System

Abstract

An object of a system according to an embodiment is to enable a user to easily grasp his or her health condition and obtain appropriate advice.SOLUTION: A system includes a measurement unit including a bioimpedance sensor, a measurement unit including an infrared sensor, an analysis unit, and a provision unit. The measuring unit including the bioimpedance sensor measures the body fat percentage of the user. The measurement unit including an infrared sensor measures a skin temperature. The analysis unit analyzes the data measured by the measurement unit. The providing unit provides a comment and advice to the user on the basis of the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult for users to easily understand their own health condition and receive appropriate advice.

[0005] The system according to the embodiment aims to enable users to easily understand their own health condition and receive appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a measurement unit including a bioimpedance sensor, a measurement unit including an infrared sensor, an analysis unit, and a provision unit. The measurement unit including the bioimpedance sensor measures the user's body fat percentage. The measurement unit including the infrared sensor measures the skin temperature. The analysis unit analyzes the data measured by the measurement unit. The provision unit provides comments and advice to the user based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily understand his / her health condition and receive appropriate advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health management system according to an embodiment of the present invention measures a user's body fat percentage and skin temperature, and a generating AI analyzes and provides the information to the user. The health management system provides personalized comments and advice tailored to the user's health status and goals, helping to improve daily life. For example, the health management system measures the user's body fat percentage and skin temperature using a bioimpedance sensor and an infrared sensor built into an IoT mirror. The generating AI then analyzes the collected data and evaluates the user's health status. For example, if the body fat percentage is increasing or the skin temperature is abnormally high, important health information is extracted. Furthermore, the generating AI provides personalized comments and advice to the user based on the analysis results. For example, if the body fat percentage is increasing, advice is provided encouraging exercise and dietary improvements. Furthermore, if the skin temperature is high, a comment recommending rest and hydration is displayed. This allows the health management system to easily grasp the user's health status and provide information for a healthier lifestyle. This allows the health management system to automatically evaluate the user's health status and provide personalized advice. For example, the user can track daily changes in their health status by checking their measurement results every morning. In addition, by following the advice provided by the generative AI, users can take specific actions to live a healthier life, thereby enabling the health management system to support users in managing their health and improving their quality of life.

[0029] A health management system according to an embodiment includes a measurement unit, an analysis unit, and a provision unit. The measurement unit includes a bioimpedance sensor that measures a user's body fat percentage and an infrared sensor that measures skin temperature. The measurement unit automatically performs measurements when the user simply stands in front of a mirror. The bioimpedance sensor measures body fat percentage by passing an electric current, for example. The infrared sensor measures skin surface temperature, for example. The analysis unit analyzes the data measured by the measurement unit. The analysis unit uses a generation AI to analyze fluctuations in body fat percentage and skin temperature and evaluate the user's health condition. For example, the generation AI detects an increase in body fat percentage or an abnormality in skin temperature. The provision unit provides comments and advice to the user based on the analysis results obtained by the analysis unit. The provision unit uses the generation AI to generate personalized comments and advice according to the user's health condition. For example, if the body fat percentage is increasing, advice is provided to encourage improvement in exercise and diet. Furthermore, if the skin temperature is high, a comment recommending rest and hydration is provided. This enables the health management system according to an embodiment to automatically evaluate the user's health condition and provide personalized advice.

[0030] The analysis unit can analyze fluctuations in body fat percentage and skin temperature to evaluate the user's health condition. The analysis unit, for example, analyzes fluctuations in body fat percentage. For example, it can detect increases or decreases in body fat percentage. The analysis unit can also analyze fluctuations in skin temperature. For example, it can detect abnormal increases or decreases in skin temperature. Furthermore, the analysis unit can comprehensively analyze fluctuations in body fat percentage and skin temperature to evaluate the user's health condition. For example, if an increase in body fat percentage and an increase in skin temperature occur simultaneously, it can suggest a deterioration in health condition. In this way, the user's health condition can be evaluated by analyzing fluctuations in body fat percentage and skin temperature.

[0031] The providing unit can provide advice encouraging improvement in exercise and diet if the body fat percentage is increasing. For example, the providing unit can provide advice encouraging improvement in exercise if the body fat percentage is increasing. For example, the providing unit can advise the user to increase the amount of exercise they do each day. The providing unit can also provide advice encouraging improvement in diet. For example, the providing unit can advise the user to eat a balanced diet. Furthermore, the providing unit can suggest specific actions to prevent an increase in the body fat percentage. For example, the providing unit can advise the user to continue regular exercise and a healthy diet. In this way, by providing advice encouraging improvement in exercise and diet if the body fat percentage is increasing, the user's health condition can be improved.

[0032] The providing unit can provide a comment recommending rest and hydration when the skin temperature is high. For example, the providing unit can provide a comment recommending rest when the skin temperature is high. For example, the providing unit can advise the user to take sufficient rest. The providing unit can also provide a comment recommending hydration. For example, the providing unit can advise the user to properly hydrate. Furthermore, the providing unit can suggest specific actions to prevent the skin temperature from rising. For example, the providing unit can advise the user to take a rest in a cool place. In this way, by providing a comment recommending rest and hydration when the skin temperature is high, the user's health condition can be improved.

[0033] The measurement unit can automatically perform measurements when the user stands in front of a mirror. For example, the measurement unit automatically starts measurement when the user stands in front of a mirror. For example, the measurement unit activates a bioimpedance sensor to measure the user's body fat percentage. The measurement unit can also activate an infrared sensor to measure the user's skin temperature. Furthermore, the measurement unit is equipped with a sensor that performs measurements when the user simply stands in front of the mirror. For example, the measurement unit is equipped with a motion sensor to detect the presence of the user. This allows the user to automatically perform measurements when they simply stand in front of the mirror, thereby enabling health data to be obtained without any hassle.

[0034] The analysis unit can use an algorithm that compares data with past data to detect abnormalities. The analysis unit can, for example, use an algorithm that compares data with past data to detect abnormalities. For example, the analysis unit can compare the user's past body fat percentage data with current data to detect abnormal fluctuations. The analysis unit can also compare the user's past skin temperature data with current data to detect abnormal fluctuations. Furthermore, the analysis unit can comprehensively analyze the past data and current data to detect abnormalities. For example, the analysis unit can comprehensively analyze fluctuations in body fat percentage and skin temperature to detect abnormal patterns. This allows for early detection of abnormal values ​​by comparing data with past data, making it possible to detect abnormalities in the user's health condition.

[0035] The analysis unit can use a machine learning model to predict the user's health condition. The analysis unit uses, for example, the machine learning model to predict the user's health condition. For example, the analysis unit can train the machine learning model based on the user's past health data to predict the user's future health condition. The analysis unit can also use the machine learning model to predict fluctuations in the user's health condition. For example, the analysis unit can predict fluctuations in body fat percentage or skin temperature and evaluate the user's future health condition. Furthermore, the analysis unit can use the machine learning model to predict abnormalities in the user's health condition. For example, the analysis unit can predict an abnormal increase in body fat percentage or an abnormal increase in skin temperature and issue a warning to the user. In this way, the use of the machine learning model can predict the user's health condition and provide appropriate advice.

[0036] The measurement unit can improve measurement accuracy based on the user's past measurement data during measurement. The measurement unit, for example, corrects the current measurement result based on the user's past measurement data. For example, the measurement unit can refer to past body fat percentage data and correct the current measurement result. The measurement unit can also refer to past skin temperature data and correct the current measurement result. Furthermore, the measurement unit can analyze past measurement data and apply an algorithm to improve measurement accuracy. For example, the measurement unit can detect abnormal values ​​based on past data and prompt the user to perform a remeasurement. In this way, measurement accuracy can be improved by referring to past measurement data.

[0037] The measurement unit can select the optimal measurement timing based on the user's lifestyle rhythm during measurement. For example, the measurement unit can analyze the user's lifestyle rhythm and perform measurement during the time period when the user is most relaxed. For example, the measurement unit can select the optimal measurement timing based on the user's sleep cycle and daily activity patterns. The measurement unit can also automatically set the daily measurement timing based on the user's lifestyle rhythm. For example, the measurement unit can analyze the user's past measurement data and predict the optimal measurement timing. Furthermore, the measurement unit can flexibly adjust the measurement timing taking the user's lifestyle rhythm into consideration. For example, the measurement unit can change the measurement timing according to the user's schedule. This allows more accurate data to be obtained by selecting the optimal measurement timing based on the user's lifestyle rhythm.

[0038] The measurement unit can automatically adjust the measurement method based on the user's physical condition and environmental conditions during measurement. The measurement unit, for example, adjusts the measurement method taking into account the user's physical condition. For example, if the user is tired, the measurement time can be shortened and the burden on the user can be reduced. The measurement unit can also adjust the measurement method taking into account the user's environmental conditions (temperature, humidity, etc.). For example, the measurement unit can adjust the sensitivity of the sensor according to the environmental conditions. Furthermore, the measurement unit can adjust the measurement frequency according to the user's physical condition. For example, if the user is feeling unwell, the measurement frequency can be reduced. This makes it possible to obtain more accurate data by automatically adjusting the measurement method according to the user's physical condition and environmental conditions.

[0039] The measurement unit can correct the measurement results based on the user's geographical location information during measurement. The measurement unit corrects the measurement results, for example, by taking the user's geographical location information into account. For example, if the user is at a high altitude, the measurement results can be corrected by taking into account the influence of atmospheric pressure. Also, if the user is in a humid area, the measurement results can be corrected by taking into account the influence of humidity. Furthermore, if the user is moving, the measurement results can be corrected based on location information. For example, the measurement unit can obtain the user's location information using GPS data and correct the measurement results. In this way, more accurate data can be obtained by correcting the measurement results by taking the user's geographical location information into account.

[0040] During measurement, the measurement unit can analyze the user's social media activities and obtain health information. The measurement unit, for example, uses a text analysis algorithm to analyze the user's social media activities. For example, the measurement unit can analyze exercise records and diet records shared by the user on social media to obtain health information. The measurement unit can also evaluate the user's health status based on the user's social media activities. For example, the measurement unit can analyze the user's posts and extract health-related information. Furthermore, the measurement unit can track changes in the user's health status based on the social media activities. For example, the measurement unit can analyze the user's exercise and dietary patterns to evaluate changes in the health status. In this way, by analyzing the user's social media activities, related health information can be obtained and reflected in the measurement results.

[0041] The measurement unit can customize the measurement method based on the user's past feedback during measurement. The measurement unit, for example, adjusts the measurement method based on the user's past feedback. For example, it can analyze feedback provided by the user in the past and customize the measurement method. The measurement unit can also improve measurement accuracy by reflecting the user's past feedback. For example, it can improve the measurement process based on the user's feedback. Furthermore, the measurement unit can also individually adjust the measurement method by reflecting the user's feedback. For example, it can adjust the frequency and timing of measurement according to the user's preferences. In this way, it is possible to customize the measurement method and improve measurement accuracy by reflecting the user's past feedback.

[0042] During the analysis, the analysis unit can analyze fluctuations in body fat percentage and skin temperature and detect abnormalities. The analysis unit can, for example, detect a sudden change in body fat percentage and report it as an abnormal value. For example, the analysis unit can detect a sudden increase or decrease in body fat percentage. The analysis unit can also detect an abnormal increase in skin temperature and warn the user of an abnormal health condition. For example, the analysis unit can detect a sudden increase in skin temperature and issue a warning to the user. Furthermore, the analysis unit can analyze the correlation between body fat percentage and skin temperature and identify abnormal values. For example, if an increase in body fat percentage and an increase in skin temperature occur simultaneously, the analysis unit can suggest a deterioration in health condition. In this way, by analyzing the fluctuations in body fat percentage and skin temperature in detail, abnormal values ​​can be detected and the user's health condition can be evaluated.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past health data. The analysis unit, for example, corrects the current analysis result based on the user's past health data. For example, the analysis unit can refer to past body fat percentage data and correct the current analysis result. The analysis unit can also refer to past skin temperature data and correct the current analysis result. Furthermore, the analysis unit can analyze past health data and apply an algorithm to improve the accuracy of the analysis. For example, the analysis unit can detect abnormal values ​​based on past data and perform reanalysis. In this way, the analysis accuracy can be improved by referring to the user's past health data.

[0044] During analysis, the analysis unit can correct the analysis results based on the user's lifestyle habits and environmental conditions. The analysis unit corrects the analysis results, for example, taking into account the user's lifestyle habits (diet, exercise, etc.). For example, the analysis unit can correct the analysis results based on the user's eating patterns and exercise habits. The analysis unit can also correct the analysis results by taking into account the user's environmental conditions (temperature, humidity, etc.). For example, the analysis unit can adjust the analysis algorithm according to the environmental conditions. Furthermore, the analysis unit can comprehensively analyze the user's lifestyle habits and environmental conditions and correct the analysis results. For example, the analysis unit can evaluate fluctuations in health status based on the user's lifestyle habits and environmental conditions. This makes it possible to provide more accurate analysis results by taking the user's lifestyle habits and environmental conditions into account.

[0045] During analysis, the analysis unit can correct the analysis results based on the user's geographical location information. The analysis unit corrects the analysis results, for example, by taking the user's geographical location information into account. For example, if the user is at a high altitude, the analysis results can be corrected by taking into account the influence of air pressure. Also, if the user is in a humid area, the analysis results can be corrected by taking into account the influence of humidity. Furthermore, if the user is moving, the analysis results can be corrected based on location information. For example, the analysis unit can obtain the user's location information using GPS data and correct the analysis results. In this way, more accurate data can be obtained by correcting the analysis results by taking the user's geographical location information into account.

[0046] During the analysis, the analysis unit can analyze the user's social media activity and obtain health information. The analysis unit, for example, uses a text analysis algorithm to analyze the user's social media activity. For example, the analysis unit can analyze exercise records and diet records shared by the user on social media to obtain health information. The analysis unit can also evaluate the user's health status based on the user's social media activity. For example, the analysis unit can analyze the user's posts and extract health-related information. Furthermore, the analysis unit can track changes in the user's health status based on the social media activity. For example, the analysis unit can analyze the user's exercise and diet patterns to evaluate changes in the health status. In this way, by analyzing the user's social media activity, related health information can be obtained and reflected in the analysis results.

[0047] During analysis, the analysis unit can customize the analysis algorithm based on the user's past feedback. The analysis unit, for example, adjusts the analysis algorithm based on the user's past feedback. For example, the analysis unit can analyze feedback provided by the user in the past and customize the analysis algorithm. The analysis unit can also improve the analysis accuracy by reflecting the user's past feedback. For example, the analysis process can be improved based on the user's feedback. Furthermore, the analysis unit can also individually adjust the analysis method by reflecting the user's feedback. For example, the level of detail and timing of the analysis can be adjusted according to the user's preferences. In this way, the analysis algorithm can be customized and the analysis accuracy can be improved by reflecting the user's past feedback.

[0048] The providing unit can adjust the level of detail of the advice based on the user's health condition when providing the advice. For example, when the user's health condition is good, the providing unit provides concise advice. For example, the providing unit can provide the user with basic advice for maintaining health. Furthermore, when the user's health condition is deteriorating, the providing unit can also provide detailed advice. For example, the providing unit can explain specific improvement measures and points of caution to the user in detail. Furthermore, the providing unit can flexibly adjust the level of detail of the advice based on the user's health condition. For example, the providing unit can appropriately update the content of the advice in accordance with changes in the user's health condition. In this way, more appropriate advice can be provided by adjusting the level of detail of the advice based on the user's health condition.

[0049] The providing unit can improve the accuracy of advice based on the user's past advice history when providing advice. The providing unit, for example, adjusts current advice based on the user's past advice history. For example, the providing unit can analyze the effects of advice received by the user in the past and reflect that in the current advice. The providing unit can also provide effective advice by referring to the user's past advice history. For example, the providing unit can select optimal advice based on the user's past advice history. Furthermore, the providing unit can analyze the user's past advice history and apply an algorithm to improve the accuracy of the advice. For example, the providing unit can customize the content of the advice based on the past advice history. In this way, the accuracy of the advice can be improved by referring to the user's past advice history.

[0050] The providing unit can customize the advice based on the user's lifestyle habits and environmental conditions when providing the advice. The providing unit customizes the advice, for example, by taking into consideration the user's lifestyle habits (e.g., diet, exercise). For example, the providing unit can provide optimal advice based on the user's eating patterns and exercise habits. The providing unit can also adjust the advice by taking into consideration the user's environmental conditions (e.g., temperature, humidity). For example, the providing unit can change the content of the advice depending on the environmental conditions. Furthermore, the providing unit can comprehensively analyze the user's lifestyle habits and environmental conditions and provide optimal advice. For example, the providing unit can evaluate fluctuations in health status based on the user's lifestyle habits and environmental conditions and provide appropriate advice. This makes it possible to provide more appropriate advice by taking the user's lifestyle habits and environmental conditions into consideration.

[0051] The providing unit can correct the advice based on the user's geographical location information when providing the advice. The providing unit corrects the advice, for example, by taking the user's geographical location information into consideration. For example, if the user is at a high altitude, the advice can be corrected by taking into consideration the influence of air pressure. Also, if the user is in a humid area, the advice can be corrected by taking into consideration the influence of humidity. Furthermore, if the user is moving, the advice can be corrected based on the location information. For example, the providing unit can obtain the user's location information using GPS data and correct the advice. In this way, more appropriate advice can be provided by correcting the advice by taking the user's geographical location information into consideration.

[0052] The providing unit can analyze the user's social media activity and provide advice at the time of providing the advice. The providing unit, for example, uses a text analysis algorithm to analyze the user's social media activity. For example, the providing unit can analyze exercise records and food records shared by the user on social media and provide advice. The providing unit can also evaluate the user's health status and provide advice based on the user's social media activity. For example, the providing unit can analyze the user's posts and extract health-related information. Furthermore, the providing unit can track changes in the user's health status based on the social media activity. For example, the providing unit can analyze the user's exercise and eating patterns and evaluate changes in the health status. In this way, by analyzing the user's social media activity, relevant advice can be provided to support the user's health management.

[0053] The providing unit can customize the content of the advice by reflecting the user's past feedback when providing the advice. The providing unit, for example, adjusts the content of the advice based on the user's past feedback. For example, the providing unit can analyze feedback provided by the user in the past and customize the content of the advice. The providing unit can also improve the accuracy of the advice by reflecting the user's past feedback. For example, the advice process can be improved based on the user's feedback. Furthermore, the providing unit can also individually adjust the content of the advice by reflecting the user's feedback. For example, the level of detail and content of the advice can be adjusted according to the user's preferences. In this way, the content of the advice can be customized by reflecting the user's past feedback, and the accuracy of the advice can be improved.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The analysis unit can combine and analyze the user's past health data and current data to predict the user's health condition. For example, the analysis unit can compare the user's past body fat percentage data with current data to predict future changes in body fat percentage. The analysis unit can also compare the user's past skin temperature data with current data to predict future changes in skin temperature. Furthermore, the analysis unit can comprehensively analyze the past data and current data to predict future changes in the user's health condition. This allows the user to predict their future health condition and take preventative measures.

[0056] The measurement unit can adjust the timing of measurement based on the user's lifestyle. For example, if the user is a morning person, the measurement can be performed in the morning. Alternatively, if the user is a night owl, the measurement can be performed in the evening. Furthermore, the measurement frequency can be adjusted according to the user's lifestyle. For example, the measurement frequency can be reduced when the user is busy and increased when the user has more free time. This allows measurements to be performed in accordance with the user's lifestyle, thereby obtaining more accurate data.

[0057] The analysis unit can use a machine learning model to predict the user's health condition. For example, the analysis unit can train a machine learning model based on the user's past health data to predict the user's future health condition. The analysis unit can also use the machine learning model to predict fluctuations in the user's health condition. For example, the analysis unit can predict fluctuations in body fat percentage or skin temperature to evaluate the user's future health condition. Furthermore, the analysis unit can use the machine learning model to predict abnormalities in the user's health condition. This allows the user to predict their future health condition and take preventive measures.

[0058] The measurement unit can correct the measurement results based on the user's geographical location information. For example, if the user is at a high altitude, the measurement results can be corrected taking into account the influence of atmospheric pressure. Also, if the user is in a humid area, the measurement results can be corrected taking into account the influence of humidity. Furthermore, if the user is moving, the measurement results can be corrected based on the location information. In this way, by correcting the measurement results taking into account the user's geographical location information, more accurate data can be obtained.

[0059] The providing unit can improve the accuracy of advice based on the user's past advice history. For example, the providing unit can analyze the effects of advice the user received in the past and reflect that in current advice. The providing unit can also refer to the user's past advice history to provide effective advice. Furthermore, the providing unit can analyze the user's past advice history and apply an algorithm to improve the accuracy of advice. In this way, the accuracy of advice can be improved by referring to the user's past advice history.

[0060] The analysis unit can analyze a user's social media activity to obtain health information. For example, the analysis unit can analyze exercise records and diet records shared by the user on social media to obtain health information. The analysis unit can also evaluate the user's health status based on the user's social media activity. Furthermore, the analysis unit can track changes in the user's health status based on the social media activity. In this way, by analyzing the user's social media activity, related health information can be obtained and reflected in the analysis results.

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

[0062] Step 1: The measurement unit is equipped with a bioimpedance sensor that measures the user's body fat percentage and an infrared sensor that measures skin temperature. Measurements are performed automatically when the user simply stands in front of the mirror. The bioimpedance sensor measures body fat percentage by passing an electric current through it, and the infrared sensor measures the surface temperature of the skin. Step 2: The analysis unit analyzes the data measured by the measurement unit. Using the generative AI, it analyzes fluctuations in body fat percentage and skin temperature to evaluate the user's health condition. For example, it detects increases in body fat percentage and abnormalities in skin temperature. Step 3: The provider provides comments and advice to the user based on the analysis results obtained by the analyzer. Using a generation AI, the provider generates personalized comments and advice based on the user's health condition. For example, if the body fat percentage is increasing, the provider will provide advice encouraging exercise and dietary improvements, and if the skin temperature is high, the provider will provide comments recommending rest and hydration.

[0063] (Example 2) A health management system according to an embodiment of the present invention measures a user's body fat percentage and skin temperature, and a generating AI analyzes and provides the information to the user. The health management system provides personalized comments and advice tailored to the user's health status and goals, helping to improve daily life. For example, the health management system measures the user's body fat percentage and skin temperature using a bioimpedance sensor and an infrared sensor built into an IoT mirror. The generating AI then analyzes the collected data and evaluates the user's health status. For example, if the body fat percentage is increasing or the skin temperature is abnormally high, important health information is extracted. Furthermore, the generating AI provides personalized comments and advice to the user based on the analysis results. For example, if the body fat percentage is increasing, advice is provided encouraging exercise and dietary improvements. Furthermore, if the skin temperature is high, a comment recommending rest and hydration is displayed. This allows the health management system to easily grasp the user's health status and provide information for a healthier lifestyle. This allows the health management system to automatically evaluate the user's health status and provide personalized advice. For example, the user can track daily changes in their health status by checking their measurement results every morning. In addition, by following the advice provided by the generative AI, users can take specific actions to live a healthier life, thereby enabling the health management system to support users in managing their health and improving their quality of life.

[0064] A health management system according to an embodiment includes a measurement unit, an analysis unit, and a provision unit. The measurement unit includes a bioimpedance sensor that measures a user's body fat percentage and an infrared sensor that measures skin temperature. The measurement unit automatically performs measurements when the user simply stands in front of a mirror. The bioimpedance sensor measures body fat percentage by passing an electric current, for example. The infrared sensor measures skin surface temperature, for example. The analysis unit analyzes the data measured by the measurement unit. The analysis unit uses a generation AI to analyze fluctuations in body fat percentage and skin temperature and evaluate the user's health condition. For example, the generation AI detects an increase in body fat percentage or an abnormality in skin temperature. The provision unit provides comments and advice to the user based on the analysis results obtained by the analysis unit. The provision unit uses the generation AI to generate personalized comments and advice according to the user's health condition. For example, if the body fat percentage is increasing, advice is provided to encourage improvement in exercise and diet. Furthermore, if the skin temperature is high, a comment recommending rest and hydration is provided. This enables the health management system according to an embodiment to automatically evaluate the user's health condition and provide personalized advice.

[0065] The analysis unit can analyze fluctuations in body fat percentage and skin temperature to evaluate the user's health condition. The analysis unit, for example, analyzes fluctuations in body fat percentage. For example, it can detect increases or decreases in body fat percentage. The analysis unit can also analyze fluctuations in skin temperature. For example, it can detect abnormal increases or decreases in skin temperature. Furthermore, the analysis unit can comprehensively analyze fluctuations in body fat percentage and skin temperature to evaluate the user's health condition. For example, if an increase in body fat percentage and an increase in skin temperature occur simultaneously, it can suggest a deterioration in health condition. In this way, the user's health condition can be evaluated by analyzing fluctuations in body fat percentage and skin temperature.

[0066] The providing unit can provide advice encouraging improvement in exercise and diet if the body fat percentage is increasing. For example, the providing unit can provide advice encouraging improvement in exercise if the body fat percentage is increasing. For example, the providing unit can advise the user to increase the amount of exercise they do each day. The providing unit can also provide advice encouraging improvement in diet. For example, the providing unit can advise the user to eat a balanced diet. Furthermore, the providing unit can suggest specific actions to prevent an increase in the body fat percentage. For example, the providing unit can advise the user to continue regular exercise and a healthy diet. In this way, by providing advice encouraging improvement in exercise and diet if the body fat percentage is increasing, the user's health condition can be improved.

[0067] The providing unit can provide a comment recommending rest and hydration when the skin temperature is high. For example, the providing unit can provide a comment recommending rest when the skin temperature is high. For example, the providing unit can advise the user to take sufficient rest. The providing unit can also provide a comment recommending hydration. For example, the providing unit can advise the user to properly hydrate. Furthermore, the providing unit can suggest specific actions to prevent the skin temperature from rising. For example, the providing unit can advise the user to take a rest in a cool place. In this way, by providing a comment recommending rest and hydration when the skin temperature is high, the user's health condition can be improved.

[0068] The measurement unit can automatically perform measurements when the user stands in front of a mirror. For example, the measurement unit automatically starts measurement when the user stands in front of a mirror. For example, the measurement unit activates a bioimpedance sensor to measure the user's body fat percentage. The measurement unit can also activate an infrared sensor to measure the user's skin temperature. Furthermore, the measurement unit is equipped with a sensor that performs measurements when the user simply stands in front of the mirror. For example, the measurement unit is equipped with a motion sensor to detect the presence of the user. This allows the user to automatically perform measurements when they simply stand in front of the mirror, thereby enabling health data to be obtained without any hassle.

[0069] The analysis unit can use an algorithm that compares data with past data to detect abnormalities. The analysis unit can, for example, use an algorithm that compares data with past data to detect abnormalities. For example, the analysis unit can compare the user's past body fat percentage data with current data to detect abnormal fluctuations. The analysis unit can also compare the user's past skin temperature data with current data to detect abnormal fluctuations. Furthermore, the analysis unit can comprehensively analyze the past data and current data to detect abnormalities. For example, the analysis unit can comprehensively analyze fluctuations in body fat percentage and skin temperature to detect abnormal patterns. This allows for early detection of abnormal values ​​by comparing data with past data, making it possible to detect abnormalities in the user's health condition.

[0070] The analysis unit can use a machine learning model to predict the user's health condition. The analysis unit uses, for example, the machine learning model to predict the user's health condition. For example, the analysis unit can train the machine learning model based on the user's past health data to predict the user's future health condition. The analysis unit can also use the machine learning model to predict fluctuations in the user's health condition. For example, the analysis unit can predict fluctuations in body fat percentage or skin temperature and evaluate the user's future health condition. Furthermore, the analysis unit can use the machine learning model to predict abnormalities in the user's health condition. For example, the analysis unit can predict an abnormal increase in body fat percentage or an abnormal increase in skin temperature and issue a warning to the user. In this way, the use of the machine learning model can predict the user's health condition and provide appropriate advice.

[0071] The measurement unit can estimate the user's emotion and adjust the timing of measurement based on the estimated user's emotion. The measurement unit, for example, uses an emotion recognition algorithm to estimate the user's emotion. For example, the measurement unit can analyze the user's facial expressions and voice to estimate the emotion. The measurement unit can also adjust the timing of measurement based on the estimated user's emotion. For example, if the user is feeling stressed, the timing of measurement can be delayed to allow the user to take the measurement in a relaxed state. Furthermore, if the user is relaxed, the measurement can be started immediately and accurate data can be obtained. Furthermore, if the user is in a hurry, the measurement process can be simplified to complete the measurement in a short time. As a result, more accurate data can be obtained by adjusting the timing of measurement according to the user's emotion.

[0072] The measurement unit can improve measurement accuracy based on the user's past measurement data during measurement. The measurement unit, for example, corrects the current measurement result based on the user's past measurement data. For example, the measurement unit can refer to past body fat percentage data and correct the current measurement result. The measurement unit can also refer to past skin temperature data and correct the current measurement result. Furthermore, the measurement unit can analyze past measurement data and apply an algorithm to improve measurement accuracy. For example, the measurement unit can detect abnormal values ​​based on past data and prompt the user to perform a remeasurement. In this way, measurement accuracy can be improved by referring to past measurement data.

[0073] The measurement unit can select the optimal measurement timing based on the user's lifestyle rhythm during measurement. For example, the measurement unit can analyze the user's lifestyle rhythm and perform measurement during the time period when the user is most relaxed. For example, the measurement unit can select the optimal measurement timing based on the user's sleep cycle and daily activity patterns. The measurement unit can also automatically set the daily measurement timing based on the user's lifestyle rhythm. For example, the measurement unit can analyze the user's past measurement data and predict the optimal measurement timing. Furthermore, the measurement unit can flexibly adjust the measurement timing taking the user's lifestyle rhythm into consideration. For example, the measurement unit can change the measurement timing according to the user's schedule. This allows more accurate data to be obtained by selecting the optimal measurement timing based on the user's lifestyle rhythm.

[0074] The measurement unit can automatically adjust the measurement method based on the user's physical condition and environmental conditions during measurement. The measurement unit, for example, adjusts the measurement method taking into account the user's physical condition. For example, if the user is tired, the measurement time can be shortened and the burden on the user can be reduced. The measurement unit can also adjust the measurement method taking into account the user's environmental conditions (temperature, humidity, etc.). For example, the measurement unit can adjust the sensitivity of the sensor according to the environmental conditions. Furthermore, the measurement unit can adjust the measurement frequency according to the user's physical condition. For example, if the user is feeling unwell, the measurement frequency can be reduced. This makes it possible to obtain more accurate data by automatically adjusting the measurement method according to the user's physical condition and environmental conditions.

[0075] The measurement unit can estimate the user's emotions and adjust the display method of the measurement results based on the estimated user emotions. The measurement unit, for example, uses an emotion recognition algorithm to estimate the user's emotions. For example, the measurement unit can analyze the user's facial expressions and voice to estimate emotions. The measurement unit can also adjust the display method of the measurement results based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the measurement results according to the user's emotions, a display that is easy for the user to view can be achieved.

[0076] The measurement unit can correct the measurement results based on the user's geographical location information during measurement. The measurement unit corrects the measurement results, for example, by taking the user's geographical location information into account. For example, if the user is at a high altitude, the measurement results can be corrected by taking into account the influence of atmospheric pressure. Also, if the user is in a humid area, the measurement results can be corrected by taking into account the influence of humidity. Furthermore, if the user is moving, the measurement results can be corrected based on location information. For example, the measurement unit can obtain the user's location information using GPS data and correct the measurement results. In this way, more accurate data can be obtained by correcting the measurement results by taking the user's geographical location information into account.

[0077] During measurement, the measurement unit can analyze the user's social media activities and obtain health information. The measurement unit, for example, uses a text analysis algorithm to analyze the user's social media activities. For example, the measurement unit can analyze exercise records and diet records shared by the user on social media to obtain health information. The measurement unit can also evaluate the user's health status based on the user's social media activities. For example, the measurement unit can analyze the user's posts and extract health-related information. Furthermore, the measurement unit can track changes in the user's health status based on the social media activities. For example, the measurement unit can analyze the user's exercise and dietary patterns to evaluate changes in the health status. In this way, by analyzing the user's social media activities, related health information can be obtained and reflected in the measurement results.

[0078] The measurement unit can customize the measurement method based on the user's past feedback during measurement. The measurement unit, for example, adjusts the measurement method based on the user's past feedback. For example, it can analyze feedback provided by the user in the past and customize the measurement method. The measurement unit can also improve measurement accuracy by reflecting the user's past feedback. For example, it can improve the measurement process based on the user's feedback. Furthermore, the measurement unit can also individually adjust the measurement method by reflecting the user's feedback. For example, it can adjust the frequency and timing of measurement according to the user's preferences. In this way, it is possible to customize the measurement method and improve measurement accuracy by reflecting the user's past feedback.

[0079] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. The analysis unit, for example, uses an emotion recognition algorithm to estimate the user's emotions. For example, the analysis unit can analyze the user's facial expressions and voice to estimate the emotions. The analysis unit can also adjust the analysis algorithm based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize analysis related to stress reduction. Furthermore, if the user is relaxed, it can perform a detailed analysis to evaluate the user's health condition. Furthermore, if the user is in a hurry, it can quickly perform an analysis and provide the results. In this way, by adjusting the analysis algorithm according to the user's emotions, it is possible to provide more appropriate analysis results.

[0080] During the analysis, the analysis unit can analyze fluctuations in body fat percentage and skin temperature and detect abnormalities. The analysis unit can, for example, detect a sudden change in body fat percentage and report it as an abnormal value. For example, the analysis unit can detect a sudden increase or decrease in body fat percentage. The analysis unit can also detect an abnormal increase in skin temperature and warn the user of an abnormal health condition. For example, the analysis unit can detect a sudden increase in skin temperature and issue a warning to the user. Furthermore, the analysis unit can analyze the correlation between body fat percentage and skin temperature and identify abnormal values. For example, if an increase in body fat percentage and an increase in skin temperature occur simultaneously, the analysis unit can suggest a deterioration in health condition. In this way, by analyzing the fluctuations in body fat percentage and skin temperature in detail, abnormal values ​​can be detected and the user's health condition can be evaluated.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past health data. The analysis unit, for example, corrects the current analysis result based on the user's past health data. For example, the analysis unit can refer to past body fat percentage data and correct the current analysis result. The analysis unit can also refer to past skin temperature data and correct the current analysis result. Furthermore, the analysis unit can analyze past health data and apply an algorithm to improve the accuracy of the analysis. For example, the analysis unit can detect abnormal values ​​based on past data and perform reanalysis. In this way, the analysis accuracy can be improved by referring to the user's past health data.

[0082] During analysis, the analysis unit can correct the analysis results based on the user's lifestyle habits and environmental conditions. The analysis unit corrects the analysis results, for example, taking into account the user's lifestyle habits (diet, exercise, etc.). For example, the analysis unit can correct the analysis results based on the user's eating patterns and exercise habits. The analysis unit can also correct the analysis results by taking into account the user's environmental conditions (temperature, humidity, etc.). For example, the analysis unit can adjust the analysis algorithm according to the environmental conditions. Furthermore, the analysis unit can comprehensively analyze the user's lifestyle habits and environmental conditions and correct the analysis results. For example, the analysis unit can evaluate fluctuations in health status based on the user's lifestyle habits and environmental conditions. This makes it possible to provide more accurate analysis results by taking the user's lifestyle habits and environmental conditions into account.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, uses an emotion recognition algorithm to estimate the user's emotions. For example, the analysis unit can analyze the user's facial expressions and voice to estimate emotions. The analysis unit can also adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, a display that is easy for the user to view can be achieved.

[0084] During analysis, the analysis unit can correct the analysis results based on the user's geographical location information. The analysis unit corrects the analysis results, for example, by taking the user's geographical location information into account. For example, if the user is at a high altitude, the analysis results can be corrected by taking into account the influence of air pressure. Also, if the user is in a humid area, the analysis results can be corrected by taking into account the influence of humidity. Furthermore, if the user is moving, the analysis results can be corrected based on location information. For example, the analysis unit can obtain the user's location information using GPS data and correct the analysis results. In this way, more accurate data can be obtained by correcting the analysis results by taking the user's geographical location information into account.

[0085] During the analysis, the analysis unit can analyze the user's social media activity and obtain health information. The analysis unit, for example, uses a text analysis algorithm to analyze the user's social media activity. For example, the analysis unit can analyze exercise records and diet records shared by the user on social media to obtain health information. The analysis unit can also evaluate the user's health status based on the user's social media activity. For example, the analysis unit can analyze the user's posts and extract health-related information. Furthermore, the analysis unit can track changes in the user's health status based on the social media activity. For example, the analysis unit can analyze the user's exercise and diet patterns to evaluate changes in the health status. In this way, by analyzing the user's social media activity, related health information can be obtained and reflected in the analysis results.

[0086] During analysis, the analysis unit can customize the analysis algorithm based on the user's past feedback. The analysis unit, for example, adjusts the analysis algorithm based on the user's past feedback. For example, the analysis unit can analyze feedback provided by the user in the past and customize the analysis algorithm. The analysis unit can also improve the analysis accuracy by reflecting the user's past feedback. For example, the analysis process can be improved based on the user's feedback. Furthermore, the analysis unit can also individually adjust the analysis method by reflecting the user's feedback. For example, the level of detail and timing of the analysis can be adjusted according to the user's preferences. In this way, the analysis algorithm can be customized and the analysis accuracy can be improved by reflecting the user's past feedback.

[0087] The providing unit can estimate the user's emotions and adjust the manner in which comments and advice are expressed based on the estimated user's emotions. The providing unit, for example, uses an emotion recognition algorithm to estimate the user's emotions. For example, the providing unit can analyze the user's facial expressions and voice to estimate the emotions. The providing unit can also adjust the manner in which comments and advice are expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide advice in gentle words. If the user is relaxed, the providing unit can provide detailed advice. Furthermore, if the user is in a hurry, the providing unit can provide concise and to-the-point advice. In this way, more appropriate advice can be provided by adjusting the manner in which comments and advice are expressed according to the user's emotions.

[0088] The providing unit can adjust the level of detail of the advice based on the user's health condition when providing the advice. For example, when the user's health condition is good, the providing unit provides concise advice. For example, the providing unit can provide the user with basic advice for maintaining health. Furthermore, when the user's health condition is deteriorating, the providing unit can also provide detailed advice. For example, the providing unit can explain specific improvement measures and points of caution to the user in detail. Furthermore, the providing unit can flexibly adjust the level of detail of the advice based on the user's health condition. For example, the providing unit can appropriately update the content of the advice in accordance with changes in the user's health condition. In this way, more appropriate advice can be provided by adjusting the level of detail of the advice based on the user's health condition.

[0089] The providing unit can improve the accuracy of advice based on the user's past advice history when providing advice. The providing unit, for example, adjusts current advice based on the user's past advice history. For example, the providing unit can analyze the effects of advice received by the user in the past and reflect that in the current advice. The providing unit can also provide effective advice by referring to the user's past advice history. For example, the providing unit can select optimal advice based on the user's past advice history. Furthermore, the providing unit can analyze the user's past advice history and apply an algorithm to improve the accuracy of the advice. For example, the providing unit can customize the content of the advice based on the past advice history. In this way, the accuracy of the advice can be improved by referring to the user's past advice history.

[0090] The providing unit can customize the advice based on the user's lifestyle habits and environmental conditions when providing the advice. The providing unit customizes the advice, for example, by taking into consideration the user's lifestyle habits (e.g., diet, exercise). For example, the providing unit can provide optimal advice based on the user's eating patterns and exercise habits. The providing unit can also adjust the advice by taking into consideration the user's environmental conditions (e.g., temperature, humidity). For example, the providing unit can change the content of the advice depending on the environmental conditions. Furthermore, the providing unit can comprehensively analyze the user's lifestyle habits and environmental conditions and provide optimal advice. For example, the providing unit can evaluate fluctuations in health status based on the user's lifestyle habits and environmental conditions and provide appropriate advice. This makes it possible to provide more appropriate advice by taking the user's lifestyle habits and environmental conditions into consideration.

[0091] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user's emotions. The providing unit, for example, uses an emotion recognition algorithm to estimate the user's emotions. For example, the providing unit can analyze the user's facial expressions and voice to estimate the emotions. The providing unit can also determine the priority of advice based on the estimated user's emotions. For example, if the user is feeling stressed, advice regarding stress reduction can be prioritized. Also, if the user is relaxed, advice regarding health maintenance can be prioritized. Furthermore, if the user is in a hurry, advice that can be implemented quickly can be prioritized. In this way, by determining the priority of advice according to the user's emotions, more appropriate advice can be provided.

[0092] The providing unit can correct the advice based on the user's geographical location information when providing the advice. The providing unit corrects the advice, for example, by taking the user's geographical location information into consideration. For example, if the user is at a high altitude, the advice can be corrected by taking into consideration the influence of air pressure. Also, if the user is in a humid area, the advice can be corrected by taking into consideration the influence of humidity. Furthermore, if the user is moving, the advice can be corrected based on the location information. For example, the providing unit can obtain the user's location information using GPS data and correct the advice. In this way, more appropriate advice can be provided by correcting the advice by taking the user's geographical location information into consideration.

[0093] The providing unit can analyze the user's social media activity and provide advice at the time of providing the advice. The providing unit, for example, uses a text analysis algorithm to analyze the user's social media activity. For example, the providing unit can analyze exercise records and food records shared by the user on social media and provide advice. The providing unit can also evaluate the user's health status and provide advice based on the user's social media activity. For example, the providing unit can analyze the user's posts and extract health-related information. Furthermore, the providing unit can track changes in the user's health status based on the social media activity. For example, the providing unit can analyze the user's exercise and eating patterns and evaluate changes in the health status. In this way, by analyzing the user's social media activity, relevant advice can be provided to support the user's health management.

[0094] The providing unit can customize the content of the advice by reflecting the user's past feedback when providing the advice. The providing unit, for example, adjusts the content of the advice based on the user's past feedback. For example, the providing unit can analyze feedback provided by the user in the past and customize the content of the advice. The providing unit can also improve the accuracy of the advice by reflecting the user's past feedback. For example, the advice process can be improved based on the user's feedback. Furthermore, the providing unit can also individually adjust the content of the advice by reflecting the user's feedback. For example, the level of detail and content of the advice can be adjusted according to the user's preferences. In this way, the content of the advice can be customized by reflecting the user's past feedback, and the accuracy of the advice can be improved. === Hard Collateral 1-1 === Each of the multiple elements, including the measurement unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the measurement unit can measure the user's body fat percentage and skin temperature using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the measurement data using a generative AI to evaluate the user's health condition. The provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides the user with personalized comments and advice based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the measurement unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the measurement unit can measure the user's body fat percentage and skin temperature using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the measurement data using a generative AI to evaluate the user's health condition. The provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides the user with personalized comments and advice based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned measurement unit, analysis unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the measurement unit can measure the user's body fat percentage and skin temperature using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the measurement data using a generative AI to evaluate the user's health condition. The provision unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12, and provides the user with personalized comments and advice based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the measurement unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the measurement unit can measure the user's body fat percentage and skin temperature using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the measurement data using generative AI to evaluate the user's health condition. The provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides the user with personalized comments and advice based on the analysis results.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The analysis unit can combine and analyze the user's past health data and current data to predict the user's health condition. For example, the analysis unit can compare the user's past body fat percentage data with current data to predict future changes in body fat percentage. The analysis unit can also compare the user's past skin temperature data with current data to predict future changes in skin temperature. Furthermore, the analysis unit can comprehensively analyze the past data and current data to predict future changes in the user's health condition. This allows the user to predict their future health condition and take preventative measures.

[0097] The providing unit can estimate the user's emotions and adjust the content of advice based on the estimated user emotions. For example, if the user is feeling stressed, advice to relax can be provided. Also, if the user is relaxed, specific advice to maintain health can be provided. Furthermore, if the user is in a hurry, concise advice that can be implemented in a short time can be provided. In this way, by adjusting the content of advice according to the user's emotions, more effective health management can be supported.

[0098] The measurement unit can adjust the timing of measurement based on the user's lifestyle. For example, if the user is a morning person, the measurement can be performed in the morning. Alternatively, if the user is a night owl, the measurement can be performed in the evening. Furthermore, the measurement frequency can be adjusted according to the user's lifestyle. For example, the measurement frequency can be reduced when the user is busy and increased when the user has more free time. This allows measurements to be performed in accordance with the user's lifestyle, thereby obtaining more accurate data.

[0099] The analysis unit can use a machine learning model to predict the user's health condition. For example, the analysis unit can train a machine learning model based on the user's past health data to predict the user's future health condition. The analysis unit can also use the machine learning model to predict fluctuations in the user's health condition. For example, the analysis unit can predict fluctuations in body fat percentage or skin temperature to evaluate the user's future health condition. Furthermore, the analysis unit can use the machine learning model to predict abnormalities in the user's health condition. This allows the user to predict their future health condition and take preventive measures.

[0100] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user's emotions. For example, if the user is feeling stressed, advice on stress reduction can be given priority. Also, if the user is relaxed, advice on maintaining health can be given priority. Furthermore, if the user is in a hurry, advice that can be implemented quickly can be given priority. In this way, by determining the priority of advice according to the user's emotions, more effective health management can be supported.

[0101] The measurement unit can correct the measurement results based on the user's geographical location information. For example, if the user is at a high altitude, the measurement results can be corrected taking into account the influence of atmospheric pressure. Also, if the user is in a humid area, the measurement results can be corrected taking into account the influence of humidity. Furthermore, if the user is moving, the measurement results can be corrected based on the location information. In this way, by correcting the measurement results taking into account the user's geographical location information, more accurate data can be obtained.

[0102] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize analysis related to stress reduction. If the user is relaxed, it can perform a detailed analysis to evaluate the user's health condition. Furthermore, if the user is in a hurry, it can quickly perform an analysis and provide the results. In this way, by adjusting the analysis algorithm according to the user's emotions, it is possible to provide more appropriate analysis results.

[0103] The providing unit can improve the accuracy of advice based on the user's past advice history. For example, the providing unit can analyze the effects of advice the user received in the past and reflect that in current advice. The providing unit can also refer to the user's past advice history to provide effective advice. Furthermore, the providing unit can analyze the user's past advice history and apply an algorithm to improve the accuracy of advice. In this way, the accuracy of advice can be improved by referring to the user's past advice history.

[0104] The providing unit can estimate the user's emotions and adjust the way comments and advice are expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide advice in gentle words. If the user is relaxed, the providing unit can provide detailed advice. If the user is in a hurry, the providing unit can provide concise advice that gets to the point. In this way, by adjusting the way comments and advice are expressed based on the user's emotions, more appropriate advice can be provided.

[0105] The analysis unit can analyze a user's social media activity to obtain health information. For example, the analysis unit can analyze exercise records and diet records shared by the user on social media to obtain health information. The analysis unit can also evaluate the user's health status based on the user's social media activity. Furthermore, the analysis unit can track changes in the user's health status based on the social media activity. In this way, by analyzing the user's social media activity, related health information can be obtained and reflected in the analysis results.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The measurement unit is equipped with a bioimpedance sensor that measures the user's body fat percentage and an infrared sensor that measures skin temperature. Measurements are performed automatically when the user simply stands in front of the mirror. The bioimpedance sensor measures body fat percentage by passing an electric current through it, and the infrared sensor measures the surface temperature of the skin. Step 2: The analysis unit analyzes the data measured by the measurement unit. Using the generative AI, it analyzes fluctuations in body fat percentage and skin temperature to evaluate the user's health condition. For example, it detects increases in body fat percentage and abnormalities in skin temperature. Step 3: The provider provides comments and advice to the user based on the analysis results obtained by the analyzer. Using a generation AI, the provider generates personalized comments and advice based on the user's health condition. For example, if the body fat percentage is increasing, the provider will provide advice encouraging exercise and dietary improvements, and if the skin temperature is high, the provider will provide comments recommending rest and hydration.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0152] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0162] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0163] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0164] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0165] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0166] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0167] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0168] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0169] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0170] 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.

[0171] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0172] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0173] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0174] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0175] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0176] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0177] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0178] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0179] [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a measuring unit including a bioimpedance sensor for measuring the user's body fat percentage; a measuring unit having an infrared sensor for measuring skin temperature; an analysis unit that analyzes the data measured by the measurement unit; a providing unit that provides comments and advice to a user based on the analysis result obtained by the analyzing unit; Equipped with A system characterized by:

2. The analysis unit Analyzes body fat percentage and skin temperature fluctuations to assess the user's health condition The system of claim 1 .

3. The providing unit If body fat is increasing, provide advice on improving exercise and diet The system of claim 1 .

4. The providing unit If skin temperature is high, provide a comment recommending rest and hydration The system of claim 1 .

5. The measurement unit Measurements are taken automatically when the user stands in front of the mirror The system of claim 1 .

6. The analysis unit Use algorithms to compare with historical data and detect anomalies The system of claim 1 .

7. The analysis unit Use machine learning models to predict user health status The system of claim 1 .

8. The measurement unit The user's emotions are estimated, and the timing of measurement is adjusted based on the estimated user's emotions. The system of claim 1 .

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