System

Abstract

The system according to the embodiment aims to grasp a health condition of a user in real time and to immediately share an abnormality.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a sharing unit, a measurement unit, a generation unit, and an advice unit. The collection unit collects daily heart rates and daily life sounds. The analysis unit analyzes the data collected by the collection unit, and grasps the health condition of the user in real time. The sharing unit shares the abnormality detected by the analysis unit with the owner, the family, and a medical expert. The measurement unit measures the abnormal sound, the pulse, and the body temperature detected by the analysis unit. The generation unit causes the voice generation AI to have a conversation with the user in an appropriate format based on the data obtained by the measurement unit. The advice unit provides the voice advice generated by the generation 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 do not adequately grasp a user's health condition in real time and immediately share any abnormalities, so there is room for improvement.

[0005] The system according to the embodiment aims to grasp the health condition of a user in real time and to immediately share any abnormalities. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a sharing unit, a measurement unit, a generation unit, and an advice unit. The collection unit collects daily heart rate and daily sounds. The analysis unit analyzes the data collected by the collection unit and grasps the user's health condition in real time. The sharing unit shares any abnormalities detected by the analysis unit with the user, their family, and medical professionals. The measurement unit measures abnormal sounds, pulse, and body temperature detected by the analysis unit. The generation unit uses a voice generation AI to converse with the user in an appropriate format based on the data obtained by the measurement unit. The advice unit provides voice advice generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the user's health condition in real time and immediately share any abnormalities. [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 wearable tool according to an embodiment of the present invention is an earhook-type system. This system supports early detection of heart disease and other medical conditions and continuous health management. The wearable tool collects and analyzes daily heart rate and other sounds to monitor the user's health in real time. For example, AI-based voice separation technology can accurately extract only essential sounds, enabling continuous health management tailored to individual lifestyles and physical conditions. In the event of an abnormality, information can be instantly shared with the user, their family, and medical professionals, enabling early health improvement and preventative measures. Furthermore, continuous measurement of pulse rate, body temperature, and other parameters can detect signs of illness that may not even be apparent to the user. Voice generation AI enables conversational communication in a format most suited to the user. Daily voice updates on the wearer's physical condition automatically calculate basal metabolic rate, activity metabolism, daily calorie intake, and required exercise volume. Audio advice calculated by a dedicated generation AI is delivered as needed, enabling planned lifestyle changes. This allows the wearable tool to monitor the user's health in real time and respond quickly to abnormalities.

[0029] A wearable tool according to an embodiment includes a collection unit, an analysis unit, a sharing unit, a measurement unit, a generation unit, and an advice unit. The collection unit collects daily heart rate and daily sounds. For example, the collection unit measures the heart rate using a heart rate sensor. The collection unit can also collect daily sounds using a microphone. The collection unit can also filter the collected data using AI to extract only necessary data. The analysis unit analyzes the data collected by the collection unit to grasp the user's health condition in real time. For example, the analysis unit analyzes heart rate data using AI to detect abnormalities. The analysis unit can also analyze daily sound data to detect abnormal sounds. The analysis unit can also analyze data in real time using AI to grasp the user's health condition. The sharing unit shares abnormalities detected by the analysis unit with the user, family, and medical professionals. For example, the sharing unit sends a notification to the user's smartphone when an abnormality is detected. The sharing unit can also send an email to a medical professional when an abnormality is detected. The sharing unit can also use AI to determine the priority of notifications based on the importance of the abnormality. The measurement unit measures abnormal sounds, constant pulse, and body temperature. The measurement unit, for example, measures pulse using a heart rate sensor. The measurement unit can also measure body temperature using a body temperature sensor. The measurement unit can also detect and measure abnormal sounds using AI. The generation unit uses voice generation AI to have a conversation in an optimal format for the user. The generation unit, for example, provides audio advice for relaxation based on the user's health condition. The generation unit can also provide audio exercise advice based on the user's activity level. The generation unit can also estimate the user's emotions using AI and have a conversation in an appropriate tone. The advice unit provides audio advice generated by the generation unit. The advice unit, for example, provides audio dietary advice based on the user's health condition. The advice unit can also provide audio exercise advice based on the user's activity level. The advice unit can also estimate the user's emotions using AI and provide advice in an appropriate tone.This allows the wearable tool according to the embodiment to grasp the user's health condition in real time and respond quickly in the event of an abnormality.

[0030] The collection unit can collect daily heart rates and lifestyle sounds. The collection unit can measure heart rates using, for example, a heart rate sensor. The collection unit can measure heart rates every minute, for example. The collection unit can also collect lifestyle sounds using a microphone. The collection unit can collect, for example, conversation sounds and environmental sounds. The collection unit can also filter the collected data using AI to extract only necessary data. The collection unit can remove noise using AI, for example, and extract only necessary audio data. In this way, by collecting daily heart rates and lifestyle sounds, the user's health condition can be understood.

[0031] The analysis unit analyzes the collected data and can grasp the user's health condition in real time. The analysis unit, for example, uses AI to analyze heart rate data and detect abnormalities. The analysis unit can detect an abnormality, for example, when the heart rate is higher than normal. The analysis unit can also analyze daily sound data and detect abnormal sounds. The analysis unit can, for example, detect abnormal patterns in daily sounds. The analysis unit can also analyze data in real time using AI and grasp the user's health condition. The analysis unit can, for example, analyze the collected data in seconds and detect abnormalities in real time. In this way, the user's health condition can be grasped in real time by analyzing the collected data.

[0032] The sharing unit can share abnormalities detected by the analysis unit with the user, their family, and medical professionals. For example, the sharing unit can send a notification to the user's smartphone when an abnormality is detected. For example, the sharing unit can send a notification to the user when an abnormality is detected when the heart rate is abnormally high. The sharing unit can also send an email to a medical professional when an abnormality is detected. For example, the sharing unit can send an email containing detailed data to a medical professional when an abnormality is detected. The sharing unit can also determine the priority of notifications according to the importance of the abnormality using AI. For example, the sharing unit can send a notification preferentially when a serious abnormality is detected. This allows abnormalities to be shared quickly, enabling early response.

[0033] The measurement unit can measure abnormal sounds, pulse, and body temperature. The measurement unit measures pulse using a heart rate sensor, for example. The measurement unit can measure pulse every minute, for example. The measurement unit can also measure body temperature using a body temperature sensor. The measurement unit can measure body temperature every hour, for example. The measurement unit can also detect and measure abnormal sounds using AI. The measurement unit can detect and measure sounds in a specific frequency band, for example. This makes it possible to understand the health condition in detail by constantly measuring abnormal sounds, pulse, and body temperature.

[0034] The generation unit can use voice generation AI to have a conversation with the user in an appropriate format. The generation unit, for example, provides voice advice on how to relax depending on the user's health condition. For example, the generation unit can provide advice on how to relax when the user is feeling stressed. The generation unit can also provide voice advice on exercise depending on the user's activity level. For example, the generation unit can provide exercise advice when the user is not getting enough exercise. The generation unit can also estimate the user's emotions using AI and have a conversation in an appropriate tone. For example, when the user is relaxed, the generation unit can have a conversation in a calm tone. This enables a conversation to be held in a format that is optimal for the user.

[0035] The advice unit can provide the audio advice generated by the generation unit. The advice unit can provide audio dietary advice depending on the user's health condition, for example. The advice unit can provide dietary advice when the user is lacking in nutrition, for example. The advice unit can also provide audio exercise advice depending on the user's activity level. The advice unit can provide exercise advice when the user is lacking in exercise, for example. The advice unit can also estimate the user's emotions using AI and provide advice in an appropriate tone. For example, the advice unit can provide advice in a gentle tone when the user is relaxed. In this way, the audio advice is provided to support the user's health management.

[0036] The generation unit can automatically calculate the basal metabolic rate, the active metabolic rate, the daily required calorie intake, and the required amount of exercise. The generation unit calculates the basal metabolic rate based on, for example, the user's age, gender, and weight. The generation unit can calculate the basal metabolic rate using, for example, the user's age, gender, and weight as input. The generation unit can also calculate the active metabolic rate based on the user's amount of exercise and activity level. The generation unit can calculate the active metabolic rate using, for example, the user's amount of exercise and activity level as input. The generation unit can also calculate the daily required calorie intake based on the basal metabolic rate and the active metabolic rate. The generation unit can calculate the required calorie intake using, for example, the basal metabolic rate and the active metabolic rate as input. The generation unit can also calculate the daily required amount of exercise based on the basal metabolic rate and the active metabolic rate. The generation unit can calculate the required amount of exercise using, for example, the basal metabolic rate and the active metabolic rate as input. In this way, the basal metabolic rate, the active metabolic rate, the required calorie intake, and the required amount of exercise are automatically calculated to support the user's health management.

[0037] The advice unit can provide audio advice to encourage the user to systematically review their lifestyle habits. The advice unit can provide audio advice regarding lifestyle review depending on, for example, the user's health condition. The advice unit can provide exercise advice when, for example, the user is not getting enough exercise. The advice unit can also provide audio dietary advice depending on the user's diet. For example, the advice unit can provide advice on a nutritionally balanced diet when, for example, the user is lacking in nutrition. The advice unit can also estimate the user's emotions using AI and provide advice in an appropriate tone. For example, the advice unit can provide advice in a calm tone when the user is relaxed. This contributes to improving the user's motivation by providing audio advice to encourage the user to systematically review their lifestyle habits.

[0038] The collection unit can analyze the user's past health data and select an appropriate collection method. The collection unit can, for example, use AI to analyze the user's past health data. The collection unit can, for example, analyze past heart rate data and increase the collection frequency during time periods when there are many abnormalities. The collection unit can also analyze past daily sound data and increase the collection frequency during time periods when there are many specific sounds. The collection unit can, for example, analyze past daily sound data and increase the collection frequency during time periods when there are many specific sounds. The collection unit can also comprehensively analyze the user's past health data and determine the optimal collection timing. The collection unit can, for example, comprehensively analyze past heart rate data, body temperature data, and daily sound data and determine the optimal collection timing. In this way, the optimal collection method can be selected by analyzing the past health data.

[0039] The collection unit can perform filtering based on the user's current activity status and environment when collecting data. The collection unit grasps the user's current activity status and environment using, for example, AI. The collection unit can prioritize collection of lifestyle sounds, for example, when the user is in a quiet environment. The collection unit can also prioritize collection of heart rate, for example, when the user is exercising. The collection unit can also prioritize collection of heart rate, for example, when the user is exercising. The collection unit can also collect both heart rate and lifestyle sounds, for example, when the user is sleeping. The collection unit can collect both heart rate and lifestyle sounds, for example, when the user is sleeping. This makes it possible to collect data according to the user's activity status and environment.

[0040] The collection unit can select the optimal collection means depending on the user's input method when collecting data. The collection unit grasps the user's input method using, for example, AI. For example, the collection unit can prioritize collection of voice data when the user is using voice input. Furthermore, the collection unit can prioritize collection of text data when the user is using text input. For example, the collection unit can prioritize collection of text data when the user is using text input. Furthermore, the collection unit can prioritize collection of gesture data when the user is using gesture input. For example, the collection unit can prioritize collection of gesture data when the user is using gesture input. This enables optimal data collection depending on the user's input method.

[0041] During collection, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, uses AI to determine the user's geographical location information. The collection unit can, for example, determine the user's geographical location information by using GPS data. The collection unit can also, for example, determine the user's geographical location information by using a location information service. The collection unit can, for example, determine the user's geographical location information by using a location information service. The collection unit can also, for example, determine the user's geographical location information by using a location information service. The collection unit can also, for example, prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit can, for example, prioritize collecting life sound data when the user is at home. The collection unit can also, for example, prioritize collecting heart rate data when the user is at work. The collection unit can, for example, prioritize collecting exercise data when the user is at an exercise facility. This enables data collection based on the user's geographical location information.

[0042] The collection unit can analyze the user's social media activity at the time of collection and collect related data. The collection unit can, for example, use AI to analyze the user's social media activity. The collection unit can, for example, analyze the content of the user's social media posts. The collection unit can also analyze the number of likes the user has on social media. The collection unit can, for example, analyze the number of likes the user has on social media. The collection unit can also analyze the user's social media activity and collect related data. For example, if the user posts about exercise on social media, the collection unit can prioritize collection of exercise data. For example, if the user posts about diet on social media, the collection unit can prioritize collection of diet data. For example, if the user posts about stress on social media, the collection unit can prioritize collection of heart rate data. This makes it possible to collect data based on the user's social media activity.

[0043] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit can, for example, use AI to analyze the user's past feedback. The collection unit can, for example, analyze the user's past ratings and comments. The collection unit can also customize the collection method by reflecting the user's past feedback. For example, if the user has requested collection of heart rate data in the past, the collection unit can prioritize collection of heart rate data. For example, if the user has requested collection of lifestyle sound data in the past, the collection unit can also prioritize collection of lifestyle sound data. For example, if the user has requested collection of exercise data in the past, the collection unit can prioritize collection of exercise data. This makes it possible to customize the collection method based on the user's past feedback.

[0044] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit evaluates the importance of the data using, for example, AI. The analysis unit can perform a detailed analysis, for example, when the heart rate data is abnormal. The analysis unit can also perform a detailed analysis, for example, when the life sound data is abnormal. The analysis unit can also perform a detailed analysis, for example, when the body temperature data is abnormal. The analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance and a brief analysis on data with low importance. In this way, by adjusting the level of detail of the analysis according to the importance of the data, more appropriate analysis results can be provided.

[0045] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. The analysis unit classifies data categories using, for example, AI. The analysis unit can apply, for example, a heart rate analysis algorithm to heart rate data. The analysis unit can also apply a voice analysis algorithm to daily sound data. The analysis unit can apply, for example, a body temperature analysis algorithm to body temperature data. The analysis unit can also apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an analysis algorithm dedicated to heart rate to heart rate data, and an analysis algorithm dedicated to voice to daily sound data. In this way, by applying an analysis algorithm according to the data category, more appropriate analysis results can be provided.

[0046] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, uses AI to analyze the user's past analysis results. The analysis unit, for example, can analyze current heart rate data by referring to past heart rate analysis results. The analysis unit can also analyze current lifestyle sound data by referring to past lifestyle sound analysis results. The analysis unit, for example, can analyze current body temperature data by referring to past body temperature analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, can improve the analysis accuracy of current heart rate data based on past heart rate analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0047] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, uses AI to evaluate the time when the data was collected. The analysis unit, for example, can prioritize analyzing recently collected data. The analysis unit can also prioritize analyzing data collected during a time period when many abnormalities occur. The analysis unit, for example, can prioritize analyzing data collected during a time period specified by the user. The analysis unit can also determine the priority of analysis based on the time when the data was collected. For example, the analysis unit can prioritize analyzing recently collected data and postpone analyzing older data. In this way, by determining the priority of analysis based on the time when the data was collected, more appropriate analysis results can be provided.

[0048] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit evaluates the relevance of the data using, for example, AI. The analysis unit can determine the order of analysis, for example, taking into account the relevance between heart rate data and life sound data. The analysis unit can also determine the order of analysis, for example, taking into account the relevance between body temperature data and heart rate data. The analysis unit can determine the order of analysis, for example, taking into account the relevance between life sound data and body temperature data. The analysis unit can also adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analyzing highly relevant data and postpone analyzing less relevant data. In this way, by adjusting the order of analysis based on the relevance of the data, more appropriate analysis results can be provided.

[0049] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, uses AI to evaluate the user's level of expertise. For example, if the user is a medical professional, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user is a general public, the analysis unit can provide analysis results that avoid technical terms. For example, if the user is a student, the analysis unit can provide analysis results that include educational elements. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can explain things in simple terms to beginners and use technical terms to advanced users. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0050] The sharing unit can adjust the level of detail of the information to be shared based on the importance of the anomaly when sharing. The sharing unit, for example, uses AI to evaluate the importance of the anomaly. For example, the sharing unit can share detailed information when a serious anomaly is detected. The sharing unit can also share brief information when a minor anomaly is detected. The sharing unit can adjust the level of detail of the information to be shared, for example, according to the type of anomaly. The sharing unit can also adjust the level of detail of the information to be shared based on the importance of the anomaly. For example, the sharing unit can share detailed information for anomalies with high importance and share brief information for anomalies with low importance. In this way, by adjusting the level of detail of the information to be shared according to the importance of the anomaly, more appropriate information sharing is possible.

[0051] The sharing unit can apply different sharing algorithms depending on the category of abnormality when sharing. The sharing unit classifies the category of abnormality using, for example, AI. The sharing unit can, for example, apply a sharing algorithm dedicated to heart rate to an abnormal heart rate. The sharing unit can also apply a sharing algorithm dedicated to audio to an abnormal living sound. The sharing unit can, for example, apply a sharing algorithm dedicated to body temperature to an abnormal body temperature. The sharing unit can also apply different sharing algorithms depending on the category of abnormality. For example, the sharing unit can apply a sharing algorithm dedicated to heart rate to an abnormal heart rate, and a sharing algorithm dedicated to audio to an abnormal living sound. This enables more appropriate information sharing by applying a sharing algorithm according to the category of abnormality.

[0052] When sharing, the sharing unit can improve the accuracy of sharing by referring to the user's past sharing results. The sharing unit can analyze the user's past sharing results using AI, for example. The sharing unit can share current heart rate data by referring to past heart rate sharing results. The sharing unit can also share current daily sound data by referring to past daily sound sharing results. The sharing unit can share current body temperature data by referring to past body temperature sharing results. The sharing unit can also improve the accuracy of sharing by referring to the user's past sharing results. The sharing unit can improve the accuracy of sharing current heart rate data based on past heart rate sharing results, for example. This makes it possible to improve the accuracy of sharing by referring to the user's past sharing results.

[0053] When sharing, the sharing unit can determine the priority of sharing based on the time when the abnormality occurred. The sharing unit can, for example, use AI to evaluate the time when the abnormality occurred. The sharing unit can, for example, prioritize sharing of an abnormality that occurred recently. The sharing unit can also prioritize sharing of an abnormality that occurred during a time period when there are many abnormalities. The sharing unit can, for example, prioritize sharing of an abnormality that occurred during a time period specified by the user. The sharing unit can also determine the priority of sharing based on the time when the abnormality occurred. For example, the sharing unit can prioritize sharing of an abnormality that occurred recently and postpone sharing of older abnormalities. In this way, by determining the priority of sharing based on the time when the abnormality occurred, more appropriate information sharing is possible.

[0054] The sharing unit can adjust the order of sharing based on the relevance of the abnormalities when sharing. The sharing unit evaluates the relevance of the abnormalities using, for example, AI. The sharing unit can determine the order of sharing by considering, for example, the relevance between an abnormal heart rate and an abnormality in daily life sounds. The sharing unit can also determine the order of sharing by considering the relevance between an abnormal body temperature and an abnormal heart rate. The sharing unit can determine the order of sharing by considering, for example, the relevance between an abnormality in daily life sounds and an abnormal body temperature. The sharing unit can also adjust the order of sharing based on the relevance of the abnormalities. For example, the sharing unit can prioritize sharing of highly relevant abnormalities and postpone sharing of less relevant abnormalities. This enables more appropriate information sharing by adjusting the order of sharing based on the relevance of the abnormalities.

[0055] The sharing unit can adjust the use of shared terminology according to the user's level of expertise when sharing. The sharing unit can, for example, use AI to evaluate the user's level of expertise. For example, if the user is a medical professional, the sharing unit can provide shared information that uses a lot of terminology. Furthermore, if the user is a general public, the sharing unit can provide shared information that avoids terminology. For example, if the user is a student, the sharing unit can provide shared information that includes educational elements. Furthermore, the sharing unit can adjust the use of shared terminology according to the user's level of expertise. For example, the sharing unit can explain things in simple terms to beginners and use technical terms to advanced users. This enables more appropriate information sharing by adjusting the use of shared terminology according to the user's level of expertise.

[0056] The measurement unit can adjust the level of detail of the measurement based on the importance of the data during measurement. The measurement unit evaluates the importance of the data using, for example, AI. The measurement unit can perform detailed measurement, for example, when heart rate data is abnormal. The measurement unit can also perform detailed measurement, for example, when daily sound data is abnormal. The measurement unit can also perform detailed measurement, for example, when body temperature data is abnormal. The measurement unit can also adjust the level of detail of the measurement based on the importance of the data. For example, the measurement unit can perform detailed measurement for data with high importance and simple measurement for data with low importance. This allows for more appropriate data measurement by adjusting the level of detail of the measurement according to the importance of the data.

[0057] The measurement unit can apply different measurement algorithms depending on the data category during measurement. The measurement unit classifies data categories using, for example, AI. The measurement unit can apply, for example, a heart rate measurement algorithm to heart rate data. The measurement unit can also apply a voice measurement algorithm to daily sound data. The measurement unit can apply, for example, a body temperature measurement algorithm to body temperature data. The measurement unit can also apply different measurement algorithms depending on the data category. For example, the measurement unit can apply a measurement algorithm dedicated to heart rate to heart rate data, and a measurement algorithm dedicated to voice to daily sound data. This enables more appropriate data measurement by applying a measurement algorithm according to the data category.

[0058] The measurement unit can improve the accuracy of measurement by referring to the user's past measurement results when taking measurements. The measurement unit can analyze the user's past measurement results using AI, for example. The measurement unit can measure current heart rate data by referring to past heart rate measurement results, for example. The measurement unit can also measure current daily sound data by referring to past daily sound measurement results. The measurement unit can measure current body temperature data by referring to past body temperature measurement results, for example. The measurement unit can also improve the accuracy of measurement by referring to the user's past measurement results. The measurement unit can improve the measurement accuracy of current heart rate data based on, for example, past heart rate measurement results. In this way, the measurement accuracy can be improved by referring to the user's past measurement results.

[0059] During measurement, the measurement unit can determine the priority of measurements based on the time when the data was collected. The measurement unit evaluates the time when the data was collected using, for example, AI. The measurement unit can, for example, prioritize measuring recently collected data. The measurement unit can also prioritize measuring data collected during a time period when there are many abnormalities. The measurement unit can, for example, prioritize measuring data collected during a time period specified by the user. The measurement unit can also determine the priority of measurements based on the time when the data was collected. For example, the measurement unit can prioritize measuring recently collected data and postpone older data. This enables more appropriate data measurement by determining the priority of measurements based on the time when the data was collected.

[0060] The measurement unit can adjust the order of measurements based on the relevance of data during measurement. The measurement unit evaluates the relevance of data using, for example, AI. The measurement unit can determine the order of measurements, for example, taking into account the relevance between heart rate data and daily sound data. The measurement unit can also determine the order of measurements, for example, taking into account the relevance between body temperature data and heart rate data. The measurement unit can determine the order of measurements, for example, taking into account the relevance between daily sound data and body temperature data. The measurement unit can also adjust the order of measurements based on the relevance of data. For example, the measurement unit can prioritize measuring highly relevant data and postpone measuring less relevant data. This allows for more appropriate data measurement by adjusting the order of measurements based on the relevance of data.

[0061] During measurement, the measurement unit can adjust the use of technical terminology in the measurement according to the user's level of expertise. The measurement unit, for example, uses AI to evaluate the user's level of expertise. For example, if the user is a medical professional, the measurement unit can provide measurement results that use a lot of technical terminology. Furthermore, if the user is a general public, the measurement unit can provide measurement results that avoid technical terminology. For example, if the user is a student, the measurement unit can provide measurement results that include educational elements. Furthermore, the measurement unit can adjust the use of technical terminology in the measurement according to the user's level of expertise. For example, the measurement unit can explain things in simple terms to beginners and use technical terminology to advanced users. This allows for more appropriate data measurement by adjusting the use of technical terminology in the measurement according to the user's level of expertise.

[0062] The generation unit can adjust the level of detail of the generated voice based on the importance of the data at the time of generation. The generation unit can, for example, use AI to evaluate the importance of the data. The generation unit can, for example, generate detailed voice for important data. The generation unit can also generate concise voice for minor data. The generation unit can adjust the level of detail of the generated voice based on the importance of the data, for example. The generation unit can generate detailed voice for data with high importance and concise voice for data with low importance. In this way, by adjusting the level of detail of the generated voice based on the importance of the data, more appropriate voice generation becomes possible.

[0063] The generation unit can apply different generation algorithms depending on the data category during generation. The generation unit classifies the data category using, for example, AI. The generation unit can apply, for example, a generation algorithm dedicated to heart rates to heart rate data. The generation unit can also apply a generation algorithm dedicated to audio to everyday sound data. The generation unit can apply, for example, a generation algorithm dedicated to body temperature to body temperature data. The generation unit can also apply different generation algorithms depending on the data category. For example, the generation unit can apply a generation algorithm dedicated to heart rates to heart rate data, and a generation algorithm dedicated to audio to everyday sound data. This makes it possible to generate more appropriate audio by applying a generation algorithm according to the data category.

[0064] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. The generation unit can analyze the user's past generation results using, for example, AI. The generation unit can generate current heart rate data by referring to, for example, past heart rate generation results. The generation unit can also generate current lifestyle sound data by referring to past lifestyle sound generation results. The generation unit can generate current body temperature data by referring to, for example, past body temperature generation results. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. The generation unit can improve the accuracy of generation of current heart rate data based on, for example, past heart rate generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0065] At the time of generation, the generation unit can determine a generation priority based on the time when the data was collected. The generation unit can, for example, use AI to evaluate the time when the data was collected. The generation unit can, for example, prioritize generating recently collected data. The generation unit can also prioritize generating data collected during a time period when there are many abnormalities. The generation unit can, for example, prioritize generating data collected during a time period specified by the user. The generation unit can also determine a generation priority based on the time when the data was collected. For example, the generation unit can prioritize generating recently collected data and postpone generating older data. In this way, by determining a generation priority based on the time when the data was collected, more appropriate voice generation is possible.

[0066] The generation unit can adjust the order of generation based on the relevance of the data during generation. The generation unit evaluates the relevance of the data using, for example, AI. The generation unit can determine the order of generation, for example, taking into account the relevance between heart rate data and daily life sound data. The generation unit can also determine the order of generation, for example, taking into account the relevance between body temperature data and heart rate data. The generation unit can determine the order of generation, for example, taking into account the relevance between daily life sound data and body temperature data. The generation unit can also adjust the order of generation based on the relevance of the data. For example, the generation unit can prioritize generating highly relevant data and postpone generating less relevant data. In this way, adjusting the order of generation based on the relevance of the data enables more appropriate voice generation.

[0067] During generation, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. The generation unit, for example, uses AI to evaluate the user's level of expertise. For example, if the user is a medical professional, the generation unit can provide a generated result that uses a lot of technical terminology. Furthermore, if the user is a general public, the generation unit can provide a generated result that avoids technical terminology. For example, if the user is a student, the generation unit can provide a generated result that includes educational elements. Furthermore, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, the generation unit can explain things in simple terms to beginners and use technical terminology to advanced users. This enables more appropriate speech generation by adjusting the use of technical terminology in the generation according to the user's level of expertise.

[0068] When giving advice, the advice unit can adjust the level of detail of the advice based on the importance of the data. The advice unit, for example, uses AI to evaluate the importance of the data. The advice unit, for example, can provide detailed advice for important data. The advice unit can also provide brief advice for minor data. The advice unit can adjust the level of detail of the advice according to the type of data, for example. The advice unit can also adjust the level of detail of the advice based on the importance of the data. For example, the advice unit can provide detailed advice for data with high importance and brief advice for data with low importance. In this way, by adjusting the level of detail of the advice according to the importance of the data, more appropriate advice can be provided.

[0069] When giving advice, the advice unit can apply different advice algorithms depending on the data category. The advice unit classifies the data category using, for example, AI. The advice unit can apply, for example, an advice algorithm dedicated to heart rate to heart rate data. The advice unit can also apply an advice algorithm dedicated to voice to daily sound data. The advice unit can apply, for example, an advice algorithm dedicated to body temperature to body temperature data. The advice unit can also apply different advice algorithms depending on the data category. For example, the advice unit can apply an advice algorithm dedicated to heart rate to heart rate data, and an advice algorithm dedicated to voice to daily sound data. This makes it possible to provide more appropriate advice by applying an advice algorithm according to the data category.

[0070] When providing advice, the advice unit can improve the accuracy of the advice by referring to past advice results for the user. The advice unit can analyze the past advice results for the user using AI, for example. The advice unit can provide advice based on current heart rate data by referring to past heart rate advice results. The advice unit can also provide advice based on current life sound data by referring to past life sound advice results. The advice unit can provide advice based on current body temperature data by referring to past body temperature advice results. The advice unit can also improve the accuracy of the advice by referring to past advice results for the user. The advice unit can improve the accuracy of advice for current heart rate data based on past heart rate advice results, for example. This makes it possible to improve the accuracy of advice by referring to the past advice results for the user.

[0071] When giving advice, the advice unit can determine the priority of advice based on the time when the data was collected. The advice unit can, for example, use AI to evaluate the time when the data was collected. The advice unit can, for example, give priority to advice about recently collected data. The advice unit can also, for example, give priority to advice about data collected during a time period when many abnormalities occur. The advice unit can, for example, give priority to advice about data collected during a time period specified by the user. The advice unit can also determine the priority of advice based on the time when the data was collected. For example, the advice unit can give priority to advice about recently collected data and postpone advice about older data. In this way, by determining the priority of advice based on the time when the data was collected, more appropriate advice can be provided.

[0072] When giving advice, the advice unit can adjust the order of advice based on the relevance of data. The advice unit evaluates the relevance of data using, for example, AI. The advice unit can determine the order of advice taking into consideration, for example, the relevance between heart rate data and daily life sound data. The advice unit can also determine the order of advice taking into consideration, for example, the relevance between body temperature data and heart rate data. The advice unit can determine the order of advice taking into consideration, for example, the relevance between daily life sound data and body temperature data. The advice unit can also adjust the order of advice based on the relevance of data. For example, the advice unit can give priority to advice on highly relevant data and postpone advice on less relevant data. In this way, by adjusting the order of advice based on the relevance of data, more appropriate advice can be provided.

[0073] When providing advice, the advice unit can adjust the use of technical terms in the advice according to the user's level of expertise. The advice unit, for example, uses AI to evaluate the user's level of expertise. For example, if the user is a medical professional, the advice unit can provide advice that uses a lot of technical terms. Furthermore, if the user is a general public, the advice unit can provide advice that avoids technical terms. For example, if the user is a student, the advice unit can provide advice that includes educational elements. Furthermore, the advice unit can adjust the use of technical terms in the advice according to the user's level of expertise. For example, the advice unit can explain things in simple terms to beginners and use technical terms to advanced users. This allows for more appropriate advice by adjusting the use of technical terms in the advice according to the user's level of expertise.

[0074] The generation unit can automatically calculate the basal metabolic rate, the active metabolic rate, the daily required calorie intake, and the required amount of exercise. The generation unit calculates the basal metabolic rate based on, for example, the user's age, gender, and weight. The generation unit can calculate the basal metabolic rate using, for example, the user's age, gender, and weight as input. The generation unit can also calculate the active metabolic rate based on the user's amount of exercise and activity level. The generation unit can calculate the active metabolic rate using, for example, the user's amount of exercise and activity level as input. The generation unit can also calculate the daily required calorie intake based on the basal metabolic rate and the active metabolic rate. The generation unit can calculate the required calorie intake using, for example, the basal metabolic rate and the active metabolic rate as input. The generation unit can also calculate the daily required amount of exercise based on the basal metabolic rate and the active metabolic rate. The generation unit can calculate the required amount of exercise using, for example, the basal metabolic rate and the active metabolic rate as input. In this way, the basal metabolic rate, the active metabolic rate, the required calorie intake, and the required amount of exercise are automatically calculated to support the user's health management.

[0075] The advice unit can provide audio advice to encourage the user to systematically review their lifestyle habits. The advice unit can provide audio advice regarding lifestyle review depending on, for example, the user's health condition. The advice unit can provide exercise advice when, for example, the user is not getting enough exercise. The advice unit can also provide audio dietary advice depending on the user's diet. For example, the advice unit can provide advice on a nutritionally balanced diet when, for example, the user is lacking in nutrition. The advice unit can also estimate the user's emotions using AI and provide advice in an appropriate tone. For example, the advice unit can provide advice in a calm tone when the user is relaxed. This contributes to improving the user's motivation by providing audio advice to encourage the user to systematically review their lifestyle habits.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The collection unit monitors the user's sleep patterns, and the analysis unit analyzes the collected sleep data to evaluate the user's sleep quality. For example, the collection unit can detect the user's tossing and turning or snoring sounds and estimate the depth of sleep. The analysis unit can also analyze the user's sleep cycle based on the collected data and suggest an optimal wake-up time. Furthermore, the sharing unit can provide the user with advice on improving their sleep based on the analysis results. This can improve the user's sleep quality.

[0078] The analysis unit can collect and analyze the user's dietary data. For example, when the user takes a photo of a meal, the analysis unit can use image recognition technology to analyze the meal contents and evaluate the balance of nutrients. The analysis unit can also evaluate the risk of nutritional deficiencies or overconsumption based on the user's dietary history. Furthermore, the sharing unit can suggest nutritionally balanced meals to the user based on the analysis results. This can improve the user's eating habits and support their health.

[0079] The sharing unit collects the user's exercise data, and the analysis unit can evaluate the user's exercise habits based on the collected exercise data. For example, the collection unit records the user's number of steps and exercise time, and the analysis unit can evaluate the user's exercise volume based on this data. The analysis unit can also evaluate the risk of insufficient or excessive exercise based on the user's exercise data. Furthermore, the sharing unit can suggest an appropriate exercise plan to the user based on the analysis results. This can improve the user's exercise habits and support their health.

[0080] The measurement unit measures the user's stress level, and the analysis unit can evaluate the user's stress state based on the collected stress data. For example, the measurement unit can measure the user's heart rate variability and skin electrical response to estimate the stress level. The analysis unit can also identify the cause of the user's stress based on the collected data and provide stress management advice. Furthermore, the sharing unit can suggest relaxation methods and stress-reducing activities to the user based on the analysis results. This can support the user's stress management.

[0081] The collection unit can prioritize collection of highly relevant data by taking into account the user's geographical location information. For example, when the user is at home, collection of daily life sound data can be prioritized. Also, when the user is at work, collection of heart rate data can be prioritized. Furthermore, when the user is at an exercise facility, collection of exercise data can be prioritized. This enables data collection based on the user's geographical location information, allowing for more appropriate health management.

[0082] The processing flow of the first embodiment will be briefly explained below.

[0083] Step 1: The collection unit collects daily heart rate and daily sounds. The collection unit measures heart rate using a heart rate sensor and collects daily sounds using a microphone. The collection unit also uses AI to filter the collected data and extract only the necessary data. Step 2: The analysis unit analyzes the data collected by the collection unit and grasps the user's health condition in real time. The analysis unit uses AI to analyze heart rate data and daily sound data and detect abnormalities. Step 3: The sharing unit shares any abnormalities detected by the analysis unit with the user, their family, and medical professionals. If an abnormality is detected, the sharing unit sends a notification to the user's smartphone and an email to medical professionals. It also uses AI to determine the priority of notifications based on the importance of the abnormality. Step 4: The measurement unit measures the abnormal sounds, pulse, and body temperature detected by the analysis unit. The measurement unit measures the pulse using a heart rate sensor and the body temperature using a body temperature sensor. It also uses AI to detect and measure abnormal sounds. Step 5: The generation unit uses a voice generation AI to converse with the user in an appropriate format based on the data obtained by the measurement unit. The generation unit provides voice advice on relaxation and exercise based on the user's health condition and activity level. The AI ​​also estimates the user's emotions and converses in an appropriate tone. Step 6: The advice unit provides the audio advice generated by the generation unit. The advice unit provides audio advice on diet and exercise based on the user's health condition and activity level. It also uses AI to estimate the user's emotions and provides advice in an appropriate tone.

[0084] (Example 2) A wearable tool according to an embodiment of the present invention is an earhook-type system. This system supports early detection of heart disease and other medical conditions and continuous health management. The wearable tool collects and analyzes daily heart rate and other sounds to monitor the user's health in real time. For example, AI-based voice separation technology can accurately extract only essential sounds, enabling continuous health management tailored to individual lifestyles and physical conditions. In the event of an abnormality, information can be instantly shared with the user, their family, and medical professionals, enabling early health improvement and preventative measures. Furthermore, continuous measurement of pulse rate, body temperature, and other parameters can detect signs of illness that may not even be apparent to the user. Voice generation AI enables conversational communication in a format most suited to the user. Daily voice updates on the wearer's physical condition automatically calculate basal metabolic rate, activity metabolism, daily calorie intake, and required exercise volume. Audio advice calculated by a dedicated generation AI is delivered as needed, enabling planned lifestyle changes. This allows the wearable tool to monitor the user's health in real time and respond quickly to abnormalities.

[0085] A wearable tool according to an embodiment includes a collection unit, an analysis unit, a sharing unit, a measurement unit, a generation unit, and an advice unit. The collection unit collects daily heart rate and daily sounds. For example, the collection unit measures the heart rate using a heart rate sensor. The collection unit can also collect daily sounds using a microphone. The collection unit can also filter the collected data using AI to extract only necessary data. The analysis unit analyzes the data collected by the collection unit to grasp the user's health condition in real time. For example, the analysis unit analyzes heart rate data using AI to detect abnormalities. The analysis unit can also analyze daily sound data to detect abnormal sounds. The analysis unit can also analyze data in real time using AI to grasp the user's health condition. The sharing unit shares abnormalities detected by the analysis unit with the user, family, and medical professionals. For example, the sharing unit sends a notification to the user's smartphone when an abnormality is detected. The sharing unit can also send an email to a medical professional when an abnormality is detected. The sharing unit can also use AI to determine the priority of notifications based on the importance of the abnormality. The measurement unit measures abnormal sounds, constant pulse, and body temperature. The measurement unit, for example, measures pulse using a heart rate sensor. The measurement unit can also measure body temperature using a body temperature sensor. The measurement unit can also detect and measure abnormal sounds using AI. The generation unit uses voice generation AI to have a conversation in an optimal format for the user. The generation unit, for example, provides audio advice for relaxation based on the user's health condition. The generation unit can also provide audio exercise advice based on the user's activity level. The generation unit can also estimate the user's emotions using AI and have a conversation in an appropriate tone. The advice unit provides audio advice generated by the generation unit. The advice unit, for example, provides audio dietary advice based on the user's health condition. The advice unit can also provide audio exercise advice based on the user's activity level. The advice unit can also estimate the user's emotions using AI and provide advice in an appropriate tone.This allows the wearable tool according to the embodiment to grasp the user's health condition in real time and respond quickly in the event of an abnormality.

[0086] The collection unit can collect daily heart rates and lifestyle sounds. The collection unit can measure heart rates using, for example, a heart rate sensor. The collection unit can measure heart rates every minute, for example. The collection unit can also collect lifestyle sounds using a microphone. The collection unit can collect, for example, conversation sounds and environmental sounds. The collection unit can also filter the collected data using AI to extract only necessary data. The collection unit can remove noise using AI, for example, and extract only necessary audio data. In this way, by collecting daily heart rates and lifestyle sounds, the user's health condition can be understood.

[0087] The analysis unit analyzes the collected data and can grasp the user's health condition in real time. The analysis unit, for example, uses AI to analyze heart rate data and detect abnormalities. The analysis unit can detect an abnormality, for example, when the heart rate is higher than normal. The analysis unit can also analyze daily sound data and detect abnormal sounds. The analysis unit can, for example, detect abnormal patterns in daily sounds. The analysis unit can also analyze data in real time using AI and grasp the user's health condition. The analysis unit can, for example, analyze the collected data in seconds and detect abnormalities in real time. In this way, the user's health condition can be grasped in real time by analyzing the collected data.

[0088] The sharing unit can share abnormalities detected by the analysis unit with the user, their family, and medical professionals. For example, the sharing unit can send a notification to the user's smartphone when an abnormality is detected. For example, the sharing unit can send a notification to the user when an abnormality is detected when the heart rate is abnormally high. The sharing unit can also send an email to a medical professional when an abnormality is detected. For example, the sharing unit can send an email containing detailed data to a medical professional when an abnormality is detected. The sharing unit can also determine the priority of notifications according to the importance of the abnormality using AI. For example, the sharing unit can send a notification preferentially when a serious abnormality is detected. This allows abnormalities to be shared quickly, enabling early response.

[0089] The measurement unit can measure abnormal sounds, pulse, and body temperature. The measurement unit measures pulse using a heart rate sensor, for example. The measurement unit can measure pulse every minute, for example. The measurement unit can also measure body temperature using a body temperature sensor. The measurement unit can measure body temperature every hour, for example. The measurement unit can also detect and measure abnormal sounds using AI. The measurement unit can detect and measure sounds in a specific frequency band, for example. This makes it possible to understand the health condition in detail by constantly measuring abnormal sounds, pulse, and body temperature.

[0090] The generation unit can use voice generation AI to have a conversation with the user in an appropriate format. The generation unit, for example, provides voice advice on how to relax depending on the user's health condition. For example, the generation unit can provide advice on how to relax when the user is feeling stressed. The generation unit can also provide voice advice on exercise depending on the user's activity level. For example, the generation unit can provide exercise advice when the user is not getting enough exercise. The generation unit can also estimate the user's emotions using AI and have a conversation in an appropriate tone. For example, when the user is relaxed, the generation unit can have a conversation in a calm tone. This enables a conversation to be held in a format that is optimal for the user.

[0091] The advice unit can provide the audio advice generated by the generation unit. The advice unit can provide audio dietary advice depending on the user's health condition, for example. The advice unit can provide dietary advice when the user is lacking in nutrition, for example. The advice unit can also provide audio exercise advice depending on the user's activity level. The advice unit can provide exercise advice when the user is lacking in exercise, for example. The advice unit can also estimate the user's emotions using AI and provide advice in an appropriate tone. For example, the advice unit can provide advice in a gentle tone when the user is relaxed. In this way, the audio advice is provided to support the user's health management.

[0092] The generation unit can automatically calculate the basal metabolic rate, the active metabolic rate, the daily required calorie intake, and the required amount of exercise. The generation unit calculates the basal metabolic rate based on, for example, the user's age, gender, and weight. The generation unit can calculate the basal metabolic rate using, for example, the user's age, gender, and weight as input. The generation unit can also calculate the active metabolic rate based on the user's amount of exercise and activity level. The generation unit can calculate the active metabolic rate using, for example, the user's amount of exercise and activity level as input. The generation unit can also calculate the daily required calorie intake based on the basal metabolic rate and the active metabolic rate. The generation unit can calculate the required calorie intake using, for example, the basal metabolic rate and the active metabolic rate as input. The generation unit can also calculate the daily required amount of exercise based on the basal metabolic rate and the active metabolic rate. The generation unit can calculate the required amount of exercise using, for example, the basal metabolic rate and the active metabolic rate as input. In this way, the basal metabolic rate, the active metabolic rate, the required calorie intake, and the required amount of exercise are automatically calculated to support the user's health management.

[0093] The advice unit can provide audio advice to encourage the user to systematically review their lifestyle habits. The advice unit can provide audio advice regarding lifestyle review depending on, for example, the user's health condition. The advice unit can provide exercise advice when, for example, the user is not getting enough exercise. The advice unit can also provide audio dietary advice depending on the user's diet. For example, the advice unit can provide advice on a nutritionally balanced diet when, for example, the user is lacking in nutrition. The advice unit can also estimate the user's emotions using AI and provide advice in an appropriate tone. For example, the advice unit can provide advice in a calm tone when the user is relaxed. This contributes to improving the user's motivation by providing audio advice to encourage the user to systematically review their lifestyle habits.

[0094] The collection unit can estimate the user's emotions and adjust the timing of collecting the heart rate and lifestyle sounds based on the estimated user's emotions. The collection unit, for example, uses AI to estimate the user's emotions. The collection unit can estimate the user's emotions using facial expression recognition technology, for example. The collection unit can also estimate the user's emotions using voice analysis technology. The collection unit can estimate the user's emotions by analyzing the tone and speed of the user's voice, for example. The collection unit can also adjust the timing of collecting the heart rate and lifestyle sounds based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit can increase the frequency of collecting the heart rate and reduce the collection of lifestyle sounds. For example, when the user is relaxed, the collection unit can increase the frequency of collecting lifestyle sounds and reduce the collection of the heart rate. For example, when the user is exercising, the collection unit can increase the frequency of collecting the heart rate and prioritize the collection of exercise sounds. This enables more appropriate data collection by adjusting the collection timing according to the user's emotions.

[0095] The collection unit can analyze the user's past health data and select an appropriate collection method. The collection unit can, for example, use AI to analyze the user's past health data. The collection unit can, for example, analyze past heart rate data and increase the collection frequency during time periods when there are many abnormalities. The collection unit can also analyze past daily sound data and increase the collection frequency during time periods when there are many specific sounds. The collection unit can, for example, analyze past daily sound data and increase the collection frequency during time periods when there are many specific sounds. The collection unit can also comprehensively analyze the user's past health data and determine the optimal collection timing. The collection unit can, for example, comprehensively analyze past heart rate data, body temperature data, and daily sound data and determine the optimal collection timing. In this way, the optimal collection method can be selected by analyzing the past health data.

[0096] The collection unit can perform filtering based on the user's current activity status and environment when collecting data. The collection unit grasps the user's current activity status and environment using, for example, AI. The collection unit can prioritize collection of lifestyle sounds, for example, when the user is in a quiet environment. The collection unit can also prioritize collection of heart rate, for example, when the user is exercising. The collection unit can also prioritize collection of heart rate, for example, when the user is exercising. The collection unit can also collect both heart rate and lifestyle sounds, for example, when the user is sleeping. The collection unit can collect both heart rate and lifestyle sounds, for example, when the user is sleeping. This makes it possible to collect data according to the user's activity status and environment.

[0097] The collection unit can select the optimal collection means depending on the user's input method when collecting data. The collection unit grasps the user's input method using, for example, AI. For example, the collection unit can prioritize collection of voice data when the user is using voice input. Furthermore, the collection unit can prioritize collection of text data when the user is using text input. For example, the collection unit can prioritize collection of text data when the user is using text input. Furthermore, the collection unit can prioritize collection of gesture data when the user is using gesture input. For example, the collection unit can prioritize collection of gesture data when the user is using gesture input. This enables optimal data collection depending on the user's input method.

[0098] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. The collection unit, for example, uses AI to estimate the user's emotions. The collection unit can estimate the user's emotions using facial expression recognition technology, for example. The collection unit can also estimate the user's emotions using voice analysis technology. The collection unit can estimate the user's emotions by analyzing the tone and speed of the user's voice, for example. The collection unit can also determine the priority of data to be collected based on the estimated user's emotions. For example, the collection unit can prioritize collection of heart rate data when the user is feeling stressed. For example, the collection unit can prioritize collection of daily sound data when the user is relaxed. For example, the collection unit can prioritize collection of exercise data when the user is exercising. This enables more appropriate data collection by determining the priority of data collection according to the user's emotions.

[0099] During collection, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, uses AI to determine the user's geographical location information. The collection unit can, for example, determine the user's geographical location information by using GPS data. The collection unit can also, for example, determine the user's geographical location information by using a location information service. The collection unit can, for example, determine the user's geographical location information by using a location information service. The collection unit can also, for example, determine the user's geographical location information by using a location information service. The collection unit can also, for example, prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit can, for example, prioritize collecting life sound data when the user is at home. The collection unit can also, for example, prioritize collecting heart rate data when the user is at work. The collection unit can, for example, prioritize collecting exercise data when the user is at an exercise facility. This enables data collection based on the user's geographical location information.

[0100] The collection unit can analyze the user's social media activity at the time of collection and collect related data. The collection unit can, for example, use AI to analyze the user's social media activity. The collection unit can, for example, analyze the content of the user's social media posts. The collection unit can also analyze the number of likes the user has on social media. The collection unit can, for example, analyze the number of likes the user has on social media. The collection unit can also analyze the user's social media activity and collect related data. For example, if the user posts about exercise on social media, the collection unit can prioritize collection of exercise data. For example, if the user posts about diet on social media, the collection unit can prioritize collection of diet data. For example, if the user posts about stress on social media, the collection unit can prioritize collection of heart rate data. This makes it possible to collect data based on the user's social media activity.

[0101] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit can, for example, use AI to analyze the user's past feedback. The collection unit can, for example, analyze the user's past ratings and comments. The collection unit can also customize the collection method by reflecting the user's past feedback. For example, if the user has requested collection of heart rate data in the past, the collection unit can prioritize collection of heart rate data. For example, if the user has requested collection of lifestyle sound data in the past, the collection unit can also prioritize collection of lifestyle sound data. For example, if the user has requested collection of exercise data in the past, the collection unit can prioritize collection of exercise data. This makes it possible to customize the collection method based on the user's past feedback.

[0102] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit, for example, uses AI to estimate the user's emotions. The analysis unit can estimate the user's emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. The analysis unit can estimate the user's emotions by analyzing the tone and speed of the user's voice, for example. The analysis unit can also adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit can provide simple, highly visible analysis results when the user is nervous, for example. The analysis unit can also provide detailed analysis results when the user is relaxed, for example. The analysis unit can provide analysis results that focus on the main points when the user is in a hurry, for example. This allows the analysis unit to adjust the way the analysis is presented based on the user's emotions, thereby providing more appropriate analysis results.

[0103] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit evaluates the importance of the data using, for example, AI. The analysis unit can perform a detailed analysis, for example, when the heart rate data is abnormal. The analysis unit can also perform a detailed analysis, for example, when the life sound data is abnormal. The analysis unit can also perform a detailed analysis, for example, when the body temperature data is abnormal. The analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance and a brief analysis on data with low importance. In this way, by adjusting the level of detail of the analysis according to the importance of the data, more appropriate analysis results can be provided.

[0104] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. The analysis unit classifies data categories using, for example, AI. The analysis unit can apply, for example, a heart rate analysis algorithm to heart rate data. The analysis unit can also apply a voice analysis algorithm to daily sound data. The analysis unit can apply, for example, a body temperature analysis algorithm to body temperature data. The analysis unit can also apply different analysis algorithms depending on the data category. For example, the analysis unit can apply an analysis algorithm dedicated to heart rate to heart rate data, and an analysis algorithm dedicated to voice to daily sound data. In this way, by applying an analysis algorithm according to the data category, more appropriate analysis results can be provided.

[0105] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, uses AI to analyze the user's past analysis results. The analysis unit, for example, can analyze current heart rate data by referring to past heart rate analysis results. The analysis unit can also analyze current lifestyle sound data by referring to past lifestyle sound analysis results. The analysis unit, for example, can analyze current body temperature data by referring to past body temperature analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, can improve the analysis accuracy of current heart rate data based on past heart rate analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0106] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit can estimate the user's emotions using, for example, AI. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. The analysis unit can estimate the user's emotions by analyzing the tone and speed of the user's voice, for example. The analysis unit can also adjust the length of the analysis based on the estimated user's emotions. The analysis unit can provide a short and to-the-point analysis result when the user is in a hurry, for example. The analysis unit can also provide a detailed analysis result when the user is relaxed, for example. The analysis unit can provide a visually stimulating analysis result when the user is excited, for example. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing more appropriate analysis results.

[0107] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit, for example, uses AI to evaluate the time when the data was collected. The analysis unit, for example, can prioritize analyzing recently collected data. The analysis unit can also prioritize analyzing data collected during a time period when many abnormalities occur. The analysis unit, for example, can prioritize analyzing data collected during a time period specified by the user. The analysis unit can also determine the priority of analysis based on the time when the data was collected. For example, the analysis unit can prioritize analyzing recently collected data and postpone analyzing older data. In this way, by determining the priority of analysis based on the time when the data was collected, more appropriate analysis results can be provided.

[0108] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit evaluates the relevance of the data using, for example, AI. The analysis unit can determine the order of analysis, for example, taking into account the relevance between heart rate data and life sound data. The analysis unit can also determine the order of analysis, for example, taking into account the relevance between body temperature data and heart rate data. The analysis unit can determine the order of analysis, for example, taking into account the relevance between life sound data and body temperature data. The analysis unit can also adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analyzing highly relevant data and postpone analyzing less relevant data. In this way, by adjusting the order of analysis based on the relevance of the data, more appropriate analysis results can be provided.

[0109] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, uses AI to evaluate the user's level of expertise. For example, if the user is a medical professional, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user is a general public, the analysis unit can provide analysis results that avoid technical terms. For example, if the user is a student, the analysis unit can provide analysis results that include educational elements. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can explain things in simple terms to beginners and use technical terms to advanced users. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided.

[0110] The sharing unit can estimate the user's emotions and adjust the sharing method based on the estimated user's emotions. The sharing unit can estimate the user's emotions using AI, for example. The sharing unit can estimate the user's emotions using facial expression recognition technology, for example. The sharing unit can also estimate the user's emotions using voice analysis technology. The sharing unit can estimate the user's emotions by analyzing the tone and speed of the user's voice, for example. The sharing unit can also adjust the sharing method based on the estimated user's emotions. For example, the sharing unit can provide a simple, highly visible sharing method when the user is nervous. For example, the sharing unit can provide a sharing method that includes detailed information when the user is relaxed. For example, the sharing unit can provide a sharing method that focuses on the main points when the user is in a hurry. This enables more appropriate information sharing by adjusting the sharing method according to the user's emotions.

[0111] The sharing unit can adjust the level of detail of the information to be shared based on the importance of the anomaly when sharing. The sharing unit, for example, uses AI to evaluate the importance of the anomaly. For example, the sharing unit can share detailed information when a serious anomaly is detected. The sharing unit can also share brief information when a minor anomaly is detected. The sharing unit can adjust the level of detail of the information to be shared, for example, according to the type of anomaly. The sharing unit can also adjust the level of detail of the information to be shared based on the importance of the anomaly. For example, the sharing unit can share detailed information for anomalies with high importance and share brief information for anomalies with low importance. In this way, by adjusting the level of detail of the information to be shared according to the importance of the anomaly, more appropriate information sharing is possible.

[0112] The sharing unit can apply different sharing algorithms depending on the category of abnormality when sharing. The sharing unit classifies the category of abnormality using, for example, AI. The sharing unit can, for example, apply a sharing algorithm dedicated to heart rate to an abnormal heart rate. The sharing unit can also apply a sharing algorithm dedicated to audio to an abnormal living sound. The sharing unit can, for example, apply a sharing algorithm dedicated to body temperature to an abnormal body temperature. The sharing unit can also apply different sharing algorithms depending on the category of abnormality. For example, the sharing unit can apply a sharing algorithm dedicated to heart rate to an abnormal heart rate, and a sharing algorithm dedicated to audio to an abnormal living sound. This enables more appropriate information sharing by applying a sharing algorithm according to the category of abnormality.

[0113] When sharing, the sharing unit can improve the accuracy of sharing by referring to the user's past sharing results. The sharing unit can analyze the user's past sharing results using AI, for example. The sharing unit can share current heart rate data by referring to past heart rate sharing results. The sharing unit can also share current daily sound data by referring to past daily sound sharing results. The sharing unit can share current body temperature data by referring to past body temperature sharing results. The sharing unit can also improve the accuracy of sharing by referring to the user's past sharing results. The sharing unit can improve the accuracy of sharing current heart rate data based on past heart rate sharing results, for example. This makes it possible to improve the accuracy of sharing by referring to the user's past sharing results.

[0114] The sharing unit can estimate the user's emotions and determine sharing priorities based on the estimated user's emotions. The sharing unit can estimate the user's emotions using AI, for example. The sharing unit can estimate the user's emotions using facial expression recognition technology, for example. The sharing unit can also estimate the user's emotions using voice analysis technology. The sharing unit can estimate the user's emotions by analyzing the tone and speed of the user's voice, for example. The sharing unit can also determine sharing priorities based on the estimated user's emotions. For example, the sharing unit can prioritize sharing important information when the user is nervous. For example, the sharing unit can share detailed information when the user is relaxed. For example, the sharing unit can prioritize sharing key information when the user is in a hurry. This enables more appropriate information sharing by determining sharing priorities according to the user's emotions.

[0115] When sharing, the sharing unit can determine the priority of sharing based on the time when the abnormality occurred. The sharing unit can, for example, use AI to evaluate the time when the abnormality occurred. The sharing unit can, for example, prioritize sharing of an abnormality that occurred recently. The sharing unit can also prioritize sharing of an abnormality that occurred during a time period when there are many abnormalities. The sharing unit can, for example, prioritize sharing of an abnormality that occurred during a time period specified by the user. The sharing unit can also determine the priority of sharing based on the time when the abnormality occurred. For example, the sharing unit can prioritize sharing of an abnormality that occurred recently and postpone sharing of older abnormalities. In this way, by determining the priority of sharing based on the time when the abnormality occurred, more appropriate information sharing is possible.

[0116] The sharing unit can adjust the order of sharing based on the relevance of the abnormalities when sharing. The sharing unit evaluates the relevance of the abnormalities using, for example, AI. The sharing unit can determine the order of sharing by considering, for example, the relevance between an abnormal heart rate and an abnormality in daily life sounds. The sharing unit can also determine the order of sharing by considering the relevance between an abnormal body temperature and an abnormal heart rate. The sharing unit can determine the order of sharing by considering, for example, the relevance between an abnormality in daily life sounds and an abnormal body temperature. The sharing unit can also adjust the order of sharing based on the relevance of the abnormalities. For example, the sharing unit can prioritize sharing of highly relevant abnormalities and postpone sharing of less relevant abnormalities. This enables more appropriate information sharing by adjusting the order of sharing based on the relevance of the abnormalities.

[0117] The sharing unit can adjust the use of shared terminology according to the user's level of expertise when sharing. The sharing unit can, for example, use AI to evaluate the user's level of expertise. For example, if the user is a medical professional, the sharing unit can provide shared information that uses a lot of terminology. Furthermore, if the user is a general public, the sharing unit can provide shared information that avoids terminology. For example, if the user is a student, the sharing unit can provide shared information that includes educational elements. Furthermore, the sharing unit can adjust the use of shared terminology according to the user's level of expertise. For example, the sharing unit can explain things in simple terms to beginners and use technical terms to advanced users. This enables more appropriate information sharing by adjusting the use of shared terminology according to the user's level of expertise.

[0118] The measurement unit can estimate the user's emotions and adjust the measurement method based on the estimated user's emotions. The measurement unit can estimate the user's emotions using AI, for example. The measurement unit can estimate the user's emotions using facial expression recognition technology, for example. The measurement unit can also estimate the user's emotions using voice analysis technology. The measurement unit can estimate the user's emotions by analyzing the tone and speed of the user's voice, for example. The measurement unit can also adjust the measurement method based on the estimated user's emotions. For example, the measurement unit can increase the frequency of measuring the heart rate when the user is nervous. The measurement unit can also increase the frequency of measuring everyday sounds when the user is relaxed. For example, the measurement unit can increase the frequency of measuring exercise data when the user is exercising. This allows for more appropriate data measurement by adjusting the measurement method according to the user's emotions.

[0119] The measurement unit can adjust the level of detail of the measurement based on the importance of the data during measurement. The measurement unit evaluates the importance of the data using, for example, AI. The measurement unit can perform detailed measurement, for example, when heart rate data is abnormal. The measurement unit can also perform detailed measurement, for example, when daily sound data is abnormal. The measurement unit can also perform detailed measurement, for example, when body temperature data is abnormal. The measurement unit can also adjust the level of detail of the measurement based on the importance of the data. For example, the measurement unit can perform detailed measurement for data with high importance and simple measurement for data with low importance. This allows for more appropriate data measurement by adjusting the level of detail of the measurement according to the importance of the data.

[0120] The measurement unit can apply different measurement algorithms depending on the data category during measurement. The measurement unit classifies data categories using, for example, AI. The measurement unit can apply, for example, a heart rate measurement algorithm to heart rate data. The measurement unit can also apply a voice measurement algorithm to daily sound data. The measurement unit can apply, for example, a body temperature measurement algorithm to body temperature data. The measurement unit can also apply different measurement algorithms depending on the data category. For example, the measurement unit can apply a measurement algorithm dedicated to heart rate to heart rate data, and a measurement algorithm dedicated to voice to daily sound data. This enables more appropriate data measurement by applying a measurement algorithm according to the data category.

[0121] The measurement unit can improve the accuracy of measurement by referring to the user's past measurement results when taking measurements. The measurement unit can analyze the user's past measurement results using AI, for example. The measurement unit can measure current heart rate data by referring to past heart rate measurement results, for example. The measurement unit can also measure current daily sound data by referring to past daily sound measurement results. The measurement unit can measure current body temperature data by referring to past body temperature measurement results, for example. The measurement unit can also improve the accuracy of measurement by referring to the user's past measurement results. The measurement unit can improve the measurement accuracy of current heart rate data based on, for example, past heart rate measurement results. In this way, the measurement accuracy can be improved by referring to the user's past measurement results.

[0122] The measurement unit can estimate the user's emotions and determine a measurement priority based on the estimated user's emotions. The measurement unit can estimate the user's emotions using, for example, AI. The measurement unit can estimate the user's emotions using, for example, facial expression recognition technology. The measurement unit can also estimate the user's emotions using voice analysis technology. The measurement unit can estimate the user's emotions by analyzing the tone and speed of the user's voice, for example. The measurement unit can also determine a measurement priority based on the estimated user's emotions. For example, the measurement unit can prioritize measurement of heart rate data when the user is nervous. The measurement unit can also prioritize measurement of daily sound data when the user is relaxed. For example, the measurement unit can prioritize measurement of exercise data when the user is exercising. This enables more appropriate data measurement by determining the measurement priority according to the user's emotions.

[0123] During measurement, the measurement unit can determine the priority of measurements based on the time when the data was collected. The measurement unit evaluates the time when the data was collected using, for example, AI. The measurement unit can, for example, prioritize measuring recently collected data. The measurement unit can also prioritize measuring data collected during a time period when there are many abnormalities. The measurement unit can, for example, prioritize measuring data collected during a time period specified by the user. The measurement unit can also determine the priority of measurements based on the time when the data was collected. For example, the measurement unit can prioritize measuring recently collected data and postpone older data. This enables more appropriate data measurement by determining the priority of measurements based on the time when the data was collected.

[0124] The measurement unit can adjust the order of measurements based on the relevance of data during measurement. The measurement unit evaluates the relevance of data using, for example, AI. The measurement unit can determine the order of measurements, for example, taking into account the relevance between heart rate data and daily sound data. The measurement unit can also determine the order of measurements, for example, taking into account the relevance between body temperature data and heart rate data. The measurement unit can determine the order of measurements, for example, taking into account the relevance between daily sound data and body temperature data. The measurement unit can also adjust the order of measurements based on the relevance of data. For example, the measurement unit can prioritize measuring highly relevant data and postpone measuring less relevant data. This allows for more appropriate data measurement by adjusting the order of measurements based on the relevance of data.

[0125] During measurement, the measurement unit can adjust the use of technical terminology in the measurement according to the user's level of expertise. The measurement unit, for example, uses AI to evaluate the user's level of expertise. For example, if the user is a medical professional, the measurement unit can provide measurement results that use a lot of technical terminology. Furthermore, if the user is a general public, the measurement unit can provide measurement results that avoid technical terminology. For example, if the user is a student, the measurement unit can provide measurement results that include educational elements. Furthermore, the measurement unit can adjust the use of technical terminology in the measurement according to the user's level of expertise. For example, the measurement unit can explain things in simple terms to beginners and use technical terminology to advanced users. This allows for more appropriate data measurement by adjusting the use of technical terminology in the measurement according to the user's level of expertise.

[0126] The generation unit can estimate the user's emotion and adjust the expression method of the generated voice based on the estimated user's emotion. The generation unit, for example, uses AI to estimate the user's emotion. The generation unit can estimate the user's emotion using facial expression recognition technology. The generation unit can also estimate the user's emotion using voice analysis technology. The generation unit can estimate the emotion by analyzing the tone and speed of the user's voice, for example. The generation unit can also adjust the expression method of the generated voice based on the estimated user's emotion. For example, the generation unit can generate voice with a relaxed tone when the user is relaxed. The generation unit can also generate voice with a quick and concise tone when the user is in a hurry. For example, the generation unit can generate voice with a visually stimulating effect when the user is excited. This enables more appropriate voice generation by adjusting the expression method of the voice according to the user's emotion.

[0127] The generation unit can adjust the level of detail of the generated voice based on the importance of the data at the time of generation. The generation unit can, for example, use AI to evaluate the importance of the data. The generation unit can, for example, generate detailed voice for important data. The generation unit can also generate concise voice for minor data. The generation unit can adjust the level of detail of the generated voice based on the importance of the data, for example. The generation unit can generate detailed voice for data with high importance and concise voice for data with low importance. In this way, by adjusting the level of detail of the generated voice based on the importance of the data, more appropriate voice generation becomes possible.

[0128] The generation unit can apply different generation algorithms depending on the data category during generation. The generation unit classifies the data category using, for example, AI. The generation unit can apply, for example, a generation algorithm dedicated to heart rates to heart rate data. The generation unit can also apply a generation algorithm dedicated to audio to everyday sound data. The generation unit can apply, for example, a generation algorithm dedicated to body temperature to body temperature data. The generation unit can also apply different generation algorithms depending on the data category. For example, the generation unit can apply a generation algorithm dedicated to heart rates to heart rate data, and a generation algorithm dedicated to audio to everyday sound data. This makes it possible to generate more appropriate audio by applying a generation algorithm according to the data category.

[0129] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. The generation unit can analyze the user's past generation results using, for example, AI. The generation unit can generate current heart rate data by referring to, for example, past heart rate generation results. The generation unit can also generate current lifestyle sound data by referring to past lifestyle sound generation results. The generation unit can generate current body temperature data by referring to, for example, past body temperature generation results. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. The generation unit can improve the accuracy of generation of current heart rate data based on, for example, past heart rate generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0130] The generation unit can estimate the user's emotion and adjust the length of the generated voice based on the estimated user's emotion. The generation unit can estimate the user's emotion using, for example, AI. The generation unit can estimate the user's emotion using, for example, facial expression recognition technology. The generation unit can also estimate the user's emotion using voice analysis technology. The generation unit can estimate the emotion by analyzing the tone and speed of the user's voice, for example. The generation unit can also adjust the length of the generated voice based on the estimated user's emotion. For example, the generation unit can generate short, to-the-point voice when the user is in a hurry. For example, the generation unit can generate longer voice with detailed explanations when the user is relaxed. For example, the generation unit can generate voice with visually stimulating effects when the user is excited. This enables more appropriate voice generation by adjusting the length of the voice according to the user's emotion.

[0131] At the time of generation, the generation unit can determine a generation priority based on the time when the data was collected. The generation unit can, for example, use AI to evaluate the time when the data was collected. The generation unit can, for example, prioritize generating recently collected data. The generation unit can also prioritize generating data collected during a time period when there are many abnormalities. The generation unit can, for example, prioritize generating data collected during a time period specified by the user. The generation unit can also determine a generation priority based on the time when the data was collected. For example, the generation unit can prioritize generating recently collected data and postpone generating older data. In this way, by determining a generation priority based on the time when the data was collected, more appropriate voice generation is possible.

[0132] The generation unit can adjust the order of generation based on the relevance of the data during generation. The generation unit evaluates the relevance of the data using, for example, AI. The generation unit can determine the order of generation, for example, taking into account the relevance between heart rate data and daily life sound data. The generation unit can also determine the order of generation, for example, taking into account the relevance between body temperature data and heart rate data. The generation unit can determine the order of generation, for example, taking into account the relevance between daily life sound data and body temperature data. The generation unit can also adjust the order of generation based on the relevance of the data. For example, the generation unit can prioritize generating highly relevant data and postpone generating less relevant data. In this way, adjusting the order of generation based on the relevance of the data enables more appropriate voice generation.

[0133] During generation, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. The generation unit, for example, uses AI to evaluate the user's level of expertise. For example, if the user is a medical professional, the generation unit can provide a generated result that uses a lot of technical terminology. Furthermore, if the user is a general public, the generation unit can provide a generated result that avoids technical terminology. For example, if the user is a student, the generation unit can provide a generated result that includes educational elements. Furthermore, the generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise. For example, the generation unit can explain things in simple terms to beginners and use technical terminology to advanced users. This enables more appropriate speech generation by adjusting the use of technical terminology in the generation according to the user's level of expertise.

[0134] The advice unit can estimate the user's emotion and adjust the manner in which advice is expressed based on the estimated user's emotion. The advice unit estimates the user's emotion using, for example, AI. The advice unit can estimate the user's emotion using, for example, facial expression recognition technology. The advice unit can also estimate the user's emotion using voice analysis technology. The advice unit can estimate the emotion by analyzing the tone and speed of the user's voice, for example. The advice unit can also adjust the manner in which advice is expressed based on the estimated user's emotion. For example, the advice unit can provide advice in a relaxed tone when the user is relaxed. The advice unit can also provide advice in a quick and concise tone when the user is in a hurry. For example, the advice unit can provide advice with a visually stimulating effect when the user is excited. This enables more appropriate advice to be provided by adjusting the manner in which advice is expressed based on the user's emotion.

[0135] When giving advice, the advice unit can adjust the level of detail of the advice based on the importance of the data. The advice unit, for example, uses AI to evaluate the importance of the data. The advice unit, for example, can provide detailed advice for important data. The advice unit can also provide brief advice for minor data. The advice unit can adjust the level of detail of the advice according to the type of data, for example. The advice unit can also adjust the level of detail of the advice based on the importance of the data. For example, the advice unit can provide detailed advice for data with high importance and brief advice for data with low importance. In this way, by adjusting the level of detail of the advice according to the importance of the data, more appropriate advice can be provided.

[0136] When giving advice, the advice unit can apply different advice algorithms depending on the data category. The advice unit classifies the data category using, for example, AI. The advice unit can apply, for example, an advice algorithm dedicated to heart rate to heart rate data. The advice unit can also apply an advice algorithm dedicated to voice to daily sound data. The advice unit can apply, for example, an advice algorithm dedicated to body temperature to body temperature data. The advice unit can also apply different advice algorithms depending on the data category. For example, the advice unit can apply an advice algorithm dedicated to heart rate to heart rate data, and an advice algorithm dedicated to voice to daily sound data. This makes it possible to provide more appropriate advice by applying an advice algorithm according to the data category.

[0137] When providing advice, the advice unit can improve the accuracy of the advice by referring to past advice results for the user. The advice unit can analyze the past advice results for the user using AI, for example. The advice unit can provide advice based on current heart rate data by referring to past heart rate advice results. The advice unit can also provide advice based on current life sound data by referring to past life sound advice results. The advice unit can provide advice based on current body temperature data by referring to past body temperature advice results. The advice unit can also improve the accuracy of the advice by referring to past advice results for the user. The advice unit can improve the accuracy of advice for current heart rate data based on past heart rate advice results, for example. This makes it possible to improve the accuracy of advice by referring to the past advice results for the user.

[0138] The advice unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. The advice unit can estimate the user's emotion using, for example, AI. The advice unit can estimate the user's emotion using, for example, facial expression recognition technology. The advice unit can also estimate the user's emotion using voice analysis technology. The advice unit can estimate the emotion by analyzing the tone and speed of the user's voice, for example. The advice unit can also adjust the length of the advice based on the estimated user's emotion. For example, the advice unit can provide short, to-the-point advice when the user is in a hurry. For example, the advice unit can provide longer advice including detailed explanations when the user is relaxed. For example, the advice unit can provide advice with visually stimulating effects when the user is excited. This enables more appropriate advice to be provided by adjusting the length of the advice according to the user's emotion.

[0139] When giving advice, the advice unit can determine the priority of advice based on the time when the data was collected. The advice unit can, for example, use AI to evaluate the time when the data was collected. The advice unit can, for example, give priority to advice about recently collected data. The advice unit can also, for example, give priority to advice about data collected during a time period when many abnormalities occur. The advice unit can, for example, give priority to advice about data collected during a time period specified by the user. The advice unit can also determine the priority of advice based on the time when the data was collected. For example, the advice unit can give priority to advice about recently collected data and postpone advice about older data. In this way, by determining the priority of advice based on the time when the data was collected, more appropriate advice can be provided.

[0140] When giving advice, the advice unit can adjust the order of advice based on the relevance of data. The advice unit evaluates the relevance of data using, for example, AI. The advice unit can determine the order of advice taking into consideration, for example, the relevance between heart rate data and daily life sound data. The advice unit can also determine the order of advice taking into consideration, for example, the relevance between body temperature data and heart rate data. The advice unit can determine the order of advice taking into consideration, for example, the relevance between daily life sound data and body temperature data. The advice unit can also adjust the order of advice based on the relevance of data. For example, the advice unit can give priority to advice on highly relevant data and postpone advice on less relevant data. In this way, by adjusting the order of advice based on the relevance of data, more appropriate advice can be provided.

[0141] When providing advice, the advice unit can adjust the use of technical terms in the advice according to the user's level of expertise. The advice unit, for example, uses AI to evaluate the user's level of expertise. For example, if the user is a medical professional, the advice unit can provide advice that uses a lot of technical terms. Furthermore, if the user is a general public, the advice unit can provide advice that avoids technical terms. For example, if the user is a student, the advice unit can provide advice that includes educational elements. Furthermore, the advice unit can adjust the use of technical terms in the advice according to the user's level of expertise. For example, the advice unit can explain things in simple terms to beginners and use technical terms to advanced users. This allows for more appropriate advice by adjusting the use of technical terms in the advice according to the user's level of expertise.

[0142] The generation unit can automatically calculate the basal metabolic rate, the active metabolic rate, the daily required calorie intake, and the required amount of exercise. The generation unit calculates the basal metabolic rate based on, for example, the user's age, gender, and weight. The generation unit can calculate the basal metabolic rate using, for example, the user's age, gender, and weight as input. The generation unit can also calculate the active metabolic rate based on the user's amount of exercise and activity level. The generation unit can calculate the active metabolic rate using, for example, the user's amount of exercise and activity level as input. The generation unit can also calculate the daily required calorie intake based on the basal metabolic rate and the active metabolic rate. The generation unit can calculate the required calorie intake using, for example, the basal metabolic rate and the active metabolic rate as input. The generation unit can also calculate the daily required amount of exercise based on the basal metabolic rate and the active metabolic rate. The generation unit can calculate the required amount of exercise using, for example, the basal metabolic rate and the active metabolic rate as input. In this way, the basal metabolic rate, the active metabolic rate, the required calorie intake, and the required amount of exercise are automatically calculated to support the user's health management.

[0143] The advice unit can provide audio advice to encourage the user to systematically review their lifestyle habits. The advice unit can provide audio advice regarding lifestyle review depending on, for example, the user's health condition. The advice unit can provide exercise advice when, for example, the user is not getting enough exercise. The advice unit can also provide audio dietary advice depending on the user's diet. For example, the advice unit can provide advice on a nutritionally balanced diet when, for example, the user is lacking in nutrition. The advice unit can also estimate the user's emotions using AI and provide advice in an appropriate tone. For example, the advice unit can provide advice in a calm tone when the user is relaxed. This contributes to improving the user's motivation by providing audio advice to encourage the user to systematically review their lifestyle habits. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, sharing unit, measurement unit, generation unit, and advice unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects heart rate and daily sounds using a heart rate sensor and microphone of the smart device 14 and extracts necessary data using AI. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 to grasp the user's health condition in real time. The sharing unit detects abnormalities using the specific processing unit 290 of the data processing device 12 and sends a notification via the smart device 14. The measurement unit constantly measures the pulse and body temperature using the heart rate sensor and body temperature sensor of the smart device 14. The generation unit uses voice generation AI by the specific processing unit 290 of the data processing device 12 to converse with the user in an optimal format. The advice unit provides voice advice generated by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, sharing unit, measurement unit, generation unit, and advice 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 collection unit collects heart rate and daily sounds using a heart rate sensor and microphone of the smart glasses 214 and extracts necessary data using AI. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 to grasp the user's health condition in real time. The sharing unit detects abnormalities using the specific processing unit 290 of the data processing device 12 and sends a notification through the smart glasses 214. The measurement unit constantly measures the pulse and body temperature using the heart rate sensor and body temperature sensor of the smart glasses 214. The generation unit converses with the user in an optimal format using voice generation AI by the specific processing unit 290 of the data processing device 12. The advice unit provides voice advice generated by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, sharing unit, measurement unit, generation unit, and advice unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects heart rate and daily sounds using a heart rate sensor and microphone of the headset-type terminal 314 and extracts necessary data using AI. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 to grasp the user's health condition in real time. The sharing unit detects abnormalities using the specific processing unit 290 of the data processing device 12 and sends a notification via the headset-type terminal 314. The measurement unit constantly measures the pulse and body temperature using the heart rate sensor and body temperature sensor of the headset-type terminal 314. The generation unit converses with the user in an optimal format using a voice generation AI by the specific processing unit 290 of the data processing device 12. The advice unit provides voice advice generated by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, sharing unit, measurement unit, generation unit, and advice unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects heart rate and daily sounds using a heart rate sensor and microphone of the robot 414 and extracts necessary data using AI. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and grasps the user's health condition in real time. The sharing unit detects abnormalities using the specific processing unit 290 of the data processing device 12 and sends a notification via the robot 414. The measurement unit constantly measures the pulse and body temperature using the heart rate sensor and body temperature sensor of the robot 414. The generation unit converses with the user in an optimal format using a voice generation AI by the specific processing unit 290 of the data processing device 12. The advice unit provides voice advice generated by the control unit 46A of the robot 414.

[0144] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0145] The collection unit monitors the user's sleep patterns, and the analysis unit analyzes the collected sleep data to evaluate the user's sleep quality. For example, the collection unit can detect the user's tossing and turning or snoring sounds and estimate the depth of sleep. The analysis unit can also analyze the user's sleep cycle based on the collected data and suggest an optimal wake-up time. Furthermore, the sharing unit can provide the user with advice on improving their sleep based on the analysis results. This can improve the user's sleep quality.

[0146] The analysis unit can collect and analyze the user's dietary data. For example, when the user takes a photo of a meal, the analysis unit can use image recognition technology to analyze the meal contents and evaluate the balance of nutrients. The analysis unit can also evaluate the risk of nutritional deficiencies or overconsumption based on the user's dietary history. Furthermore, the sharing unit can suggest nutritionally balanced meals to the user based on the analysis results. This can improve the user's eating habits and support their health.

[0147] The sharing unit collects the user's exercise data, and the analysis unit can evaluate the user's exercise habits based on the collected exercise data. For example, the collection unit records the user's number of steps and exercise time, and the analysis unit can evaluate the user's exercise volume based on this data. The analysis unit can also evaluate the risk of insufficient or excessive exercise based on the user's exercise data. Furthermore, the sharing unit can suggest an appropriate exercise plan to the user based on the analysis results. This can improve the user's exercise habits and support their health.

[0148] The measurement unit measures the user's stress level, and the analysis unit can evaluate the user's stress state based on the collected stress data. For example, the measurement unit can measure the user's heart rate variability and skin electrical response to estimate the stress level. The analysis unit can also identify the cause of the user's stress based on the collected data and provide stress management advice. Furthermore, the sharing unit can suggest relaxation methods and stress-reducing activities to the user based on the analysis results. This can support the user's stress management.

[0149] The generation unit can estimate the user's emotion and adjust the content of the feedback to the user based on the estimated emotion. For example, if the user is feeling stressed, the generation unit can provide advice on how to relax. Also, if the user needs motivation, the generation unit can provide an encouraging message. Furthermore, the generation unit can adjust the tone and content of the feedback according to the user's emotion. This makes it possible to provide more appropriate feedback to the user.

[0150] The collection unit can estimate the user's emotions and adjust the type and frequency of data collection based on the estimated emotions. For example, if the user is feeling stressed, priority can be given to collecting data on heart rate and skin galvanic response. Also, if the user is relaxed, priority can be given to collecting data on daily sounds and environmental sounds. Furthermore, the collection unit can adjust the frequency of data collection according to the user's emotions. This makes it possible to collect data according to the user's emotions, allowing for more appropriate health management.

[0151] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, it can provide simple, highly visible analysis results. Alternatively, if the user is relaxed, it can provide detailed analysis results. Furthermore, the analysis unit can adjust the display format and content of the analysis results according to the user's emotions. This makes it possible to provide more appropriate analysis results to the user.

[0152] The sharing unit can estimate the user's emotions and adjust the information sharing method based on the estimated emotions. For example, if the user is nervous, a simple and highly visible information sharing method can be provided. Alternatively, if the user is relaxed, a sharing method including detailed information can be provided. Furthermore, the sharing unit can adjust the timing and content of information sharing according to the user's emotions. This enables more appropriate information sharing for the user.

[0153] The advice unit can estimate the user's emotions and adjust the content and tone of the advice based on the estimated emotions. For example, if the user is feeling stressed, the advice unit can provide advice to help the user relax. Also, if the user needs motivation, the advice unit can provide an encouraging message. Furthermore, the advice unit can adjust the tone and content of the advice according to the user's emotions. This makes it possible to provide more appropriate advice to the user.

[0154] The collection unit can prioritize collection of highly relevant data by taking into account the user's geographical location information. For example, when the user is at home, collection of daily life sound data can be prioritized. Also, when the user is at work, collection of heart rate data can be prioritized. Furthermore, when the user is at an exercise facility, collection of exercise data can be prioritized. This enables data collection based on the user's geographical location information, allowing for more appropriate health management.

[0155] The processing flow of the second embodiment will be briefly explained below.

[0156] Step 1: The collection unit collects daily heart rate and daily sounds. The collection unit measures heart rate using a heart rate sensor and collects daily sounds using a microphone. The collection unit also uses AI to filter the collected data and extract only the necessary data. Step 2: The analysis unit analyzes the data collected by the collection unit and grasps the user's health condition in real time. The analysis unit uses AI to analyze heart rate data and daily sound data and detect abnormalities. Step 3: The sharing unit shares any abnormalities detected by the analysis unit with the user, their family, and medical professionals. If an abnormality is detected, the sharing unit sends a notification to the user's smartphone and an email to medical professionals. It also uses AI to determine the priority of notifications based on the importance of the abnormality. Step 4: The measurement unit measures the abnormal sounds, pulse, and body temperature detected by the analysis unit. The measurement unit measures the pulse using a heart rate sensor and the body temperature using a body temperature sensor. It also uses AI to detect and measure abnormal sounds. Step 5: The generation unit uses a voice generation AI to converse with the user in an appropriate format based on the data obtained by the measurement unit. The generation unit provides voice advice on relaxation and exercise based on the user's health condition and activity level. The AI ​​also estimates the user's emotions and converses in an appropriate tone. Step 6: The advice unit provides the audio advice generated by the generation unit. The advice unit provides audio advice on diet and exercise based on the user's health condition and activity level. It also uses AI to estimate the user's emotions and provides advice in an appropriate tone.

[0157] 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.

[0158] 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.

[0159] 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.

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

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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 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.

[0175] 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.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0178] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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 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.

[0191] 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.

[0192] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0193] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0194] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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).

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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 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.

[0208] 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.

[0209] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0215] 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."

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] [Explanation of symbols]

[0229] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects daily heart rate and daily sounds, an analysis unit that analyzes the data collected by the collection unit and grasps the user's health condition in real time; a sharing unit that shares the abnormality detected by the analysis unit with the patient, their family, and medical professionals; a measuring unit that measures abnormal sounds, pulse, and body temperature detected by the analyzing unit; A generation unit in which a speech generation AI converses with a user in an appropriate format based on the data obtained by the measurement unit; an advice unit that provides the audio advice generated by the generation unit; A system characterized by:

2. The collecting unit Collecting daily heart rate and lifestyle sounds 2. The system of claim 1.

3. The analysis unit Analyze the collected data and understand the user's health condition in real time 2. The system of claim 1.

4. The common part is The abnormalities detected by the analysis unit are shared with the individual, their family, and medical professionals.

2. The system of claim 1.

5. The measurement unit Measure abnormal sounds, pulse, and body temperature 2. The system of claim 1.

6. The generation unit Speech generation AI allows users to converse in an appropriate format 2. The system of claim 1.

7. The advice unit providing the audio advice generated by the generation unit; 2. The system of claim 1.

8. The generation unit Automatically calculates basal metabolic rate, activity metabolic rate, daily calorie intake and required exercise amount 2. The system of claim 1.

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