system

Abstract

The system according to this embodiment aims to propose an optimal health plan based on individual health data and to promote interaction with the local community. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a consultation unit, and an interaction unit. The collection unit collects the user's health data. The analysis unit analyzes the data collected by the collection unit and evaluates the user's health status. The proposal unit proposes a health plan based on the evaluation results obtained by the analysis unit. The consultation unit provides online consultations based on the health plan proposed by the proposal unit. The interaction unit promotes interaction with the local community based on the consultation results provided by the consultation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] The system according to this embodiment can propose an optimal health plan based on an individual's health data and promote interaction with the local community. [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 applied 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 plan proposal system according to an embodiment of the present invention is a system that uses generation AI to analyze an individual's health data and proposes a healthy lifestyle plan for rural areas. This health plan proposal system targets middle-aged and elderly people in urban areas and aims to extend healthy life expectancy while promoting migration to rural areas. The system uses AI to analyze the user's health checkup data, daily activity levels, and dietary records, and provides personalized health advice. Furthermore, it collaborates with local governments to create health plans that take into account the characteristics of each region (climate, food culture, natural environment, etc.). For example, it may suggest meal recipes using local ingredients or introduce walking courses that utilize the local natural environment. It also has an online consultation function with healthcare professionals (doctors, public health nurses, nutritionists, etc.) residing in rural areas, allowing users to receive expert advice remotely. It also includes a function to promote interaction with local communities, such as introducing participation in health events and local volunteer activities. Through this system, urban people can support healthy lifestyles through migration to rural areas, while simultaneously contributing to the revitalization of rural areas. As a result, the health plan proposal system can automatically collect and analyze the user's health data and propose an optimal health plan.

[0029] The health plan proposal system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a consultation unit, and an interaction unit. The data collection unit collects the user's health data. For example, the data collection unit collects the user's health checkup data, daily activity levels, and meal records. For example, the data collection unit can collect blood pressure, blood glucose levels, cholesterol levels, etc., as health checkup data. The data collection unit can also collect steps taken, calories burned, exercise time, etc., as daily activity levels. Furthermore, the data collection unit can collect calorie intake, nutrient balance, meal timing, etc., as meal records. The analysis unit analyzes the data collected by the data collection unit and evaluates the user's health status. The analysis unit analyzes the collected data in detail using techniques such as data mining, statistical analysis, and machine learning. For example, the analysis unit can extract patterns related to the user's health status using data mining techniques. Furthermore, the analysis unit can quantify and evaluate the user's health status using statistical analysis techniques. Furthermore, the analysis unit can predict and evaluate the user's health status using machine learning techniques. The Proposal Department proposes health plans based on the evaluation results obtained by the Analysis Department. For example, the Proposal Department collaborates with local governments to create health plans that take into account the characteristics of each region. For example, the Proposal Department can propose meal recipes using local ingredients or introduce walking courses that utilize the local natural environment. For example, as a meal recipe using local ingredients, the Proposal Department can propose healthy recipes using local vegetables and fish. Also, as a walking course that utilizes the local natural environment, the Proposal Department can introduce local parks and nature reserves. The Consultation Department provides online consultations based on the health plans proposed by the Proposal Department. For example, the Consultation Department provides an online consultation function with healthcare professionals (doctors, public health nurses, nutritionists, etc.) residing in the region. For example, the Consultation Department allows users to receive expert advice through methods such as video calls, chat, and email consultations. The Interaction Department promotes interaction with local communities based on the consultation results provided by the Consultation Department. For example, the Interaction Department promotes interaction with local communities by providing information on health events and volunteer activities held in the region.The communication department can provide information on events such as walking events, health seminars, and health check events. It can also provide information on volunteer activities such as health consultations, exercise guidance, and dietary guidance. This enables the health plan proposal system according to the embodiment to efficiently collect, analyze, propose, consult on, and communicate with users regarding their health data.

[0030] The data collection unit collects user health data. For example, it collects user health checkup data, daily activity levels, and meal records. Specifically, as health checkup data, it can collect detailed health indicators such as blood pressure, blood sugar levels, cholesterol levels, weight, BMI, and heart rate. This data is automatically obtained from the results of regular health checkups the user receives. It can also collect daily activity levels such as steps taken, calories burned, exercise time, and type of exercise (e.g., running, cycling, yoga). This data is collected in real time through wearable devices worn by the user or smartphone applications. Furthermore, as meal records, it can collect calorie intake, nutrient balance (e.g., protein, fat, carbohydrates, vitamins, minerals), meal timing, and meal types (e.g., breakfast, lunch, dinner, snacks). Users record their meals with photos and text using a dedicated application, and this data is automatically analyzed. The data collection unit centrally manages this diverse data and creates individual health profiles for each user. Furthermore, the data collection unit can flexibly adapt to the user's lifestyle and health condition by adjusting the frequency and accuracy of data collection. For example, if an abnormality is detected in a specific health indicator, the collection frequency can be increased to collect more detailed data, allowing for early detection of the problem. This enables the data collection unit to comprehensively understand the user's health condition and improve the overall system performance.

[0031] The analysis department analyzes data collected by the data collection department to assess the user's health status. The analysis department uses techniques such as data mining, statistical analysis, and machine learning to analyze the collected data in detail. Specifically, data mining techniques can be used to extract patterns related to the user's health status. For example, it can identify patterns of fluctuation in the user's health status from past data and predict health risks under specific times and conditions. Statistical analysis techniques can also be used to quantify and evaluate the user's health status. For example, it can calculate the average and standard deviation of blood pressure and blood glucose levels and evaluate whether they fall within the normal range. Furthermore, machine learning techniques can be used to predict and evaluate the user's health status. For example, based on past data, future health risks can be predicted, allowing for early intervention. The analysis department combines these techniques to comprehensively evaluate the user's health status and create individual health profiles. Additionally, the analysis department can utilize past data and statistical information to conduct long-term health risk assessments and trend analyses. For example, based on past data, it can analyze fluctuation trends in specific health indicators and predict future health risks. Anomaly detection algorithms can also be used to detect unusual patterns and abnormal data, enabling early warnings. 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 Proposal Department proposes health plans based on evaluation results obtained by the Analysis Department. For example, the Proposal Department collaborates with local governments to create health plans that take into account the characteristics of each region. Specifically, this can include proposing meal recipes that utilize local ingredients and introducing walking courses that take advantage of the local natural environment. For instance, they could propose healthy recipes using local vegetables and fish to help users eat balanced meals. They could also introduce walking courses that utilize local parks and nature reserves to help users exercise regularly. The Proposal Department customizes these proposals to suit the user's health condition and lifestyle to create individual health plans. Furthermore, the Proposal Department can collect user feedback to continuously improve the accuracy and effectiveness of the proposals. For example, they can collect results from users who have tried the proposed meal recipes or their impressions of using the walking courses and incorporate them into future proposals. The Proposal Department can also collaborate with local healthcare professionals and fitness instructors to provide expert advice and guidance. In this way, the Proposal Department can provide users with effective health plans and support them in maintaining and improving their health.

[0033] The Consultation Department provides online consultations based on the health plans proposed by the Proposal Department. For example, the Consultation Department offers online consultations with healthcare professionals (doctors, public health nurses, nutritionists, etc.) residing in local areas. Specifically, users can receive expert advice through methods such as video calls, chat, and email consultations. For instance, a user can consult a nutritionist about a proposed meal plan and receive advice on specific meal content and cooking methods. They can also consult a fitness instructor about an exercise plan and receive guidance on appropriate exercise methods and training plans. Furthermore, they can consult a doctor or public health nurse about their health status and receive advice on necessary tests and treatments. Through these online consultations, the Consultation Department helps users resolve any questions or concerns they may have about implementing the proposed health plan, supporting effective health management. Additionally, the Consultation Department collects user consultation content and feedback, and collaborates with the Proposal and Analysis Departments to continuously improve the accuracy and effectiveness of the health plans. For example, they analyze user consultation content to identify common challenges and problems and incorporate them into future proposals. The Consultation Department also implements thorough security measures to protect user privacy and provide a safe and secure environment for consultations. This allows the consultation department to provide users with expert advice and support them in implementing their health plans.

[0034] The Exchange Department promotes interaction with local communities based on the consultation results provided by the Consultation Department. For example, the Exchange Department promotes interaction with local communities by providing information on health events and volunteer activities held in the area. Specifically, it can provide information on walking events, health seminars, and health check events. For example, by participating in a walking event held in a local park, users can interact with other participants and share healthy lifestyle habits. Health seminars allow users to deepen their knowledge of health through lectures and workshops by experts. Furthermore, health check events allow users to undergo health checks such as blood pressure measurement and body fat percentage measurement, allowing them to check their health status. Through these events and activities, the Exchange Department provides support for users to actively interact with local communities and lead healthy lives. In addition, the Exchange Department can provide information on volunteer activities such as health consultations, exercise guidance, and dietary guidance. For example, by collaborating with local volunteer groups to hold health consultation sessions and exercise instruction classes, it provides users with opportunities to receive expert advice and guidance. Furthermore, in dietary guidance classes, users can acquire knowledge and skills to practice healthy eating habits through dietary guidance and cooking classes led by registered dietitians. This allows the communication department to connect with local communities, provide support for healthy living, and improve the overall effectiveness of the system.

[0035] The data collection unit can collect user health check data, daily activity levels, and meal records. For example, the data collection unit can collect blood pressure, blood sugar levels, cholesterol levels, etc., as user health check data. For example, the data collection unit can collect steps, calories burned, exercise time, etc., as daily activity levels. For example, the data collection unit can collect calorie intake, nutrient balance, meal timing, etc., as meal records. By collecting user health check data, daily activity levels, meal records, etc., detailed health data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user health check data into AI, and the AI ​​can analyze and collect the data.

[0036] The analysis unit can analyze the collected data in detail and comprehensively evaluate the user's health status. For example, the analysis unit can use data mining techniques to analyze the collected data in detail. For example, the analysis unit can use statistical analysis techniques to quantify and evaluate the user's health status. For example, the analysis unit can use machine learning techniques to predict and evaluate the user's health status. In this way, by analyzing the collected data in detail, the user's health status can be comprehensively evaluated. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can analyze and evaluate the data.

[0037] The proposal department can collaborate with local governments to create health plans that take into account the characteristics of each region. For example, the proposal department can suggest meal recipes using local ingredients or introduce walking courses that utilize the local natural environment. For example, as a meal recipe using local ingredients, the proposal department can suggest healthy recipes using local vegetables and fish. For example, as a walking course that utilizes the local natural environment, the proposal department can introduce local parks and nature reserves. In this way, by collaborating with local governments, health plans that take into account the characteristics of each region can be created. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on local ingredients and the natural environment into AI, and the AI ​​can analyze the data to create a health plan.

[0038] The consultation service can provide an online consultation function with healthcare professionals residing in rural areas. The consultation service allows users to receive expert advice through methods such as video calls, chat, and email consultations. For example, the consultation service allows users to directly consult with doctors and nutritionists using video calls. For example, the consultation service allows users to consult with public health nurses in real time using chat. For example, the consultation service allows users to receive advice from professionals using email consultations. This enables access to high-quality healthcare services even in remote locations by providing an online consultation function with healthcare professionals residing in rural areas. Some or all of the above-described processes in the consultation service may be performed using AI, or not. For example, the consultation service can input user health data into AI, which can then analyze the data and provide it to professionals.

[0039] The Community Interaction Department can promote interaction with the local community by providing information on health events and volunteer activities held in the area. For example, the department can provide information on walking events, health seminars, and health check-up events. For instance, it could introduce walking events held in local parks. For example, it could introduce health seminars conducted by local doctors. For example, it could introduce health check-up events conducted by local public health nurses. The Community Interaction Department also provides information on volunteer activities such as health consultations, exercise guidance, and dietary guidance. For example, it could introduce health consultations by local doctors. For example, it could introduce exercise guidance by local fitness trainers. For example, it could introduce dietary guidance by local nutritionists. This allows for increased interaction with the local community by providing information on health events and volunteer activities held in the area. Some or all of the above processing in the Community Interaction Department may be performed using AI, or not. For example, the communication department can input local event information into the AI, which then analyzes the data and provides it to users.

[0040] The data collection unit can analyze the user's past health data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on the devices and applications the user has used in the past. For example, the data collection unit can analyze the user's past data collection frequency and suggest the optimal collection schedule. For example, the data collection unit can select the most effective collection method based on the user's past data collection results. In this way, the optimal collection method can be selected by analyzing the user's past health data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI, which can then analyze the data and select the optimal collection method.

[0041] The data collection unit can filter health data based on the user's current lifestyle and areas of interest. For example, if the user is on a diet, the data collection unit will prioritize collecting meal records. If the user is interested in exercise, the data collection unit will prioritize collecting activity level data. If the user is interested in stress management, the data collection unit will prioritize collecting heart rate and sleep data. By filtering based on the user's current lifestyle and areas of interest, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into an AI, which can then analyze and filter the data.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit prioritizes the collection of oxygen saturation data. If the user is in an urban area, the data collection unit prioritizes the collection of walking distance and activity level data. If the user is at the beach, the data collection unit prioritizes the collection of heart rate and relaxation level data. By prioritizing the collection of highly relevant data while considering the user's geographical location, more appropriate data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user posts about exercise on social media, the data collection unit prioritizes collecting activity level data. For example, if a user posts about food, the data collection unit prioritizes collecting food records. For example, if a user posts about stress, the data collection unit prioritizes collecting heart rate and sleep data. This allows the data collection to be made relevant by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, which can then analyze the data and collect relevant data.

[0044] The analysis unit can improve the accuracy of its assessments by referring to the user's past health status when analyzing health data. For example, the analysis unit can refer to the user's past health checkup data to assess their current health status. For example, the analysis unit can refer to the user's past activity data to assess their current exercise habits. For example, the analysis unit can refer to the user's past meal records to assess their current nutritional status. By referring to the user's past health status, the accuracy of the assessment can be improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past health data into AI, which can then analyze the data to improve the accuracy of the assessment.

[0045] The analysis unit can perform evaluations of health data while considering the user's lifestyle and environmental factors. For example, the analysis unit can evaluate the user's health status by considering the user's sleep habits. For example, the analysis unit can evaluate the user's nutritional status by considering the user's eating habits. For example, the analysis unit can evaluate the user's fitness level by considering the user's exercise habits. This allows for a more accurate evaluation by considering the user's lifestyle and environmental factors. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's lifestyle and environmental factors into AI, which can then analyze the data and perform evaluations.

[0046] The analysis unit can consider the geographical distribution of users when analyzing health data. For example, if a user lives at high altitude, the analysis unit will consider oxygen saturation when evaluating their health status. For example, if a user lives in an urban area, the analysis unit will consider lack of exercise when evaluating their health status. For example, if a user lives by the sea, the analysis unit will consider their level of relaxation when evaluating their health status. This allows for a more appropriate evaluation by considering the geographical distribution of users. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of users into AI, and the AI ​​can analyze the data and perform the evaluation.

[0047] The analysis unit can improve the accuracy of its assessments by referring to relevant medical literature when analyzing health data. For example, the analysis unit can refer to the latest medical literature to assess the user's health status. For example, the analysis unit can refer to relevant research papers to assess the user's health risks. For example, the analysis unit can refer to medical databases to comprehensively assess the user's health status. This allows the accuracy of the assessment to be improved by referring to relevant medical literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant medical literature data into AI, which can then analyze the data to improve the accuracy of the assessment.

[0048] The suggestion unit can adjust the level of detail in a health plan proposal based on the importance of the user's health condition. For example, if the user's health condition is good, the suggestion unit will propose a simple health plan. If the user's health condition is moderate, the suggestion unit will propose a detailed health plan. If the user's health condition is poor, the suggestion unit will propose a very detailed health plan. By adjusting the level of detail in the suggestion unit based on the importance of the user's health condition, a more appropriate health plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's health condition data into AI, which can then analyze the data and adjust the level of detail in the suggestion.

[0049] The proposal unit can apply different proposal algorithms to each region's characteristics when proposing health plans. For example, the proposal unit might propose a health plan that takes oxygen saturation into account to users living at high altitudes. For example, it might propose a health plan to address lack of exercise to users living in urban areas. For example, it might propose a health plan to enhance relaxation to users living by the sea. By applying proposal algorithms tailored to each region's characteristics, a more appropriate health plan can be provided. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input regional characteristic data into AI, which can then analyze the data and apply a proposal algorithm.

[0050] The proposal department can prioritize proposals based on the user's submission timing when proposing health plans. For example, if the user is in a hurry, the proposal department will propose a health plan that can be implemented quickly. If the user has more time, the proposal department will propose a more detailed health plan. If the user is preparing for a specific event, the proposal department will propose a health plan tailored to that event. This allows for the provision of more appropriate plans by prioritizing proposals based on the user's submission timing. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input user submission timing data into an AI, which can then analyze the data to determine the priority of proposals.

[0051] The suggestion unit can adjust the order of suggestions based on user relevance when proposing a health plan. For example, the suggestion unit may first display the suggestion most relevant to the user's health condition. For example, the suggestion unit may first display the suggestion most relevant to the user's lifestyle. For example, the suggestion unit may first display the suggestion most relevant to the user's areas of interest. By adjusting the order of suggestions based on user relevance, a more appropriate plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user relevance data into AI, which can then analyze the data and adjust the order of suggestions.

[0052] The consultation department can provide optimal advice during online consultations by referring to the user's past consultation history. For example, the consultation department can provide relevant advice based on the content of past consultations the user has had. For example, the consultation department can select the most effective advice from the user's past consultation history. For example, the consultation department can analyze the user's past consultation history and provide optimal advice. In this way, optimal advice can be provided by referring to the user's past consultation history. Some or all of the above processes in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the user's past consultation history data into AI, and the AI ​​can analyze the data and provide optimal advice.

[0053] The consultation department can customize the content of online consultations based on the user's current health status. For example, the consultation department can customize the content based on the user's current health checkup data. For example, the consultation department can customize the content based on the user's current activity level data. For example, the consultation department can customize the content based on the user's current dietary record. By customizing the consultation content based on the user's current health status, more appropriate consultations become possible. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the user's current health status data into AI, and the AI ​​can analyze the data to customize the consultation content.

[0054] The consultation department can select the most appropriate consultation method during online consultations by considering the user's geographical location. For example, if the user lives at high altitude, the consultation department will consider oxygen saturation. If the user lives in an urban area, the consultation department will consider lack of exercise. If the user lives by the sea, the consultation department will consider relaxation level. By considering the user's geographical location, more appropriate consultations become possible. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the user's geographical location data into AI, which can then analyze the data and select the most appropriate consultation method.

[0055] The consultation department can analyze a user's social media activity during an online consultation and suggest consultation topics. For example, if a user posts about exercise on social media, the consultation department will suggest consultations about exercise. For example, if a user posts about food, the consultation department will suggest consultations about food. For example, if a user posts about stress, the consultation department will suggest consultations about stress management. By analyzing the user's social media activity, the consultation department can suggest more appropriate consultation topics. Some or all of the above processing in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the user's social media activity data into AI, which can then analyze the data and suggest consultation topics.

[0056] The interaction unit can provide the optimal interaction method when interacting with local communities by referring to the user's past interaction history. For example, the interaction unit can suggest relevant interaction methods based on events the user has previously participated in. For example, the interaction unit can select the most effective interaction method from the user's past interaction history. For example, the interaction unit can analyze the user's past interaction history and provide the optimal interaction method. In this way, the optimal interaction method can be provided by referring to the user's past interaction history. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input the user's past interaction history data into AI, and the AI ​​can analyze the data and provide the optimal interaction method.

[0057] The interaction unit can customize the means of interaction with the local community based on the user's current living situation. For example, the interaction unit can suggest an appropriate means of interaction considering the user's current living situation. For example, the interaction unit can provide the optimal method of interaction based on the user's current living situation. For example, the interaction unit can analyze the user's current living situation and customize the optimal means of interaction. This makes it possible to have more appropriate interactions by customizing the means of interaction based on the user's current living situation. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input the user's current living situation data into AI, and the AI ​​can analyze the data and customize the means of interaction.

[0058] The interaction unit can select the optimal interaction method when interacting with local communities, taking into account the user's geographical location information. For example, if the user lives at high altitude, the interaction unit will suggest an interaction method that takes oxygen saturation into consideration. If the user lives in an urban area, the interaction unit will suggest an interaction method that helps alleviate lack of exercise. If the user lives by the sea, the interaction unit will suggest an interaction method that enhances relaxation. By considering the user's geographical location information, more appropriate interactions become possible. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input the user's geographical location data into AI, which can then analyze the data and select the optimal interaction method.

[0059] The interaction unit can analyze a user's social media activity and suggest interaction methods when interacting with local communities. For example, if a user posts about exercise on social media, the interaction unit will suggest interaction methods related to exercise. For example, if a user posts about food, the interaction unit will suggest interaction methods related to food. For example, if a user posts about stress, the interaction unit will suggest interaction methods related to stress management. In this way, by analyzing the user's social media activity, more appropriate interaction methods can be suggested. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input the user's social media activity data into AI, which can then analyze the data and suggest interaction methods.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The health plan proposal system can also collect and analyze user sleep data. The data collection unit can collect data such as the user's sleep duration, sleep quality, and number of times they toss and turn during sleep. The analysis unit can evaluate the user's sleep patterns based on the collected data and suggest areas for improvement. The proposal unit can offer advice on improving the user's sleep quality and suggest methods for creating a suitable sleep environment. This allows the system to utilize user sleep data to provide a more comprehensive health plan.

[0062] The health plan suggestion system can also monitor and analyze the user's stress level. The data collection unit can collect data such as the user's heart rate, blood pressure, and respiratory rate to estimate their stress level. The analysis unit can evaluate the user's stress patterns based on the collected data and provide advice for stress management. The suggestion unit can propose relaxation techniques and exercise plans to reduce stress. This helps manage the user's stress level and supports a healthier lifestyle.

[0063] The health plan suggestion system can further analyze the user's dietary data in detail and evaluate nutritional balance. The data collection unit can collect, for example, the user's diet, calorie intake, and nutrient balance. The analysis unit can evaluate the user's nutritional status based on the collected data and suggest areas for improvement. The suggestion unit can suggest, for example, a meal plan to improve the user's nutritional balance or recipes to supplement specific nutrients. In this way, the system can utilize the user's dietary data to support a healthier lifestyle.

[0064] The health plan suggestion system can further analyze the user's exercise data in detail and evaluate their exercise habits. The data collection unit can collect data such as the user's exercise time, exercise intensity, and calories burned. The analysis unit can evaluate the user's exercise habits based on the collected data and suggest areas for improvement. The suggestion unit can provide, for example, exercise plans to improve the user's habits or advice on incorporating specific exercises. This allows the system to utilize the user's exercise data to support healthier exercise habits.

[0065] The health plan proposal system can further utilize the user's geographical location information to assess region-specific health risks. The data collection unit can collect data such as the climate of the user's place of residence, environmental pollution levels, and access to medical facilities. The analysis unit can evaluate the user's health risks based on the collected data and propose countermeasures for region-specific health risks. The proposal unit can provide, for example, health plans tailored to the user's place of residence and advice to mitigate region-specific health risks. This allows for the provision of more appropriate health support by utilizing the user's geographical location information.

[0066] The health plan suggestion system can further analyze users' social media activity and utilize this information to suggest health plans. The data collection unit can collect, for example, the content of users' social media posts, the number of likes, and the content of comments. The analysis unit can evaluate users' areas of interest and lifestyle habits based on the collected data and reflect this in the health plan suggestions. The suggestion unit can provide, for example, health plans based on users' areas of interest and advice related to their social media activity. This allows for the provision of more personalized health plans by leveraging users' social media activity.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The data collection unit collects the user's health data. The data collection unit collects, for example, the user's health checkup data, daily activity levels, and meal records. Specifically, it can collect data such as blood pressure, blood sugar levels, and cholesterol levels as health checkup data, steps taken, calories burned, and exercise time as daily activity levels, and calories consumed, nutrient balance, and meal timing as meal records. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the user's health status. The analysis unit uses techniques such as data mining, statistical analysis, and machine learning to analyze the collected data in detail. Specifically, it can use data mining techniques to extract patterns related to the user's health status, statistical analysis techniques to quantify and evaluate the user's health status, and machine learning techniques to predict and evaluate the user's health status. Step 3: The proposal department proposes a health plan based on the evaluation results obtained by the analysis department. The proposal department works with local governments to create health plans that take into account the characteristics of each region. Specifically, this may include proposing meal recipes that utilize local ingredients and introducing walking courses that take advantage of the local natural environment. For example, they could propose healthy recipes using local vegetables and fish, and introduce local parks and nature reserves. Step 4: The consultation department provides online consultations based on the health plan proposed by the proposal department. The consultation department provides online consultation functions with healthcare professionals (doctors, public health nurses, nutritionists, etc.) residing in the local area. Specifically, users can receive expert advice through methods such as video calls, chat, and email consultations. Step 5: The Exchange Department promotes interaction with the local community based on the consultation results provided by the Consultation Department. The Exchange Department promotes interaction with the local community by providing information on health events and volunteer activities held in the area. Specifically, it can provide information on walking events, health seminars, health check events, and volunteer activities such as health consultations, exercise guidance, and dietary guidance.

[0069] (Example of form 2) The health plan proposal system according to an embodiment of the present invention is a system that uses generation AI to analyze an individual's health data and proposes a healthy lifestyle plan for rural areas. This health plan proposal system targets middle-aged and elderly people in urban areas and aims to extend healthy life expectancy while promoting migration to rural areas. The system uses AI to analyze the user's health checkup data, daily activity levels, and dietary records, and provides personalized health advice. Furthermore, it collaborates with local governments to create health plans that take into account the characteristics of each region (climate, food culture, natural environment, etc.). For example, it may suggest meal recipes using local ingredients or introduce walking courses that utilize the local natural environment. It also has an online consultation function with healthcare professionals (doctors, public health nurses, nutritionists, etc.) residing in rural areas, allowing users to receive expert advice remotely. It also includes a function to promote interaction with local communities, such as introducing participation in health events and local volunteer activities. Through this system, urban people can support healthy lifestyles through migration to rural areas, while simultaneously contributing to the revitalization of rural areas. As a result, the health plan proposal system can automatically collect and analyze the user's health data and propose an optimal health plan.

[0070] The health plan proposal system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a consultation unit, and an interaction unit. The data collection unit collects the user's health data. For example, the data collection unit collects the user's health checkup data, daily activity levels, and meal records. For example, the data collection unit can collect blood pressure, blood glucose levels, cholesterol levels, etc., as health checkup data. The data collection unit can also collect steps taken, calories burned, exercise time, etc., as daily activity levels. Furthermore, the data collection unit can collect calorie intake, nutrient balance, meal timing, etc., as meal records. The analysis unit analyzes the data collected by the data collection unit and evaluates the user's health status. The analysis unit analyzes the collected data in detail using techniques such as data mining, statistical analysis, and machine learning. For example, the analysis unit can extract patterns related to the user's health status using data mining techniques. Furthermore, the analysis unit can quantify and evaluate the user's health status using statistical analysis techniques. Furthermore, the analysis unit can predict and evaluate the user's health status using machine learning techniques. The Proposal Department proposes health plans based on the evaluation results obtained by the Analysis Department. For example, the Proposal Department collaborates with local governments to create health plans that take into account the characteristics of each region. For example, the Proposal Department can propose meal recipes using local ingredients or introduce walking courses that utilize the local natural environment. For example, as a meal recipe using local ingredients, the Proposal Department can propose healthy recipes using local vegetables and fish. Also, as a walking course that utilizes the local natural environment, the Proposal Department can introduce local parks and nature reserves. The Consultation Department provides online consultations based on the health plans proposed by the Proposal Department. For example, the Consultation Department provides an online consultation function with healthcare professionals (doctors, public health nurses, nutritionists, etc.) residing in the region. For example, the Consultation Department allows users to receive expert advice through methods such as video calls, chat, and email consultations. The Interaction Department promotes interaction with local communities based on the consultation results provided by the Consultation Department. For example, the Interaction Department promotes interaction with local communities by providing information on health events and volunteer activities held in the region.The communication department can provide information on events such as walking events, health seminars, and health check events. It can also provide information on volunteer activities such as health consultations, exercise guidance, and dietary guidance. This enables the health plan proposal system according to the embodiment to efficiently collect, analyze, propose, consult on, and communicate with users regarding their health data.

[0071] The data collection unit collects user health data. For example, it collects user health checkup data, daily activity levels, and meal records. Specifically, as health checkup data, it can collect detailed health indicators such as blood pressure, blood sugar levels, cholesterol levels, weight, BMI, and heart rate. This data is automatically obtained from the results of regular health checkups the user receives. It can also collect daily activity levels such as steps taken, calories burned, exercise time, and type of exercise (e.g., running, cycling, yoga). This data is collected in real time through wearable devices worn by the user or smartphone applications. Furthermore, as meal records, it can collect calorie intake, nutrient balance (e.g., protein, fat, carbohydrates, vitamins, minerals), meal timing, and meal types (e.g., breakfast, lunch, dinner, snacks). Users record their meals with photos and text using a dedicated application, and this data is automatically analyzed. The data collection unit centrally manages this diverse data and creates individual health profiles for each user. Furthermore, the data collection unit can flexibly adapt to the user's lifestyle and health condition by adjusting the frequency and accuracy of data collection. For example, if an abnormality is detected in a specific health indicator, the collection frequency can be increased to collect more detailed data, allowing for early detection of the problem. This enables the data collection unit to comprehensively understand the user's health condition and improve the overall system performance.

[0072] The analysis department analyzes data collected by the data collection department to assess the user's health status. The analysis department uses techniques such as data mining, statistical analysis, and machine learning to analyze the collected data in detail. Specifically, data mining techniques can be used to extract patterns related to the user's health status. For example, it can identify patterns of fluctuation in the user's health status from past data and predict health risks under specific times and conditions. Statistical analysis techniques can also be used to quantify and evaluate the user's health status. For example, it can calculate the average and standard deviation of blood pressure and blood glucose levels and evaluate whether they fall within the normal range. Furthermore, machine learning techniques can be used to predict and evaluate the user's health status. For example, based on past data, future health risks can be predicted, allowing for early intervention. The analysis department combines these techniques to comprehensively evaluate the user's health status and create individual health profiles. Additionally, the analysis department can utilize past data and statistical information to conduct long-term health risk assessments and trend analyses. For example, based on past data, it can analyze fluctuation trends in specific health indicators and predict future health risks. Anomaly detection algorithms can also be used to detect unusual patterns and abnormal data, enabling early warnings. 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.

[0073] The Proposal Department proposes health plans based on evaluation results obtained by the Analysis Department. For example, the Proposal Department collaborates with local governments to create health plans that take into account the characteristics of each region. Specifically, this could include suggesting meal recipes using local ingredients or introducing walking courses that utilize the local natural environment. For instance, they could propose healthy recipes using local vegetables and fish to help users maintain a balanced diet. They could also introduce walking courses utilizing local parks and nature reserves to encourage regular exercise. The Proposal Department customizes these suggestions to match the user's health condition and lifestyle, creating individualized health plans. Furthermore, the Proposal Department collects user feedback to continuously improve the accuracy and effectiveness of its suggestions. For example, they collect results from users who have tried the suggested meal recipes or used the walking courses, and incorporate this feedback into future suggestions. The Proposal Department can also collaborate with local healthcare professionals and fitness instructors to provide expert advice and guidance. This allows the Proposal Department to provide users with effective health plans and support their health maintenance and improvement.

[0074] The Consultation Department provides online consultations based on the health plans proposed by the Proposal Department. For example, the Consultation Department offers online consultations with healthcare professionals (doctors, public health nurses, nutritionists, etc.) residing in local areas. Specifically, users can receive expert advice through methods such as video calls, chat, and email consultations. For instance, a user can consult a nutritionist about a proposed meal plan and receive advice on specific meal content and cooking methods. They can also consult a fitness instructor about an exercise plan and receive guidance on appropriate exercise methods and training plans. Furthermore, they can consult a doctor or public health nurse about their health status and receive advice on necessary tests and treatments. Through these online consultations, the Consultation Department helps users resolve any questions or concerns they may have about implementing the proposed health plan, supporting effective health management. Additionally, the Consultation Department collects user consultation content and feedback, and collaborates with the Proposal and Analysis Departments to continuously improve the accuracy and effectiveness of the health plans. For example, they analyze user consultation content to identify common challenges and problems and incorporate them into future proposals. The Consultation Department also implements thorough security measures to protect user privacy and provide a safe and secure environment for consultations. This allows the consultation department to provide users with expert advice and support them in implementing their health plans.

[0075] The Exchange Department promotes interaction with local communities based on the consultation results provided by the Consultation Department. For example, the Exchange Department promotes interaction with local communities by providing information on health events and volunteer activities held in the area. Specifically, it can provide information on walking events, health seminars, and health check events. For example, by participating in a walking event held in a local park, users can interact with other participants and share healthy lifestyle habits. Health seminars allow users to deepen their knowledge of health through lectures and workshops by experts. Furthermore, health check events allow users to undergo health checks such as blood pressure measurement and body fat percentage measurement, allowing them to check their health status. Through these events and activities, the Exchange Department provides support for users to actively interact with local communities and lead healthy lives. In addition, the Exchange Department can provide information on volunteer activities such as health consultations, exercise guidance, and dietary guidance. For example, by collaborating with local volunteer groups to hold health consultation sessions and exercise instruction classes, it provides users with opportunities to receive expert advice and guidance. Furthermore, in dietary guidance classes, users can acquire knowledge and skills to practice healthy eating habits through dietary guidance and cooking classes led by registered dietitians. This allows the communication department to connect with local communities, provide support for healthy living, and improve the overall effectiveness of the system.

[0076] The data collection unit can collect user health check data, daily activity levels, and meal records. For example, the data collection unit can collect blood pressure, blood sugar levels, cholesterol levels, etc., as user health check data. For example, the data collection unit can collect steps, calories burned, exercise time, etc., as daily activity levels. For example, the data collection unit can collect calorie intake, nutrient balance, meal timing, etc., as meal records. By collecting user health check data, daily activity levels, meal records, etc., detailed health data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user health check data into AI, and the AI ​​can analyze and collect the data.

[0077] The analysis unit can analyze the collected data in detail and comprehensively evaluate the user's health status. For example, the analysis unit can use data mining techniques to analyze the collected data in detail. For example, the analysis unit can use statistical analysis techniques to quantify and evaluate the user's health status. For example, the analysis unit can use machine learning techniques to predict and evaluate the user's health status. In this way, by analyzing the collected data in detail, the user's health status can be comprehensively evaluated. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, and the AI ​​can analyze and evaluate the data.

[0078] The proposal department can collaborate with local governments to create health plans that take into account the characteristics of each region. For example, the proposal department can suggest meal recipes using local ingredients or introduce walking courses that utilize the local natural environment. For example, as a meal recipe using local ingredients, the proposal department can suggest healthy recipes using local vegetables and fish. For example, as a walking course that utilizes the local natural environment, the proposal department can introduce local parks and nature reserves. In this way, by collaborating with local governments, health plans that take into account the characteristics of each region can be created. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on local ingredients and the natural environment into AI, and the AI ​​can analyze the data to create a health plan.

[0079] The consultation service can provide an online consultation function with healthcare professionals residing in rural areas. The consultation service allows users to receive expert advice through methods such as video calls, chat, and email consultations. For example, the consultation service allows users to directly consult with doctors and nutritionists using video calls. For example, the consultation service allows users to consult with public health nurses in real time using chat. For example, the consultation service allows users to receive advice from professionals using email consultations. This enables access to high-quality healthcare services even in remote locations by providing an online consultation function with healthcare professionals residing in rural areas. Some or all of the above-described processes in the consultation service may be performed using AI, or not. For example, the consultation service can input user health data into AI, which can then analyze the data and provide it to professionals.

[0080] The Community Interaction Department can promote interaction with the local community by providing information on health events and volunteer activities held in the area. For example, the department can provide information on walking events, health seminars, and health check-up events. For instance, it could introduce walking events held in local parks. For example, it could introduce health seminars conducted by local doctors. For example, it could introduce health check-up events conducted by local public health nurses. The Community Interaction Department also provides information on volunteer activities such as health consultations, exercise guidance, and dietary guidance. For example, it could introduce health consultations by local doctors. For example, it could introduce exercise guidance by local fitness trainers. For example, it could introduce dietary guidance by local nutritionists. This allows for increased interaction with the local community by providing information on health events and volunteer activities held in the area. Some or all of the above processing in the Community Interaction Department may be performed using AI, or not. For example, the communication department can input local event information into the AI, which then analyzes the data and provides it to users.

[0081] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can adjust the timing to collect health data during relaxed periods. For example, if the user is relaxed, the data collection unit can adjust the timing to collect health data during periods of high activity. For example, if the user is tired, the data collection unit can adjust the timing to collect health data after rest. By adjusting the timing of health data collection based on the user's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as 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 AI, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can estimate the emotion and adjust the collection timing.

[0082] The data collection unit can analyze the user's past health data collection history and select the optimal collection method. For example, the data collection unit can suggest the optimal collection method based on the devices and applications the user has used in the past. For example, the data collection unit can analyze the user's past data collection frequency and suggest the optimal collection schedule. For example, the data collection unit can select the most effective collection method based on the user's past data collection results. In this way, the optimal collection method can be selected by analyzing the user's past health data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI, which can then analyze the data and select the optimal collection method.

[0083] The data collection unit can filter health data based on the user's current lifestyle and areas of interest. For example, if the user is on a diet, the data collection unit will prioritize collecting meal records. If the user is interested in exercise, the data collection unit will prioritize collecting activity level data. If the user is interested in stress management, the data collection unit will prioritize collecting heart rate and sleep data. By filtering based on the user's current lifestyle and areas of interest, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into an AI, which can then analyze and filter the data.

[0084] The data collection unit can estimate the user's emotions and determine the priority of health data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting heart rate and sleep data. For example, if the user is relaxed, the data collection unit will prioritize collecting activity level data. For example, if the user is tired, the data collection unit will prioritize collecting post-rest data. This allows for the priority collection of more important data by determining the priority of health data to collect 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 AI or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can estimate the emotions and determine the priority of health data to collect.

[0085] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at high altitude, the data collection unit prioritizes the collection of oxygen saturation data. If the user is in an urban area, the data collection unit prioritizes the collection of walking distance and activity level data. If the user is at the beach, the data collection unit prioritizes the collection of heart rate and relaxation level data. By prioritizing the collection of highly relevant data while considering the user's geographical location, more appropriate data can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0086] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user posts about exercise on social media, the data collection unit prioritizes collecting activity level data. For example, if a user posts about food, the data collection unit prioritizes collecting food records. For example, if a user posts about stress, the data collection unit prioritizes collecting heart rate and sleep data. This allows the data collection to be made relevant by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, which can then analyze the data and collect relevant data.

[0087] The analysis unit can estimate the user's emotions and adjust the health assessment method based on the estimated emotions. For example, if the user is stressed, the analysis unit will adopt an assessment method that focuses on stress management. For example, if the user is relaxed, the analysis unit will adopt a method that assesses the overall health status. For example, if the user is tired, the analysis unit will adopt an assessment method that focuses on rest and recovery. By adjusting the health assessment method based on the user's emotions, a more appropriate assessment becomes possible. 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 AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the assessment method.

[0088] The analysis unit can improve the accuracy of its assessments by referring to the user's past health status when analyzing health data. For example, the analysis unit can refer to the user's past health checkup data to assess their current health status. For example, the analysis unit can refer to the user's past activity data to assess their current exercise habits. For example, the analysis unit can refer to the user's past meal records to assess their current nutritional status. By referring to the user's past health status, the accuracy of the assessment can be improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past health data into AI, which can then analyze the data to improve the accuracy of the assessment.

[0089] The analysis unit can perform evaluations of health data while considering the user's lifestyle and environmental factors. For example, the analysis unit can evaluate the user's health status by considering the user's sleep habits. For example, the analysis unit can evaluate the user's nutritional status by considering the user's eating habits. For example, the analysis unit can evaluate the user's fitness level by considering the user's exercise habits. This allows for a more accurate evaluation by considering the user's lifestyle and environmental factors. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's lifestyle and environmental factors into AI, which can then analyze the data and perform evaluations.

[0090] The analysis unit can estimate the user's emotions and adjust the order in which health status assessment results are displayed based on the estimated emotions. For example, if the user is stressed, the analysis unit will display stress-related assessment results first. For example, if the user is relaxed, the analysis unit will display the overall health status assessment results first. For example, if the user is tired, the analysis unit will display assessment results related to rest and recovery first. This allows for the provision of more appropriate information by adjusting the display order of assessment results 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the display order of assessment results.

[0091] The analysis unit can consider the geographical distribution of users when analyzing health data. For example, if a user lives at high altitude, the analysis unit will consider oxygen saturation when evaluating their health status. For example, if a user lives in an urban area, the analysis unit will consider lack of exercise when evaluating their health status. For example, if a user lives by the sea, the analysis unit will consider their level of relaxation when evaluating their health status. This allows for a more appropriate evaluation by considering the geographical distribution of users. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of users into AI, and the AI ​​can analyze the data and perform the evaluation.

[0092] The analysis unit can improve the accuracy of its assessments by referring to relevant medical literature when analyzing health data. For example, the analysis unit can refer to the latest medical literature to assess the user's health status. For example, the analysis unit can refer to relevant research papers to assess the user's health risks. For example, the analysis unit can refer to medical databases to comprehensively assess the user's health status. This allows the accuracy of the assessment to be improved by referring to relevant medical literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant medical literature data into AI, which can then analyze the data to improve the accuracy of the assessment.

[0093] The suggestion unit can estimate the user's emotions and adjust the presentation of the health plan based on the estimated emotions. For example, if the user is stressed, the suggestion unit will suggest a simple and easy-to-understand health plan. If the user is relaxed, the suggestion unit will suggest a health plan with detailed explanations. If the user is excited, the suggestion unit will suggest a visually appealing health plan. By adjusting the presentation of the health plan based on the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the presentation.

[0094] The suggestion unit can adjust the level of detail in a health plan proposal based on the importance of the user's health condition. For example, if the user's health condition is good, the suggestion unit will propose a simple health plan. If the user's health condition is moderate, the suggestion unit will propose a detailed health plan. If the user's health condition is poor, the suggestion unit will propose a very detailed health plan. By adjusting the level of detail in the suggestion unit based on the importance of the user's health condition, a more appropriate health plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's health condition data into AI, which can then analyze the data and adjust the level of detail in the suggestion.

[0095] The proposal unit can apply different proposal algorithms to each region's characteristics when proposing health plans. For example, the proposal unit might propose a health plan that takes oxygen saturation into account to users living at high altitudes. For example, it might propose a health plan to address lack of exercise to users living in urban areas. For example, it might propose a health plan to enhance relaxation to users living by the sea. By applying proposal algorithms tailored to each region's characteristics, a more appropriate health plan can be provided. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input regional characteristic data into AI, which can then analyze the data and apply a proposal algorithm.

[0096] The suggestion unit can estimate the user's emotions and adjust the length of the health plan based on the estimated emotions. For example, if the user is stressed, the suggestion unit will suggest a short, concise health plan. If the user is relaxed, the suggestion unit will suggest a longer health plan with detailed explanations. If the user is excited, the suggestion unit will suggest a health plan with visually stimulating effects. By adjusting the length of the health plan based on the user's emotions, a more appropriate plan can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the length of the health plan.

[0097] The proposal department can prioritize proposals based on the user's submission timing when proposing health plans. For example, if the user is in a hurry, the proposal department will propose a health plan that can be implemented quickly. If the user has more time, the proposal department will propose a more detailed health plan. If the user is preparing for a specific event, the proposal department will propose a health plan tailored to that event. This allows for the provision of more appropriate plans by prioritizing proposals based on the user's submission timing. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input user submission timing data into an AI, which can then analyze the data to determine the priority of proposals.

[0098] The suggestion unit can adjust the order of suggestions based on user relevance when proposing a health plan. For example, the suggestion unit may first display the suggestion most relevant to the user's health condition. For example, the suggestion unit may first display the suggestion most relevant to the user's lifestyle. For example, the suggestion unit may first display the suggestion most relevant to the user's areas of interest. By adjusting the order of suggestions based on user relevance, a more appropriate plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user relevance data into AI, which can then analyze the data and adjust the order of suggestions.

[0099] The consultation unit can estimate the user's emotions and adjust the online consultation method based on the estimated emotions. For example, if the user is stressed, the consultation unit will conduct the consultation in a relaxed atmosphere. If the user is relaxed, the consultation unit will conduct the consultation with detailed explanations. If the user is in a hurry, the consultation unit will conduct the consultation quickly and to the point. By adjusting the online consultation method based on the user's emotions, more appropriate consultations become possible. Emotion estimation is achieved using an emotion estimation function, such as 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 consultation unit may be performed using AI or not using AI. For example, the consultation unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the consultation method.

[0100] The consultation department can provide optimal advice during online consultations by referring to the user's past consultation history. For example, the consultation department can provide relevant advice based on the content of past consultations the user has had. For example, the consultation department can select the most effective advice from the user's past consultation history. For example, the consultation department can analyze the user's past consultation history and provide optimal advice. In this way, optimal advice can be provided by referring to the user's past consultation history. Some or all of the above processes in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the user's past consultation history data into AI, and the AI ​​can analyze the data and provide optimal advice.

[0101] The consultation department can customize the content of online consultations based on the user's current health status. For example, the consultation department can customize the content based on the user's current health checkup data. For example, the consultation department can customize the content based on the user's current activity level data. For example, the consultation department can customize the content based on the user's current dietary record. By customizing the consultation content based on the user's current health status, more appropriate consultations become possible. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the user's current health status data into AI, and the AI ​​can analyze the data to customize the consultation content.

[0102] The consultation unit can estimate the user's emotions and prioritize online consultations based on the estimated emotions. For example, if the user is feeling stressed, the consultation unit will prioritize their consultation. If the user is relaxed, the consultation unit will accept consultations in the normal order. If the user is in a hurry, the consultation unit will accept their consultation quickly. This allows for more appropriate consultations by prioritizing online consultations 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 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 consultation unit may be performed using AI or not. For example, the consultation unit can input user emotion data into a generative AI, which can estimate emotions and determine the priority of consultations.

[0103] The consultation department can select the most appropriate consultation method during online consultations by considering the user's geographical location. For example, if the user lives at high altitude, the consultation department will consider oxygen saturation. If the user lives in an urban area, the consultation department will consider lack of exercise. If the user lives by the sea, the consultation department will consider relaxation level. By considering the user's geographical location, more appropriate consultations become possible. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the user's geographical location data into AI, which can then analyze the data and select the most appropriate consultation method.

[0104] The consultation department can analyze a user's social media activity during an online consultation and suggest consultation topics. For example, if a user posts about exercise on social media, the consultation department will suggest consultations about exercise. For example, if a user posts about food, the consultation department will suggest consultations about food. For example, if a user posts about stress, the consultation department will suggest consultations about stress management. By analyzing the user's social media activity, the consultation department can suggest more appropriate consultation topics. Some or all of the above processing in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the user's social media activity data into AI, which can then analyze the data and suggest consultation topics.

[0105] The interaction unit can estimate the user's emotions and adjust the way the user interacts with the local community based on those emotions. For example, if the user is stressed, the interaction unit will suggest a relaxing way to interact. If the user is relaxed, the interaction unit will suggest a more proactive way to interact. If the user is excited, the interaction unit will suggest a visually appealing way to interact. By adjusting the way the user interacts with the local community based on their emotions, more appropriate interactions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the interaction unit may be performed using AI, or not using AI. For example, the interaction unit can input user emotion data into a generative AI, which can then estimate the emotion and adjust the way the user interacts.

[0106] The interaction unit can provide the optimal interaction method when interacting with local communities by referring to the user's past interaction history. For example, the interaction unit can suggest relevant interaction methods based on events the user has previously participated in. For example, the interaction unit can select the most effective interaction method from the user's past interaction history. For example, the interaction unit can analyze the user's past interaction history and provide the optimal interaction method. In this way, the optimal interaction method can be provided by referring to the user's past interaction history. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input the user's past interaction history data into AI, and the AI ​​can analyze the data and provide the optimal interaction method.

[0107] The interaction unit can customize the means of interaction with the local community based on the user's current living situation. For example, the interaction unit can suggest an appropriate means of interaction considering the user's current living situation. For example, the interaction unit can provide the optimal method of interaction based on the user's current living situation. For example, the interaction unit can analyze the user's current living situation and customize the optimal means of interaction. This makes it possible to have more appropriate interactions by customizing the means of interaction based on the user's current living situation. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input the user's current living situation data into AI, and the AI ​​can analyze the data and customize the means of interaction.

[0108] The interaction unit can estimate the user's emotions and, based on the estimated emotions, determine the priority of interactions with the local community. For example, if the user is stressed, the interaction unit will prioritize relaxing interactions. If the user is relaxed, the interaction unit will prioritize positive interactions. If the user is excited, the interaction unit will prioritize visually appealing interactions. This allows for more appropriate interactions by prioritizing interactions with the local community 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 interaction unit may be performed using AI or not. For example, the interaction unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of interactions.

[0109] The interaction unit can select the optimal interaction method when interacting with local communities, taking into account the user's geographical location information. For example, if the user lives at high altitude, the interaction unit will suggest an interaction method that takes oxygen saturation into consideration. If the user lives in an urban area, the interaction unit will suggest an interaction method that helps alleviate lack of exercise. If the user lives by the sea, the interaction unit will suggest an interaction method that enhances relaxation. By considering the user's geographical location information, more appropriate interactions become possible. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input the user's geographical location data into AI, which can then analyze the data and select the optimal interaction method.

[0110] The interaction unit can analyze a user's social media activity and suggest interaction methods when interacting with local communities. For example, if a user posts about exercise on social media, the interaction unit will suggest interaction methods related to exercise. For example, if a user posts about food, the interaction unit will suggest interaction methods related to food. For example, if a user posts about stress, the interaction unit will suggest interaction methods related to stress management. In this way, by analyzing the user's social media activity, more appropriate interaction methods can be suggested. Some or all of the above processing in the interaction unit may be performed using AI, for example, or without AI. For example, the interaction unit can input the user's social media activity data into AI, which can then analyze the data and suggest interaction methods.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The health plan proposal system can also collect and analyze user sleep data. The data collection unit can collect data such as the user's sleep duration, sleep quality, and number of times they toss and turn during sleep. The analysis unit can evaluate the user's sleep patterns based on the collected data and suggest areas for improvement. The proposal unit can offer advice on improving the user's sleep quality and suggest methods for creating a suitable sleep environment. This allows the system to utilize user sleep data to provide a more comprehensive health plan.

[0113] The health plan suggestion system can also monitor and analyze the user's stress level. The data collection unit can collect data such as the user's heart rate, blood pressure, and respiratory rate to estimate their stress level. The analysis unit can evaluate the user's stress patterns based on the collected data and provide advice for stress management. The suggestion unit can propose relaxation techniques and exercise plans to reduce stress. This helps manage the user's stress level and supports a healthier lifestyle.

[0114] The health plan suggestion system can further estimate the user's emotions and adjust the health plan based on those emotions. For example, if the user is feeling stressed, the analysis unit can suggest a health plan focused on stress reduction. Similarly, if the user is relaxed, the suggestion unit can suggest a health plan to maintain that relaxation. By adjusting the health plan based on the user's emotions, the system can provide more effective health support.

[0115] The health plan suggestion system can further analyze the user's dietary data in detail and evaluate nutritional balance. The data collection unit can collect, for example, the user's diet, calorie intake, and nutrient balance. The analysis unit can evaluate the user's nutritional status based on the collected data and suggest areas for improvement. The suggestion unit can suggest, for example, a meal plan to improve the user's nutritional balance or recipes to supplement specific nutrients. In this way, the system can utilize the user's dietary data to support a healthier lifestyle.

[0116] The health plan suggestion system can further analyze the user's exercise data in detail and evaluate their exercise habits. The data collection unit can collect data such as the user's exercise time, exercise intensity, and calories burned. The analysis unit can evaluate the user's exercise habits based on the collected data and suggest areas for improvement. The suggestion unit can provide, for example, exercise plans to improve the user's habits or advice on incorporating specific exercises. This allows the system to utilize the user's exercise data to support healthier exercise habits.

[0117] The health plan suggestion system can further estimate the user's emotions and adjust the content of online consultations based on those emotions. For example, if the user is feeling stressed, the consultation department can prioritize consultations on stress management. If the user is relaxed, for example, the consultation department can provide consultations on maintaining good health. By adjusting the content of online consultations based on the user's emotions, the system can provide more appropriate advice.

[0118] The health plan proposal system can further utilize the user's geographical location information to assess region-specific health risks. The data collection unit can collect data such as the climate of the user's place of residence, environmental pollution levels, and access to medical facilities. The analysis unit can evaluate the user's health risks based on the collected data and propose countermeasures for region-specific health risks. The proposal unit can provide, for example, health plans tailored to the user's place of residence and advice to mitigate region-specific health risks. This allows for the provision of more appropriate health support by utilizing the user's geographical location information.

[0119] The health plan suggestion system can further estimate the user's emotions and adjust how they interact with the local community based on those emotions. For example, if the user is feeling stressed, the interaction function can suggest relaxing social events. Conversely, if the user is relaxed, the interaction function can suggest more engaging social events. This allows for more appropriate interactions by adjusting how users interact with the local community based on their emotions.

[0120] The health plan suggestion system can further analyze users' social media activity and utilize this information to suggest health plans. The data collection unit can collect, for example, the content of users' social media posts, the number of likes, and the content of comments. The analysis unit can evaluate users' areas of interest and lifestyle habits based on the collected data and reflect this in the health plan suggestions. The suggestion unit can provide, for example, health plans based on users' areas of interest and advice related to their social media activity. This allows for the provision of more personalized health plans by leveraging users' social media activity.

[0121] The health plan suggestion system can further estimate the user's emotions and prioritize health plans based on those emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggesting a health plan that focuses on stress reduction. For example, if the user is relaxed, the suggestion unit can prioritize suggesting a plan that focuses on overall health maintenance. By prioritizing health plans based on the user's emotions, the system can provide more effective health support.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The data collection unit collects the user's health data. The data collection unit collects, for example, the user's health checkup data, daily activity levels, and meal records. Specifically, it can collect data such as blood pressure, blood sugar levels, and cholesterol levels as health checkup data, steps taken, calories burned, and exercise time as daily activity levels, and calories consumed, nutrient balance, and meal timing as meal records. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the user's health status. The analysis unit uses techniques such as data mining, statistical analysis, and machine learning to analyze the collected data in detail. Specifically, it can use data mining techniques to extract patterns related to the user's health status, statistical analysis techniques to quantify and evaluate the user's health status, and machine learning techniques to predict and evaluate the user's health status. Step 3: The proposal department proposes a health plan based on the evaluation results obtained by the analysis department. The proposal department works with local governments to create health plans that take into account the characteristics of each region. Specifically, this may include proposing meal recipes that utilize local ingredients and introducing walking courses that take advantage of the local natural environment. For example, they could propose healthy recipes using local vegetables and fish, and introduce local parks and nature reserves. Step 4: The consultation department provides online consultations based on the health plan proposed by the proposal department. The consultation department provides online consultation functions with healthcare professionals (doctors, public health nurses, nutritionists, etc.) residing in the local area. Specifically, users can receive expert advice through methods such as video calls, chat, and email consultations. Step 5: The Exchange Department promotes interaction with the local community based on the consultation results provided by the Consultation Department. The Exchange Department promotes interaction with the local community by providing information on health events and volunteer activities held in the area. Specifically, it can provide information on walking events, health seminars, health check events, and volunteer activities such as health consultations, exercise guidance, and dietary guidance.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, consultation unit, and communication 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 user health data using the camera 42 and microphone 38B of the smart device 14 and processes the data with the control unit 46A. The analysis unit is implemented in detail by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in detail by the specific processing unit 290 of the data processing unit 12 and proposes a health plan based on the analysis results. The consultation unit is implemented in detail by the control unit 46A of the smart device 14 and provides an online consultation function. The communication unit is implemented in detail by the control unit 46A of the smart device 14 and promotes interaction with the local community. 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.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, consultation unit, and communication 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 user health data using the camera 42 and microphone 238 of the smart glasses 214 and processes the data with the control unit 46A. The analysis unit is implemented in detail by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in detail by the specific processing unit 290 of the data processing unit 12 and proposes a health plan based on the analysis results. The consultation unit is implemented in detail by the control unit 46A of the smart glasses 214 and provides an online consultation function. The communication unit is implemented in detail by the control unit 46A of the smart glasses 214 and promotes interaction with the local community. 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.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, consultation unit, and communication 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 user health data using the camera 42 and microphone 238 of the headset terminal 314 and processes the data with the control unit 46A. The analysis unit is implemented in detail by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in detail by the specific processing unit 290 of the data processing unit 12 and proposes a health plan based on the analysis results. The consultation unit is implemented in detail by the control unit 46A of the headset terminal 314 and provides an online consultation function. The communication unit is implemented in detail by the control unit 46A of the headset terminal 314 and promotes interaction with the local community. 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.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, consultation unit, and communication unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit collects user health data using the camera 42 and microphone 238 of the robot 414 and processes the data with the control unit 46A. The analysis unit is implemented in detail by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented in detail by the specific processing unit 290 of the data processing unit 12 and proposes a health plan based on the analysis results. The consultation unit is implemented in detail by the control unit 46A of the robot 414 and provides an online consultation function. The communication unit is implemented in detail by the control unit 46A of the robot 414 and promotes interaction with the local community. 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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."

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] (Note 1) A collection unit that collects user health data, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates the user's health status, A proposal unit that proposes a health plan based on the evaluation results obtained by the aforementioned analysis unit, A consultation department that provides online consultations based on the health plan proposed by the aforementioned proposal department, The system includes an exchange department that promotes interaction with local communities based on the consultation results provided by the aforementioned consultation department. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects user health check data, daily activity levels, and dietary records. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed in detail to provide a comprehensive assessment of the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We collaborate with local governments to create health plans that take into account the specific characteristics of each region. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned consultation department, We offer an online consultation service with healthcare professionals residing in rural areas. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned AC unit is We provide information on local health events and volunteer activities, and promote interaction with the local community. 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 health data collection 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 data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting health data, 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 health data 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 data, the system prioritizes collecting highly relevant data by considering 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 data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is The system estimates the user's emotions and adjusts the health assessment method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When analyzing health data, referencing the user's past health status improves the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is When analyzing health data, the evaluation should take into account the user's lifestyle and environmental factors. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the order in which health status assessment results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When analyzing health data, the evaluation should take into account the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is When analyzing health data, referencing relevant medical literature improves the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts how the health plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When proposing a health plan, adjust the level of detail in the proposal based on the importance of the user's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When proposing health plans, different proposal algorithms are applied according to the characteristics of each region. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the health plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When proposing health plans, we prioritize proposals based on when the user submitted them. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When proposing a health plan, the order of suggestions is adjusted based on the user's relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned consultation department, The system estimates the user's emotions and adjusts the online consultation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned consultation department, During online consultations, we refer to the user's past consultation history to provide the most suitable advice. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned consultation department, During online consultations, the consultation content is customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned consultation department, The system estimates the user's emotions and prioritizes online consultations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned consultation department, During online consultations, the system selects the most suitable consultation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned consultation department, During online consultations, we analyze the user's social media activity to suggest consultation topics. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned AC unit is It estimates user emotions and adjusts how users interact with local communities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned AC unit is When interacting with local communities, the system provides the most suitable interaction method by referring to the user's past interaction history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned AC unit is When interacting with local communities, the means of interaction are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned AC unit is It estimates user emotions and prioritizes interactions with local communities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned AC unit is When interacting with local communities, the system selects the most appropriate interaction method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned AC unit is When interacting with local communities, we analyze users' social media activity and suggest ways to engage with them. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 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 collection unit that collects user health data, An analysis unit analyzes the data collected by the aforementioned collection unit and evaluates the user's health status, A proposal unit that proposes a health plan based on the evaluation results obtained by the aforementioned analysis unit, A consultation department that provides online consultations based on the health plan proposed by the aforementioned proposal department, The system includes an exchange department that promotes interaction with local communities based on the consultation results provided by the aforementioned consultation department. A system characterized by the following features.

2. The aforementioned collection unit is It collects user health check data, daily activity levels, and dietary records. The system according to feature 1.

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

4. The aforementioned proposal section is, We collaborate with local governments to create health plans that take into account the specific characteristics of each region. The system according to feature 1.

5. The aforementioned consultation department, We offer an online consultation service with healthcare professionals residing in rural areas. The system according to feature 1.

6. The aforementioned AC unit is We provide information on local health events and volunteer activities, and promote interaction with the local community. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past health data collection history and select the optimal collection method. The system according to feature 1.

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