System

Abstract

To provide a system capable of providing advice on best health by analyzing individual health data.SOLUTION: A system in an embodiment includes a collection unit, an analysis unit, and a providing unit. The collection unit collects health data. The analysis unit analyzes the data collected by the collection unit. The providing unit provides advice based on the analysis results obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not yet effectively utilized personal health data to provide appropriate advice, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze an individual's health data and provide advice on optimal health practices. [Means for solving the problem]

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

[0007] The system according to the embodiment can analyze an individual's health data and provide advice on optimal health practices. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health monitoring system according to an embodiment of the present invention is a system in which an AI health monitor linked to a communication application analyzes personal health data collected by a smartwatch and provides optimal health advice to the user. This health monitoring system collects daily health data (heart rate, number of steps, sleep time, etc.) and transmits it to the AI ​​health monitor through a communication application. The AI ​​health monitor analyzes these data to understand the user's health condition and patterns. In addition, the AI ​​health monitor predicts the risk of diseases that the user is likely to suffer from, taking into account the user's genetic information and family history. For example, if there is a history of a particular disease in the family, the AI ​​estimates the risk high and suggests preventive measures. This allows the user to implement preventive measures in their daily lives. The system also provides health coaching tailored to the user's goals and needs. For example, for a user who wants to lose weight, the AI ​​provides dietary advice and exercise plans. This makes it easier for the user to achieve their health goals. The system supports health management by monitoring the user's health data in real time and providing necessary advice immediately. For example, if stress is increasing, the AI ​​provides advice on how to relax. Also, if the quality of sleep is declining, the AI ​​suggests improvement measures. This allows the user to always be aware of their health condition and take appropriate measures. This enables the health monitoring system to monitor the user's health data in real time and provide necessary advice immediately.

[0029] The health monitoring system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects health data. The health data includes, for example, heart rate, blood pressure, body temperature, and the like, but is not limited to such examples. The collection unit measures the heart rate, for example, using a smart watch. The collection unit can also measure blood pressure using a blood pressure monitor. Furthermore, the collection unit can also measure body temperature using a thermometer. For example, the collection unit monitors the heart rate in real time using a smart watch and collects data. The blood pressure monitor periodically measures blood pressure and collects data. The thermometer records body temperature fluctuations and collects data. The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to such examples. For example, the analysis unit analyzes the trend of the data using statistical analysis. The analysis unit can also detect abnormal values ​​using machine learning algorithms. Furthermore, the analysis unit can analyze the correlation of the data. For example, the analysis unit calculates the average value and standard deviation of the data using statistical analysis to grasp the trend. The machine learning algorithm automatically detects abnormal values ​​and issues an alert. By analyzing the correlation of data, it is possible to predict changes in health status. The providing unit provides advice based on the analysis results obtained by the analyzing unit. The advice includes, for example, but is not limited to, recommendations for improving diet and exercise. For example, the providing unit provides advice regarding improving diet. The providing unit can also provide advice regarding recommended exercise. Furthermore, the providing unit can also provide advice regarding stress management. For example, the providing unit recommends a balanced diet and suggests a specific menu. As a recommendation for exercise, an appropriate exercise plan is provided and a method of implementation is explained. As a stress management, relaxation methods and stress relief methods are suggested. As a result, the health monitoring system according to the embodiment can support the user's health management by collecting and analyzing the user's health data and providing advice.

[0030] The collection unit collects health data. Examples of health data include, but are not limited to, heart rate, blood pressure, and body temperature. For example, the collection unit measures the heart rate using a smart watch. The smart watch is equipped with an optical heart rate sensor, and by wearing it on the user's wrist, it can detect changes in blood flow and monitor the heart rate in real time. Furthermore, the smart watch has a function of transmitting data to a smartphone or a cloud server via Bluetooth (registered trademark) or Wi-Fi, and the collected data is immediately stored in a central database. There are types of blood pressure monitors that measure blood pressure by wrapping a cuff around the arm and types that are worn on the wrist, and they measure blood pressure periodically and collect data. These devices are designed so that users can easily use them at home, and the measurement results are automatically recorded. There are types of thermometers that measure body temperature by contacting the ear or forehead, and non-contact infrared thermometers that record body temperature fluctuations and collect data. This allows the collection unit to collect health data from the user's daily life in a multifaceted manner and monitor it in real time. Furthermore, the collection department can centrally manage the data obtained from these devices and link it with other systems and departments as necessary. For example, the collected data is stored on a cloud server and made accessible to the analysis department and provision department. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection department to collect data efficiently and effectively and improve the performance of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to such examples. Specifically, the trend of the data is analyzed using statistical analysis. For example, the data of the heart rate, blood pressure, and body temperature are plotted in a time series, and the average value and standard deviation are calculated, so that the trend of the user's health condition can be grasped. Furthermore, abnormal values ​​can be detected using machine learning algorithms. For example, a normal range is learned based on past data, and an alert is issued when an abnormal value is detected by comparing it with data collected in real time. This makes it possible to detect abnormalities in the health condition early and take appropriate measures. Furthermore, the analysis unit can also analyze the correlation of data. For example, by performing correlation analysis on the data of the heart rate, blood pressure, and body temperature, and clarifying the relationship between these data, it is possible to predict changes in the health condition. This allows the analysis unit to analyze the collected data from multiple angles and comprehensively evaluate the health condition of the user. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term health risk assessment and trend analysis. For example, based on past data, it can predict fluctuations in health risks during specific seasons or times of the day and devise future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The providing unit provides advice based on the analysis results obtained by the analyzing unit. The advice includes, for example, recommendations for improving diet and exercise, but is not limited to such examples. Specifically, advice on improving diet is provided. For example, based on the health data of the user, a balanced diet is recommended and a specific menu is proposed. This includes a meal plan that takes into account nutrient balance and calorie intake. The providing unit can also provide advice on recommended exercise. For example, an appropriate exercise plan is provided according to the physical strength and health condition of the user, and a method for implementing the plan is explained. This includes aerobic exercise, muscle training, stretching, and the like. Furthermore, the providing unit can also provide advice on stress management. For example, relaxation methods and stress relief methods are proposed, and specific methods that the user can practice in daily life are provided. This includes deep breathing, meditation, yoga, and the like. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice content. For example, the meal plan and exercise plan are reviewed based on feedback from the user who received the advice. Furthermore, the providing unit can reliably transmit information using multiple communication means. For example, important information can be delivered reliably not only through smartphone notifications but also through voice calls, SMS, emails, etc. This enables the provider to provide advice to users quickly and reliably and support their health management.

[0033] The analysis unit can predict the risk of disease based on the user's genetic information and family history. The analysis unit, for example, analyzes the user's genetic information and predicts the risk of disease. The genetic information includes, for example, DNA sequencing and genetic markers, but is not limited to such examples. For example, the analysis unit analyzes the user's genetic information using DNA sequencing to detect a specific genetic mutation. The risk of disease can also be evaluated using genetic markers. The analysis unit also predicts the risk of disease taking into account the user's family history. The family history includes, for example, a family medical history and the presence or absence of a genetic disease, but is not limited to such examples. For example, the analysis unit collects the family's medical history and evaluates the risk of a specific disease. It is also possible to confirm the presence or absence of a genetic disease and predict the risk. As a result, the analysis unit can more accurately predict the risk of disease by considering the user's genetic information and family history. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's genetic information into AI and cause the AI ​​to execute a prediction of the risk of disease.

[0034] The providing unit can perform health coaching according to the user's goals and needs. The providing unit, for example, collects the user's health goals and provides advice based thereon. Health goals include, for example, weight loss, muscle building, stress management, and the like, but are not limited to such examples. For example, the providing unit provides a diet improvement and exercise plan based on the user's weight loss goal. An appropriate training plan can also be provided based on the muscle building goal. A relaxation method or a stress relief method can also be suggested based on the stress management goal. In this way, the providing unit can support the achievement of the health goal by providing advice according to the user's goals and needs. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's health goals into AI and cause the AI ​​to generate advice.

[0035] The collection unit can collect at least one of daily health data of a heart rate, a step count, and a sleep time. The collection unit measures a heart rate using, for example, a smart watch. Measurement of a heart rate includes, for example, a heart rate monitor and a wearable device, but is not limited to such examples. For example, the collection unit monitors a heart rate in real time using a heart rate monitor and collects data. The wearable device can record fluctuations in a heart rate and collect data. In addition, the collection unit can use a pedometer or a smartphone app to measure the number of steps. For example, the collection unit counts the number of steps per day using a pedometer and collects data. The smartphone app can automatically record the number of steps and collect data. In addition, the collection unit can use a sleep tracker or a wearable device to measure sleep time. For example, the collection unit uses a sleep tracker to evaluate the quality of sleep and collect data. The wearable device can analyze the stages of sleep and collect data. As a result, the collection unit can grasp the user's health condition in detail by collecting daily health data such as a heart rate, a step count, and a sleep time. Some or all of the above-mentioned processes in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input heart rate data acquired by a smart watch to the generation AI and have the generation AI analyze the data.

[0036] The providing unit can provide advice to relax when stress is high. The providing unit, for example, evaluates the stress level of the user and provides advice to relax. The evaluation of the stress level includes, for example, but is not limited to, stress hormone measurement and questionnaire survey. For example, the providing unit suggests a relaxation method based on the measurement result of the stress hormone. The result of the questionnaire survey can also be analyzed to provide advice on stress management. In this way, the providing unit can support the user's stress management by providing appropriate advice when stress is high. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the measurement data of the stress hormone to AI and cause AI to execute the suggestion of a relaxation method.

[0037] The providing unit can propose an improvement measure when the quality of sleep is deteriorating. The providing unit, for example, evaluates the quality of sleep of the user and proposes an improvement measure. The evaluation of the quality of sleep includes, for example, sleep score and sleep stage analysis, but is not limited to such examples. For example, the providing unit proposes an improvement measure for the sleep environment based on the sleep score. A specific improvement measure can also be provided based on the analysis result of the sleep stage. Thereby, the providing unit can improve the quality of sleep of the user by proposing an appropriate improvement measure when the quality of sleep is deteriorating. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the sleep score data to AI and cause the AI ​​to execute the proposal of the improvement measure.

[0038] The collection unit can analyze the user's past health data and select the optimal collection method. The collection unit, for example, analyzes the user's past health data and selects the optimal collection method. The optimal collection method includes, for example, the selection of a device and the frequency of data collection, but is not limited to such examples. For example, the collection unit analyzes the user's past heart rate data and collects data with a focus on the time period when abnormalities are observed. The collection unit can also collect data during the time period when the quality of sleep is poor, based on the user's past sleep data. Furthermore, the collection unit can increase the collection frequency on days when the amount of exercise is low, referring to the user's past step count data. As a result, the collection unit can select the optimal collection method by analyzing the past data, and improve the efficiency of data collection. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past health data into AI and cause the AI ​​to select the optimal collection method.

[0039] The collection unit can perform filtering based on the user's current activity status and environment when collecting health data. The collection unit performs filtering based on the user's current activity status and environment when collecting health data, for example. The activity status includes, for example, the amount of exercise and the activity level, but is not limited to such examples. The environment includes, for example, the temperature, humidity, and the noise level, but is not limited to such examples. For example, the collection unit prioritizes collecting exercise data when the user is exercising. In addition, the collection unit can also prioritize collecting sleep data when the user is sleeping. Furthermore, the collection unit can also prioritize collecting stress levels when the user is at work. This allows the collection unit to collect more relevant data by filtering data according to the user's activity status and environment. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input activity status and environmental data to AI and have AI perform filtering.

[0040] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting health data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting health data. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is at a high altitude, the collection unit preferentially collects data related to altitude. In addition, when the user is in an urban area, the collection unit can also preferentially collect data related to noise levels. Furthermore, when the user is in a natural environment, the collection unit can also preferentially collect data related to air quality. Thereby, the collection unit can preferentially collect highly relevant data by considering the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information into AI and determine the priority order of highly relevant data.

[0041] The collection unit can analyze the social media activity of the user and collect related data when collecting health data. The collection unit, for example, analyzes the social media activity of the user and collects related data when collecting health data. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to such examples. For example, the collection unit collects stress-related data when the user feels stressed on social media. In addition, the collection unit can also collect relaxation-related data when the user is relaxing on social media. Furthermore, the collection unit can also collect exercise-related data when the user posts about exercise on social media. This makes it easier for the collection unit to collect related data by analyzing social media activity. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media activity data to AI and cause AI to collect related data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the health data. The level of importance includes, for example, the impact level and urgency of the data, but is not limited to such examples. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can also perform an analysis with a moderate level of detail on data with medium importance. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI and cause the AI ​​to adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. The analysis unit applies different analysis algorithms depending on the category of health data, for example. The categories include, but are not limited to, heart rate data and sleep data. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a walking pattern analysis algorithm to step count data. Furthermore, the analysis unit can apply a sleep stage analysis algorithm to sleep data. Thereby, the analysis unit applies an appropriate analysis algorithm depending on the category of data, thereby improving the analysis accuracy. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to AI and cause AI to apply an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of the analysis based on the collection time of the health data at the time of analysis. The analysis unit determines the priority of the analysis based on, for example, the collection time of the health data. The collection time includes, for example, the freshness of the data and the frequency of collection, but is not limited to such examples. For example, the analysis unit prioritizes the analysis of recently collected data. The analysis unit can also analyze current data while referring to past data. Furthermore, the analysis unit can also focus on analyzing data collected during a specific period. This allows the analysis unit to determine the priority of the analysis based on the collection time of the data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collection time data to AI and have the AI ​​execute the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of the health data. The relevance includes, for example, correlation and co-occurrence frequency of data, but is not limited to such examples. For example, the analysis unit prioritizes analysis of highly related data. The analysis unit can also analyze data with low relevance later. Furthermore, the analysis unit can appropriately analyze data with medium relevance. This allows the analysis unit to adjust the order of analysis based on the relevance of the data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to AI and cause the AI ​​to adjust the order of analysis.

[0046] The providing unit can adjust the level of detail of the advice based on the importance of the health data when providing the advice. The providing unit adjusts the level of detail of the advice based on, for example, the importance of the health data. The level of importance includes, for example, the impact level and urgency of the data, but is not limited to such examples. For example, the providing unit provides detailed advice based on data with high importance. The providing unit can also provide simplified advice based on data with low importance. Furthermore, the providing unit can provide advice based on data with medium importance with appropriate level of detail. Thereby, the providing unit adjusts the level of detail of the advice according to the importance of the data, thereby enabling efficient advice. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance of the data to AI and cause the AI ​​to adjust the level of detail of the advice.

[0047] The providing unit can apply different advice algorithms according to the category of health data when providing advice. The providing unit applies different advice algorithms according to the category of health data, for example. The categories include, but are not limited to, heart rate data and sleep data. For example, the providing unit applies a heart rate management algorithm to advice based on heart rate data. The providing unit can also apply a walking management algorithm to advice based on step count data. Furthermore, the providing unit can also apply a sleep management algorithm to advice based on sleep data. Thereby, the providing unit applies an appropriate advice algorithm according to the category of data, thereby improving the accuracy of advice. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the category of data to AI and cause AI to apply an appropriate advice algorithm.

[0048] The providing unit can determine the priority of advice based on the collection time of health data when providing advice. The providing unit determines the priority of advice based on, for example, the collection time of health data. The collection time includes, for example, the freshness of data and the frequency of collection, but is not limited to such examples. For example, the providing unit provides advice based on recently collected data with priority. The providing unit can also provide advice based on current data while referring to past data. Furthermore, the providing unit can also provide advice based on data collected during a specific period with a focus on it. As a result, the providing unit determines the priority of advice based on the collection time of data, thereby enabling efficient advice. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input collection time data to AI and cause AI to execute the determination of the priority of advice.

[0049] The providing unit can adjust the order of advice based on the relevance of health data when providing advice. The providing unit adjusts the order of advice based on, for example, the relevance of health data. The relevance includes, for example, correlation of data and co-occurrence frequency, but is not limited to such examples. For example, the providing unit provides advice based on highly relevant data with priority. The providing unit can also provide advice based on data with low relevance later. Furthermore, the providing unit can also provide advice based on data with medium relevance appropriately. Thereby, the providing unit adjusts the order of advice based on the relevance of data, thereby enabling efficient advice. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of data to AI and cause AI to adjust the order of advice.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The collection unit can record the user's dietary content when collecting the user's health data. For example, the collection unit can analyze images of the user's meals and calculate the nutrients and calories ingested. The collection unit can also allow the user to manually input the dietary content and integrate the data with the health data. Furthermore, the collection unit can analyze the user's dietary patterns and record the timing and frequency of meals. This allows the collection unit to grasp the user's dietary content in detail and use it for health management.

[0052] The analysis unit can take into account the user's lifestyle habits when analyzing the user's health data. For example, the analysis unit can analyze the user's sleep patterns and exercise habits and evaluate the impact on the health condition. The analysis unit can also predict health risks by taking into account the user's stress level and work schedule. Furthermore, the analysis unit can analyze the user's diet and drinking habits and evaluate the impact on the health condition. This allows the analysis unit to comprehensively evaluate the user's lifestyle habits and make more accurate predictions of health risks.

[0053] The providing unit can provide health advice tailored to the user's lifestyle based on the user's health data. For example, when the user is busy, the providing unit can suggest an effective exercise plan in a short time. Also, when the user is traveling, the providing unit can provide health advice that can be practiced at the travel destination. Furthermore, when the user has specific dietary restrictions, the providing unit can suggest a meal plan that matches those restrictions. This allows the providing unit to provide flexible health advice tailored to the user's lifestyle.

[0054] The collection unit can evaluate the user's athletic performance when collecting the user's health data. For example, the collection unit can measure the user's running speed and distance to evaluate the effectiveness of the exercise. The collection unit can also record the number of times and weight of the user's strength training to evaluate the improvement of muscle strength. Furthermore, the collection unit can measure the user's flexibility and balance to evaluate the overall athletic performance. This allows the collection unit to grasp the user's athletic performance in detail and use it for health management.

[0055] The process flow of the first embodiment will be briefly described below.

[0056] Step 1: The collection unit collects health data. The health data includes heart rate, blood pressure, body temperature, etc. For example, the collection unit uses a smart watch to monitor the heart rate in real time and collect data. The blood pressure monitor periodically measures blood pressure and collects data. The thermometer records body temperature fluctuations and collects data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit uses statistical analysis to analyze data trends and machine learning algorithms to detect outliers. By analyzing correlations in the data, it is possible to predict changes in health status. Step 3: The advice provider provides advice based on the analysis results obtained by the analyzer. The advice includes advice on improving diet, exercise recommendations, and stress management. For example, the advice provider may recommend a balanced diet and suggest a specific menu. As an exercise recommendation, the advice provider may provide an appropriate exercise plan and explain how to carry it out. As a stress management recommendation, the advice provider may suggest relaxation techniques and stress relief methods.

[0057] (Example 2) A health monitoring system according to an embodiment of the present invention is a system in which an AI health monitor linked to a communication application analyzes personal health data collected by a smartwatch and provides optimal health advice to the user. This health monitoring system collects daily health data (heart rate, number of steps, sleep time, etc.) and transmits it to the AI ​​health monitor through a communication application. The AI ​​health monitor analyzes these data to understand the user's health condition and patterns. In addition, the AI ​​health monitor predicts the risk of diseases that the user is likely to suffer from, taking into account the user's genetic information and family history. For example, if there is a history of a particular disease in the family, the AI ​​estimates the risk high and suggests preventive measures. This allows the user to implement preventive measures in their daily lives. The system also provides health coaching tailored to the user's goals and needs. For example, for a user who wants to lose weight, the AI ​​provides dietary advice and exercise plans. This makes it easier for the user to achieve their health goals. The system supports health management by monitoring the user's health data in real time and providing necessary advice immediately. For example, if stress is increasing, the AI ​​provides advice on how to relax. Also, if the quality of sleep is declining, the AI ​​suggests improvement measures. This allows the user to always be aware of their health condition and take appropriate measures. This enables the health monitoring system to monitor the user's health data in real time and provide necessary advice immediately.

[0058] The health monitoring system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects health data. The health data includes, for example, heart rate, blood pressure, body temperature, and the like, but is not limited to such examples. The collection unit measures the heart rate, for example, using a smart watch. The collection unit can also measure blood pressure using a blood pressure monitor. Furthermore, the collection unit can also measure body temperature using a thermometer. For example, the collection unit monitors the heart rate in real time using a smart watch and collects data. The blood pressure monitor periodically measures blood pressure and collects data. The thermometer records body temperature fluctuations and collects data. The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to such examples. For example, the analysis unit analyzes the trend of the data using statistical analysis. The analysis unit can also detect abnormal values ​​using machine learning algorithms. Furthermore, the analysis unit can analyze the correlation of the data. For example, the analysis unit calculates the average value and standard deviation of the data using statistical analysis to grasp the trend. The machine learning algorithm automatically detects abnormal values ​​and issues an alert. By analyzing the correlation of data, it is possible to predict changes in health status. The providing unit provides advice based on the analysis results obtained by the analyzing unit. The advice includes, for example, but is not limited to, recommendations for improving diet and exercise. For example, the providing unit provides advice regarding improving diet. The providing unit can also provide advice regarding recommended exercise. Furthermore, the providing unit can also provide advice regarding stress management. For example, the providing unit recommends a balanced diet and suggests a specific menu. As a recommendation for exercise, an appropriate exercise plan is provided and a method of implementation is explained. As a stress management, relaxation methods and stress relief methods are suggested. As a result, the health monitoring system according to the embodiment can support the user's health management by collecting and analyzing the user's health data and providing advice.

[0059] The collection unit collects health data. Examples of health data include, but are not limited to, heart rate, blood pressure, and body temperature. The collection unit measures the heart rate using, for example, a smart watch. The smart watch is equipped with an optical heart rate sensor, and by wearing it on the user's wrist, it can detect changes in blood flow and monitor the heart rate in real time. Furthermore, the smart watch has a function of transmitting data to a smartphone or a cloud server via Bluetooth or Wi-Fi, and the collected data is immediately stored in a central database. There are types of blood pressure monitors that measure blood pressure by wrapping a cuff around the arm and types that are worn on the wrist, and they measure blood pressure periodically and collect data. These devices are designed so that users can easily use them at home, and the measurement results are automatically recorded. There are types of thermometers that measure body temperature by contacting the ear or forehead, and non-contact infrared thermometers that record body temperature fluctuations and collect data. This allows the collection unit to collect health data from the user's daily life in a multifaceted manner and monitor it in real time. Furthermore, the collection department can centrally manage the data obtained from these devices and link it with other systems and departments as necessary. For example, the collected data is stored on a cloud server and made accessible to the analysis department and provision department. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection department to collect data efficiently and effectively and improve the performance of the entire system.

[0060] The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis or machine learning algorithms, but is not limited to such examples. Specifically, the trend of the data is analyzed using statistical analysis. For example, the data of the heart rate, blood pressure, and body temperature are plotted in a time series, and the average value and standard deviation are calculated, so that the trend of the user's health condition can be grasped. Furthermore, abnormal values ​​can be detected using machine learning algorithms. For example, a normal range is learned based on past data, and an alert is issued when an abnormal value is detected by comparing it with data collected in real time. This makes it possible to detect abnormalities in the health condition early and take appropriate measures. Furthermore, the analysis unit can also analyze the correlation of data. For example, by performing correlation analysis on the data of the heart rate, blood pressure, and body temperature, and clarifying the relationship between these data, it is possible to predict changes in the health condition. This allows the analysis unit to analyze the collected data from multiple angles and comprehensively evaluate the health condition of the user. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term health risk assessment and trend analysis. For example, based on past data, it can predict fluctuations in health risks during specific seasons or times of the day and devise future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time, but also handle long-term health management and anomaly detection, improving the reliability and safety of the entire system.

[0061] The providing unit provides advice based on the analysis results obtained by the analyzing unit. The advice includes, for example, recommendations for improving diet and exercise, but is not limited to such examples. Specifically, advice on improving diet is provided. For example, based on the health data of the user, a balanced diet is recommended and a specific menu is proposed. This includes a meal plan that takes into account nutrient balance and calorie intake. The providing unit can also provide advice on recommended exercise. For example, an appropriate exercise plan is provided according to the physical strength and health condition of the user, and a method for implementing the plan is explained. This includes aerobic exercise, muscle training, stretching, and the like. Furthermore, the providing unit can also provide advice on stress management. For example, relaxation methods and stress relief methods are proposed, and specific methods that the user can practice in daily life are provided. This includes deep breathing, meditation, yoga, and the like. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the advice content. For example, the meal plan and exercise plan are reviewed based on feedback from the user who received the advice. Furthermore, the providing unit can reliably transmit information using multiple communication means. For example, important information can be delivered reliably not only through smartphone notifications but also through voice calls, SMS, emails, etc. This enables the provider to provide advice to users quickly and reliably and support their health management.

[0062] The analysis unit can predict the risk of disease based on the user's genetic information and family history. The analysis unit, for example, analyzes the user's genetic information and predicts the risk of disease. The genetic information includes, for example, DNA sequencing and genetic markers, but is not limited to such examples. For example, the analysis unit analyzes the user's genetic information using DNA sequencing to detect a specific genetic mutation. The risk of disease can also be evaluated using genetic markers. The analysis unit also predicts the risk of disease taking into account the user's family history. The family history includes, for example, a family medical history and the presence or absence of a genetic disease, but is not limited to such examples. For example, the analysis unit collects the family's medical history and evaluates the risk of a specific disease. It is also possible to confirm the presence or absence of a genetic disease and predict the risk. As a result, the analysis unit can more accurately predict the risk of disease by considering the user's genetic information and family history. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's genetic information into AI and cause the AI ​​to execute a prediction of the risk of disease.

[0063] The providing unit can perform health coaching according to the user's goals and needs. The providing unit, for example, collects the user's health goals and provides advice based thereon. Health goals include, for example, weight loss, muscle building, stress management, and the like, but are not limited to such examples. For example, the providing unit provides a diet improvement and exercise plan based on the user's weight loss goal. An appropriate training plan can also be provided based on the muscle building goal. A relaxation method or a stress relief method can also be suggested based on the stress management goal. In this way, the providing unit can support the achievement of the health goal by providing advice according to the user's goals and needs. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's health goals into AI and cause the AI ​​to generate advice.

[0064] The collection unit can collect at least one of daily health data of a heart rate, a step count, and a sleep time. The collection unit measures a heart rate using, for example, a smart watch. Measurement of a heart rate includes, for example, a heart rate monitor and a wearable device, but is not limited to such examples. For example, the collection unit monitors a heart rate in real time using a heart rate monitor and collects data. The wearable device can record fluctuations in a heart rate and collect data. In addition, the collection unit can use a pedometer or a smartphone app to measure the number of steps. For example, the collection unit counts the number of steps per day using a pedometer and collects data. The smartphone app can automatically record the number of steps and collect data. In addition, the collection unit can use a sleep tracker or a wearable device to measure the sleep time. For example, the collection unit uses a sleep tracker to evaluate the quality of sleep and collect data. The wearable device can analyze the stages of sleep and collect data. As a result, the collection unit can grasp the user's health condition in detail by collecting daily health data such as a heart rate, a step count, and a sleep time. Some or all of the above-mentioned processes in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input heart rate data acquired by a smart watch to the generation AI and have the generation AI analyze the data.

[0065] The providing unit can provide advice to relax when stress is high. The providing unit, for example, evaluates the stress level of the user and provides advice to relax. The evaluation of the stress level includes, for example, but is not limited to, stress hormone measurement and questionnaire survey. For example, the providing unit suggests a relaxation method based on the measurement result of the stress hormone. The result of the questionnaire survey can also be analyzed to provide advice on stress management. In this way, the providing unit can support the user's stress management by providing appropriate advice when stress is high. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the measurement data of the stress hormone to AI and cause AI to execute the suggestion of a relaxation method.

[0066] The providing unit can propose an improvement measure when the quality of sleep is deteriorating. The providing unit, for example, evaluates the quality of sleep of the user and proposes an improvement measure. The evaluation of the quality of sleep includes, for example, sleep score and sleep stage analysis, but is not limited to such examples. For example, the providing unit proposes an improvement measure for the sleep environment based on the sleep score. A specific improvement measure can also be provided based on the analysis result of the sleep stage. Thereby, the providing unit can improve the quality of sleep of the user by proposing an appropriate improvement measure when the quality of sleep is deteriorating. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the sleep score data to AI and cause the AI ​​to execute the proposal of the improvement measure.

[0067] The collection unit can estimate the user's emotion and adjust the timing of collecting health data based on the estimated emotion of the user. The collection unit, for example, estimates the user's emotion and adjusts the timing of collecting health data based on the estimated emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, the collection unit estimates the user's emotion using facial expression recognition technology, and increases the collection frequency when the user is stressed. The collection unit can also estimate the user's emotion using voice analysis technology, and decreases the collection frequency when the user is relaxed. Furthermore, when the user is exercising, the collection unit can also collect data in accordance with the recovery time after exercise. This allows the collection unit to adjust the collection timing according to the user's emotion, thereby enabling more appropriate data collection. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input data acquired using facial expression recognition technology into the generation AI and have the generation AI estimate emotions.

[0068] The collection unit can analyze the user's past health data and select the optimal collection method. The collection unit, for example, analyzes the user's past health data and selects the optimal collection method. The optimal collection method includes, for example, the selection of a device and the frequency of data collection, but is not limited to such examples. For example, the collection unit analyzes the user's past heart rate data and collects data with a focus on the time period when abnormalities are observed. The collection unit can also collect data during the time period when the quality of sleep is poor, based on the user's past sleep data. Furthermore, the collection unit can increase the collection frequency on days when the amount of exercise is low, referring to the user's past step count data. As a result, the collection unit can select the optimal collection method by analyzing the past data, and improve the efficiency of data collection. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past health data into AI and cause the AI ​​to select the optimal collection method.

[0069] The collection unit can perform filtering based on the user's current activity status and environment when collecting health data. The collection unit performs filtering based on the user's current activity status and environment when collecting health data, for example. The activity status includes, for example, the amount of exercise and the activity level, but is not limited to such examples. The environment includes, for example, the temperature, humidity, and the noise level, but is not limited to such examples. For example, the collection unit prioritizes collecting exercise data when the user is exercising. In addition, the collection unit can also prioritize collecting sleep data when the user is sleeping. Furthermore, the collection unit can also prioritize collecting stress levels when the user is at work. This allows the collection unit to collect more relevant data by filtering data according to the user's activity status and environment. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input activity status and environmental data to AI and have AI perform filtering.

[0070] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of data to be collected based on the estimated emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, the collection unit estimates the user's emotion using a facial expression recognition technology, and when the user is stressed, prioritizes and collects stress-related data. The collection unit can also estimate the user's emotion using a voice analysis technology, and when the user is relaxed, prioritizes and collects relaxation-related data. Furthermore, when the user is exercising, the collection unit can also prioritize and collect exercise-related data. Thereby, the collection unit can prioritize and collect important data by determining the priority of data according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input data acquired using facial expression recognition technology into the generation AI and have the generation AI estimate emotions.

[0071] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting health data. For example, the collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting health data. The geographical location information includes, for example, GPS data and location information services, but is not limited to such examples. For example, when the user is at a high altitude, the collection unit preferentially collects data related to altitude. In addition, when the user is in an urban area, the collection unit can also preferentially collect data related to noise levels. Furthermore, when the user is in a natural environment, the collection unit can also preferentially collect data related to air quality. Thereby, the collection unit can preferentially collect highly relevant data by considering the geographical location information. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information into AI and determine the priority order of highly relevant data.

[0072] The collection unit can analyze the social media activity of the user and collect related data when collecting health data. The collection unit, for example, analyzes the social media activity of the user and collects related data when collecting health data. Social media activity includes, for example, the content of posts and the number of likes, but is not limited to such examples. For example, the collection unit collects stress-related data when the user feels stressed on social media. In addition, the collection unit can also collect relaxation-related data when the user is relaxing on social media. Furthermore, the collection unit can also collect exercise-related data when the user posts about exercise on social media. This makes it easier for the collection unit to collect related data by analyzing social media activity. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input social media activity data to AI and cause AI to collect related data.

[0073] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. The analysis unit can estimate the user's emotion, and adjust the expression method of the analysis based on the estimated emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, the analysis unit can estimate the user's emotion using facial expression recognition technology, and provide a simple and visually easy-to-understand analysis result when the user is stressed. The analysis unit can also estimate the user's emotion using voice analysis technology, and provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide a visually stimulating analysis result when the user is excited. Thereby, the analysis unit can provide a more easy-to-understand analysis result by adjusting the expression method of the analysis according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input data obtained using facial expression recognition technology into the generation AI and have the generation AI estimate emotions.

[0074] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the health data. The level of importance includes, for example, the impact level and urgency of the data, but is not limited to such examples. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can also perform an analysis with a moderate level of detail on data with medium importance. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI and cause the AI ​​to adjust the level of detail of the analysis.

[0075] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. The analysis unit applies different analysis algorithms depending on the category of health data, for example. The categories include, but are not limited to, heart rate data and sleep data. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a walking pattern analysis algorithm to step count data. Furthermore, the analysis unit can apply a sleep stage analysis algorithm to sleep data. Thereby, the analysis unit applies an appropriate analysis algorithm depending on the category of data, thereby improving the analysis accuracy. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to AI and cause AI to apply an appropriate analysis algorithm.

[0076] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, the analysis unit can estimate the user's emotion using facial expression recognition technology, and provide a short and to the point analysis result when the user is in a hurry. The analysis unit can also estimate the user's emotion using voice analysis technology, and provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide a visually stimulating analysis result when the user is excited. Thereby, the analysis unit can provide a more appropriate analysis result by adjusting the length of the analysis according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input data obtained using facial expression recognition technology into the generation AI and have the generation AI estimate emotions.

[0077] The analysis unit can determine the priority of the analysis based on the collection time of the health data at the time of analysis. The analysis unit determines the priority of the analysis based on, for example, the collection time of the health data. The collection time includes, for example, the freshness of the data and the frequency of collection, but is not limited to such examples. For example, the analysis unit prioritizes the analysis of recently collected data. The analysis unit can also analyze current data while referring to past data. Furthermore, the analysis unit can also focus on analyzing data collected during a specific period. This allows the analysis unit to determine the priority of the analysis based on the collection time of the data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collection time data to AI and have the AI ​​execute the determination of the analysis priority.

[0078] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. The analysis unit adjusts the order of analysis based on, for example, the relevance of the health data. The relevance includes, for example, correlation and co-occurrence frequency of data, but is not limited to such examples. For example, the analysis unit prioritizes analysis of highly related data. The analysis unit can also analyze data with low relevance later. Furthermore, the analysis unit can appropriately analyze data with medium relevance. This allows the analysis unit to adjust the order of analysis based on the relevance of the data, thereby enabling efficient analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to AI and cause the AI ​​to adjust the order of analysis.

[0079] The providing unit can estimate the user's emotion and adjust the way of expressing advice based on the estimated emotion of the user. The providing unit, for example, estimates the user's emotion and adjusts the way of expressing advice based on the estimated emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, the providing unit estimates the user's emotion using facial expression recognition technology, and provides simple and visually easy-to-understand advice when the user is stressed. The providing unit can also estimate the user's emotion using voice analysis technology, and provide detailed advice when the user is relaxed. Furthermore, the providing unit can also provide visually stimulating advice when the user is excited. Thereby, the providing unit can provide more easily understandable advice by adjusting the way of expressing advice according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input data obtained using facial expression recognition technology into the generating AI and have the generating AI estimate emotions.

[0080] The providing unit can adjust the level of detail of the advice based on the importance of the health data when providing the advice. The providing unit adjusts the level of detail of the advice based on, for example, the importance of the health data. The level of importance includes, for example, the impact level and urgency of the data, but is not limited to such examples. For example, the providing unit provides detailed advice based on data with high importance. The providing unit can also provide simplified advice based on data with low importance. Furthermore, the providing unit can provide advice based on data with medium importance with appropriate level of detail. Thereby, the providing unit adjusts the level of detail of the advice according to the importance of the data, thereby enabling efficient advice. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance of the data to AI and cause the AI ​​to adjust the level of detail of the advice.

[0081] The providing unit can apply different advice algorithms according to the category of health data when providing advice. The providing unit applies different advice algorithms according to the category of health data, for example. The categories include, but are not limited to, heart rate data and sleep data. For example, the providing unit applies a heart rate management algorithm to advice based on heart rate data. The providing unit can also apply a walking management algorithm to advice based on step count data. Furthermore, the providing unit can also apply a sleep management algorithm to advice based on sleep data. Thereby, the providing unit applies an appropriate advice algorithm according to the category of data, thereby improving the accuracy of advice. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the category of data to AI and cause AI to apply an appropriate advice algorithm.

[0082] The providing unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the length of the advice based on the estimated emotion. The estimation of emotion includes, for example, facial expression recognition and voice analysis, but is not limited to such examples. For example, the providing unit can estimate the user's emotion using facial expression recognition technology, and provide short and to the point advice when the user is in a hurry. The providing unit can also estimate the user's emotion using voice analysis technology, and provide detailed advice when the user is relaxed. Furthermore, the providing unit can also provide visually stimulating advice when the user is excited. Thereby, the providing unit can provide more appropriate advice by adjusting the length of the advice according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input data obtained using facial expression recognition technology into the generating AI and have the generating AI estimate emotions.

[0083] The providing unit can determine the priority of advice based on the collection time of health data when providing advice. The providing unit determines the priority of advice based on, for example, the collection time of health data. The collection time includes, for example, the freshness of data and the frequency of collection, but is not limited to such examples. For example, the providing unit provides advice based on recently collected data with priority. The providing unit can also provide advice based on current data while referring to past data. Furthermore, the providing unit can also provide advice based on data collected during a specific period with a focus on it. As a result, the providing unit determines the priority of advice based on the collection time of data, thereby enabling efficient advice. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input collection time data to AI and cause AI to execute the determination of the priority of advice.

[0084] The providing unit can adjust the order of advice based on the relevance of health data when providing advice. The providing unit adjusts the order of advice based on, for example, the relevance of health data. The relevance includes, for example, correlation of data and co-occurrence frequency, but is not limited to such examples. For example, the providing unit provides advice based on highly relevant data with priority. The providing unit can also provide advice based on data with low relevance later. Furthermore, the providing unit can also provide advice based on data with medium relevance appropriately. Thereby, the providing unit adjusts the order of advice based on the relevance of data, thereby enabling efficient advice. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the relevance of data to AI and cause AI to adjust the order of advice.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The collection unit can record the user's dietary content when collecting the user's health data. For example, the collection unit can analyze images of the user's meals and calculate the nutrients and calories ingested. The collection unit can also allow the user to manually input the dietary content and integrate the data with the health data. Furthermore, the collection unit can analyze the user's dietary patterns and record the timing and frequency of meals. This allows the collection unit to grasp the user's dietary content in detail and use it for health management.

[0087] The analysis unit can take into account the user's lifestyle habits when analyzing the user's health data. For example, the analysis unit can analyze the user's sleep patterns and exercise habits and evaluate the impact on the health condition. The analysis unit can also predict health risks by taking into account the user's stress level and work schedule. Furthermore, the analysis unit can analyze the user's diet and drinking habits and evaluate the impact on the health condition. This allows the analysis unit to comprehensively evaluate the user's lifestyle habits and make more accurate predictions of health risks.

[0088] The providing unit can provide health advice tailored to the user's lifestyle based on the user's health data. For example, when the user is busy, the providing unit can suggest an effective exercise plan in a short time. Also, when the user is traveling, the providing unit can provide health advice that can be practiced at the travel destination. Furthermore, when the user has specific dietary restrictions, the providing unit can suggest a meal plan that matches those restrictions. This allows the providing unit to provide flexible health advice tailored to the user's lifestyle.

[0089] The collection unit can evaluate the user's athletic performance when collecting the user's health data. For example, the collection unit can measure the user's running speed and distance to evaluate the effectiveness of the exercise. The collection unit can also record the number of times and weight of the user's strength training to evaluate the improvement of muscle strength. Furthermore, the collection unit can measure the user's flexibility and balance to evaluate the overall athletic performance. This allows the collection unit to grasp the user's athletic performance in detail and use it for health management.

[0090] The providing unit can provide advice to support the user's mental health based on the user's health data. For example, if the user feels stressed, the providing unit can suggest relaxation techniques or stress relief methods. In addition, if the user feels anxious, the providing unit can recommend consulting with a mental health professional. Furthermore, if the user feels depressed, the providing unit can provide advice to encourage positive thinking. In this way, the providing unit can support the user's mental health and contribute to improving the overall health condition.

[0091] When analyzing the user's health data, the analysis unit can estimate the user's emotions and adjust the analysis results based on the estimated emotions. For example, the analysis unit can emphasize analysis results related to stress management when the user is stressed. The analysis unit can also provide detailed analysis results related to the overall health condition when the user is relaxed. Furthermore, the analysis unit can emphasize analysis results related to athletic performance when the user is excited. In this way, the analysis unit can adjust the analysis results according to the user's emotions and provide more appropriate information.

[0092] The providing unit can estimate the user's emotions based on the user's health data, and adjust the content of the advice based on the estimated emotions. For example, when the user feels stressed, the providing unit can suggest a relaxation method or a stress relief method. Furthermore, when the user is relaxed, the providing unit can provide advice for maintaining health. Furthermore, when the user is excited, the providing unit can provide advice for improving exercise performance. In this way, the providing unit can adjust the content of the advice according to the user's emotions and provide more effective support.

[0093] When collecting the user's health data, the collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting stress-related data. Also, when the user is relaxed, the collection unit can also prioritize collecting relaxation-related data. Furthermore, when the user is exercising, the collection unit can also prioritize collecting exercise-related data. In this way, the collection unit can adjust the data collection method according to the user's emotions and collect more relevant data.

[0094] When analyzing the user's health data, the analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, when the user is feeling stressed, the analysis unit can prioritize the analysis of stress-related data. In addition, when the user is relaxed, the analysis unit can prioritize the analysis of data related to the overall health condition. Furthermore, when the user is excited, the analysis unit can prioritize the analysis of data related to athletic performance. In this way, the analysis unit can determine the priority of the analysis according to the user's emotions and provide more appropriate analysis results.

[0095] The providing unit can estimate the user's emotions based on the user's health data, and adjust the timing of advice based on the estimated emotions. For example, when the user is feeling stressed, the providing unit can immediately provide advice on relaxation techniques. When the user is relaxed, the providing unit can also provide advice for maintaining health at an appropriate time. Furthermore, when the user is excited, the providing unit can also provide advice for improving exercise performance at an appropriate time. In this way, the providing unit can adjust the timing of advice according to the user's emotions and provide more effective support.

[0096] The process flow of the second embodiment will be briefly described below.

[0097] Step 1: The collection unit collects health data. The health data includes heart rate, blood pressure, body temperature, etc. For example, the collection unit uses a smart watch to monitor the heart rate in real time and collect data. The blood pressure monitor periodically measures blood pressure and collects data. The thermometer records body temperature fluctuations and collects data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the analysis unit uses statistical analysis to analyze data trends and machine learning algorithms to detect outliers. By analyzing correlations in the data, it is possible to predict changes in health status. Step 3: The advice provider provides advice based on the analysis results obtained by the analyzer. The advice includes advice on improving diet, exercise recommendations, and stress management. For example, the advice provider may recommend a balanced diet and suggest a specific menu. As an exercise recommendation, the advice provider may provide an appropriate exercise plan and explain how to carry it out. As a stress management recommendation, the advice provider may suggest relaxation techniques and stress relief methods.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0100] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0101] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and provision 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 is realized by the computer 36 of the smart device 14 and its control unit 46A, and collects health data such as heart rate, blood pressure, and body temperature. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using statistical analysis or machine learning algorithms. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides advice such as dietary improvement and exercise recommendations based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] 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.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0109] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart glasses 214 and its control unit 46A, and collects health data such as heart rate, blood pressure, and body temperature. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using statistical analysis or machine learning algorithms. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides advice such as dietary improvement and exercise recommendations based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0128] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the headset type terminal 314 and its control unit 46A, and collects health data such as heart rate, blood pressure, and body temperature. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using statistical analysis or machine learning algorithms. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides advice such as dietary improvement and exercise recommendations based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] 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.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] 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.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0145] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the robot 414 and its control unit 46A, and collects health data such as heart rate, blood pressure, and body temperature. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using statistical analysis or machine learning algorithms. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides advice such as dietary improvement and exercise recommendations based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0151] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0152] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0156] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0158] 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.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0161] 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.

[0162] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0163] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0167] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0169] (Appendix 1) A collection unit that collects health data; an analysis unit that analyzes the data collected by the collection unit; A providing unit that provides advice based on the analysis result obtained by the analyzing unit. A system characterized by: (Appendix 2) The analysis unit is Predicts disease risk based on a user's genetic information and family history 2. The system of claim 1, (Appendix 3) The providing unit is Providing health coaching tailored to the user's goals and needs 2. The system of claim 1, (Appendix 4) The collecting unit includes: Collect at least one of the following daily health data: heart rate, steps, and sleep time 2. The system of claim 1, (Appendix 5) The providing unit is Offering tips to help you relax when you're feeling stressed 2. The system of claim 1, (Appendix 6) The providing unit is If your sleep quality is poor, we'll suggest ways to improve it. 2. The system of claim 1, (Appendix 7) The collecting unit includes: To estimate a user's emotion and adjust the timing of collection of health data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze the user's past health data and select the optimal collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: Filtering health data collection based on the user's current activity and environment 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting health data, prioritize collection of relevant data by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting health data, analyze users' social media activity to collect relevant data 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the health data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of health data. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the health data was collected. 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the relevance of health data 2. The system of claim 1, (Appendix 19) The providing unit is Inferring the user's emotions and adjusting the way advice is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 20) The providing unit is When providing advice, adjust the level of detail based on the importance of the health data 2. The system of claim 1, (Appendix 21) The providing unit is When providing advice, different advice algorithms are applied depending on the category of health data. 2. The system of claim 1, (Appendix 22) The providing unit is Estimate the user's emotions and adjust the length of the advice based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The providing unit is When providing advice, prioritize advice based on when the health data was collected 2. The system of claim 1, (Appendix 24) The providing unit is When providing advice, tailor the order of advice based on the relevance of your health data 2. The system of claim 1, [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects health data; an analysis unit that analyzes the data collected by the collection unit; A providing unit that provides advice based on the analysis result obtained by the analyzing unit. A system characterized by:

2. The analysis unit is Predicts disease risk based on a user's genetic information and family history 2. The system of claim 1 .

3. The providing unit is Providing health coaching tailored to the user's goals and needs 2. The system of claim 1 .

4. The collecting unit includes: Collect at least one of the following daily health data: heart rate, steps, and sleep time 2. The system of claim 1 .

5. The providing unit is Offering tips to help you relax when you're feeling stressed 2. The system of claim 1 .

6. The providing unit is If your sleep quality is poor, we'll suggest ways to improve it.

2. The system of claim 1 .

7. The collecting unit includes: To estimate a user's emotion and adjust the timing of collection of health data based on the estimated user's emotion.

2. The system of claim 1 .

8. The collecting unit includes: Analyze the user's past health data and select the optimal collection method 2. The system of claim 1 .

Images