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 Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a system that integrally supports mental aspects and physical condition management has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to integrally support mental aspects and physical condition management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a goal setting unit, and an activity suggestion unit. The data collection unit collects information from health checkup results, online medical consultations, smart body composition analyzers, and wearable watches. The analysis unit analyzes the information collected by the data collection unit. The goal setting unit sets goals based on the analysis results obtained by the analysis unit. The activity suggestion unit suggests activities based on the goals set by the goal setting unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide integrated support for managing both mental and physical health. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health management system according to an embodiment of the present invention is a system that provides total maintenance and support for mental care and physical health management. This health management system collects information from the user's annual health checkup results, online medical consultations, smart body composition analyzers, and wearable watches, and analyzes it in conjunction with a generating AI. The generating AI comprehensively evaluates the user's health status and sets optimal goals from both mental and physical perspectives. Furthermore, the user can discuss mental health concerns and ailments while interacting with the generating AI. The generating AI analyzes the user's mental state and provides appropriate advice, such as suggesting stress management and relaxation methods. The generating AI then creates activities to help the user achieve their goals, such as suggesting exercise plans, meal plans, and mental care activities. This allows the user to have a concrete action plan to maintain their health from both mental and physical perspectives. This solution enables the user to comprehensively manage their health from both mental and physical perspectives and to execute concrete activities to achieve their goals. As a result, the health management system can comprehensively evaluate the user's health status, set optimal goals, and suggest concrete activities.

[0029] The health management system according to this embodiment comprises a data collection unit, an analysis unit, a goal setting unit, and an activity suggestion unit. The data collection unit collects information from health checkup results, online medical consultations, smart body composition analyzers, and wearable watches. For example, the data collection unit can collect annual health checkup results. The data collection unit can also collect information from online medical consultations, smart body composition analyzers, and wearable watches. The analysis unit analyzes the information collected by the data collection unit and comprehensively evaluates the user's health status. For example, the analysis unit analyzes the collected information using statistical analysis or machine learning algorithms. The goal setting unit sets optimal goals from both mental and physical perspectives based on the analysis results obtained by the analysis unit. For example, the goal setting unit sets goals such as weight loss or increased exercise frequency based on the user's health status. The activity suggestion unit proposes exercise plans, meal plans, and mental care activities based on the goals set by the goal setting unit. For example, the activity suggestion unit proposes specific activities to help the user achieve their goals. Thus, the health management system according to this embodiment can comprehensively evaluate the user's health status, set optimal goals, and propose specific activities.

[0030] The data collection unit gathers information from health checkup results, online medical consultations, smart body composition scales, and wearable watches. Specifically, it can collect detailed data such as blood tests, urine tests, electrocardiograms, and blood pressure measurements from annual health checkup results. This data serves as foundational information for comprehensively understanding the user's health status. From online medical consultations, it collects information such as doctors' diagnoses, prescriptions, and medical records to understand the user's medical history and current health status. From smart body composition scales, it collects data such as weight, body fat percentage, muscle mass, bone mass, and water content in real time to monitor changes in the user's body composition. From wearable watches, it collects data such as heart rate, steps, calories burned, and sleep patterns to understand the user's activity level and sleep quality in daily life. This data is automatically collected via Bluetooth® and Wi-Fi and stored on a cloud server. The data collection unit centrally manages information from these diverse data sources and can collaborate with other systems and departments as needed. For example, by making the collected data accessible to the analysis unit and goal-setting unit, and by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the data collection unit to comprehensively evaluate the user's health status. Specifically, it analyzes the collected information using statistical analysis and machine learning algorithms. In statistical analysis, it calculates statistical indicators such as the mean, standard deviation, and correlation coefficient based on the user's health data to detect trends and outliers in health status. In machine learning algorithms, it learns from past data and builds predictive models based on newly collected data. For example, it can create a model to predict future health risks using the user's heart rate and step count data. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to analyze collected data quickly and accurately, and understand the user's health status in real time. In addition, the analysis unit can utilize past data and statistical information to perform long-term health assessments and trend analyses. For example, it can predict fluctuations in specific health indicators based on past health checkup results and evaluate future health risks. Moreover, the analysis unit can perform more accurate health assessments by considering the user's lifestyle and environmental factors. This allows the analysis unit to not only monitor health status in real time, but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The goal-setting unit sets optimal goals from both a mental and physical perspective, based on the analysis results obtained by the analysis unit. Specifically, it sets goals such as weight loss, increased exercise frequency, and stress management based on the user's health status. For example, if the user's weight is above the standard value, it sets a weight loss goal and a specific weight loss target. If a lack of exercise is identified, it sets an exercise frequency target, such as how many times a week to exercise. Furthermore, it can also consider mental health and set goals for stress management and relaxation. The goal-setting unit sets realistic and achievable goals, taking into account the user's lifestyle and individual needs. For example, it sets goals within a reasonable range, taking into account work and family circumstances. In addition, the goal-setting unit monitors the degree of goal achievement and progress based on user feedback and can revise goals as needed. This allows the goal-setting unit to set optimal goals according to the user's health status and support the user in effectively managing their health. Furthermore, the goal-setting unit can also provide incentives and rewards for achieving goals in order to maintain the user's motivation. For example, it can award points when a goal is achieved, and allow users to receive rewards by accumulating points. This allows the goal-setting unit to support the user's health management and promote sustainable health improvement.

[0033] The Activity Proposal Department proposes exercise plans, meal plans, and mental care activities based on the goals set by the Goal Setting Department. Specifically, it proposes concrete activities to help users achieve their goals. For example, for users aiming for weight loss, it proposes calorie restriction and balanced meal plans; for users aiming to increase exercise frequency, it proposes exercise plans such as how many times a week to exercise. Furthermore, for users aiming for stress management, it proposes activities such as meditation, yoga, and relaxation. The Activity Proposal Department proposes realistic and effective activities considering the user's lifestyle and individual needs. For example, it proposes activities that are within a reasonable range, taking into account work and family circumstances. In addition, the Activity Proposal Department can monitor the effectiveness and progress of activities based on user feedback and modify activities as needed. This allows the Activity Proposal Department to effectively support users' health management and promote sustainable health improvement. Furthermore, to maintain user motivation, the Activity Proposal Department can also provide incentives and rewards based on the degree of activity completion. For example, it can award points when activities are completed, and users can receive rewards by accumulating points. This allows the activity suggestion department to support users' health management and promote sustainable health improvement.

[0034] The data collection unit can collect the results of annual health checkups. For example, the data collection unit can collect the results of regular health checkups and specific health examinations. The data collection unit can collect the health checkup results in digital format and input them into the generating AI. This enables continuous health management by collecting the results of annual health checkups. Some or all of the above-described processing in the data collection unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the data collection unit can input the health checkup results into the generating AI, and the generating AI can analyze the data.

[0035] The data collection unit can collect information from online medical consultations, smart body composition analyzers, and wearable watches. For example, it can collect online medical consultation information such as video call consultations and online prescription issuance. It can collect information such as body fat percentage, muscle mass, and bone density from smart body composition analyzers. It can collect information such as heart rate, steps taken, and sleep patterns from wearable watches. This allows for the collection of comprehensive health information by collecting information from online medical consultations, smart body composition analyzers, and wearable watches. Some or all of the above-described processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input online medical consultation information into a generative AI, which can then analyze the data.

[0036] The analysis unit can analyze the collected information and comprehensively evaluate the user's health status. For example, the analysis unit can analyze the collected information using statistical analysis or machine learning algorithms. The analysis unit can use a method that integrates and evaluates multiple health indicators. This allows for a comprehensive evaluation of the user's health status by analyzing the collected information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected information into a generative AI, which can then analyze the data.

[0037] The goal-setting unit can set optimal goals from both a mental and physical perspective based on the analysis results. For example, the goal-setting unit can set goals such as weight loss or increased exercise frequency based on the user's health status. The goal-setting unit can set goals using mental evaluation criteria such as stress levels and psychological test results. The goal-setting unit can set goals using physical evaluation criteria 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-described processes in the goal-setting unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the goal-setting unit can input the analysis results into a generating AI, and the generating AI can set goals.

[0038] The activity suggestion unit can propose exercise plans, meal plans, and mental care activities to help users achieve their goals. For example, the activity suggestion unit can propose specific exercise plans to help users achieve their goals. The activity suggestion unit can propose meal plans that take into account calorie restriction and nutritional balance. The activity suggestion unit can propose mental care activities such as relaxation techniques and counseling. In this way, by proposing specific activities to help users achieve their goals, user health management is realized. Some or all of the above processing in the activity suggestion unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the activity suggestion unit can input goals into a generative AI, and the generative AI can propose activities.

[0039] The data collection unit can analyze the user's past health check results and select the optimal data collection method. For example, the data collection unit can collect information from the user's past health check results, focusing on specific items. Based on the user's past health check results, the data collection unit can select the most effective data collection method. The data collection unit can analyze the user's past health check results and determine the priority of the information to be collected. This allows the optimal information collection method to be selected by analyzing past health check results. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit can input past health check results into a generating AI, which can then select the optimal data collection method.

[0040] The data collection unit can filter health checkup results based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize the collection of highly relevant information based on the user's current lifestyle. The data collection unit can filter and collect specific health checkup items based on the user's areas of interest. The data collection unit can adjust the scope of information to be collected based on the user's lifestyle and areas of interest. This allows for the collection of more relevant information by filtering information based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generative AI, which can then filter the information.

[0041] The data collection unit can prioritize the collection of highly relevant information based on the user's geographical location when collecting health checkup results. For example, the data collection unit can prioritize the collection of region-specific health information based on the user's geographical location. The data collection unit can collect the most relevant health checkup results, taking into account the user's geographical location. The data collection unit can collect information related to health risks in a specific region based on the user's geographical location. This allows for addressing region-specific health risks by collecting information based on geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant information.

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

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the health status during the analysis. For example, if the health status is deteriorating, the analysis unit can perform a detailed analysis and propose specific countermeasures. If the health status is good, the analysis unit can perform a concise analysis and provide advice for maintenance. The analysis unit can adjust the level of detail of the analysis based on the importance of the health status to provide optimal information. In this way, by adjusting the level of detail of the analysis based on the importance of the health status, optimal information can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input health status data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

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

[0045] The analysis unit can determine the priority of analysis based on the submission date of the health check results. For example, the analysis unit can prioritize analysis of health check results that have been recently submitted. The analysis unit can determine the priority of analysis based on the submission date of the health check results. If the health check results are old, the analysis unit can prioritize other information for analysis. This enables rapid analysis by determining the priority of analysis based on the submission date of the health check results. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the health check result submission date data into a generating AI, and the generating AI can determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the correlation of health status during the analysis. For example, if the health status is deteriorating, the analysis unit can prioritize the analysis. The analysis unit can adjust the order of analysis based on the correlation of health status. If the health status is good, the analysis unit can prioritize the analysis of other information. In this way, by adjusting the order of analysis based on the correlation of health status, more important information can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input health status correlation data into a generating AI, and the generating AI can adjust the order of analysis.

[0047] The goal-setting unit can set optimal goals by analyzing the user's past health status when setting goals. For example, the goal-setting unit can set realistic and achievable goals based on the user's past health status. The goal-setting unit can analyze the user's past health status and set optimal goals. The goal-setting unit can set goals for maintaining health based on the user's past health status. This allows for the setting of realistic and achievable goals by analyzing past health status. Some or all of the above-described processes in the goal-setting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the goal-setting unit can input past health status data into a generative AI, which can then set optimal goals.

[0048] The goal-setting unit can customize goals based on the user's current living situation when setting goals. For example, the goal-setting unit can set realistic and achievable goals based on the user's current living situation. The goal-setting unit can customize goals according to the user's living situation. The goal-setting unit can set optimal goals considering the user's current living situation. This allows for the setting of more realistic and achievable goals by customizing goals based on the current living situation. Some or all of the above-described processes in the goal-setting unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the goal-setting unit can input current living situation data into a generating AI, which can then customize the goals.

[0049] The goal-setting unit can set optimal goals by considering the user's geographical location information when setting goals. For example, the goal-setting unit can set goals that address region-specific health risks based on the user's geographical location information. The goal-setting unit can set optimal goals by considering the user's geographical location information. The goal-setting unit can set realistic and achievable goals based on the user's geographical location information. This allows for addressing region-specific health risks by setting goals based on geographical location information. Some or all of the above processing in the goal-setting unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the goal-setting unit can input geographical location data into a generating AI, which can then set optimal goals.

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

[0051] The activity suggestion unit can analyze the user's past activity history to make optimal suggestions when suggesting activities. For example, the activity suggestion unit can suggest the 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 activities. The activity suggestion unit can suggest activities for maintaining health based on the user's past activity history. This makes it possible to suggest optimal activities by analyzing past activity history. Some or all of the above processing in the activity suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the activity suggestion unit can input past activity history data into a generative AI, which can then make optimal suggestions.

[0052] The activity suggestion unit can customize activity suggestions based on the user's current living situation. For example, the activity suggestion unit can suggest realistic and feasible activities based on the user's current living situation. The activity suggestion unit can customize activities according to the user's living situation. The activity suggestion unit can suggest the optimal activity considering the user's current living situation. This makes it possible to make more realistic and feasible suggestions by customizing activities based on the current living situation. Some or all of the above processing in the activity suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the activity suggestion unit can input current living situation data into a generative AI, which can then customize the activity.

[0053] The activity suggestion unit can make optimal suggestions by considering the user's geographical location information when suggesting activities. For example, the activity suggestion unit can suggest region-specific activities based on the user's geographical location information. The activity suggestion unit can suggest optimal activities by considering the user's geographical location information. The activity suggestion unit can suggest realistic and feasible activities based on the user's geographical location information. In this way, region-specific activities can be provided by suggesting activities based on geographical location information. Some or all of the above processing in the activity suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the activity suggestion unit can input geographical location data into a generative AI, which can then make optimal suggestions.

[0054] The activity suggestion unit can analyze the user's social media activity and make suggestions when suggesting activities. For example, the activity suggestion unit can identify health-related interests from the user's social media activity and suggest relevant activities. The activity suggestion unit can analyze the user's social media activity and suggest the most suitable activities. Based on the user's social media activity, the activity suggestion unit can suggest realistic and actionable activities. This makes it possible to suggest activities based on the user's interests by analyzing social media activity. Some or all of the above processing in the activity suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the activity suggestion unit can input social media activity data into a generative AI, which can then make the most suitable suggestions.

[0055] The system according to the embodiment is not limited to the example described above, 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 sleep depth and frequency of interruptions. Based on the analysis results, the goal setting unit can set appropriate sleep duration and bedtime / wake-up times for the user. The activity suggestion unit can suggest nighttime routines for relaxation and environmental settings to improve sleep quality. This allows the user to have a concrete action plan for achieving better sleep.

[0057] The health management system can further collect users' meal records and analyze their nutritional balance. For example, the collection unit can provide an interface for users to input their daily meals. The analysis unit can analyze the collected meal data and evaluate any excesses or deficiencies in nutrients. The goal setting unit can set appropriate nutritional balance goals for the user based on the analysis results. The activity suggestion unit can suggest specific meal plans and recipes to improve nutritional balance. This allows users to obtain concrete guidance for maintaining a healthy diet.

[0058] The health management system can further analyze the user's exercise history and provide advice to maximize the effects of exercise. For example, the data collection unit can collect data such as the type, duration, and intensity of exercise performed by the user. The analysis unit can analyze the collected exercise data and evaluate the effects of the exercise. The goal setting unit can set appropriate exercise goals for the user based on the analysis results. The activity suggestion unit can propose specific training plans and stretching methods to maximize the effects of exercise. This allows the user to obtain concrete guidance for developing 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 data collection unit can collect the user's heart rate, breathing patterns, and self-reported stress levels. The analysis unit can analyze the collected data and evaluate the user's stress level. The goal setting unit can set appropriate stress management goals for the user based on the analysis results. The activity suggestion unit can suggest specific relaxation methods and mindfulness practices for stress reduction. This allows the user to have a concrete action plan for effectively managing stress.

[0060] The health management system can further monitor users' social activities and provide advice to improve their social health. For example, the data collection unit can collect users' social media activity and communication history. The analysis unit can analyze the collected data and evaluate the frequency and quality of users' social activities. The goal setting unit can set appropriate social activity goals for users based on the analysis results. The activity suggestion unit can suggest specific events to participate in and communication methods to improve social health. This allows users to obtain concrete guidance for strengthening their social connections.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects information from health checkup results, online medical consultations, smart body composition scales, and wearable watches. For example, it can collect data from annual health checkup results, online medical consultations, smart body composition scales, and wearable watches. Step 2: The analysis unit analyzes the information collected by the collection unit and comprehensively evaluates the user's health status. For example, the collected information is analyzed using statistical analysis or machine learning algorithms. Step 3: The goal-setting unit sets optimal goals from both a mental and physical perspective, based on the analysis results obtained by the analysis unit. For example, it sets goals such as weight loss or increased exercise frequency based on the user's health status. Step 4: The Activity Proposal Unit proposes exercise plans, meal plans, and mental care activities based on the goals set by the Goal Setting Unit. For example, it proposes specific activities to help the user achieve their goals.

[0063] (Example of form 2) The 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 checkup results, online medical consultations, smart body composition analyzers, and wearable watches, and analyzes it in conjunction with a generating AI. The generating AI comprehensively evaluates the user's health status and sets optimal goals from both mental and physical perspectives. Furthermore, the user can discuss mental health concerns and ailments while interacting with the generating AI. The generating AI analyzes the user's mental state and provides appropriate advice, such as suggesting stress management and relaxation methods. The generating AI then creates activities to help the user achieve their goals, such as suggesting exercise plans, meal plans, and mental care activities. This allows the user to have a concrete action plan to maintain their health from both mental and physical perspectives. This solution enables the user to comprehensively manage their health from both mental and physical perspectives and to execute concrete activities to achieve their goals. As a result, the health management system can comprehensively evaluate the user's health status, set optimal goals, and suggest concrete activities.

[0064] The health management system according to this embodiment comprises a data collection unit, an analysis unit, a goal setting unit, and an activity suggestion unit. The data collection unit collects information from health checkup results, online medical consultations, smart body composition analyzers, and wearable watches. For example, the data collection unit can collect annual health checkup results. The data collection unit can also collect information from online medical consultations, smart body composition analyzers, and wearable watches. The analysis unit analyzes the information collected by the data collection unit and comprehensively evaluates the user's health status. For example, the analysis unit analyzes the collected information using statistical analysis or machine learning algorithms. The goal setting unit sets optimal goals from both mental and physical perspectives based on the analysis results obtained by the analysis unit. For example, the goal setting unit sets goals such as weight loss or increased exercise frequency based on the user's health status. The activity suggestion unit proposes exercise plans, meal plans, and mental care activities based on the goals set by the goal setting unit. For example, the activity suggestion unit proposes specific activities to help the user achieve their goals. Thus, the health management system according to this embodiment can comprehensively evaluate the user's health status, set optimal goals, and propose specific activities.

[0065] The data collection unit gathers information from health checkup results, online medical consultations, smart body composition scales, and wearable watches. Specifically, it can collect detailed data such as blood tests, urine tests, electrocardiograms, and blood pressure measurements from annual health checkup results. This data serves as foundational information for comprehensively understanding the user's health status. From online medical consultations, it collects information such as doctor's diagnoses, prescriptions, and medical records to understand the user's medical history and current health status. From smart body composition scales, it collects data such as weight, body fat percentage, muscle mass, bone mass, and water content in real time to monitor changes in the user's body composition. From wearable watches, it collects data such as heart rate, steps, calories burned, and sleep patterns to understand the user's activity level and sleep quality in daily life. This data is automatically collected via Bluetooth and Wi-Fi and stored on a cloud server. The data collection unit centrally manages information from these diverse data sources and can collaborate with other systems and departments as needed. For example, by making the collected data accessible to the analysis unit and goal-setting unit, and by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis unit analyzes the information collected by the data collection unit to comprehensively evaluate the user's health status. Specifically, it analyzes the collected information using statistical analysis and machine learning algorithms. In statistical analysis, it calculates statistical indicators such as the mean, standard deviation, and correlation coefficient based on the user's health data to detect trends and outliers in health status. In machine learning algorithms, it learns from past data and builds predictive models based on newly collected data. For example, it can create a model to predict future health risks using the user's heart rate and step count data. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to analyze collected data quickly and accurately, and understand the user's health status in real time. In addition, the analysis unit can utilize past data and statistical information to perform long-term health assessments and trend analyses. For example, it can predict fluctuations in specific health indicators based on past health checkup results and evaluate future health risks. Moreover, the analysis unit can perform more accurate health assessments by considering the user's lifestyle and environmental factors. This allows the analysis unit to not only monitor health status in real time, but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0067] The goal-setting unit sets optimal goals from both a mental and physical perspective, based on the analysis results obtained by the analysis unit. Specifically, it sets goals such as weight loss, increased exercise frequency, and stress management based on the user's health status. For example, if the user's weight is above the standard value, it sets a weight loss goal and a specific weight loss target. If a lack of exercise is identified, it sets an exercise frequency target, such as how many times a week to exercise. Furthermore, it can also consider mental health and set goals for stress management and relaxation. The goal-setting unit sets realistic and achievable goals, taking into account the user's lifestyle and individual needs. For example, it sets goals within a reasonable range, taking into account work and family circumstances. In addition, the goal-setting unit monitors the degree of goal achievement and progress based on user feedback and can revise goals as needed. This allows the goal-setting unit to set optimal goals according to the user's health status and support the user in effectively managing their health. Furthermore, the goal-setting unit can also provide incentives and rewards for achieving goals in order to maintain the user's motivation. For example, it can award points when a goal is achieved, and allow users to receive rewards by accumulating points. This allows the goal-setting unit to support the user's health management and promote sustainable health improvement.

[0068] The Activity Proposal Department proposes exercise plans, meal plans, and mental care activities based on the goals set by the Goal Setting Department. Specifically, it proposes concrete activities to help users achieve their goals. For example, for users aiming for weight loss, it proposes calorie restriction and balanced meal plans; for users aiming to increase exercise frequency, it proposes exercise plans such as how many times a week to exercise. Furthermore, for users aiming for stress management, it proposes activities such as meditation, yoga, and relaxation. The Activity Proposal Department proposes realistic and effective activities considering the user's lifestyle and individual needs. For example, it proposes activities that are within a reasonable range, taking into account work and family circumstances. In addition, the Activity Proposal Department can monitor the effectiveness and progress of activities based on user feedback and modify activities as needed. This allows the Activity Proposal Department to effectively support users' health management and promote sustainable health improvement. Furthermore, to maintain user motivation, the Activity Proposal Department can also provide incentives and rewards based on the degree of activity completion. For example, it can award points when activities are completed, and users can receive rewards by accumulating points. This allows the activity suggestion department to support users' health management and promote sustainable health improvement.

[0069] The data collection unit can collect the results of annual health checkups. For example, the data collection unit can collect the results of regular health checkups and specific health examinations. The data collection unit can collect the health checkup results in digital format and input them into the generating AI. This enables continuous health management by collecting the results of annual health checkups. Some or all of the above-described processing in the data collection unit may be performed using the generating AI, or it may be performed without the generating AI. For example, the data collection unit can input the health checkup results into the generating AI, and the generating AI can analyze the data.

[0070] The data collection unit can collect information from online medical consultations, smart body composition analyzers, and wearable watches. For example, it can collect online medical consultation information such as video call consultations and online prescription issuance. It can collect information such as body fat percentage, muscle mass, and bone density from smart body composition analyzers. It can collect information such as heart rate, steps taken, and sleep patterns from wearable watches. This allows for the collection of comprehensive health information by collecting information from online medical consultations, smart body composition analyzers, and wearable watches. Some or all of the above-described processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input online medical consultation information into a generative AI, which can then analyze the data.

[0071] The analysis unit can analyze the collected information and comprehensively evaluate the user's health status. For example, the analysis unit can analyze the collected information using statistical analysis or machine learning algorithms. The analysis unit can use a method that integrates and evaluates multiple health indicators. This allows for a comprehensive evaluation of the user's health status by analyzing the collected information. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the collected information into a generative AI, which can then analyze the data.

[0072] The goal-setting unit can set optimal goals from both a mental and physical perspective based on the analysis results. For example, the goal-setting unit can set goals such as weight loss or increased exercise frequency based on the user's health status. The goal-setting unit can set goals using mental evaluation criteria such as stress levels and psychological test results. The goal-setting unit can set goals using physical evaluation criteria 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-described processes in the goal-setting unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the goal-setting unit can input the analysis results into a generating AI, and the generating AI can set goals.

[0073] The activity suggestion unit can propose exercise plans, meal plans, and mental care activities to help users achieve their goals. For example, the activity suggestion unit can propose specific exercise plans to help users achieve their goals. The activity suggestion unit can propose meal plans that take into account calorie restriction and nutritional balance. The activity suggestion unit can propose mental care activities such as relaxation techniques and counseling. In this way, by proposing specific activities to help users achieve their goals, user health management is realized. Some or all of the above processing in the activity suggestion unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the activity suggestion unit can input goals into a generative AI, and the generative AI can propose activities.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of collecting health checkup results and online medical information based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the timing of collection to collect information when the user is relaxed. If the user is relaxed, the data collection unit can collect information immediately and start analysis quickly. If the user is in a hurry, the data collection unit can adjust the timing of collection to collect information in the most efficient way. This allows for more appropriate information collection by adjusting the timing of collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the timing of collection.

[0075] The data collection unit can analyze the user's past health check results and select the optimal data collection method. For example, the data collection unit can collect information from the user's past health check results, focusing on specific items. Based on the user's past health check results, the data collection unit can select the most effective data collection method. The data collection unit can analyze the user's past health check results and determine the priority of the information to be collected. This allows the optimal information collection method to be selected by analyzing past health check results. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit can input past health check results into a generating AI, which can then select the optimal data collection method.

[0076] The data collection unit can filter health checkup results based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize the collection of highly relevant information based on the user's current lifestyle. The data collection unit can filter and collect specific health checkup items based on the user's areas of interest. The data collection unit can adjust the scope of information to be collected based on the user's lifestyle and areas of interest. This allows for the collection of more relevant information by filtering information based on the user's lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generative AI, which can then filter the information.

[0077] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting information related to mental health care. If the user is relaxed, the data collection unit can prioritize collecting information related to health management. If the user is in a hurry, the data collection unit can prioritize collecting the most important information. This allows for the priority collection of more important information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the priority of the information.

[0078] The data collection unit can prioritize the collection of highly relevant information based on the user's geographical location when collecting health checkup results. For example, the data collection unit can prioritize the collection of region-specific health information based on the user's geographical location. The data collection unit can collect the most relevant health checkup results, taking into account the user's geographical location. The data collection unit can collect information related to health risks in a specific region based on the user's geographical location. This allows for addressing region-specific health risks by collecting information based on geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant information.

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

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the presentation of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the health status during the analysis. For example, if the health status is deteriorating, the analysis unit can perform a detailed analysis and propose specific countermeasures. If the health status is good, the analysis unit can perform a concise analysis and provide advice for maintenance. The analysis unit can adjust the level of detail of the analysis based on the importance of the health status to provide optimal information. In this way, by adjusting the level of detail of the analysis based on the importance of the health status, optimal information can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input health status data into a generating AI, and the generating AI can adjust the level of detail of the analysis.

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

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is in a hurry, the analysis unit can perform a rapid analysis and provide results in a short time. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI, which can then adjust the length of the analysis.

[0084] The analysis unit can determine the priority of analysis based on the submission date of the health check results. For example, the analysis unit can prioritize analysis of health check results that have been recently submitted. The analysis unit can determine the priority of analysis based on the submission date of the health check results. If the health check results are old, the analysis unit can prioritize other information for analysis. This enables rapid analysis by determining the priority of analysis based on the submission date of the health check results. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the health check result submission date data into a generating AI, and the generating AI can determine the priority of analysis.

[0085] The analysis unit can adjust the order of analysis based on the correlation of health status during the analysis. For example, if the health status is deteriorating, the analysis unit can prioritize the analysis. The analysis unit can adjust the order of analysis based on the correlation of health status. If the health status is good, the analysis unit can prioritize the analysis of other information. In this way, by adjusting the order of analysis based on the correlation of health status, more important information can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input health status correlation data into a generating AI, and the generating AI can adjust the order of analysis.

[0086] The goal-setting unit can estimate the user's emotions and adjust the goal-setting method based on the estimated emotions. For example, if the user is stressed, the goal-setting unit can set simple and easily achievable goals. If the user is relaxed, the goal-setting unit can set detailed and challenging goals. If the user is in a hurry, the goal-setting unit can set goals that can be achieved quickly. This allows for the setting of more appropriate goals by adjusting the goal-setting method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the goal-setting unit may be performed using a generative AI, or not using a generative AI. For example, the goal-setting unit can input user emotion data into a generative AI, which can then adjust the goal-setting method.

[0087] The goal-setting unit can set optimal goals by analyzing the user's past health status when setting goals. For example, the goal-setting unit can set realistic and achievable goals based on the user's past health status. The goal-setting unit can analyze the user's past health status and set optimal goals. The goal-setting unit can set goals for maintaining health based on the user's past health status. This allows for the setting of realistic and achievable goals by analyzing past health status. Some or all of the above-described processes in the goal-setting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the goal-setting unit can input past health status data into a generative AI, which can then set optimal goals.

[0088] The goal-setting unit can customize goals based on the user's current living situation when setting goals. For example, the goal-setting unit can set realistic and achievable goals based on the user's current living situation. The goal-setting unit can customize goals according to the user's living situation. The goal-setting unit can set optimal goals considering the user's current living situation. This allows for the setting of more realistic and achievable goals by customizing goals based on the current living situation. Some or all of the above-described processes in the goal-setting unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the goal-setting unit can input current living situation data into a generating AI, which can then customize the goals.

[0089] The goal-setting unit can estimate the user's emotions and determine the priority of goal setting based on the estimated user emotions. For example, if the user is stressed, the goal-setting unit can prioritize goals related to mental care. If the user is relaxed, the goal-setting unit can prioritize goals related to physical health management. If the user is in a hurry, the goal-setting unit can prioritize goals that can be achieved quickly. This allows for prioritizing more important goals by determining the priority of goal setting based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the goal-setting unit may be performed using a generative AI, or not using a generative AI. For example, the goal-setting unit can input user emotion data into a generative AI, which can then determine the priority of goal setting.

[0090] The goal-setting unit can set optimal goals by considering the user's geographical location information when setting goals. For example, the goal-setting unit can set goals that address region-specific health risks based on the user's geographical location information. The goal-setting unit can set optimal goals by considering the user's geographical location information. The goal-setting unit can set realistic and achievable goals based on the user's geographical location information. This allows for addressing region-specific health risks by setting goals based on geographical location information. Some or all of the above processing in the goal-setting unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the goal-setting unit can input geographical location data into a generating AI, which can then set optimal goals.

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

[0092] The activity suggestion unit can estimate the user's emotions and adjust its activity suggestion method based on the estimated emotions. For example, if the user is feeling 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 completed in a short time. In this way, by adjusting the activity suggestion method based on the user's emotions, more appropriate activities can be suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the activity suggestion unit may be performed using a generative AI, or not using a generative AI. For example, the activity suggestion unit can input user emotion data into a generative AI, which can then adjust its activity suggestion method.

[0093] The activity suggestion unit can analyze the user's past activity history to make optimal suggestions when suggesting activities. For example, the activity suggestion unit can suggest the 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 activities. The activity suggestion unit can suggest activities for maintaining health based on the user's past activity history. This makes it possible to suggest optimal activities by analyzing past activity history. Some or all of the above processing in the activity suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the activity suggestion unit can input past activity history data into a generative AI, which can then make optimal suggestions.

[0094] The activity suggestion unit can customize activity suggestions based on the user's current living situation. For example, the activity suggestion unit can suggest realistic and feasible activities based on the user's current living situation. The activity suggestion unit can customize activities according to the user's living situation. The activity suggestion unit can suggest the optimal activity considering the user's current living situation. This makes it possible to make more realistic and feasible suggestions by customizing activities based on the current living situation. Some or all of the above processing in the activity suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the activity suggestion unit can input current living situation data into a generative AI, which can then customize the activity.

[0095] The activity suggestion unit can estimate the user's emotions and prioritize activity suggestions based on those emotions. For example, if the user is stressed, the activity suggestion unit can prioritize suggesting relaxation activities. If the user is relaxed, the activity suggestion unit can prioritize suggesting challenging activities. If the user is in a hurry, the activity suggestion unit can prioritize suggesting activities that can be completed in a short amount of time. This allows for prioritizing more important activities by determining the priority of activity suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the activity suggestion unit may be performed using or without a generative AI. For example, the activity suggestion unit can input user emotion data into a generative AI, which can then determine the priority of activity suggestions.

[0096] The activity suggestion unit can make optimal suggestions by considering the user's geographical location information when suggesting activities. For example, the activity suggestion unit can suggest region-specific activities based on the user's geographical location information. The activity suggestion unit can suggest optimal activities by considering the user's geographical location information. The activity suggestion unit can suggest realistic and feasible activities based on the user's geographical location information. In this way, region-specific activities can be provided by suggesting activities based on geographical location information. Some or all of the above processing in the activity suggestion unit may be performed using, for example, a generative AI, or without a generative AI. For example, the activity suggestion unit can input geographical location data into a generative AI, which can then make optimal suggestions.

[0097] The activity suggestion unit can analyze the user's social media activity and make suggestions when suggesting activities. For example, the activity suggestion unit can identify health-related interests from the user's social media activity and suggest relevant activities. The activity suggestion unit can analyze the user's social media activity and suggest the most suitable activities. Based on the user's social media activity, the activity suggestion unit can suggest realistic and actionable activities. This makes it possible to suggest activities based on the user's interests by analyzing social media activity. Some or all of the above processing in the activity suggestion unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the activity suggestion unit can input social media activity data into a generative AI, which can then make the most suitable suggestions.

[0098] The system according to the embodiment is not limited to the example described above, 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 sleep depth and frequency of interruptions. Based on the analysis results, the goal setting unit can set appropriate sleep duration and bedtime / wake-up times for the user. The activity suggestion unit can suggest nighttime routines for relaxation and environmental settings to improve sleep quality. This allows the user to have a concrete action plan for achieving better sleep.

[0100] The health management system can further collect users' meal records and analyze their nutritional balance. For example, the collection unit can provide an interface for users to input their daily meals. The analysis unit can analyze the collected meal data and evaluate any excesses or deficiencies in nutrients. The goal setting unit can set appropriate nutritional balance goals for the user based on the analysis results. The activity suggestion unit can suggest specific meal plans and recipes to improve nutritional balance. This allows users to obtain concrete guidance for maintaining a healthy diet.

[0101] The health management system can further analyze the user's exercise history and provide advice to maximize the effects of exercise. For example, the data collection unit can collect data such as the type, duration, and intensity of exercise performed by the user. The analysis unit can analyze the collected exercise data and evaluate the effects of the exercise. The goal setting unit can set appropriate exercise goals for the user based on the analysis results. The activity suggestion unit can propose specific training plans and stretching methods to maximize the effects of exercise. This allows the user to obtain concrete guidance for developing 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 data collection unit can collect the user's heart rate, breathing patterns, and self-reported stress levels. The analysis unit can analyze the collected data and evaluate the user's stress level. The goal setting unit can set appropriate stress management goals for the user based on the analysis results. The activity suggestion unit can suggest specific relaxation methods and mindfulness practices for stress reduction. This allows the user to have a concrete action plan for effectively managing stress.

[0103] The health management system can further monitor users' social activities and provide advice to improve their social health. For example, the data collection unit can collect users' social media activity and communication history. The analysis unit can analyze the collected data and evaluate the frequency and quality of users' social activities. The goal setting unit can set appropriate social activity goals for users based on the analysis results. The activity suggestion unit can suggest specific events to participate in and communication methods to improve social health. This allows users to obtain concrete guidance for strengthening their social connections.

[0104] A health management system can estimate a user's emotions and adjust their exercise plan based on those emotions. For example, if a user is stressed, it can suggest relaxing yoga or stretching. If a user is relaxed, it can suggest challenging running or strength training. If a user is in a hurry, it can suggest short, effective high-intensity interval training (HIIT). By providing exercise plans tailored to the user's emotions, it can support more effective exercise habits.

[0105] A health management system can estimate a user's emotions and adjust meal plans based on those emotions. For example, if a user is stressed, it can suggest relaxing herbal teas or snacks. If a user is relaxed, it can suggest nutritionally balanced meals. If a user is in a hurry, it can suggest healthy snacks or meal preps that can be prepared quickly. By providing meal plans tailored to the user's emotions, it can support healthier eating habits.

[0106] A health management system can estimate a user's emotions and adjust mental care activities based on those estimates. For example, if a user is stressed, it can suggest relaxation activities such as meditation or deep breathing exercises. If a user is relaxed, it can suggest self-improvement activities such as reading or hobbies. If a user is in a hurry, it can suggest short, effective mindfulness exercises. This allows for more effective mental care support by providing mental care activities tailored to the user's emotions.

[0107] A health management system can estimate a user's emotions and adjust the feedback method for health checkup results based on those emotions. For example, if a user is stressed, it can provide simple, positive feedback. If a user is relaxed, it can provide detailed analysis results and specific advice. If a user is in a hurry, it can provide concise, to-the-point feedback. This allows for more effective health management by providing feedback tailored to the user's emotions.

[0108] A health management system can estimate a user's emotions and suggest ways to maintain motivation for goal achievement based on those emotions. For example, if a user is stressed, it can provide relaxing music or positive messages. If a user is relaxed, it can provide encouraging messages for challenging goals. If a user is in a hurry, it can set small, achievable goals in a short time to give them a sense of accomplishment. In this way, it can support goal achievement by providing motivational methods tailored to the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects information from health checkup results, online medical consultations, smart body composition scales, and wearable watches. For example, it can collect data from annual health checkup results, online medical consultations, smart body composition scales, and wearable watches. Step 2: The analysis unit analyzes the information collected by the collection unit and comprehensively evaluates the user's health status. For example, the collected information is analyzed using statistical analysis or machine learning algorithms. Step 3: The goal-setting unit sets optimal goals from both a mental and physical perspective, based on the analysis results obtained by the analysis unit. For example, it sets goals such as weight loss or increased exercise frequency based on the user's health status. Step 4: The Activity Proposal Unit proposes exercise plans, meal plans, and mental care activities based on the goals set by the Goal Setting Unit. For example, it proposes specific activities to help the user achieve their goals.

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

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, goal setting unit, and activity suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects health checkup results and online medical consultation information using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using statistical analysis and machine learning algorithms. The goal setting unit is implemented in the specific processing unit 290 of the data processing unit 12 and sets optimal goals based on the analysis results. The activity suggestion unit is implemented in the control unit 46A of the smart device 14 and proposes specific activities to help the user achieve their goals. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, goal setting unit, and activity suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects health checkup results and online medical consultation information using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using statistical analysis and machine learning algorithms. The goal setting unit is implemented in the specific processing unit 290 of the data processing unit 12 and sets optimal goals based on the analysis results. The activity suggestion unit is implemented in the control unit 46A of the smart glasses 214 and proposes specific activities to help the user achieve their goals. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, goal setting unit, and activity suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects health checkup results and online medical consultation information using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using statistical analysis and machine learning algorithms. The goal setting unit is implemented in the specific processing unit 290 of the data processing unit 12 and sets optimal goals based on the analysis results. The activity suggestion unit is implemented in the control unit 46A of the headset terminal 314 and proposes specific activities to help the user achieve their goals. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, goal setting unit, and activity suggestion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects health checkup results and online medical consultation information using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected information using statistical analysis and machine learning algorithms. The goal setting unit is implemented in the specific processing unit 290 of the data processing unit 12 and sets the optimal goal based on the analysis results. The activity suggestion unit is implemented in the control unit 46A of the robot 414 and proposes specific activities to help the user achieve their goals. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A data collection unit that collects information from health checkup results, online medical consultations, smart body composition scales, and wearable watches, An analysis unit analyzes the information collected by the aforementioned collection unit, A target setting unit sets targets based on the analysis results obtained by the aforementioned analysis unit, The system includes an activity suggestion unit that suggests activities based on the goals set by the goal setting unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect the results of annual health checkups. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Collect information from online medical consultations, smart body composition scales, or wearable watches. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The collected information is analyzed to provide a comprehensive assessment of the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned target setting unit, Based on the analysis results, set optimal goals from both a mental and physical perspective. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned activity proposal unit, We propose exercise plans, meal plans, and mental care activities to help you achieve your goals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting health checkup results and online medical consultation information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past health checkup results and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting health checkup results, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting health checkup results, the system prioritizes collecting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting health checkup results, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the representation of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the health status. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the health status category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the health checkup results were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of health status. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned target setting unit, We estimate the user's emotions and adjust the goal-setting method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned target setting unit, When setting goals, the system analyzes the user's past health history to set optimal goals. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned target setting unit, When setting goals, customize them based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned target setting unit, The system estimates user emotions and prioritizes goal setting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned target setting unit, When setting goals, consider the user's geographical location to set the optimal goals. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned target setting unit, When setting goals, analyze users' social media activity to set objectives. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned activity proposal unit, It estimates the user's emotions and adjusts how activity suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned activity proposal unit, When suggesting activities, the system analyzes the user's past activity history to provide the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned activity proposal unit, When suggesting activities, customize the suggestions based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned activity proposal unit, It estimates the user's emotions and prioritizes activity suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned activity proposal unit, When suggesting activities, the system takes the user's geographical location into consideration to provide the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned activity proposal unit, When suggesting activities, the system analyzes the user's social media activity to make recommendations. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects information from health checkup results, online medical consultations, smart body composition scales, and wearable watches, An analysis unit analyzes the information collected by the collection unit using statistical analysis and machine learning algorithms, and detects trends or abnormal values ​​in the user's health status as analysis results. A goal setting unit sets at least one goal selected from weight loss, increased exercise frequency, and stress management based on the trend or abnormal value detected by the analysis unit. The system includes an activity suggestion unit that suggests activities based on the goals set by the aforementioned goal setting unit, The aforementioned collection unit is Before the information collection process, a neural network, pre-trained based on multiple training data sets consisting of user inputs received from the user via a touch panel and microphone, and emotion values ​​representing each emotion in an emotion map, is used to estimate the user's emotion by inputting user input. If the estimated user emotion is stress, the timing of collecting the health checkup results or online medical consultation information is adjusted to delay it. A system characterized by the following features.

2. The aforementioned collection unit is Collect the results of annual health checkups. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed to provide a comprehensive assessment of the user's health status. The system according to feature 1.

4. The aforementioned target setting unit, Based on the analysis results, set optimal goals from both a mental and physical perspective. The system according to feature 1.

5. The aforementioned activity proposal unit, We propose exercise plans, meal plans, and mental care activities to help you achieve your goals. The system according to feature 1.

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