System

Abstract

To provide support for both mental and physical health management in an integrate manner.SOLUTION: A system according to an embodiment comprises a collection unit, an analysis unit, a goal setting unit, and an activity proposal unit. The collection unit collects information from health checkup results or online medical consultation, a smart body composition scale, and a wearable watch. The analysis unit analyzes the information collected by the collection unit. The goal setting unit sets a goal on the basis of the analysis results obtained by the analysis unit. The activity proposal unit proposes an activity on the basis of the goal set by the goal setting 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 technology does not adequately provide a system that comprehensively supports the management of mental and physical health conditions, and there is room for improvement.

[0005] The system according to the embodiment aims to provide integrated support for mental and physical health management. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a goal setting unit, and an activity suggestion unit. The collection unit collects information from health checkup results or online medical treatment, a smart body composition scale, and a wearable watch. The analysis unit analyzes the information collected by the collection unit. The goal setting unit sets a goal based on the analysis result obtained by the analysis unit. The activity suggestion unit suggests an activity based on the goal set by the goal setting unit. Effect of the Invention

[0007] The system according to the embodiment can provide integrated support for managing mental and physical health conditions. [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 management system according to an embodiment of the present invention is a system that provides total maintenance and support for mental care and physical health management. This health management system collects information from the user's annual health check results, online medical treatment, smart body composition scale, and wearable watch, and analyzes the information in cooperation with a generation AI. The generation AI comprehensively evaluates the user's health condition and sets optimal goals in terms of both mental and physical health. In addition, the user discusses mental consultations and ailments while interacting with the generation AI. The generation AI analyzes the user's mental condition and provides appropriate advice. For example, it suggests methods of stress management and relaxation. The generation AI puts together activities aimed at achieving the user's goals. For example, it suggests exercise plans, meal plans, and mental care activities. This allows the user to have a specific action plan for maintaining health in terms of both mental and physical health. This solution allows the user to comprehensively manage their health in terms of both mental and physical health, and to carry out specific activities toward achieving their goals. This allows the health management system to comprehensively evaluate the user's health condition, set optimal goals, and suggest specific activities.

[0029] The health management system according to the embodiment includes a collection unit, an analysis unit, a goal setting unit, and an activity suggestion unit. The collection unit collects information from health checkup results, online medical treatment, a smart body composition scale, and a wearable watch. The collection unit can collect, for example, annual health checkup results. The collection unit can also collect information from online medical treatment, a smart body composition scale, and a wearable watch. The analysis unit analyzes the information collected by the collection unit and comprehensively evaluates the user's health condition. The analysis unit analyzes the collected information using, for example, statistical analysis or a machine learning algorithm. The goal setting unit sets optimal goals in terms of both mental and physical conditions based on the analysis results obtained by the analysis unit. The goal setting unit sets goals such as weight loss and increased exercise frequency based on the user's health condition. The activity suggestion unit proposes exercise plans, meal plans, and mental care activities based on the goals set by the goal setting unit. The activity suggestion unit proposes, for example, specific activities aimed at achieving the user's goals. As a result, the health management system according to the embodiment can comprehensively evaluate the user's health condition, set optimal goals, and propose specific activities.

[0030] The collection unit collects information from health checkup results, online medical examinations, smart body composition scales, and wearable watches. Specifically, detailed data such as blood tests, urine tests, electrocardiograms, and blood pressure measurements can be collected as annual health checkup results. These data are basic information for comprehensively understanding the user's health condition. From online medical examinations, information such as doctor's diagnosis results, prescriptions, and medical records are collected to understand the user's medical history and current health condition. From the smart body composition scale, data such as weight, body fat percentage, muscle mass, bone mass, and water content are collected in real time to monitor changes in the user's body composition. From the wearable watch, data such as heart rate, number of steps, calories burned, and sleep patterns are collected to understand the user's activity level and sleep quality in daily life. These data are automatically collected via Bluetooth (registered trademark) and Wi-Fi and stored in a cloud server. The collection unit can centrally manage information from these diverse data sources and link with other systems and departments as necessary. For example, the collected data can be made accessible to the analysis unit and goal setting unit, and the frequency and accuracy of data collection can be adjusted to accommodate specific situations and conditions. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The analysis unit analyzes the information collected by the collection unit and comprehensively evaluates the user's health condition. Specifically, the collected information is analyzed using statistical analysis and machine learning algorithms. In statistical analysis, statistical indicators such as average values, standard deviations, and correlation coefficients are calculated based on the user's health data to detect trends and abnormal values ​​in the health condition. In machine learning algorithms, past data is learned and a prediction model is constructed based on newly collected data. For example, a model predicting future health risks can be created using the user's heart rate and step count data. Furthermore, the analysis unit can use an anomaly detection algorithm to detect patterns that are different from normal or abnormal data and issue an early warning. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the user's health condition in real time. Furthermore, the analysis unit can also perform long-term health evaluations and trend analysis using past data and statistical information. For example, based on past health checkup results, fluctuations in specific health indicators are predicted and future health risks are evaluated. In addition, the analysis unit can perform more accurate health evaluations by taking into account the user's lifestyle and environmental factors. This enables the analysis unit to not only grasp health conditions in real time, but also handle long-term health management and abnormality detection, improving the reliability and safety of the entire system.

[0032] The goal setting unit sets optimal goals in terms of both mental and physical health based on the analysis results obtained by the analysis unit. Specifically, goals such as weight loss, increased exercise frequency, and stress management are set based on the user's health condition. For example, if the user's weight exceeds the standard value, a specific weight loss goal is set with weight loss as the goal. In addition, if a lack of exercise is indicated, a goal for the frequency of exercise, such as how many times per week to exercise, is set. Furthermore, it is possible to set goals for stress management and relaxation, taking into account mental health. The goal setting unit sets realistic and achievable goals in consideration of the user's lifestyle and individual needs. For example, it sets goals within a reasonable range in consideration of work and family situations. In addition, the goal setting unit can monitor the degree of goal achievement and progress based on the user's feedback, and modify the goals as necessary. As a result, the goal setting unit can set optimal goals according to the user's health condition and support the user in effectively managing his or her health. Furthermore, the goal setting unit can provide incentives and rewards for goal achievement in order to maintain the user's motivation. For example, points are awarded when a goal is achieved, and a user can receive benefits by accumulating points. This enables the goal setting unit to support the user's health management and promote sustained health improvement.

[0033] The activity suggestion unit proposes an exercise plan, a meal plan, and mental care activities based on the goal set by the goal setting unit. Specifically, the activity suggestion unit proposes specific activities aimed at achieving the user's goal. For example, a calorie restriction and a balanced meal plan is proposed for a user aiming to lose weight, and an exercise plan such as how many times a week to exercise is proposed for a user aiming to increase the frequency of exercise. Furthermore, meditation, yoga, and relaxation activities are proposed for a user aiming to manage stress. The activity suggestion unit proposes realistic and effective activities in consideration of the user's lifestyle and individual needs. For example, the activity suggestion unit proposes activities within a reasonable range in consideration of work and family situations. In addition, the activity suggestion unit can monitor the effect and progress of the activity based on the user's feedback and modify the activity as necessary. This allows the activity suggestion unit to effectively support the user's health management and promote sustained health improvement. Furthermore, the activity suggestion unit can provide incentives and rewards according to the degree of achievement of the activity in order to maintain the user's motivation. For example, points are awarded when an activity is achieved, and a user can receive benefits by accumulating points. This enables the activity suggestion unit to support the user's health management and promote sustained health improvement.

[0034] The collection unit can collect annual health checkup results. The collection unit can collect, for example, the results of regular health checkups and specific health checkups. The collection unit can collect the health checkup results in a digital format and input them to the generation AI. This enables continuous health management by collecting annual health checkup results. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the health checkup results to the generation AI, which can analyze the data.

[0035] The collection unit can collect information from online medical care, a smart body composition scale, and a wearable watch. The collection unit can collect online medical information, such as a medical examination by video call or issuance of a prescription online. The collection unit can collect information such as body fat percentage, muscle mass, and bone density from the smart body composition scale. The collection unit can collect information such as heart rate, number of steps, and sleep pattern from the wearable watch. This makes it possible to collect comprehensive health information by collecting information from online medical care, a smart body composition scale, and a wearable watch. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input online medical information to a generation AI, and the generation AI can analyze the data.

[0036] The analysis unit can analyze the collected information and comprehensively evaluate the health condition of the user. The analysis unit can analyze the collected information using, for example, statistical analysis or a machine learning algorithm. The analysis unit can use a method of integrating and evaluating multiple health indicators. This allows the collected information to be analyzed to comprehensively evaluate the health condition of the user. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected information to a generation AI, which can analyze the data.

[0037] The goal setting unit can set optimal goals in terms of both mental and physical health based on the analysis results. The goal setting unit can set goals such as weight loss and increased exercise frequency based on the user's health condition. The goal setting unit can set goals using mental evaluation criteria such as stress level and psychological test results. The goal setting unit can set goals using evaluation criteria for physical health such as body temperature, blood pressure, and heart rate. This improves the user's health management by setting optimal goals based on the analysis results. Some or all of the above-mentioned processing in the goal setting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the goal setting unit can input the analysis results to the generation AI, which can set goals.

[0038] The activity suggestion unit can suggest an exercise plan, a meal plan, and a mental care activity for achieving the goal. The activity suggestion unit can, for example, suggest a specific exercise plan for achieving the user's goal. The activity suggestion unit can suggest a meal plan that takes into account calorie restriction and nutritional balance. The activity suggestion unit can suggest mental care activities such as relaxation techniques and counseling. As a result, health management of the user is realized by suggesting specific activities for achieving the goal. Some or all of the above-mentioned processing in the activity suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the activity suggestion unit can input a goal into the generation AI, which then suggests an activity.

[0039] The collection unit can analyze the user's past health check results and select the optimal collection method. The collection unit can, for example, collect information from the user's past health check results with emphasis on a specific item. The collection unit can select the most effective collection method based on the user's past health check results. The collection unit can analyze the user's past health check results and determine the priority of the information to be collected. In this way, the optimal information collection method can be selected by analyzing the past health check results. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the past health check results to the generation AI, which can select the optimal collection method.

[0040] The collection unit can perform filtering based on the user's current living situation and field of interest when collecting the health checkup results. The collection unit can, for example, preferentially collect highly relevant information based on the user's current living situation. The collection unit can filter and collect specific health checkup items based on the user's field of interest. The collection unit can adjust the range of information to be collected based on the user's living situation and field of interest. This makes it possible to collect more relevant information by filtering information based on the user's living situation and field of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input data on the user's living situation and field of interest into the generation AI, and the generation AI can filter the information.

[0041] The collection unit can preferentially collect highly relevant information based on the geographical location information of the user when collecting the health checkup results. The collection unit can preferentially collect region-specific health information based on the geographical location information of the user, for example. The collection unit can collect the most relevant health checkup results in consideration of the geographical location information of the user. The collection unit can collect information related to health risks in a specific region based on the geographical location information of the user. This makes it possible to respond to region-specific health risks by collecting information based on the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the geographical location information of the user to the generation AI, and the generation AI can preferentially collect highly relevant information.

[0042] The collection unit can analyze the user's social media activity and collect related information when collecting the health checkup result. For example, the collection unit can identify health concerns from the user's social media activity and collect related information. The collection unit can analyze the user's social media activity and collect information related to the health checkup result. The collection unit can collect information related to a specific health risk based on the user's social media activity. This makes it possible to collect information based on the user's interests by analyzing the social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's social media activity data to the generation AI, and the generation AI can collect the related information.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the health condition during the analysis. For example, when the health condition is deteriorating, the analysis unit can perform a detailed analysis and propose specific measures. When the health condition is good, the analysis unit can perform a concise analysis and provide advice for maintaining the health condition. The analysis unit can adjust the level of detail of the analysis based on the importance of the health condition and provide optimal information. As a result, optimal information can be provided by adjusting the level of detail of the analysis based on the importance of the health condition. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data on the health condition to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the health condition during analysis. The analysis unit can apply a specific analysis algorithm to information related to mental care, for example. The analysis unit can apply another analysis algorithm to information related to physical condition management. The analysis unit can select and apply an optimal analysis algorithm depending on the category of the health condition. This improves the analysis accuracy by applying the optimal analysis algorithm depending on the category of the health condition. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input health condition category data to the generation AI, and the generation AI can apply the optimal analysis algorithm.

[0045] The analysis unit can determine the priority of the analysis based on the time of submission of the health check result at the time of analysis. For example, if the health check result has been submitted recently, the analysis unit can prioritize the analysis. The analysis unit can determine the priority of the analysis based on the time of submission of the health check result. If the health check result is old, the analysis unit can prioritize other information in the analysis. This enables rapid analysis by determining the priority of the analysis based on the time of submission of the health check result. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time of submission of the health check result to the generation AI, and the generation AI can determine the priority of the analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the health condition during analysis. For example, when the health condition is deteriorating, the analysis unit can prioritize analysis. The analysis unit can adjust the order of analysis based on the relevance of the health condition. When the health condition is good, the analysis unit can prioritize analysis of other information. In this way, by adjusting the order of analysis based on the relevance of the health condition, more important information can be prioritized in analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the relevance data of the health condition to the generation AI, and the generation AI can adjust the order of analysis.

[0047] The goal setting unit can set an optimal goal by analyzing the user's past health condition when setting a goal. The goal setting unit can set a realistic and achievable goal based on the user's past health condition, for example. The goal setting unit can set an optimal goal by analyzing the user's past health condition. The goal setting unit can set a goal for maintaining health based on the user's past health condition. This makes it possible to set a realistic and achievable goal by analyzing the past health condition. A part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the goal setting unit can input past health condition data into the generation AI, which can set an optimal goal.

[0048] The goal setting unit can customize the goal based on the user's current living situation when setting the goal. The goal setting unit can set a realistic and achievable goal based on the user's current living situation, for example. The goal setting unit can customize the goal according to the user's living situation. The goal setting unit can set an optimal goal in consideration of the user's current living situation. As a result, by customizing the goal based on the current living situation, a more realistic and achievable goal can be set. A part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the goal setting unit can input current living situation data to the generation AI, and the generation AI can customize the goal.

[0049] The goal setting unit can set an optimal goal in consideration of the geographical location information of the user when setting a goal. The goal setting unit can set a goal corresponding to a region-specific health risk based on, for example, the geographical location information of the user. The goal setting unit can set an optimal goal in consideration of the geographical location information of the user. The goal setting unit can set a realistic and achievable goal based on the geographical location information of the user. As a result, by setting a goal based on the geographical location information, it is possible to respond to a region-specific health risk. A part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the goal setting unit can input geographical location information data to the generation AI, which can set an optimal goal.

[0050] The goal setting unit can set a goal by analyzing the user's social media activity when setting a goal. The goal setting unit can, for example, identify health-related interests from the user's social media activity and set a related goal. The goal setting unit can analyze the user's social media activity and set an optimal goal. The goal setting unit can set a realistic and achievable goal based on the user's social media activity. This makes it possible to set a goal based on the user's interests by analyzing the social media activity. A part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the goal setting unit can input social media activity data to the generation AI, which can set a goal.

[0051] The activity suggestion unit can analyze the user's past activity history and make optimal suggestions when suggesting an activity. The activity suggestion unit can, for example, suggest an optimal exercise plan based on the user's past activity history. The activity suggestion unit can analyze the user's past activity history and suggest the most effective activity. The activity suggestion unit can suggest an activity for maintaining health based on the user's past activity history. This makes it possible to propose optimal activities by analyzing past activity history. Some or all of the above-mentioned processing in the activity suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the activity suggestion unit inputs past activity history data into the generation AI, which can make optimal suggestions.

[0052] The activity suggestion unit can customize the suggestion based on the user's current living situation when suggesting an activity. The activity suggestion unit can suggest a realistic and executable activity based on the user's current living situation, for example. The activity suggestion unit can customize the activity according to the user's living situation. The activity suggestion unit can suggest an optimal activity in consideration of the user's current living situation. This enables a more realistic and executable suggestion by customizing the activity based on the current living situation. A part or all of the above-mentioned processing in the activity suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the activity suggestion unit can input current living situation data to the generation AI, which can customize the activity.

[0053] The activity suggestion unit can make optimal suggestions in consideration of the geographical location information of the user when suggesting an activity. The activity suggestion unit can, for example, suggest an activity specific to a region based on the geographical location information of the user. The activity suggestion unit can suggest optimal activities in consideration of the geographical location information of the user. The activity suggestion unit can suggest realistic and executable activities based on the geographical location information of the user. As a result, activities specific to a region can be provided by suggesting activities based on the geographical location information. A part or all of the above-mentioned processing in the activity suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the activity suggestion unit inputs geographical location information data to the generation AI, which can make optimal suggestions.

[0054] The activity suggestion unit can analyze the user's social media activity and make a suggestion when suggesting an activity. The activity suggestion unit can, for example, identify health-related interests from the user's social media activity and suggest related activities. The activity suggestion unit can analyze the user's social media activity and suggest an optimal activity. The activity suggestion unit can suggest a realistic and executable activity based on the user's social media activity. This makes it possible to suggest an activity based on the user's interests by analyzing the social media activity. Some or all of the above-mentioned processing in the activity suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the activity suggestion unit can input social media activity data to the generation AI, which can make an optimal suggestion.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The health management system can further analyze the user's sleep patterns and provide advice to improve sleep quality. For example, the analysis unit can collect the user's sleep data and analyze the depth of sleep and frequency of interruptions. The goal setting unit can set sleep time and bedtime / wake-up time suitable for the user based on the analysis results. The activity suggestion unit can suggest a nighttime routine for relaxation and environmental settings for improving sleep quality. This allows the user to have a specific action plan to get better sleep.

[0057] The health management system can further collect the user's dietary records and analyze the nutritional balance. For example, the collection unit can provide an interface for the user to input daily meal contents. The analysis unit can analyze the collected dietary data and evaluate nutrient excess or deficiency. The goal setting unit can set a nutritional balance goal suitable for the user based on the analysis result. The activity suggestion unit can suggest a specific meal plan or recipe for improving nutritional balance. This allows the user to obtain a specific guideline for maintaining a healthy diet.

[0058] The health management system can further analyze the user's exercise history and provide advice for maximizing the effect of exercise. For example, the collection unit can collect data such as the type, time, and intensity of exercise performed by the user. The analysis unit can analyze the collected exercise data and evaluate the effect of the exercise. The goal setting unit can set an exercise goal suitable for the user based on the analysis result. The activity suggestion unit can suggest a specific training plan and stretching method for maximizing the effect of exercise. This allows the user to obtain specific guidelines for acquiring effective exercise habits.

[0059] The health management system can further monitor the user's stress level and provide advice for stress reduction. For example, the collection unit can collect the user's heart rate, breathing pattern, and self-reported stress level. The analysis unit can analyze the collected data and evaluate the user's stress level. The goal setting unit can set a stress management goal suitable for the user based on the analysis result. The activity suggestion unit can suggest specific relaxation techniques and mindfulness exercises for stress reduction. This allows the user to have a specific action plan for effectively managing stress.

[0060] The health management system can further monitor the user's social activities and provide advice for improving social health. For example, the collection unit can collect the user's social media activities and communication history. The analysis unit can analyze the collected data and evaluate the frequency and quality of the user's social activities. The goal setting unit can set social activity goals suitable for the user based on the analysis results. The activity suggestion unit can suggest specific event participation and communication methods for improving social health. This allows the user to obtain specific guidelines for strengthening social connections.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects information from health checkup results, online medical examinations, smart body composition scales, and wearable watches. For example, annual health checkup results, online medical examination data, and information from smart body composition scales and wearable watches can be collected. Step 2: The analysis unit analyzes the information collected by the collection unit and comprehensively evaluates the user's health condition. For example, the analysis unit analyzes the collected information using statistical analysis or machine learning algorithms. Step 3: The goal setting unit sets optimal goals in terms of both mental and physical health based on the analysis results obtained by the analysis unit. For example, goals such as weight loss and increased exercise frequency are set based on the user's health condition. Step 4: The activity suggestion unit suggests an exercise plan, a meal plan, and mental care activities based on the goal set by the goal setting unit. For example, it suggests specific activities to help the user achieve their goal.

[0063] (Example 2) A health management system according to an embodiment of the present invention is a system that provides total maintenance and support for mental care and physical health management. This health management system collects information from the user's annual health check results, online medical treatment, smart body composition scale, and wearable watch, and analyzes the information in cooperation with a generation AI. The generation AI comprehensively evaluates the user's health condition and sets optimal goals in terms of both mental and physical health. In addition, the user discusses mental consultations and ailments while interacting with the generation AI. The generation AI analyzes the user's mental condition and provides appropriate advice. For example, it suggests methods of stress management and relaxation. The generation AI puts together activities aimed at achieving the user's goals. For example, it suggests exercise plans, meal plans, and mental care activities. This allows the user to have a specific action plan for maintaining health in terms of both mental and physical health. This solution allows the user to comprehensively manage their health in terms of both mental and physical health, and to carry out specific activities toward achieving their goals. This allows the health management system to comprehensively evaluate the user's health condition, set optimal goals, and suggest specific activities.

[0064] The health management system according to the embodiment includes a collection unit, an analysis unit, a goal setting unit, and an activity suggestion unit. The collection unit collects information from health checkup results, online medical treatment, a smart body composition scale, and a wearable watch. The collection unit can collect, for example, annual health checkup results. The collection unit can also collect information from online medical treatment, a smart body composition scale, and a wearable watch. The analysis unit analyzes the information collected by the collection unit and comprehensively evaluates the user's health condition. The analysis unit analyzes the collected information using, for example, statistical analysis or a machine learning algorithm. The goal setting unit sets optimal goals in terms of both mental and physical conditions based on the analysis results obtained by the analysis unit. The goal setting unit sets goals such as weight loss and increased exercise frequency based on the user's health condition. The activity suggestion unit proposes exercise plans, meal plans, and mental care activities based on the goals set by the goal setting unit. The activity suggestion unit proposes, for example, specific activities aimed at achieving the user's goals. As a result, the health management system according to the embodiment can comprehensively evaluate the user's health condition, set optimal goals, and propose specific activities.

[0065] The collection unit collects information from health checkup results, online medical examinations, smart body composition scales, and wearable watches. Specifically, detailed data such as blood tests, urine tests, electrocardiograms, and blood pressure measurements can be collected as annual health checkup results. These data are basic information for comprehensively understanding the user's health condition. From online medical examinations, information such as doctor's diagnosis results, prescriptions, and medical records are collected to understand the user's medical history and current health condition. From the smart body composition scale, data such as weight, body fat percentage, muscle mass, bone mass, and water content are collected in real time to monitor changes in the user's body composition. From the wearable watch, data such as heart rate, number of steps, calories burned, and sleep patterns are collected to understand the user's activity level and sleep quality in daily life. These data are automatically collected via Bluetooth and Wi-Fi and stored on a cloud server. The collection unit can centrally manage information from these diverse data sources and link with other systems and departments as necessary. For example, the collected data can be made accessible to the analysis unit and goal setting unit, and the frequency and accuracy of data collection can be adjusted to accommodate specific situations and conditions. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0066] The analysis unit analyzes the information collected by the collection unit and comprehensively evaluates the user's health condition. Specifically, the collected information is analyzed using statistical analysis and machine learning algorithms. In statistical analysis, statistical indicators such as average values, standard deviations, and correlation coefficients are calculated based on the user's health data to detect trends and abnormal values ​​in the health condition. In machine learning algorithms, past data is learned and a prediction model is constructed based on newly collected data. For example, a model predicting future health risks can be created using the user's heart rate and step count data. Furthermore, the analysis unit can use an anomaly detection algorithm to detect patterns that are different from normal or abnormal data and issue an early warning. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the user's health condition in real time. Furthermore, the analysis unit can also perform long-term health evaluations and trend analysis using past data and statistical information. For example, based on past health checkup results, fluctuations in specific health indicators are predicted and future health risks are evaluated. In addition, the analysis unit can perform more accurate health evaluations by taking into account the user's lifestyle and environmental factors. This enables the analysis unit to not only grasp health conditions in real time, but also handle long-term health management and abnormality detection, improving the reliability and safety of the entire system.

[0067] The goal setting unit sets optimal goals in terms of both mental and physical health based on the analysis results obtained by the analysis unit. Specifically, goals such as weight loss, increased exercise frequency, and stress management are set based on the user's health condition. For example, if the user's weight exceeds the standard value, a specific weight loss goal is set with weight loss as the goal. In addition, if a lack of exercise is indicated, a goal for the frequency of exercise, such as how many times per week to exercise, is set. Furthermore, it is possible to set goals for stress management and relaxation, taking into account mental health. The goal setting unit sets realistic and achievable goals in consideration of the user's lifestyle and individual needs. For example, it sets goals within a reasonable range in consideration of work and family situations. In addition, the goal setting unit can monitor the degree of goal achievement and progress based on the user's feedback, and modify the goals as necessary. As a result, the goal setting unit can set optimal goals according to the user's health condition and support the user in effectively managing his or her health. Furthermore, the goal setting unit can provide incentives and rewards for goal achievement in order to maintain the user's motivation. For example, points are awarded when a goal is achieved, and a user can receive benefits by accumulating points. This enables the goal setting unit to support the user's health management and promote sustained health improvement.

[0068] The activity suggestion unit proposes an exercise plan, a meal plan, and mental care activities based on the goal set by the goal setting unit. Specifically, the activity suggestion unit proposes specific activities aimed at achieving the user's goal. For example, a calorie restriction and a balanced meal plan is proposed for a user aiming to lose weight, and an exercise plan such as how many times a week to exercise is proposed for a user aiming to increase the frequency of exercise. Furthermore, meditation, yoga, and relaxation activities are proposed for a user aiming to manage stress. The activity suggestion unit proposes realistic and effective activities in consideration of the user's lifestyle and individual needs. For example, the activity suggestion unit proposes activities within a reasonable range in consideration of work and family situations. In addition, the activity suggestion unit can monitor the effect and progress of the activity based on the user's feedback and modify the activity as necessary. This allows the activity suggestion unit to effectively support the user's health management and promote sustained health improvement. Furthermore, the activity suggestion unit can provide incentives and rewards according to the degree of achievement of the activity in order to maintain the user's motivation. For example, points are awarded when an activity is achieved, and a user can receive benefits by accumulating points. This enables the activity suggestion unit to support the user's health management and promote sustained health improvement.

[0069] The collection unit can collect annual health checkup results. The collection unit can collect, for example, the results of regular health checkups and specific health checkups. The collection unit can collect the health checkup results in a digital format and input them to the generation AI. This enables continuous health management by collecting annual health checkup results. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the health checkup results to the generation AI, which can analyze the data.

[0070] The collection unit can collect information from online medical care, a smart body composition scale, and a wearable watch. The collection unit can collect online medical information, such as a medical examination by video call or issuance of a prescription online. The collection unit can collect information such as body fat percentage, muscle mass, and bone density from the smart body composition scale. The collection unit can collect information such as heart rate, number of steps, and sleep pattern from the wearable watch. This makes it possible to collect comprehensive health information by collecting information from online medical care, a smart body composition scale, and a wearable watch. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input online medical information to a generation AI, and the generation AI can analyze the data.

[0071] The analysis unit can analyze the collected information and comprehensively evaluate the health condition of the user. The analysis unit can analyze the collected information using, for example, statistical analysis or a machine learning algorithm. The analysis unit can use a method of integrating and evaluating multiple health indicators. This allows the collected information to be analyzed to comprehensively evaluate the health condition of the user. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected information to a generation AI, which can analyze the data.

[0072] The goal setting unit can set optimal goals in terms of both mental and physical health based on the analysis results. The goal setting unit can set goals such as weight loss and increased exercise frequency based on the user's health condition. The goal setting unit can set goals using mental evaluation criteria such as stress level and psychological test results. The goal setting unit can set goals using evaluation criteria for physical health such as body temperature, blood pressure, and heart rate. This improves the user's health management by setting optimal goals based on the analysis results. Some or all of the above-mentioned processing in the goal setting unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the goal setting unit can input the analysis results to the generation AI, which can set goals.

[0073] The activity suggestion unit can suggest an exercise plan, a meal plan, and a mental care activity for achieving the goal. The activity suggestion unit can, for example, suggest a specific exercise plan for achieving the user's goal. The activity suggestion unit can suggest a meal plan that takes into account calorie restriction and nutritional balance. The activity suggestion unit can suggest mental care activities such as relaxation techniques and counseling. As a result, health management of the user is realized by suggesting specific activities for achieving the goal. Some or all of the above-mentioned processing in the activity suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the activity suggestion unit can input a goal into the generation AI, which then suggests an activity.

[0074] The collection unit can estimate the user's emotion and adjust the collection timing of the health checkup result and the online medical information based on the estimated emotion of the user. For example, when the user is stressed, the collection unit can delay the collection timing in order to collect information in a relaxed state. When the user is relaxed, the collection unit can collect information immediately and start analysis quickly. When the user is in a hurry, the collection unit can adjust the collection timing and collect information in the most efficient manner. This enables more appropriate information collection by adjusting the collection timing based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data to the generation AI, and the generation AI can adjust the collection timing.

[0075] The collection unit can analyze the user's past health check results and select the optimal collection method. The collection unit can, for example, collect information from the user's past health check results with emphasis on a specific item. The collection unit can select the most effective collection method based on the user's past health check results. The collection unit can analyze the user's past health check results and determine the priority of the information to be collected. In this way, the optimal information collection method can be selected by analyzing the past health check results. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the past health check results to the generation AI, which can select the optimal collection method.

[0076] The collection unit can perform filtering based on the user's current living situation and field of interest when collecting the health checkup results. The collection unit can, for example, preferentially collect highly relevant information based on the user's current living situation. The collection unit can filter and collect specific health checkup items based on the user's field of interest. The collection unit can adjust the range of information to be collected based on the user's living situation and field of interest. This makes it possible to collect more relevant information by filtering information based on the user's living situation and field of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can input data on the user's living situation and field of interest into the generation AI, and the generation AI can filter the information.

[0077] The collection unit can estimate the user's emotion and determine the priority of information to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit can preferentially collect information related to mental care. When the user is relaxed, the collection unit can preferentially collect information related to physical condition management. When the user is in a hurry, the collection unit can preferentially collect the most important information. In this way, by determining the priority of information based on the user's emotion, more important information can be preferentially collected. 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, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of information.

[0078] The collection unit can preferentially collect highly relevant information based on the geographical location information of the user when collecting the health checkup results. The collection unit can preferentially collect region-specific health information based on the geographical location information of the user, for example. The collection unit can collect the most relevant health checkup results in consideration of the geographical location information of the user. The collection unit can collect information related to health risks in a specific region based on the geographical location information of the user. This makes it possible to respond to region-specific health risks by collecting information based on the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the geographical location information of the user to the generation AI, and the generation AI can preferentially collect highly relevant information.

[0079] The collection unit can analyze the user's social media activity and collect related information when collecting the health checkup result. For example, the collection unit can identify health concerns from the user's social media activity and collect related information. The collection unit can analyze the user's social media activity and collect information related to the health checkup result. The collection unit can collect information related to a specific health risk based on the user's social media activity. This makes it possible to collect information based on the user's interests by analyzing the social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's social media activity data to the generation AI, and the generation AI can collect the related information.

[0080] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is stressed, the analysis unit can provide a simple and highly visible analysis result. When the user is relaxed, the analysis unit can provide a detailed analysis result. When the user is in a hurry, the analysis unit can provide an analysis result that covers the main points. This allows a more appropriate analysis result to be provided by adjusting the expression method of the analysis based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data to the generation AI, and the generation AI can adjust the expression method of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the health condition during the analysis. For example, when the health condition is deteriorating, the analysis unit can perform a detailed analysis and propose specific measures. When the health condition is good, the analysis unit can perform a concise analysis and provide advice for maintaining the health condition. The analysis unit can adjust the level of detail of the analysis based on the importance of the health condition and provide optimal information. As a result, optimal information can be provided by adjusting the level of detail of the analysis based on the importance of the health condition. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data on the health condition to the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the health condition during analysis. The analysis unit can apply a specific analysis algorithm to information related to mental care, for example. The analysis unit can apply another analysis algorithm to information related to physical condition management. The analysis unit can select and apply an optimal analysis algorithm depending on the category of the health condition. This improves the analysis accuracy by applying the optimal analysis algorithm depending on the category of the health condition. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input health condition category data to the generation AI, and the generation AI can apply the optimal analysis algorithm.

[0083] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is stressed, the analysis unit can provide a short and to-the-point analysis result. When the user is relaxed, the analysis unit can provide a detailed analysis result. When the user is in a hurry, the analysis unit can perform the analysis quickly and provide the result in a short time. This allows a more appropriate analysis result to be provided by adjusting the length of the analysis based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data to the generation AI, and the generation AI can adjust the length of the analysis.

[0084] The analysis unit can determine the priority of the analysis based on the time of submission of the health check result at the time of analysis. For example, if the health check result has been submitted recently, the analysis unit can prioritize the analysis. The analysis unit can determine the priority of the analysis based on the time of submission of the health check result. If the health check result is old, the analysis unit can prioritize other information in the analysis. This enables rapid analysis by determining the priority of the analysis based on the time of submission of the health check result. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time of submission of the health check result to the generation AI, and the generation AI can determine the priority of the analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the health condition during analysis. For example, when the health condition is deteriorating, the analysis unit can prioritize analysis. The analysis unit can adjust the order of analysis based on the relevance of the health condition. When the health condition is good, the analysis unit can prioritize analysis of other information. In this way, by adjusting the order of analysis based on the relevance of the health condition, more important information can be prioritized in analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the relevance data of the health condition to the generation AI, and the generation AI can adjust the order of analysis.

[0086] The goal setting unit can estimate the user's emotion and adjust the goal setting method based on the estimated user's emotion. For example, when the user is stressed, the goal setting unit can set a simple and easy-to-achieve goal. When the user is relaxed, the goal setting unit can set a detailed and challenging goal. When the user is in a hurry, the goal setting unit can set a goal that can be achieved quickly. This allows a more appropriate goal to be set by adjusting the goal setting method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the goal setting unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the goal setting unit can input the user's emotion data to the generation AI, and the generation AI can adjust the goal setting method.

[0087] The goal setting unit can set an optimal goal by analyzing the user's past health condition when setting a goal. The goal setting unit can set a realistic and achievable goal based on the user's past health condition, for example. The goal setting unit can set an optimal goal by analyzing the user's past health condition. The goal setting unit can set a goal for maintaining health based on the user's past health condition. This makes it possible to set a realistic and achievable goal by analyzing the past health condition. A part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the goal setting unit can input past health condition data into the generation AI, which can set an optimal goal.

[0088] The goal setting unit can customize the goal based on the user's current living situation when setting the goal. The goal setting unit can set a realistic and achievable goal based on the user's current living situation, for example. The goal setting unit can customize the goal according to the user's living situation. The goal setting unit can set an optimal goal in consideration of the user's current living situation. As a result, by customizing the goal based on the current living situation, a more realistic and achievable goal can be set. A part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the goal setting unit can input current living situation data to the generation AI, and the generation AI can customize the goal.

[0089] The goal setting unit can estimate the user's emotion and determine the priority of goal setting based on the estimated user's emotion. For example, when the user is feeling stressed, the goal setting unit can prioritize goals related to mental care. When the user is relaxed, the goal setting unit can prioritize goals related to physical condition management. When the user is in a hurry, the goal setting unit can prioritize goals that can be achieved quickly. In this way, by determining the priority of goal setting based on the user's emotion, more important goals can be prioritized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the goal setting unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the goal setting unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of goal setting.

[0090] The goal setting unit can set an optimal goal in consideration of the geographical location information of the user when setting a goal. The goal setting unit can set a goal corresponding to a region-specific health risk based on, for example, the geographical location information of the user. The goal setting unit can set an optimal goal in consideration of the geographical location information of the user. The goal setting unit can set a realistic and achievable goal based on the geographical location information of the user. As a result, by setting a goal based on the geographical location information, it is possible to respond to a region-specific health risk. A part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the goal setting unit can input geographical location information data to the generation AI, which can set an optimal goal.

[0091] The goal setting unit can set a goal by analyzing the user's social media activity when setting a goal. The goal setting unit can, for example, identify health-related interests from the user's social media activity and set a related goal. The goal setting unit can analyze the user's social media activity and set an optimal goal. The goal setting unit can set a realistic and achievable goal based on the user's social media activity. This makes it possible to set a goal based on the user's interests by analyzing the social media activity. A part or all of the above-mentioned processing in the goal setting unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the goal setting unit can input social media activity data to the generation AI, which can set a goal.

[0092] The activity suggestion unit can estimate the user's emotion and adjust the activity suggestion method based on the estimated user's emotion. For example, if the user is stressed, the activity suggestion unit can suggest a relaxation activity. If the user is relaxed, the activity suggestion unit can suggest a challenging activity. If the user is in a hurry, the activity suggestion unit can suggest an activity that can be performed in a short time. This allows a more appropriate activity to be suggested by adjusting the activity suggestion method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the activity suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the activity suggestion unit can input the user's emotion data to the generation AI, and the generation AI can adjust the activity suggestion method.

[0093] The activity suggestion unit can analyze the user's past activity history and make optimal suggestions when suggesting an activity. The activity suggestion unit can, for example, suggest an optimal exercise plan based on the user's past activity history. The activity suggestion unit can analyze the user's past activity history and suggest the most effective activity. The activity suggestion unit can suggest an activity for maintaining health based on the user's past activity history. This makes it possible to propose optimal activities by analyzing past activity history. Some or all of the above-mentioned processing in the activity suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the activity suggestion unit inputs past activity history data into the generation AI, which can make optimal suggestions.

[0094] The activity suggestion unit can customize the suggestion based on the user's current living situation when suggesting an activity. The activity suggestion unit can suggest a realistic and executable activity based on the user's current living situation, for example. The activity suggestion unit can customize the activity according to the user's living situation. The activity suggestion unit can suggest an optimal activity in consideration of the user's current living situation. This enables a more realistic and executable suggestion by customizing the activity based on the current living situation. A part or all of the above-mentioned processing in the activity suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the activity suggestion unit can input current living situation data to the generation AI, which can customize the activity.

[0095] The activity suggestion unit can estimate the user's emotion and determine the priority of activity suggestions based on the estimated user's emotion. For example, when the user is feeling stressed, the activity suggestion unit can preferentially suggest a relaxation activity. When the user is relaxed, the activity suggestion unit can preferentially suggest a challenging activity. When the user is in a hurry, the activity suggestion unit can preferentially suggest an activity that can be performed in a short time. In this way, by determining the priority of activity suggestions based on the user's emotion, more important activities can be preferentially suggested. 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 activity suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the activity suggestion unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of activity suggestions.

[0096] The activity suggestion unit can make optimal suggestions in consideration of the geographical location information of the user when suggesting an activity. The activity suggestion unit can, for example, suggest an activity specific to a region based on the geographical location information of the user. The activity suggestion unit can suggest optimal activities in consideration of the geographical location information of the user. The activity suggestion unit can suggest realistic and executable activities based on the geographical location information of the user. As a result, activities specific to a region can be provided by suggesting activities based on the geographical location information. A part or all of the above-mentioned processing in the activity suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the activity suggestion unit inputs geographical location information data to the generation AI, which can make optimal suggestions.

[0097] The activity suggestion unit can analyze the user's social media activity and make a suggestion when suggesting an activity. The activity suggestion unit can, for example, identify health-related interests from the user's social media activity and suggest related activities. The activity suggestion unit can analyze the user's social media activity and suggest an optimal activity. The activity suggestion unit can suggest a realistic and executable activity based on the user's social media activity. This makes it possible to suggest an activity based on the user's interests by analyzing the social media activity. Some or all of the above-mentioned processing in the activity suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the activity suggestion unit can input social media activity data to the generation AI, which can make an optimal suggestion.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The health management system can further analyze the user's sleep patterns and provide advice to improve sleep quality. For example, the analysis unit can collect the user's sleep data and analyze the depth of sleep and frequency of interruptions. The goal setting unit can set sleep time and bedtime / wake-up time suitable for the user based on the analysis results. The activity suggestion unit can suggest a nighttime routine for relaxation and environmental settings for improving sleep quality. This allows the user to have a specific action plan to get better sleep.

[0100] The health management system can further collect the user's dietary records and analyze the nutritional balance. For example, the collection unit can provide an interface for the user to input daily meal contents. The analysis unit can analyze the collected dietary data and evaluate nutrient excess or deficiency. The goal setting unit can set a nutritional balance goal suitable for the user based on the analysis result. The activity suggestion unit can suggest a specific meal plan or recipe for improving nutritional balance. This allows the user to obtain a specific guideline for maintaining a healthy diet.

[0101] The health management system can further analyze the user's exercise history and provide advice for maximizing the effect of exercise. For example, the collection unit can collect data such as the type, time, and intensity of exercise performed by the user. The analysis unit can analyze the collected exercise data and evaluate the effect of the exercise. The goal setting unit can set an exercise goal suitable for the user based on the analysis result. The activity suggestion unit can suggest a specific training plan and stretching method for maximizing the effect of exercise. This allows the user to obtain specific guidelines for acquiring effective exercise habits.

[0102] The health management system can further monitor the user's stress level and provide advice for stress reduction. For example, the collection unit can collect the user's heart rate, breathing pattern, and self-reported stress level. The analysis unit can analyze the collected data and evaluate the user's stress level. The goal setting unit can set a stress management goal suitable for the user based on the analysis result. The activity suggestion unit can suggest specific relaxation techniques and mindfulness exercises for stress reduction. This allows the user to have a specific action plan for effectively managing stress.

[0103] The health management system can further monitor the user's social activities and provide advice for improving social health. For example, the collection unit can collect the user's social media activities and communication history. The analysis unit can analyze the collected data and evaluate the frequency and quality of the user's social activities. The goal setting unit can set social activity goals suitable for the user based on the analysis results. The activity suggestion unit can suggest specific event participation and communication methods for improving social health. This allows the user to obtain specific guidelines for strengthening social connections.

[0104] The health management system can estimate the user's emotions and adjust the exercise plan based on the estimated emotions. For example, if the user is feeling stressed, relaxing yoga or stretching can be suggested. If the user is relaxed, challenging running or strength training can be suggested. If the user is in a hurry, effective high-intensity interval training (HIIT) can be suggested. This makes it possible to support more effective exercise habits by providing an exercise plan according to the user's emotions.

[0105] The health management system can estimate the user's emotions and adjust the meal plan based on the estimated emotions. For example, if the user is stressed, relaxing herbal tea or light meals can be suggested. If the user is relaxed, a nutritionally balanced meal can be suggested. If the user is in a hurry, healthy snacks or meal preps that can be prepared in a short time can be suggested. This can support a healthier diet by providing a meal plan according to the user's emotions.

[0106] The health management system can estimate the user's emotions and adjust mental care activities based on the estimated emotions. For example, if the user is feeling stressed, meditation or deep breathing exercises for relaxation can be suggested. If the user is relaxed, reading or hobby activities for self-development can be suggested. If the user is in a hurry, short and effective mindfulness exercises can be suggested. This makes it possible to support more effective mental care by providing mental care activities according to the user's emotions.

[0107] The health management system can estimate the user's emotions and adjust the feedback method of the health check result based on the estimated emotions. For example, if the user is feeling stressed, simple positive feedback can be provided. If the user is relaxed, detailed analysis results and specific advice can be provided. If the user is in a hurry, short feedback that focuses on the main points can be provided. In this way, more effective health management can be supported by providing feedback according to the user's emotions.

[0108] The health management system can estimate the user's emotions and suggest a method of maintaining motivation to achieve goals based on the estimated emotions. For example, if the user is feeling stressed, relaxing music and positive messages can be provided. If the user is relaxed, encouraging messages can be provided for challenging goals. If the user is in a hurry, small goals that can be achieved in a short time can be set, allowing the user to feel a sense of accomplishment. In this way, goal achievement can be supported by providing a method of maintaining motivation according to the user's emotions.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The collection unit collects information from health checkup results, online medical examinations, smart body composition scales, and wearable watches. For example, annual health checkup results, online medical examination data, and information from smart body composition scales and wearable watches can be collected. Step 2: The analysis unit analyzes the information collected by the collection unit and comprehensively evaluates the user's health condition. For example, the analysis unit analyzes the collected information using statistical analysis or machine learning algorithms. Step 3: The goal setting unit sets optimal goals in terms of both mental and physical health based on the analysis results obtained by the analysis unit. For example, goals such as weight loss and increased exercise frequency are set based on the user's health condition. Step 4: The activity suggestion unit suggests an exercise plan, a meal plan, and mental care activities based on the goal set by the goal setting unit. For example, it suggests specific activities to help the user achieve their goal.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements including the above-mentioned collection unit, analysis unit, goal setting unit, and activity suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information on health checkup results and online medical treatment using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using statistical analysis or machine learning algorithms. The goal setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sets an optimal goal based on the analysis result. The activity suggestion unit is realized, for example, by the control unit 46A of the smart device 14, and suggests a specific activity for achieving the user's goal. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements including the above-mentioned collection unit, analysis unit, goal setting unit, and activity suggestion 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 collects information on health checkup results and online medical treatment using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using statistical analysis or machine learning algorithms. The goal setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sets an optimal goal based on the analysis result. The activity suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214, and suggests a specific activity for achieving the user's goal. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements including the above-mentioned collection unit, analysis unit, goal setting unit, and activity suggestion 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 collects information on health checkup results and online medical treatment using the camera 42 and communication I / F 44 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using statistical analysis and machine learning algorithms. The goal setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sets an optimal goal based on the analysis result. The activity suggestion unit is realized, for example, by the control unit 46A of the headset type terminal 314, and proposes a specific activity for achieving the user's goal. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements including the above-mentioned collection unit, analysis unit, goal setting unit, and activity suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information on medical examination results and online medical treatment using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using statistical analysis and machine learning algorithms. The goal setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sets an optimal goal based on the analysis result. The activity suggestion unit is realized, for example, by the control unit 46A of the robot 414, and proposes a specific activity for achieving the user's goal. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] 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.

[0165] 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.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] 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).

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Appendix 1) A collection unit that collects information from health checkup results, online medical examinations, smart body composition scales, and wearable watches; an analysis unit that analyzes the information collected by the collection unit; a goal setting unit that sets a goal based on the analysis result obtained by the analysis unit; an activity suggestion unit that suggests an activity based on the goal set by the goal setting unit; A system characterized by: (Appendix 2) The collecting unit includes: Collect annual health check results 2. The system of claim 1, (Appendix 3) The collecting unit includes: Collect information from online medical examinations, smart body composition scales, and wearable watches 2. The system of claim 1, (Appendix 4) The analysis unit is Analyze the collected information and provide a comprehensive assessment of the user's health condition 2. The system of claim 1, (Appendix 5) The goal setting unit, Set optimal goals for both mental and physical health based on the analysis results 2. The system of claim 1, (Appendix 6) The activity suggestion unit, Providing exercise plans, meal plans, and mental care activities to help you achieve your goals 2. The system of claim 1, (Appendix 7) The collecting unit includes: To estimate a user's emotions and adjust the timing of collection of health checkup results and online medical information based on the estimated user's emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze the user's past health checkup results and select the optimal collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting health checkup results, filtering is performed based on the user's current life situation and areas of interest. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Estimate user emotions and prioritize information to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting health checkup results, the system prioritizes collection of relevant information based on the user's geographic location. 2. The system of claim 1, (Appendix 12) The collecting unit includes: Analyze users' social media activity and collect relevant information when collecting health check results 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 analysis detail based on the importance of health conditions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the health category. 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, the analysis priority will be determined based on the timing of submission of medical examination results. 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 conditions 2. The system of claim 1, (Appendix 19) The goal setting unit, Inferring a user's emotions and adjusting a goal setting method based on the estimated user's emotions 2. The system of claim 1, (Appendix 20) The goal setting unit, When setting goals, the system analyzes the user's past health condition to set optimal goals. 2. The system of claim 1, (Appendix 21) The goal setting unit, Customize goals based on the user's current life situation when setting goals 2. The system of claim 1, (Appendix 22) The goal setting unit, Estimate user emotions and prioritize goal setting based on the estimated user emotions 2. The system of claim 1, (Appendix 23) The goal setting unit, When setting goals, consider the user's geographic location to set optimal goals. 2. The system of claim 1, (Appendix 24) The goal setting unit, When setting goals, set them based on the analysis of users' social media activity. 2. The system of claim 1, (Appendix 25) The activity suggestion unit, Estimate a user's emotions and adjust the activity suggestion method based on the estimated user's emotions. 2. The system of claim 1, (Appendix 26) The activity suggestion unit, When suggesting activities, the system analyzes the user's past activity history to provide optimal suggestions. 2. The system of claim 1, (Appendix 27) The activity suggestion unit, When suggesting activities, tailor suggestions based on the user's current life situation 2. The system of claim 1, (Appendix 28) The activity suggestion unit, Estimate user emotions and prioritize activity suggestions based on the estimated user emotions. 2. The system of claim 1, (Appendix 29) The activity suggestion unit, When suggesting activities, the system takes into account the user's geographic location to provide optimal suggestions. 2. The system of claim 1, (Appendix 30) The activity suggestion unit, When suggesting activities, the system analyzes the user's social media activity to provide suggestions. 2. The system of claim 1, [Explanation of symbols]

[0183] 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 information from health checkup results, online medical examinations, smart body composition scales, and wearable watches; an analysis unit that analyzes the information collected by the collection unit; a goal setting unit that sets a goal based on the analysis result obtained by the analysis unit; an activity suggestion unit that suggests an activity based on the goal set by the goal setting unit; A system characterized by:

2. The collecting unit includes: Collect annual health check results 2. The system of claim 1 .

3. The analysis unit is Analyze the collected information and provide a comprehensive assessment of the user's health condition 2. The system of claim 1 .

4. The goal setting unit, Set optimal goals for both mental and physical health based on the analysis results 2. The system of claim 1 .

5. The activity suggestion unit is Providing exercise plans, meal plans, and mental care activities to help you achieve your goals 2. The system of claim 1 .

6. The collecting unit includes: To estimate a user's emotions and adjust the timing of collection of health checkup results and online medical information based on the estimated user's emotions 2. The system of claim 1 .

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

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