usage analysis unit can analyze the frequency and interval of toilet use. For example, the usage analysis unit can count the number of times the toilet is used. The usage analysis unit can also measure the time interval between toilet uses. Furthermore, the usage analysis unit can analyze toilet use patterns. For example, the usage analysis unit counts the number of times the toilet is used on a daily basis and analyzes the frequency of use. The usage analysis unit measures the time interval between toilet uses and analyzes the interval of use. The usage analysis unit analyzes toilet use patterns and detects changes in usage frequency and interval. As a result, changes in health status can be detected by analyzing the frequency and interval of toilet use. Some or all of the above processing in the usage analysis unit may be performed using AI, for example, or without AI. For example, the usage analysis unit can input data on the number of times the toilet is used and the interval of use into a generating AI and have the generating AI perform the analysis of usage frequency and interval.

[0071] The usage analysis unit can analyze the frequency and interval of toilet use and detect changes in health status. For example, the usage analysis unit can analyze increases or decreases in the number of times the toilet is used. It can also analyze changes in the interval of toilet use. Furthermore, it can analyze changes in toilet use patterns. For example, the usage analysis unit detects a change in health status if the number of times the toilet is used increases. The usage analysis unit detects a change in health status if the interval of toilet use becomes shorter. The usage analysis unit detects a change in health status if the toilet use pattern changes. In this way, changes in health status can be detected by analyzing the frequency and interval of toilet use. Some or all of the above processing in the usage analysis unit may be performed using AI, for example, or without AI. For example, the usage analysis unit can input data on the number of times the toilet is used and the interval of use into a generating AI and have the generating AI perform the detection of changes in health status.

[0072] The advice unit can provide advice on diet and exercise based on the analysis results. For example, the advice unit can provide advice on meal menus. It can also provide advice on the type and frequency of exercise. Furthermore, it can provide advice on improving lifestyle habits. For example, if body fat is increasing, the advice unit will advise reviewing meal menus. If lack of exercise is the cause, the advice unit will advise increasing the type and frequency of exercise. If lifestyle improvements are necessary, the advice unit will provide advice indicating specific areas for improvement. This enables the provision of appropriate diet and exercise advice based on the analysis results. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the analysis results into a generating AI and have the generating AI generate advice on diet and exercise.

[0073] The advice unit can provide advice on adjusting fluid intake or recommending a doctor's consultation based on the analysis results. For example, the advice unit can provide advice indicating a guideline for fluid intake. It can also provide advice on the timing of fluid intake. Furthermore, the advice unit can provide advice recommending a doctor's consultation if specific symptoms are observed. For example, the advice unit may advise increasing fluid intake if body fat is increasing. The advice unit may advise adjusting the timing of fluid intake if the interval between toilet visits is becoming shorter. The advice unit may advise seeking a doctor's consultation if specific symptoms are observed. This enables the provision of appropriate advice on adjusting fluid intake or recommending a doctor's consultation based on the analysis results. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the analysis results into a generating AI and have the generating AI generate advice recommending adjustment of fluid intake or a doctor's consultation.

[0074] The measurement unit can estimate the user's emotions and adjust the timing of the measurement based on the estimated emotions. For example, if the user is relaxed, the measurement unit can select a suitable time to perform the measurement and conduct it in a low-stress state. If the user is stressed, the measurement unit can temporarily postpone the measurement and conduct it again when the user is relaxed. Furthermore, if the user is in a hurry, the measurement unit can conduct the measurement quickly to obtain results in a short time. For example, if the measurement unit is relaxed, it can select a suitable time to perform the measurement and conduct it in a low-stress state. If the user is stressed, the measurement unit can temporarily postpone the measurement and conduct it again when the user is relaxed. If the user is in a hurry, the measurement unit can conduct the measurement quickly to obtain results in a short time. In this way, by adjusting the timing of the measurement according to the user's emotions, measurements can be taken at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input user emotion data into a generating AI and have the generating AI adjust the timing of the measurements.

[0075] The measurement unit can analyze the user's past measurement data and select the optimal measurement method. For example, the measurement unit can select the most accurate measurement method from the user's past measurement data to improve measurement accuracy. The measurement unit can also adjust the measurement frequency based on the user's past measurement data and perform measurements at appropriate times. Furthermore, the measurement unit can analyze the user's past measurement data and customize and individually optimize the measurement method. For example, the measurement unit can select the most accurate measurement method from the user's past measurement data to improve measurement accuracy. The measurement unit can adjust the measurement frequency based on the user's past measurement data and perform measurements at appropriate times. The measurement unit analyzes the user's past measurement data and customizes and individually optimizes the measurement method. As a result, by analyzing the user's past measurement data, the optimal measurement method can be selected and measurement accuracy can be improved. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's past measurement data into a generating AI and have the generating AI select the optimal measurement method.

[0076] The measurement unit can customize measurement items based on the user's current health status and lifestyle during measurement. For example, the measurement unit can add or remove necessary measurement items considering the user's current health status. It can also adjust measurement items based on the user's lifestyle to collect more appropriate data. Furthermore, the measurement unit can prioritize measurement items according to the user's health status and lifestyle, prioritizing the collection of important data. For example, the measurement unit can add or remove necessary measurement items considering the user's current health status. The measurement unit adjusts measurement items based on the user's lifestyle to collect more appropriate data. The measurement unit prioritizes measurement items according to the user's health status and lifestyle, prioritizing the collection of important data. This allows for the collection of more appropriate data by customizing measurement items based on the user's current health status and lifestyle. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input data on the user's health status and lifestyle into a generating AI and have the generating AI perform the customization of measurement items.

[0077] The measurement unit can estimate the user's emotions and adjust the display method of the measurement results based on the estimated emotions. For example, if the user is relaxed, the measurement unit can display detailed measurement results in an easy-to-understand format. If the user is stressed, the measurement unit can display concise and to-the-point measurement results to reduce the burden. Furthermore, if the user is in a hurry, the measurement unit can display results quickly and provide them in a format that can be understood in a short time. For example, if the user is relaxed, the measurement unit can display detailed measurement results in an easy-to-understand format. If the user is stressed, the measurement unit can display concise and to-the-point measurement results to reduce the burden. If the user is in a hurry, the measurement unit can display results quickly and provide them in a format that can be understood in a short time. In this way, by adjusting the display method of the measurement results according to the user's emotions, the results can be provided in a more easily understandable format. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input user emotion data into a generating AI and have the generating AI adjust the display method of the measurement results.

[0078] The measurement unit can prioritize acquiring highly relevant data by considering the user's geographical location information during measurement. For example, if the user is at high altitude, the measurement unit will perform measurements that take into account atmospheric pressure and oxygen concentration. Furthermore, if the user is in an urban area, the measurement unit can perform measurements that take into account the effects of environmental pollution. Additionally, if the user is traveling, the measurement unit can perform measurements that are appropriate to the local climate and environment. This allows the measurement unit to prioritize acquiring highly relevant data by considering the user's geographical location information. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant data.

[0079] The measurement unit can analyze the user's social media activity during measurement and acquire relevant health data. For example, the measurement unit can acquire information about diet and exercise from the user's social media posts and reflect it in the measurement data. The measurement unit can also estimate the stress level from the user's social media activity and reflect it in the measurement data. Furthermore, the measurement unit can analyze the sleep pattern from the user's social media activity and reflect it in the measurement data. For example, the measurement unit can acquire information about diet and exercise from the user's social media posts and reflect it in the measurement data. The measurement unit can estimate the stress level from the user's social media activity and reflect it in the measurement data. The measurement unit can analyze the sleep pattern from the user's social media activity and reflect it in the measurement data. In this way, by analyzing the user's social media activity, relevant health data can be acquired and reflected in the measurement data. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's social media activity data into a generating AI and have the generating AI acquire the relevant health data.

[0080] The authentication unit can estimate the user's emotions and adjust the authentication method based on the estimated emotions. For example, if the user is relaxed, the authentication unit can perform smooth authentication using facial recognition. If the user is stressed, the authentication unit can also perform rapid authentication using fingerprint recognition. Furthermore, if the user is in a hurry, the authentication unit can also perform rapid authentication using voice recognition. For example, if the user is relaxed, the authentication unit can perform smooth authentication using facial recognition. If the user is stressed, the authentication unit can perform rapid authentication using fingerprint recognition. If the user is in a hurry, the authentication unit can perform rapid authentication using voice recognition. This allows for more appropriate authentication by adjusting the authentication method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input user emotion data into a generating AI and have the generating AI adjust the authentication method.

[0081] The authentication unit can analyze the user's past authentication history and select the optimal authentication method during authentication. For example, the authentication unit can select the authentication method with the highest success rate based on the user's past authentication history. The authentication unit can also customize and individually optimize authentication methods based on the user's past authentication history. Furthermore, the authentication unit can analyze the user's past authentication history and set priorities for authentication methods. For example, the authentication unit can select the authentication method with the highest success rate based on the user's past authentication history. The authentication unit can customize and individually optimize authentication methods based on the user's past authentication history. The authentication unit analyzes the user's past authentication history and sets priorities for authentication methods. This allows the authentication unit to select the optimal authentication method and improve authentication accuracy by analyzing the user's past authentication history. Some or all of the above processes in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input the user's past authentication history data into a generating AI and have the generating AI select the optimal authentication method.

[0082] The authentication unit can customize authentication items based on the user's current lifestyle and health status during authentication. For example, the authentication unit can consider the user's current health status and add or remove necessary authentication items. The authentication unit can also adjust authentication items based on the user's lifestyle to perform more appropriate authentication. Furthermore, the authentication unit can set priorities for authentication items according to the user's health and lifestyle, and authenticate important items first. For example, the authentication unit can consider the user's current health status and add or remove necessary authentication items. The authentication unit can adjust authentication items based on the user's lifestyle to perform more appropriate authentication. The authentication unit can set priorities for authentication items according to the user's health and lifestyle, and authenticate important items first. This allows for more appropriate authentication by customizing authentication items based on the user's current lifestyle and health status. Some or all of the above processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input data on the user's lifestyle and health status into a generating AI and have the generating AI perform the customization of authentication items.

[0083] The authentication unit can estimate the user's emotions and adjust how the authentication results are displayed based on the estimated emotions. For example, if the user is relaxed, the authentication unit can display detailed authentication results in an easy-to-understand format. If the user is stressed, the authentication unit can display concise and to-the-point authentication results to reduce the burden. Furthermore, if the user is in a hurry, the authentication unit can display results quickly and provide them in a format that can be understood in a short time. For example, if the user is relaxed, the authentication unit can display detailed authentication results in an easy-to-understand format. If the user is stressed, the authentication unit can display concise and to-the-point authentication results to reduce the burden. If the user is in a hurry, the authentication unit can display results quickly and provide them in a format that can be understood in a short time. This allows the system to provide results in a more easily understandable format by adjusting how the authentication results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input user emotion data into a generating AI and have the generating AI adjust how the authentication results are displayed.

[0084] The authentication unit can prioritize the acquisition of highly relevant authentication data by considering the user's geographical location information during authentication. For example, if the user is at high altitude, the authentication unit will perform authentication considering atmospheric pressure and oxygen concentration. Furthermore, if the user is in an urban area, the authentication unit can perform authentication considering the effects of environmental pollution. Additionally, if the user is traveling, the authentication unit can perform authentication according to the local climate and environment. This allows the authentication unit to prioritize the acquisition of highly relevant authentication data by considering the user's geographical location information. Some or all of the above processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant authentication data.

[0085] The authentication unit can analyze the user's social media activity during authentication and obtain relevant authentication data. For example, the authentication unit can obtain information about the user's lifestyle and health status from the user's social media posts and reflect it in the authentication data. The authentication unit can also estimate the user's stress level from the user's social media activity and reflect it in the authentication data. Furthermore, the authentication unit can analyze the user's sleep patterns from the user's social media activity and reflect it in the authentication data. For example, the authentication unit can obtain information about the user's lifestyle and health status from the user's social media posts and reflect it in the authentication data. The authentication unit can estimate the stress level from the user's social media activity and reflect it in the authentication data. The authentication unit can analyze the user's sleep patterns from the user's social media activity and reflect it in the authentication data. In this way, by analyzing the user's social media activity, relevant authentication data can be obtained and authentication accuracy can be improved. Some or all of the above processing in the authentication unit may be performed using AI, for example, or without AI. For example, the authentication unit can input the user's social media activity data into a generating AI and have the generating AI perform the acquisition of relevant authentication data.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. If the user is stressed, the analysis unit can also perform a concise analysis and provide results quickly. Furthermore, if the user is in a hurry, the analysis unit can perform a rapid analysis and provide results in a short time. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide highly accurate results. If the user is stressed, the analysis unit can perform a concise analysis and provide results quickly. If the user is in a hurry, the analysis unit can perform a rapid analysis and provide results in a short time. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the analysis method.

[0087] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can optimize the analysis algorithm based on past analysis data to improve accuracy. The analysis unit can also adjust the parameters of the analysis algorithm by referring to past analysis data. Furthermore, the analysis unit can analyze past analysis data to identify areas for improvement in the analysis algorithm. For example, the analysis unit optimizes the analysis algorithm based on past analysis data to improve accuracy. The analysis unit adjusts the parameters of the analysis algorithm by referring to past analysis data. The analysis unit analyzes past analysis data to identify areas for improvement in the analysis algorithm. In this way, by referring to past analysis data, the analysis algorithm can be optimized and accuracy improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0088] The analysis unit can customize analysis items based on the user's health status and lifestyle during analysis. For example, the analysis unit can add or remove necessary analysis items considering the user's health status. It can also adjust analysis items based on the user's lifestyle to provide more appropriate data. Furthermore, the analysis unit can set priorities for analysis items according to the user's health status and lifestyle, prioritizing the analysis of important data. For example, the analysis unit can add or remove necessary analysis items considering the user's health status. The analysis unit can adjust analysis items based on the user's lifestyle to provide more appropriate data. The analysis unit can set priorities for analysis items according to the user's health status and lifestyle, prioritizing the analysis of important data. This allows for the provision of more appropriate data by customizing analysis items based on the user's health status and lifestyle. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's health status and lifestyle into a generating AI and have the generating AI perform the customization of analysis items.

[0089] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results in an easy-to-understand format. If the user is stressed, the analysis unit can display concise and to-the-point analysis results to reduce the burden. Furthermore, if the user is in a hurry, the analysis unit can display results quickly and provide them in a format that can be understood in a short time. For example, if the user is relaxed, the analysis unit can display detailed analysis results in an easy-to-understand format. If the user is stressed, the analysis unit can display concise and to-the-point analysis results to reduce the burden. If the user is in a hurry, the analysis unit can display results quickly and provide them in a format that can be understood in a short time. In this way, by adjusting how the analysis results are displayed according to the user's emotions, the results can be provided in a more easily understandable format. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust how the analysis results are displayed.

[0090] The analysis unit can prioritize the acquisition of highly relevant analysis data by considering the user's geographical location information during analysis. For example, if the user is at high altitude, the analysis unit will perform analysis that takes into account atmospheric pressure and oxygen concentration. Furthermore, if the user is in an urban area, the analysis unit can perform analysis that takes into account the effects of environmental pollution. Additionally, if the user is traveling, the analysis unit can perform analysis that adapts to the local climate and environment. This allows the analysis unit to prioritize the acquisition of highly relevant analysis data by considering the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant analysis data.

[0091] The analysis unit can analyze the user's social media activity and acquire relevant analytical data during analysis. For example, the analysis unit can acquire information on lifestyle habits and health status from the user's social media posts and reflect it in the analytical data. The analysis unit can also estimate the stress level from the user's social media activity and reflect it in the analytical data. Furthermore, the analysis unit can analyze the sleep pattern from the user's social media activity and reflect it in the analytical data. For example, the analysis unit can acquire information on lifestyle habits and health status from the user's social media posts and reflect it in the analytical data. The analysis unit can estimate the stress level from the user's social media activity and reflect it in the analytical data. The analysis unit can analyze the sleep pattern from the user's social media activity and reflect it in the analytical data. In this way, by analyzing the user's social media activity, relevant analytical data can be acquired and the accuracy of the analysis can be improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI acquire the relevant analytical data.

[0092] The advice unit can estimate the user's emotions and adjust the way it presents advice based on those emotions. For example, if the user is relaxed, the advice unit can provide detailed advice in an easy-to-understand format. If the user is stressed, the advice unit can provide concise and to-the-point advice to alleviate their burden. Furthermore, if the user is in a hurry, the advice unit can provide quick advice in a format that can be understood in a short amount of time. For example, if the user is relaxed, the advice unit can provide detailed advice in an easy-to-understand format. If the user is stressed, the advice unit can provide concise and to-the-point advice to alleviate their burden. If the user is in a hurry, the advice unit can provide quick advice in a format that can be understood in a short amount of time. This allows the advice unit to provide advice in a more easily understandable format by adjusting the way it presents advice according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input user emotion data into a generating AI and have the generating AI adjust the way the advice is expressed.

[0093] The advice unit can adjust the level of detail of its advice based on the importance of the analysis results. For example, the advice unit can provide detailed advice and specific action guidelines based on important analysis results. It can also provide concise advice and basic guidelines based on less important analysis results. Furthermore, the advice unit can adjust the level of detail of its advice in stages according to the importance of the analysis results to provide appropriate information. For example, the advice unit can provide detailed advice and specific action guidelines based on important analysis results. It can provide concise advice and basic guidelines based on less important analysis results. It can adjust the level of detail of its advice in stages according to the importance of the analysis results to provide appropriate information. In this way, by adjusting the level of detail of the advice based on the importance of the analysis results, appropriate information can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the importance data of the analysis results into a generating AI and have the generating AI perform the adjustment of the level of detail of the advice.

[0094] The advice unit can customize the advice it provides based on the user's health condition and lifestyle. For example, the advice unit can provide advice on appropriate diet and exercise, taking into account the user's health condition. It can also provide actionable advice, suggesting specific areas for improvement based on the user's lifestyle. Furthermore, the advice unit can individually customize the advice based on the user's health condition and lifestyle to provide optimal guidance. For example, the advice unit can provide advice on appropriate diet and exercise, taking into account the user's health condition. The advice unit can provide actionable advice, suggesting specific areas for improvement based on the user's lifestyle. The advice unit can individually customize the advice based on the user's health condition and lifestyle to provide optimal guidance. This allows for the provision of more appropriate advice by customizing the advice based on the user's health condition and lifestyle. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input data on the user's health condition and lifestyle into a generating AI and have the generating AI customize the advice.

[0095] The advice section can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is relaxed, the advice section will provide detailed advice in an easy-to-understand format. If the user is stressed, the advice section can provide concise and to-the-point advice to alleviate the burden. Furthermore, if the user is in a hurry, the advice section can provide quick advice in a format that can be understood in a short amount of time. For example, if the user is relaxed, the advice section will provide detailed advice in an easy-to-understand format. If the user is stressed, the advice section will provide concise and to-the-point advice to alleviate the burden. If the user is in a hurry, the advice section will provide quick advice in a format that can be understood in a short amount of time. This allows the advice section to provide advice in a more easily understandable format by adjusting the length of the advice according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input user emotion data into a generating AI and have the generating AI adjust the length of the advice.

[0096] The advice unit can determine the priority of advice based on the timing of the analysis results submission. For example, if important analysis results are submitted, the advice unit will provide priority advice and respond quickly. Conversely, if less important analysis results are submitted, the advice unit may postpone providing advice. Furthermore, the advice unit can adjust the priority of advice in stages according to the timing of the analysis results submission, providing information at the appropriate time. For example, if important analysis results are submitted, the advice unit will provide priority advice and respond quickly. If less important analysis results are submitted, the advice unit will postpone providing advice. The advice unit can adjust the priority of advice in stages according to the timing of the analysis results submission, providing information at the appropriate time. This allows the advice unit to provide information at the appropriate time by determining the priority of advice based on the timing of the analysis results submission. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the data on the timing of the analysis result submission into a generating AI and have the generating AI determine the priority of advice.

[0097] The advice unit can adjust the advice content by referring to the user's relevant market data when providing advice. For example, the advice unit can refer to market data related to the user's health status and provide advice based on the latest information. The advice unit can also refer to market data related to the user's lifestyle and suggest specific areas for improvement. Furthermore, the advice unit can customize the advice content based on relevant market data according to the user's health status and lifestyle. For example, the advice unit can refer to market data related to the user's health status and provide advice based on the latest information. The advice unit can refer to market data related to the user's lifestyle and suggest specific areas for improvement. The advice unit customizes the advice content based on relevant market data according to the user's health status and lifestyle. This allows the advice unit to provide advice based on the latest information by referring to the user's relevant market data. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's relevant market data into a generating AI and have the generating AI adjust the advice content.

[0098] The usage analysis unit can estimate the user's emotions and adjust the usage analysis method based on the estimated emotions. For example, if the user is relaxed, the usage analysis unit can perform a detailed usage analysis and provide highly accurate results. If the user is stressed, the usage analysis unit can perform a concise usage analysis and provide results quickly. Furthermore, if the user is in a hurry, the usage analysis unit can perform a rapid usage analysis and provide results in a short time. For example, if the user is relaxed, the usage analysis unit can perform a detailed usage analysis and provide highly accurate results. If the user is stressed, the usage analysis unit can perform a concise usage analysis and provide results quickly. If the user is in a hurry, the usage analysis unit can perform a rapid usage analysis and provide results in a short time. In this way, by adjusting the usage analysis method according to the user's emotions, more appropriate analysis can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the usage analysis unit may be performed using AI, for example, or without AI. For example, the usage analysis unit can input user emotion data into a generating AI and have the generating AI adjust the usage analysis method.

[0099] The usage analysis unit can optimize the analysis algorithm by referring to past usage data during usage analysis. For example, the usage analysis unit can optimize the analysis algorithm based on past usage data to improve accuracy. The usage analysis unit can also adjust the parameters of the analysis algorithm by referring to past usage data. Furthermore, the usage analysis unit can analyze past usage data to identify areas for improvement in the analysis algorithm. For example, the usage analysis unit optimizes the analysis algorithm based on past usage data to improve accuracy. The usage analysis unit adjusts the parameters of the analysis algorithm by referring to past usage data. The usage analysis unit analyzes past usage data to identify areas for improvement in the analysis algorithm. In this way, by referring to past usage data, the analysis algorithm can be optimized and accuracy improved. Some or all of the above processes in the usage analysis unit may be performed using AI, for example, or without AI. For example, the usage analysis unit can input past usage data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0100] The user analysis unit can estimate the user's emotions and adjust the display method of the user analysis results based on the estimated emotions. For example, if the user is relaxed, the user analysis unit can display detailed user analysis results in an easy-to-understand format. If the user is stressed, the user analysis unit can display concise and to-the-point user analysis results to reduce the burden. Furthermore, if the user is in a hurry, the user analysis unit can display results quickly and provide them in a format that can be understood in a short time. This allows for the display method of the user analysis results to be adjusted according to the user's emotions, providing results in a more easily understandable format. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the usage analysis unit may be performed using AI, or not using AI. For example, the usage analysis unit can input user emotion data into the generating AI and have the generating AI adjust how the usage analysis results are displayed.

[0101] The usage analysis unit can prioritize the acquisition of highly relevant usage data by considering the user's geographical location information during usage analysis. For example, if the user is at high altitude, the usage analysis unit will perform usage analysis that takes into account atmospheric pressure and oxygen concentration. Furthermore, if the user is in an urban area, the usage analysis unit can perform usage analysis that takes into account the effects of environmental pollution. Additionally, if the user is traveling, the usage analysis unit can perform usage analysis that adapts to the local climate and environment. This allows for the priority acquisition of highly relevant usage data by considering the user's geographical location information. Some or all of the above-described processes in the usage analysis unit may be performed using AI, for example, or without AI. For example, the usage analysis unit can input the user's geographical location information into a generating AI and have the generating AI acquire highly relevant usage data.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] A health management system can include a sleep analysis unit that acquires and analyzes the user's sleep data. The sleep analysis unit can, for example, monitor the user's sleep duration and quality. It can also analyze the user's sleep patterns and identify areas for improvement. Furthermore, based on the user's sleep data, the sleep analysis unit can provide appropriate sleep advice. For example, if the user's sleep duration is short, it might advise extending their sleep time. If the user's sleep quality is poor, it might suggest improvements to the sleep environment. The sleep analysis unit analyzes the user's sleep patterns and provides advice to maintain a proper sleep rhythm. This allows for the provision of more appropriate sleep advice by analyzing the user's sleep data.

[0104] A health management system can include a dietary analysis unit that acquires and analyzes the user's dietary data. The dietary analysis unit can, for example, monitor the user's diet and calorie intake. It can also analyze the user's eating patterns and identify areas for improvement. Furthermore, based on the user's dietary data, the dietary analysis unit can provide appropriate dietary advice. For example, if the user's calorie intake is high, it may advise calorie restriction. If the user's diet is unbalanced, it may suggest a balanced diet. The dietary analysis unit analyzes the user's eating patterns and provides advice to maintain a proper eating rhythm. In this way, by analyzing the user's dietary data, more appropriate dietary advice can be provided.

[0105] A health management system can include an exercise analysis unit that acquires and analyzes the user's exercise data. The exercise analysis unit can, for example, monitor the user's exercise volume and type. It can also analyze the user's exercise patterns and identify areas for improvement. Furthermore, based on the user's exercise data, the exercise analysis unit can provide appropriate exercise advice. For example, if the user's exercise volume is low, it will advise increasing it. If the user's exercise patterns are unbalanced, it will suggest a more balanced exercise routine. The exercise analysis unit analyzes the user's exercise patterns and provides advice to maintain an appropriate exercise rhythm. In this way, by analyzing the user's exercise data, more appropriate exercise advice can be provided.

[0106] A health management system can include a stress analysis unit that monitors and analyzes the user's stress level. The stress analysis unit can, for example, monitor the user's heart rate and blood pressure. It can also analyze the user's stress patterns and identify areas for stress reduction. Furthermore, based on the user's stress data, the stress analysis unit can provide appropriate stress management advice. For example, if the user's heart rate is high, it might advise on relaxation techniques. If the user's blood pressure is high, it might suggest ways to reduce stress. The stress analysis unit analyzes the user's stress patterns and provides advice to maintain an appropriate stress management rhythm. This allows for the provision of more appropriate stress management advice by analyzing the user's stress data.

[0107] A health management system can include a hydration analysis unit that acquires and analyzes the user's hydration data. The hydration analysis unit can, for example, monitor the user's hydration volume and timing. It can also analyze the user's hydration patterns and identify areas for improvement. Furthermore, based on the user's hydration data, the hydration analysis unit can provide appropriate hydration advice. For example, if the user's hydration is low, it might advise increasing their intake. If the user's hydration timing is irregular, it might suggest appropriate timing for hydration. The hydration analysis unit analyzes the user's hydration patterns and provides advice to maintain a proper hydration rhythm. This allows for the provision of more appropriate hydration advice by analyzing the user's hydration data.

[0108] A health management system can estimate a user's emotions and adjust the content of health advice based on those emotions. For example, if a user is relaxed, it can provide detailed health advice in an easy-to-understand format. If a user is stressed, it can provide concise and to-the-point health advice to reduce their burden. Furthermore, if a user is in a hurry, it can provide quick health advice in a format that can be understood in a short amount of time. In this way, by adjusting the content of health advice according to the user's emotions, it is possible to provide advice in a more easily understandable format.

[0109] A health management system can estimate a user's emotions and adjust how health data is displayed based on those emotions. For example, if a user is relaxed, detailed health data can be displayed in an easy-to-understand format. If a user is stressed, concise and to-the-point health data can be displayed to reduce their burden. Furthermore, if a user is in a hurry, health data can be displayed quickly and in a format that can be understood in a short amount of time. In this way, by adjusting how health data is displayed according to the user's emotions, data can be provided in a more easily understandable format.

[0110] A health management system can estimate a user's emotions and adjust the timing of health data collection based on those emotions. For example, if a user is relaxed, the system can select a time to collect data in a low-stress state. If a user is stressed, data collection can be temporarily postponed and collected again when the user is relaxed. Furthermore, if a user is in a hurry, data collection can be expedited to obtain results quickly. By adjusting the timing of health data collection according to the user's emotions, data can be collected at a more appropriate time.

[0111] A health management system can estimate a user's emotions and prioritize health advice based on those emotions. For example, if a user is relaxed, it can prioritize important health advice and provide detailed explanations. If a user is stressed, it can prioritize concise and to-the-point health advice to reduce their burden. Furthermore, if a user is in a hurry, it can quickly provide important health advice and explain it in a format that can be understood in a short amount of time. In this way, by prioritizing health advice according to the user's emotions, advice can be provided at a more appropriate time.

[0112] A health management system can estimate a user's emotions and adjust the analysis of health data based on those emotions. For example, if a user is relaxed, it can perform a detailed analysis and provide highly accurate results. If a user is stressed, it can perform a concise analysis and provide results quickly. Furthermore, if a user is in a hurry, it can perform a rapid analysis and provide results in a short time. In this way, by adjusting the analysis of health data according to the user's emotions, more appropriate analysis can be performed.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The measurement unit measures body fat and weight. The measurement unit can measure body fat using methods such as bioimpedance or dual-energy X-ray absorptiometry. Alternatively, weight can be measured using a scale, such as a scale built into a toilet seat. Step 2: The authentication unit performs personal authentication using a camera. The authentication unit can authenticate users using, for example, facial recognition technology, fingerprint recognition technology, or iris recognition technology. For example, it can use a camera installed in a restroom to perform facial recognition and identify the user. Step 3: The analysis unit analyzes the data collected by the measurement unit and the authentication unit. The analysis unit can perform statistical analysis of the data, analysis using machine learning algorithms, and trend analysis of the data, for example. For example, it can analyze fluctuations in body fat and weight to monitor the user's health status. Step 4: The advice unit provides health advice based on the analysis results obtained by the analysis unit. The advice unit can, for example, provide dietary guidance, exercise guidance, and suggestions for improving lifestyle habits. For example, if body fat is increasing, it will provide advice on diet and exercise.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the measurement unit, authentication unit, analysis unit, advice unit, and usage analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the measurement unit is implemented by a function that measures body fat using a scale or bioimpedance method built into the toilet seat of the smart device 14. The authentication unit performs facial recognition using the camera of the smart device 14 to identify the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides health advice based on the analysis results. The usage analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the frequency and interval of toilet use. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the measurement unit, authentication unit, analysis unit, advice unit, and usage analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the measurement unit is implemented by a function that measures body fat using a scale or bioimpedance method built into the toilet seat of the smart glasses 214. The authentication unit performs facial recognition using the camera of the smart glasses 214 to identify the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides health advice based on the analysis results. The usage analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the frequency and interval of toilet use. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the measurement unit, authentication unit, analysis unit, advice unit, and usage analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the measurement unit is implemented by a function that measures body fat using a scale or bioimpedance method built into the toilet seat of the headset terminal 314. The authentication unit performs facial recognition using the camera of the headset terminal 314 to identify the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides health advice based on the analysis results. The usage analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the frequency and interval of toilet use. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the measurement unit, authentication unit, analysis unit, advice unit, and usage analysis unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the measurement unit is implemented by a function that measures body fat using a scale or bioimpedance method built into the toilet seat of the robot 414. The authentication unit performs facial recognition using the camera of the robot 414 to identify the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides health advice based on the analysis results. The usage analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the frequency and interval of toilet use. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A measuring unit that measures body fat and weight, The authentication unit performs personal authentication using a camera, An analysis unit that analyzes the data collected by the measurement unit and the authentication unit, An advice unit provides health advice based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features. (Note 2) It is equipped with a usage analysis unit that analyzes the frequency and interval of toilet use. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned usage analysis unit is: Analyze toilet usage frequency and intervals to detect changes in health status. The system described in Appendix 2, characterized by the features described herein. (Note 4) The aforementioned advice section, Based on the analysis results, we provide advice on diet and exercise. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned advice section, Based on the analysis results, we provide advice such as adjusting fluid intake or recommending a doctor's consultation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned measuring unit is It estimates the user's emotions and adjusts the timing of measurements based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned measuring unit is Analyze the user's past measurement data and select the optimal measurement method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned measuring unit is During measurement, the measurement items are customized based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned measuring unit is It estimates the user's emotions and adjusts how the measurement results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned measuring unit is During measurement, the system prioritizes acquiring highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned measuring unit is During measurement, the system analyzes the user's social media activity and obtains relevant health data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The authentication unit, It estimates the user's emotions and adjusts the authentication method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The authentication unit, During authentication, the system analyzes the user's past authentication history and selects the most suitable authentication method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The authentication unit, During authentication, the authentication process is customized based on the user's current lifestyle and health status. The system described in Appendix 1, characterized by the features described herein. (Note 15) The authentication unit, The system estimates the user's emotions and adjusts how authentication results are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The authentication unit, During authentication, the system prioritizes obtaining highly relevant authentication data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The authentication unit, During authentication, the system analyzes the user's social media activity and retrieves relevant authentication data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the analysis items are customized based on the user's health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, the system prioritizes acquiring highly relevant analysis data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, the user's social media activity is analyzed, and relevant analytical data is obtained. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice section, When providing advice, the advice is customized based on the user's health condition and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, When providing advice, we prioritize the advice based on when the analysis results are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advice section, When providing advice, we adjust the advice based on the user's relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned usage analysis unit is: We estimate the user's emotions and adjust the usage analysis method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned usage analysis unit is: During usage analysis, the analysis algorithm is optimized by referring to past usage data. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned usage analysis unit is: It estimates the user's emotions and adjusts how the usage analysis results are displayed based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned usage analysis unit is: During usage analysis, the system prioritizes acquiring highly relevant usage data by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A measuring unit that measures body fat and weight, The authentication unit performs personal authentication using a camera, An analysis unit that analyzes the data collected by the measurement unit and the authentication unit, An advice unit provides health advice based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. It is equipped with a usage analysis unit that analyzes the frequency and interval of toilet use. The system according to feature 1.

3. The aforementioned usage analysis unit is: Analyze toilet usage frequency and intervals to detect changes in health status. The system according to feature 2.

4. The aforementioned advice section, Based on the analysis results, we provide advice on diet and exercise. The system according to feature 1.

5. The aforementioned advice section, Based on the analysis results, we provide advice such as adjusting fluid intake or recommending a doctor's consultation. The system according to feature 1.

6. The aforementioned measuring unit is It estimates the user's emotions and adjusts the timing of measurements based on the estimated user emotions. The system according to feature 1.

7. The aforementioned measuring unit is Analyze the user's past measurement data and select the optimal measurement method. The system according to feature 1.

8. The aforementioned measuring unit is During measurement, the measurement items are customized based on the user's current health status and lifestyle. The system according to feature 1.

9. The aforementioned measuring unit is It estimates the user's emotions and adjusts how the measurement results are displayed based on the estimated user emotions. The system according to feature 1.

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