system

Abstract

The system according to this embodiment aims to predict the risk of lifestyle-related diseases based on health checkup results and medical history, and to propose specific improvement measures. [Solution] The system according to the embodiment comprises a registration unit, a recording unit, an analysis unit, a proposal unit, and a hospital candidate unit. The registration unit registers the user's health checkup results and medical history. The recording unit records lifestyle habits based on the data registered by the registration unit. The analysis unit analyzes the data recorded by the recording unit to determine the risk of lifestyle-related diseases. The proposal unit proposes specific improvement measures based on the risk determined by the analysis unit. The hospital candidate unit selects hospital candidates based on the improvement measures proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the risk of lifestyle diseases has not been sufficiently predicted based on health examination results and medical histories, and specific improvement measures have not been proposed enough, leaving room for improvement.

[0005] The system according to the embodiment aims to predict the risk of lifestyle diseases based on health examination results and medical histories and propose specific improvement measures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a registration unit, a recording unit, an analysis unit, a proposal unit, and a hospital candidate unit. The registration unit registers the user's health checkup results and medical history. The recording unit records lifestyle habits based on the data registered by the registration unit. The analysis unit analyzes the data recorded by the recording unit to determine the risk of lifestyle-related diseases. The proposal unit proposes specific improvement measures based on the risk determined by the analysis unit. The hospital candidate unit selects hospital candidates based on the improvement measures proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can predict the risk of lifestyle-related diseases based on health checkup results and medical history, and propose specific improvement measures. [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, etc. 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 reception 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 reception 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 healthcare service "HealthGuard" according to an embodiment of the present invention is a system that utilizes a generating AI and IoT devices to automatically record and analyze the user's lifestyle habits and share their risk of lifestyle-related diseases simply by registering their health checkup results and medical history. This system focuses on preventive medicine with the aim of maintaining the user's health and reducing medical expenses. For example, the user registers their past health checkup results and medical history. Next, IoT devices automatically record the user's lifestyle habits. For example, a smartwatch records the user's steps and heart rate, and a smart scale measures their weight. This data is transmitted to the generating AI, which analyzes the data to determine the risk of lifestyle-related diseases. The generating AI proposes specific improvement measures based on the user's lifestyle habits. For example, it suggests daily walking to users who are not getting enough exercise, and suggests a balanced meal menu to users with unbalanced diets. Furthermore, if the user falls ill, the generating AI selects and provides potential hospitals to the user. This allows the user to quickly visit an appropriate medical institution. This service not only supports the user's health maintenance but also contributes to reducing medical expenses. By focusing on preventive medicine, early detection and treatment of diseases become possible, preventing them from becoming severe. Furthermore, by suggesting specific improvement measures, users' lifestyles improve, leading to better health. In this way, the HealthGuard system can support users in maintaining their health and reducing medical expenses.

[0029] The HealthGuard system according to this embodiment comprises a registration unit, a recording unit, an analysis unit, a proposal unit, and a hospital candidate unit. The registration unit registers the user's health checkup results and medical history. The registration unit provides, for example, an interface for the user to input the results of past health checkups and medical history. The recording unit records lifestyle habits based on the data registered by the registration unit. The recording unit automatically records data such as the user's steps, heart rate, and weight using, for example, an IoT device such as a smartwatch or smart scale. The analysis unit analyzes the data recorded by the recording unit to determine the risk of lifestyle-related diseases. The analysis unit analyzes the user's lifestyle data using, for example, a generative AI to determine the risk of lifestyle-related diseases with high accuracy. The proposal unit proposes specific improvement measures based on the risk determined by the analysis unit. The proposal unit uses, for example, a generative AI to suggest daily walking to users who are not getting enough exercise and to suggest a balanced meal menu to users with unbalanced diets. The hospital candidate unit selects hospital candidates based on the improvement measures proposed by the proposal unit. The hospital selection department, for example, uses generative AI to select and provide users with appropriate hospitals based on their symptoms and risks. This allows the HealthGuard system to support users in maintaining their health and reducing medical expenses.

[0030] The registration section registers users' health checkup results and medical history. For example, the registration section provides an interface for users to input past health checkup results and medical history. Specifically, users can input numerical data from health checkups and doctor's diagnoses via a web browser or mobile app. This includes basic health indicators such as blood pressure, blood sugar levels, and cholesterol levels, as well as detailed medical history information such as past illnesses and surgical history. Furthermore, the registration section also includes a function for users to upload electronic medical records and diagnostic reports provided by medical institutions, allowing users to register accurate medical information in the system without hassle. Registered data is stored in secure cloud storage and encrypted for privacy protection. This minimizes the risk of users' personal information being leaked to third parties. The registration section also includes a function to send reminders to users to undergo regular health checkups, enabling users to consistently manage their health without neglecting it.

[0031] The recording unit records lifestyle habits based on data registered by the registration unit. The recording unit automatically records data such as the user's steps, heart rate, and weight using IoT devices such as smartwatches and smart scales. Specifically, smartwatches measure the user's daily steps and heart rate in real time, while smart scales periodically measure weight and body fat percentage. These devices transmit data to the HealthGuard system via Bluetooth® or Wi-Fi, and the recording unit centrally manages this data. Furthermore, the recording unit also accepts data manually entered by the user. For example, users can record more detailed lifestyle data by entering information such as meal content, type and duration of exercise, and sleep duration into the app. The recording unit organizes this data daily, weekly, and monthly, and also features graphs and charts to help users understand their lifestyle trends. This allows users to visually review their lifestyle habits and easily identify areas for improvement.

[0032] The analysis unit analyzes the data recorded by the recording unit to determine the risk of lifestyle-related diseases. For example, the analysis unit uses generative AI to analyze the user's lifestyle data and determine the risk of lifestyle-related diseases with high accuracy. Specifically, the generative AI comprehensively analyzes data such as the user's steps, heart rate, weight, diet, and sleep duration to evaluate the risk of lifestyle-related diseases. The generative AI has learned from a vast amount of historical datasets and identifies high-risk patterns by comparing them with the user's data. For example, if a user is sedentary or consumes a high-calorie diet, the generative AI detects this and determines that the risk of lifestyle-related diseases is increasing. Furthermore, the generative AI also takes into account the user's age, gender, and genetic factors in its risk assessment, enabling risk assessment optimized for each individual user. The analysis unit notifies the user of the risk assessment results and shows the degree of risk using specific numbers and graphs. This allows the user to accurately understand their health status and take necessary measures.

[0033] The proposal department proposes specific improvement measures based on the risks determined by the analysis department. For example, using generative AI, the proposal department suggests daily walking to users who are sedentary and balanced meal plans to users with unbalanced diets. Specifically, the generative AI generates individually optimized improvement measures based on the user's lifestyle data and risk assessment results. For example, for users determined to be sedentary, it suggests daily walking or going to the gym a few times a week, and provides specific exercise menus and target steps. For users with unbalanced diets, it suggests nutritionally balanced meal plans, providing specific recipes and guidance on ingredient selection. Furthermore, because the proposal department proposes improvement measures that can be easily implemented according to the user's lifestyle and preferences, users can work on improving their health without stress. The proposal department also has a function to regularly review its suggestions and update improvement measures based on user feedback. This allows the proposal department to always provide the latest information and optimal improvement measures tailored to the user's situation.

[0034] The Hospital Candidate Department selects hospital candidates based on improvement measures proposed by the Proposal Department. For example, using a generative AI, the Hospital Candidate Department selects and provides appropriate hospitals to users based on their symptoms and risks. Specifically, the generative AI lists hospitals and clinics with specialists based on the user's health data and proposed improvement measures. For instance, it suggests hospitals with internal medicine departments or nutritional guidance for users at high risk of lifestyle-related diseases, and orthopedic clinics or rehabilitation facilities for users with muscle or joint problems due to lack of exercise. The Hospital Candidate Department provides information such as hospital location, operating hours, and specialties to help users choose the most suitable hospital. Furthermore, the Hospital Candidate Department prioritizes hospitals that are easily accessible based on the user's place of residence and commute route. This allows users to find hospitals they can comfortably visit. In addition, the Hospital Candidate Department manages the user's appointment status and medical history, and provides support for smoothly scheduling follow-up appointments. In this way, the Hospital Candidate Department comprehensively supports users' health management and facilitates the use of medical services.

[0035] The registration unit can analyze the user's past health check results and medical history to select the optimal registration method. For example, the registration unit can analyze the data format previously entered by the user and suggest registration in the same format. It can also analyze the user's past registration frequency and suggest the optimal registration timing. Furthermore, the registration unit can analyze the user's past health check results and prioritize the registration of important data. In this way, by analyzing the user's past data, the optimal registration method is selected, and efficient data registration is achieved. Some or all of the above processing in the registration unit may be performed using AI, for example, or without using AI.

[0036] The registration unit can filter data based on the user's current health status and areas of interest when registering health checkup results or medical history. For example, the registration unit can analyze the user's current health status and register only relevant data. The registration unit can also prioritize the registration of important data based on the user's areas of interest. Furthermore, the registration unit can exclude unnecessary data based on the user's health status and areas of interest. This allows for the priority registration of highly relevant data by filtering data based on the user's current health status and areas of interest. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI.

[0037] The recording unit can record the user's lifestyle habits using IoT devices such as smartwatches and smart scales. For example, the recording unit can record the user's steps and heart rate using a smartwatch. It can also measure the user's weight using a smart scale. Furthermore, the recording unit can record the user's diet and sleep data using a smartphone app. This allows for the automatic recording of the user's lifestyle habits using IoT devices, improving the accuracy and efficiency of the data. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI.

[0038] The analysis unit can use generative AI to analyze data recorded by the recording unit and determine the risk of lifestyle-related diseases. For example, the analysis unit uses generative AI to analyze data such as the user's steps, heart rate, weight, diet, and sleep to determine the risk of lifestyle-related diseases with high accuracy. The generative AI analyzes the user's lifestyle data using deep learning models and natural language generation technology. As a result, by using generative AI, the risk of lifestyle-related diseases can be determined with high accuracy, and appropriate advice can be provided to the user.

[0039] The proposal unit can use generative AI to suggest specific improvement measures based on the risks determined by the analysis unit. For example, the proposal unit can use generative AI to suggest daily walking to users who are not getting enough exercise, and suggest a balanced meal plan to users with unbalanced diets. The generative AI uses an algorithm that analyzes the user's lifestyle data and suggests the optimal improvement measures. In this way, by using generative AI, the system can suggest specific and effective improvement measures to users and support them in maintaining their health.

[0040] The hospital candidate department can use generative AI to select hospital candidates based on improvement measures proposed by the proposal department. For example, the hospital candidate department can use generative AI to select and provide appropriate hospitals to users based on their symptoms and risks. The generative AI uses an algorithm that selects the optimal hospital based on information such as the presence of specialists and facilities at each hospital. In this way, by using generative AI, appropriate hospital candidates can be quickly provided to users, supporting them in selecting medical institutions.

[0041] The recording unit can analyze the user's past lifestyle data and select the optimal recording method. For example, the recording unit can analyze the user's past recording method and suggest the same method. It can also analyze the user's past recording frequency and suggest the optimal recording timing. Furthermore, the recording unit can analyze the user's past lifestyle data and prioritize recording important data. In this way, by analyzing the user's past data, the optimal recording method is selected, and efficient data recording is achieved. Some or all of the above processing in the recording unit may be performed using AI, for example, or without using AI.

[0042] The recording unit can filter data based on the user's current health status and areas of interest when recording lifestyle habits. For example, the recording unit can analyze the user's current health status and record only relevant data. The recording unit can also prioritize recording important data based on the user's areas of interest. Furthermore, the recording unit can exclude unnecessary data based on the user's health status and areas of interest. This allows for the priority recording of highly relevant data by filtering data based on the user's current health status and areas of interest. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI.

[0043] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of lifestyle data. For example, the analysis unit can correlate and analyze data on diet and exercise. It can also correlate and analyze data on sleep and stress. Furthermore, it can correlate and analyze data on weight and activity levels. By considering the interrelationships of lifestyle data, the analysis accuracy is improved, and a more accurate risk assessment is made. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without using generative AI.

[0044] The analysis unit can perform analysis while considering the user's attribute information. For example, the analysis unit can perform analysis while considering the user's age and gender. It can also perform analysis while considering the user's occupation and lifestyle. Furthermore, the analysis unit can perform analysis while considering the user's medical history and family history. By considering the user's attribute information, it is possible to perform individually tailored analysis and make a more accurate risk assessment. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI.

[0045] The proposal unit can adjust the level of detail of its proposals based on the severity of the risk. For example, in the case of high risk, the proposal unit will propose detailed improvement measures. In the case of medium risk, the proposal unit may also propose specific improvement measures. Furthermore, in the case of low risk, the proposal unit may also propose simple improvement measures. In this way, by adjusting the level of detail of the proposals according to the severity of the risk, the proposal unit provides appropriate improvement measures to the user. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI.

[0046] The suggestion unit can apply different suggestion algorithms depending on the risk category. For example, in the case of a risk related to diet, the suggestion unit can apply a meal menu suggestion algorithm. It can also apply an exercise plan suggestion algorithm in the case of a risk related to lack of exercise. Furthermore, it can apply a relaxation method suggestion algorithm in the case of a stress risk. By applying the suggestion algorithm according to the risk category, the suggestion unit provides the user with the most suitable improvement solution. Some or all of the processing described above in the suggestion unit may be performed using, for example, generative AI, or without using generative AI.

[0047] The hospital candidate department can select the most suitable hospital by analyzing the user's past medical history when selecting potential hospitals. For example, the hospital candidate department can select a hospital with specialists based on the user's past medical history. It can also analyze the user's past treatment history and select a hospital where the same treatment can be received. Furthermore, the hospital candidate department can select an appropriate medical institution by considering the user's past medical history. In this way, by analyzing the user's past medical history, the optimal hospital is selected and appropriate medical care is provided. Some or all of the above processing in the hospital candidate department may be performed using, for example, generative AI, or without using generative AI.

[0048] The hospital candidate department can customize hospital candidates based on the user's current health condition when selecting hospital candidates. For example, the hospital candidate department can analyze the user's current health condition and select hospitals with specialists. It can also select hospitals with appropriate medical departments based on the user's current symptoms. Furthermore, the hospital candidate department can select hospitals that can respond quickly, taking into account the user's current health condition. In this way, by customizing hospital candidates based on the user's current health condition, an appropriate medical institution is selected. Some or all of the above processing in the hospital candidate department may be performed using, for example, generative AI, or without generative AI.

[0049] The hospital candidate department can select the most suitable hospital by considering the user's geographical location when selecting potential hospitals. For example, the hospital candidate department can select the nearest hospital based on the user's current location. It can also analyze the user's geographical location and select a hospital that is easily accessible. Furthermore, the hospital candidate department can consider the user's geographical location and select a hospital that can make the most use of local medical resources. In this way, by considering the user's geographical location, it selects the most accessible and optimal hospital. Some or all of the above processing in the hospital candidate department may be performed using, for example, generative AI, or without using generative AI.

[0050] The hospital candidate department can analyze a user's social media activity and propose hospital candidates when selecting hospital candidates. For example, the hospital candidate department can analyze a user's social media activity and propose hospitals recommended by friends and followers. The hospital candidate department can also propose appropriate hospitals based on the user's health-related posts on social media. Furthermore, the hospital candidate department can identify medical institutions of interest from the user's social media activity and propose hospital candidates. In this way, by analyzing the user's social media activity, it proposes hospitals of interest to the user. Some or all of the above processing in the hospital candidate department may be performed using, for example, generative AI, or without generative AI.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The HealthGuard system can be equipped with a registration unit that analyzes the user's past health checkup results and medical history to select the optimal registration method. For example, it can analyze the data format the user has entered in the past and suggest registration in the same format. It can also analyze the user's past registration frequency and suggest the optimal registration timing. Furthermore, it can analyze the user's past health checkup results and prioritize the registration of important data. In this way, by analyzing the user's past data, the system can select the optimal registration method and achieve efficient data registration.

[0053] The HealthGuard system can include a registration unit that filters data based on the user's current health status and areas of interest. For example, it can analyze the user's current health status and register only relevant data. It can also prioritize the registration of important data based on the user's areas of interest. Furthermore, it can exclude unnecessary data based on the user's health status and areas of interest. This allows for the priority registration of highly relevant data by filtering data based on the user's current health status and areas of interest.

[0054] The HealthGuard system can be equipped with a recording unit that analyzes the user's past lifestyle data and selects the optimal recording method. For example, it can analyze the user's past recording methods and suggest the same method again. It can also analyze the user's past recording frequency and suggest the optimal recording timing. Furthermore, it can analyze the user's past lifestyle data and prioritize recording important data. In this way, by analyzing the user's past data, the system can select the optimal recording method and achieve efficient data recording.

[0055] The HealthGuard system can be equipped with an analysis unit that improves the accuracy of analysis by considering the interrelationships of lifestyle data. For example, it can analyze data on diet and exercise in relation to each other. It can also analyze data on sleep and stress in relation to each other. Furthermore, it can analyze data on weight and activity levels in relation to each other. By considering the interrelationships of lifestyle data, the accuracy of the analysis can be improved, and a more accurate risk assessment can be made.

[0056] The HealthGuard system can be equipped with an analysis unit that performs analysis while considering the user's attribute information. For example, it can perform analysis while considering the user's age and gender. It can also perform analysis while considering the user's occupation and lifestyle. Furthermore, it can perform analysis while considering the user's medical history and family history. By considering the user's attribute information, it is possible to perform analysis tailored to each individual and make a more accurate risk assessment.

[0057] The HealthGuard system can include a suggestion section that adjusts the level of detail of suggestions based on the severity of the risk. For example, in the case of high risk, detailed improvement measures can be suggested. In the case of medium risk, specific improvement measures can be suggested. Furthermore, in the case of low risk, simple improvement measures can be suggested. This allows the system to provide users with appropriate improvement measures by adjusting the level of detail of suggestions according to the severity of the risk.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The registration unit registers the user's health check results and medical history. The registration unit provides an interface for users to input, for example, the results of past health checks and their medical history. Step 2: The recording unit records lifestyle habits based on the data registered by the registration unit. The recording unit automatically records data such as the user's steps, heart rate, and weight using IoT devices such as smartwatches and smart scales. Step 3: The analysis unit analyzes the data recorded by the recording unit to determine the risk of lifestyle-related diseases. The analysis unit, for example, uses a generation AI to analyze the user's lifestyle data and determine the risk of lifestyle-related diseases with high accuracy. Step 4: The proposal department proposes specific improvement measures based on the risks determined by the analysis department. For example, the proposal department might use generative AI to suggest daily walking to users who are not getting enough exercise, or suggest a balanced meal plan to users with unbalanced diets. Step 5: The hospital candidate department selects hospital candidates based on the improvement measures proposed by the proposal department. The hospital candidate department uses, for example, generative AI to select appropriate hospitals according to the user's symptoms and risks, and provides them to the user.

[0060] (Example of form 2) The healthcare service "HealthGuard" according to an embodiment of the present invention is a system that utilizes a generating AI and IoT devices to automatically record and analyze the user's lifestyle habits and share their risk of lifestyle-related diseases simply by registering their health checkup results and medical history. This system focuses on preventive medicine with the aim of maintaining the user's health and reducing medical expenses. For example, the user registers their past health checkup results and medical history. Next, IoT devices automatically record the user's lifestyle habits. For example, a smartwatch records the user's steps and heart rate, and a smart scale measures their weight. This data is transmitted to the generating AI, which analyzes the data to determine the risk of lifestyle-related diseases. The generating AI proposes specific improvement measures based on the user's lifestyle habits. For example, it suggests daily walking to users who are not getting enough exercise, and suggests a balanced meal menu to users with unbalanced diets. Furthermore, if the user falls ill, the generating AI selects and provides potential hospitals to the user. This allows the user to quickly visit an appropriate medical institution. This service not only supports the user's health maintenance but also contributes to reducing medical expenses. By focusing on preventive medicine, early detection and treatment of diseases become possible, preventing them from becoming severe. Furthermore, by suggesting specific improvement measures, users' lifestyles improve, leading to better health. In this way, the HealthGuard system can support users in maintaining their health and reducing medical expenses.

[0061] The HealthGuard system according to this embodiment comprises a registration unit, a recording unit, an analysis unit, a proposal unit, and a hospital candidate unit. The registration unit registers the user's health checkup results and medical history. The registration unit provides, for example, an interface for the user to input the results of past health checkups and medical history. The recording unit records lifestyle habits based on the data registered by the registration unit. The recording unit automatically records data such as the user's steps, heart rate, and weight using, for example, an IoT device such as a smartwatch or smart scale. The analysis unit analyzes the data recorded by the recording unit to determine the risk of lifestyle-related diseases. The analysis unit analyzes the user's lifestyle data using, for example, a generative AI to determine the risk of lifestyle-related diseases with high accuracy. The proposal unit proposes specific improvement measures based on the risk determined by the analysis unit. The proposal unit uses, for example, a generative AI to suggest daily walking to users who are not getting enough exercise and to suggest a balanced meal menu to users with unbalanced diets. The hospital candidate unit selects hospital candidates based on the improvement measures proposed by the proposal unit. The hospital selection department, for example, uses generative AI to select and provide users with appropriate hospitals based on their symptoms and risks. This allows the HealthGuard system to support users in maintaining their health and reducing medical expenses.

[0062] The registration section registers users' health checkup results and medical history. For example, the registration section provides an interface for users to input past health checkup results and medical history. Specifically, users can input numerical data from health checkups and doctor's diagnoses via a web browser or mobile app. This includes basic health indicators such as blood pressure, blood sugar levels, and cholesterol levels, as well as detailed medical history information such as past illnesses and surgical history. Furthermore, the registration section also includes a function for users to upload electronic medical records and diagnostic reports provided by medical institutions, allowing users to register accurate medical information in the system without hassle. Registered data is stored in secure cloud storage and encrypted for privacy protection. This minimizes the risk of users' personal information being leaked to third parties. The registration section also includes a function to send reminders to users to undergo regular health checkups, enabling users to consistently manage their health without neglecting it.

[0063] The recording unit records lifestyle habits based on data registered by the registration unit. The recording unit automatically records data such as the user's steps, heart rate, and weight using IoT devices such as smartwatches and smart scales. Specifically, the smartwatch measures the user's daily steps and heart rate in real time, while the smart scale periodically measures weight and body fat percentage. These devices transmit data to the HealthGuard system via Bluetooth or Wi-Fi, and the recording unit centrally manages this data. Furthermore, the recording unit also accepts data manually entered by the user. For example, users can record more detailed lifestyle data by entering information such as their meals, type and duration of exercise, and sleep duration into the app. The recording unit organizes this data daily, weekly, and monthly, and also features graphs and charts to help users understand their lifestyle trends. This allows users to visually review their lifestyle habits and easily identify areas for improvement.

[0064] The analysis unit analyzes the data recorded by the recording unit to determine the risk of lifestyle-related diseases. For example, the analysis unit uses generative AI to analyze the user's lifestyle data and determine the risk of lifestyle-related diseases with high accuracy. Specifically, the generative AI comprehensively analyzes data such as the user's steps, heart rate, weight, diet, and sleep duration to evaluate the risk of lifestyle-related diseases. The generative AI has learned from a vast amount of historical datasets and identifies high-risk patterns by comparing them with the user's data. For example, if a user is sedentary or consumes a high-calorie diet, the generative AI detects this and determines that the risk of lifestyle-related diseases is increasing. Furthermore, the generative AI also takes into account the user's age, gender, and genetic factors in its risk assessment, enabling risk assessment optimized for each individual user. The analysis unit notifies the user of the risk assessment results and shows the degree of risk using specific numbers and graphs. This allows the user to accurately understand their health status and take necessary measures.

[0065] The proposal department proposes specific improvement measures based on the risks determined by the analysis department. For example, using generative AI, the proposal department suggests daily walking to users who are sedentary and balanced meal plans to users with unbalanced diets. Specifically, the generative AI generates individually optimized improvement measures based on the user's lifestyle data and risk assessment results. For example, for users determined to be sedentary, it suggests daily walking or going to the gym a few times a week, and provides specific exercise menus and target steps. For users with unbalanced diets, it suggests nutritionally balanced meal plans, providing specific recipes and guidance on ingredient selection. Furthermore, because the proposal department proposes improvement measures that can be easily implemented according to the user's lifestyle and preferences, users can work on improving their health without stress. The proposal department also has a function to regularly review its suggestions and update improvement measures based on user feedback. This allows the proposal department to always provide the latest information and optimal improvement measures tailored to the user's situation.

[0066] The Hospital Candidate Department selects hospital candidates based on improvement measures proposed by the Proposal Department. For example, using a generative AI, the Hospital Candidate Department selects and provides appropriate hospitals to users based on their symptoms and risks. Specifically, the generative AI lists hospitals and clinics with specialists based on the user's health data and proposed improvement measures. For instance, it suggests hospitals with internal medicine departments or nutritional guidance for users at high risk of lifestyle-related diseases, and orthopedic clinics or rehabilitation facilities for users with muscle or joint problems due to lack of exercise. The Hospital Candidate Department provides information such as hospital location, operating hours, and specialties to help users choose the most suitable hospital. Furthermore, the Hospital Candidate Department prioritizes hospitals that are easily accessible based on the user's place of residence and commute route. This allows users to find hospitals they can comfortably visit. In addition, the Hospital Candidate Department manages the user's appointment status and medical history, and provides support for smoothly scheduling follow-up appointments. In this way, the Hospital Candidate Department comprehensively supports users' health management and facilitates the use of medical services.

[0067] The registration unit can estimate the user's emotions and adjust the timing of registering health check results and medical history based on the estimated emotions. For example, if the user is feeling stressed, the registration unit will prompt them to register at a time when they can relax. It can also prompt the user to register when they are relaxed. Furthermore, if the user is busy, the registration unit can find a free time to prompt them to register. This reduces the user's burden and promotes registration at the appropriate time by adjusting the registration timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0068] The registration unit can analyze the user's past health check results and medical history to select the optimal registration method. For example, the registration unit can analyze the data format previously entered by the user and suggest registration in the same format. It can also analyze the user's past registration frequency and suggest the optimal registration timing. Furthermore, the registration unit can analyze the user's past health check results and prioritize the registration of important data. In this way, by analyzing the user's past data, the optimal registration method is selected, and efficient data registration is achieved. Some or all of the above processing in the registration unit may be performed using AI, for example, or without using AI.

[0069] The registration unit can filter data based on the user's current health status and areas of interest when registering health checkup results or medical history. For example, the registration unit can analyze the user's current health status and register only relevant data. The registration unit can also prioritize the registration of important data based on the user's areas of interest. Furthermore, the registration unit can exclude unnecessary data based on the user's health status and areas of interest. This allows for the priority registration of highly relevant data by filtering data based on the user's current health status and areas of interest. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI.

[0070] The recording unit can record the user's lifestyle habits using IoT devices such as smartwatches and smart scales. For example, the recording unit can record the user's steps and heart rate using a smartwatch. It can also measure the user's weight using a smart scale. Furthermore, the recording unit can record the user's diet and sleep data using a smartphone app. This allows for the automatic recording of the user's lifestyle habits using IoT devices, improving the accuracy and efficiency of the data. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI.

[0071] The analysis unit can use generative AI to analyze data recorded by the recording unit and determine the risk of lifestyle-related diseases. For example, the analysis unit uses generative AI to analyze data such as the user's steps, heart rate, weight, diet, and sleep to determine the risk of lifestyle-related diseases with high accuracy. The generative AI analyzes the user's lifestyle data using deep learning models and natural language generation technology. As a result, by using generative AI, the risk of lifestyle-related diseases can be determined with high accuracy, and appropriate advice can be provided to the user.

[0072] The proposal unit can use generative AI to suggest specific improvement measures based on the risks determined by the analysis unit. For example, the proposal unit can use generative AI to suggest daily walking to users who are not getting enough exercise, and suggest a balanced meal plan to users with unbalanced diets. The generative AI uses an algorithm that analyzes the user's lifestyle data and suggests the optimal improvement measures. In this way, by using generative AI, the system can suggest specific and effective improvement measures to users and support them in maintaining their health.

[0073] The hospital candidate department can use generative AI to select hospital candidates based on improvement measures proposed by the proposal department. For example, the hospital candidate department can use generative AI to select and provide appropriate hospitals to users based on their symptoms and risks. The generative AI uses an algorithm that selects the optimal hospital based on information such as the presence of specialists and facilities at each hospital. In this way, by using generative AI, appropriate hospital candidates can be quickly provided to users, supporting them in selecting medical institutions.

[0074] The recording unit can estimate the user's emotions and adjust the method of recording lifestyle habits based on the estimated emotions. For example, if the user is feeling stressed, the recording unit can suggest a simple recording method. If the user is relaxed, the recording unit can also suggest a more detailed recording method. Furthermore, if the user is busy, the recording unit can suggest a method that allows for quick recording. By adjusting the recording method according to the user's emotions, the system reduces the user's burden and promotes appropriate recording. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The recording unit can analyze the user's past lifestyle data and select the optimal recording method. For example, the recording unit can analyze the user's past recording method and suggest the same method. It can also analyze the user's past recording frequency and suggest the optimal recording timing. Furthermore, the recording unit can analyze the user's past lifestyle data and prioritize recording important data. In this way, by analyzing the user's past data, the optimal recording method is selected, and efficient data recording is achieved. Some or all of the above processing in the recording unit may be performed using AI, for example, or without using AI.

[0076] The recording unit can filter data based on the user's current health status and areas of interest when recording lifestyle habits. For example, the recording unit can analyze the user's current health status and record only relevant data. The recording unit can also prioritize recording important data based on the user's areas of interest. Furthermore, the recording unit can exclude unnecessary data based on the user's health status and areas of interest. This allows for the priority recording of highly relevant data by filtering data based on the user's current health status and areas of interest. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI.

[0077] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit will focus on stress reduction. If the user is relaxed, the analysis unit can focus on overall health. Furthermore, if the user is busy, the analysis unit can focus on important data. By adjusting the analysis criteria according to the user's emotions, the system provides analysis results that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of lifestyle data. For example, the analysis unit can correlate and analyze data on diet and exercise. It can also correlate and analyze data on sleep and stress. Furthermore, it can correlate and analyze data on weight and activity levels. By considering the interrelationships of lifestyle data, the analysis accuracy is improved, and a more accurate risk assessment is made. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without using generative AI.

[0079] The analysis unit can perform analysis while considering the user's attribute information. For example, the analysis unit can perform analysis while considering the user's age and gender. It can also perform analysis while considering the user's occupation and lifestyle. Furthermore, the analysis unit can perform analysis while considering the user's medical history and family history. By considering the user's attribute information, it is possible to perform individually tailored analysis and make a more accurate risk assessment. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without using a generative AI.

[0080] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion function will present suggestions in gentle language. If the user is relaxed, the suggestion function can also present suggestions with detailed explanations. Furthermore, if the user is in a hurry, the suggestion function can present concise and to-the-point suggestions. By adjusting the way suggestions are presented according to the user's emotions, the system can provide suggestions that are more acceptable to the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The proposal unit can adjust the level of detail of its proposals based on the severity of the risk. For example, in the case of high risk, the proposal unit will propose detailed improvement measures. In the case of medium risk, the proposal unit may also propose specific improvement measures. Furthermore, in the case of low risk, the proposal unit may also propose simple improvement measures. In this way, by adjusting the level of detail of the proposals according to the severity of the risk, the proposal unit provides appropriate improvement measures to the user. Some or all of the above processing in the proposal unit may be performed using, for example, a generative AI, or without using a generative AI.

[0082] The suggestion unit can apply different suggestion algorithms depending on the risk category. For example, in the case of a risk related to diet, the suggestion unit can apply a meal menu suggestion algorithm. It can also apply an exercise plan suggestion algorithm in the case of a risk related to lack of exercise. Furthermore, it can apply a relaxation method suggestion algorithm in the case of a stress risk. By applying the suggestion algorithm according to the risk category, the suggestion unit provides the user with the most suitable improvement solution. Some or all of the processing described above in the suggestion unit may be performed using, for example, generative AI, or without using generative AI.

[0083] The hospital selection system can estimate the user's emotions and adjust its hospital selection method based on those emotions. For example, if the user is stressed, the system will prioritize hospitals that offer a relaxing environment. If the user is relaxed, the system can also select hospitals that provide detailed information. Furthermore, if the user is in a hurry, the system can select hospitals that can respond quickly. By adjusting the hospital selection method according to the user's emotions, the system can select the most suitable hospital for the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The hospital candidate department can select the most suitable hospital by analyzing the user's past medical history when selecting potential hospitals. For example, the hospital candidate department can select a hospital with specialists based on the user's past medical history. It can also analyze the user's past treatment history and select a hospital where the same treatment can be received. Furthermore, the hospital candidate department can select an appropriate medical institution by considering the user's past medical history. In this way, by analyzing the user's past medical history, the optimal hospital is selected and appropriate medical care is provided. Some or all of the above processing in the hospital candidate department may be performed using, for example, generative AI, or without using generative AI.

[0085] The hospital candidate department can customize hospital candidates based on the user's current health condition when selecting hospital candidates. For example, the hospital candidate department can analyze the user's current health condition and select hospitals with specialists. It can also select hospitals with appropriate medical departments based on the user's current symptoms. Furthermore, the hospital candidate department can select hospitals that can respond quickly, taking into account the user's current health condition. In this way, by customizing hospital candidates based on the user's current health condition, an appropriate medical institution is selected. Some or all of the above processing in the hospital candidate department may be performed using, for example, generative AI, or without generative AI.

[0086] The hospital recommendation system can estimate the user's emotions and prioritize hospital candidates based on those emotions. For example, if the user is stressed, the system will prioritize suggesting hospitals that offer a relaxing environment. If the user is relaxed, the system can also prioritize suggesting hospitals that provide detailed information. Furthermore, if the user is in a hurry, the system can prioritize suggesting hospitals that can respond quickly. By prioritizing hospital candidates according to the user's emotions, the system can suggest the most suitable hospital for the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The hospital candidate department can select the most suitable hospital by considering the user's geographical location when selecting potential hospitals. For example, the hospital candidate department can select the nearest hospital based on the user's current location. It can also analyze the user's geographical location and select a hospital that is easily accessible. Furthermore, the hospital candidate department can consider the user's geographical location and select a hospital that can make the most use of local medical resources. In this way, by considering the user's geographical location, it selects the most accessible and optimal hospital. Some or all of the above processing in the hospital candidate department may be performed using, for example, generative AI, or without using generative AI.

[0088] The hospital candidate department can analyze a user's social media activity and propose hospital candidates when selecting hospital candidates. For example, the hospital candidate department can analyze a user's social media activity and propose hospitals recommended by friends and followers. The hospital candidate department can also propose appropriate hospitals based on the user's health-related posts on social media. Furthermore, the hospital candidate department can identify medical institutions of interest from the user's social media activity and propose hospital candidates. In this way, by analyzing the user's social media activity, it proposes hospitals of interest to the user. Some or all of the above processing in the hospital candidate department may be performed using, for example, generative AI, or without generative AI.

[0089] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0090] The HealthGuard system can also include a registration unit that estimates the user's emotions and adjusts the timing of registering health check results and medical history based on the estimated emotions. For example, if the user is feeling stressed, it can prompt them to register at a time when they can relax. Similarly, if the user is relaxed, it can prompt them to register at that time. Furthermore, if the user is busy, it can prompt them to register when they have free time. This reduces the user's burden and promotes registration at the appropriate time by adjusting the registration timing according to the user's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI.

[0091] The HealthGuard system can be equipped with a registration unit that analyzes the user's past health checkup results and medical history to select the optimal registration method. For example, it can analyze the data format the user has entered in the past and suggest registration in the same format. It can also analyze the user's past registration frequency and suggest the optimal registration timing. Furthermore, it can analyze the user's past health checkup results and prioritize the registration of important data. In this way, by analyzing the user's past data, the system can select the optimal registration method and achieve efficient data registration.

[0092] The HealthGuard system can include a registration unit that filters data based on the user's current health status and areas of interest. For example, it can analyze the user's current health status and register only relevant data. It can also prioritize the registration of important data based on the user's areas of interest. Furthermore, it can exclude unnecessary data based on the user's health status and areas of interest. This allows for the priority registration of highly relevant data by filtering data based on the user's current health status and areas of interest.

[0093] The HealthGuard system may include a recording unit that estimates the user's emotions and adjusts the method of recording lifestyle habits based on those emotions. For example, if the user is feeling stressed, it can suggest a simple recording method. If the user is relaxed, it can suggest a more detailed recording method. Furthermore, if the user is busy, it can suggest a method that allows for quick recording. By adjusting the recording method according to the user's emotions, it can reduce the user's burden and promote appropriate recording. Emotion estimation can be achieved, for example, using an emotion engine or generative AI.

[0094] The HealthGuard system can be equipped with a recording unit that analyzes the user's past lifestyle data and selects the optimal recording method. For example, it can analyze the user's past recording methods and suggest the same method again. It can also analyze the user's past recording frequency and suggest the optimal recording timing. Furthermore, it can analyze the user's past lifestyle data and prioritize recording important data. In this way, by analyzing the user's past data, the system can select the optimal recording method and achieve efficient data recording.

[0095] The HealthGuard system can include an analysis unit that estimates the user's emotions and adjusts the analysis criteria based on those emotions. For example, if the user is stressed, the analysis can focus on stress reduction. If the user is relaxed, the analysis can focus on overall health. Furthermore, if the user is busy, the analysis can be narrowed down to important data. This allows the system to provide analysis results tailored to the user by adjusting the analysis criteria according to their emotions. Emotion estimation can be achieved, for example, using an emotion engine or generative AI.

[0096] The HealthGuard system can be equipped with an analysis unit that improves the accuracy of analysis by considering the interrelationships of lifestyle data. For example, it can analyze data on diet and exercise in relation to each other. It can also analyze data on sleep and stress in relation to each other. Furthermore, it can analyze data on weight and activity levels in relation to each other. By considering the interrelationships of lifestyle data, the accuracy of the analysis can be improved, and a more accurate risk assessment can be made.

[0097] The HealthGuard system can be equipped with an analysis unit that performs analysis while considering the user's attribute information. For example, it can perform analysis while considering the user's age and gender. It can also perform analysis while considering the user's occupation and lifestyle. Furthermore, it can perform analysis while considering the user's medical history and family history. By considering the user's attribute information, it is possible to perform analysis tailored to each individual and make a more accurate risk assessment.

[0098] The HealthGuard system may include a suggestion unit that estimates the user's emotions and adjusts the way suggestions are presented based on those emotions. For example, if the user is stressed, the system can offer suggestions in gentle language. If the user is relaxed, it can offer suggestions with detailed explanations. Furthermore, if the user is in a hurry, it can offer concise and to-the-point suggestions. By adjusting the way suggestions are presented according to the user's emotions, the system can offer suggestions that are more easily accepted by the user. Emotion estimation can be achieved, for example, using an emotion engine or generative AI.

[0099] The HealthGuard system can include a suggestion section that adjusts the level of detail of suggestions based on the severity of the risk. For example, in the case of high risk, detailed improvement measures can be suggested. In the case of medium risk, specific improvement measures can be suggested. Furthermore, in the case of low risk, simple improvement measures can be suggested. This allows the system to provide users with appropriate improvement measures by adjusting the level of detail of suggestions according to the severity of the risk.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The registration unit registers the user's health check results and medical history. The registration unit provides an interface for users to input, for example, the results of past health checks and their medical history. Step 2: The recording unit records lifestyle habits based on the data registered by the registration unit. The recording unit automatically records data such as the user's steps, heart rate, and weight using IoT devices such as smartwatches and smart scales. Step 3: The analysis unit analyzes the data recorded by the recording unit to determine the risk of lifestyle-related diseases. The analysis unit, for example, uses a generation AI to analyze the user's lifestyle data and determine the risk of lifestyle-related diseases with high accuracy. Step 4: The proposal department proposes specific improvement measures based on the risks determined by the analysis department. For example, the proposal department might use generative AI to suggest daily walking to users who are not getting enough exercise, or suggest a balanced meal plan to users with unbalanced diets. Step 5: The hospital candidate department selects hospital candidates based on the improvement measures proposed by the proposal department. The hospital candidate department uses, for example, generative AI to select appropriate hospitals according to the user's symptoms and risks, and provides them to the user.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] Each of the multiple elements described above, including the registration unit, recording unit, analysis unit, proposal unit, and hospital candidate unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14 and provides an interface for inputting the user's health checkup results and medical history. The recording unit is implemented by the control unit 46A of the smart device 14 and automatically records data such as the user's steps, heart rate, and weight using IoT devices such as smartwatches and smart scales. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's lifestyle data using generating AI to determine the risk of lifestyle-related diseases. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes specific improvement measures using generating AI. The hospital candidate unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects an appropriate hospital using generating AI and provides it to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the registration unit, recording unit, analysis unit, proposal unit, and hospital candidate unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for inputting the user's health checkup results and medical history. The recording unit is implemented by the control unit 46A of the smart glasses 214 and automatically records data such as the user's steps, heart rate, and weight using IoT devices such as smartwatches and smart scales. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's lifestyle data using generating AI to determine the risk of lifestyle-related diseases. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes specific improvement measures using generating AI. The hospital candidate unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects an appropriate hospital using generating AI and provides it to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the registration unit, recording unit, analysis unit, proposal unit, and hospital candidate unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for inputting the user's health checkup results and medical history. The recording unit is implemented by the control unit 46A of the headset terminal 314 and automatically records data such as the user's steps, heart rate, and weight using IoT devices such as smartwatches and smart scales. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's lifestyle data using generating AI to determine the risk of lifestyle-related diseases. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes specific improvement measures using generating AI. The hospital candidate unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects an appropriate hospital using generating AI and provides it to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the registration unit, recording unit, analysis unit, proposal unit, and hospital candidate unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the robot 414 and provides an interface for inputting the user's health checkup results and medical history. The recording unit is implemented by the control unit 46A of the robot 414 and automatically records data such as the user's steps, heart rate, and weight using IoT devices such as smartwatches and smart scales. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's lifestyle data using generating AI to determine the risk of lifestyle-related diseases. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes specific improvement measures using generating AI. The hospital candidate unit is implemented by the specific processing unit 290 of the data processing unit 12 and selects an appropriate hospital using generating AI and provides it to the user. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] (Note 1) A registration unit for registering the user's health check results and medical history, A recording unit records lifestyle habits based on the data registered by the aforementioned registration unit, An analysis unit analyzes the data recorded by the recording unit to determine the risk of lifestyle-related diseases, A proposal unit that proposes specific improvement measures based on the risks determined by the aforementioned analysis unit, The system includes a hospital candidate department that selects hospital candidates based on improvement measures proposed by the aforementioned proposal department. A system characterized by the following features. (Note 2) The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of registering health check results and medical history based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned registration unit is The system analyzes the user's past health check results and medical history to select the optimal registration method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned registration unit is When registering health checkup results or medical history, filtering is performed based on the user's current health status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned recording unit is IoT devices such as smartwatches and smart scales are used to record users' daily habits. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Using a generation AI, the data recorded by the recording unit is analyzed to determine the risk of lifestyle-related diseases. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, Using the generated AI, specific improvement measures are proposed based on the risks determined by the analysis unit. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned hospital candidate department, Using a generation AI, hospital candidates are selected based on the improvement measures proposed by the proposal unit. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recording unit is The system estimates the user's emotions and adjusts the method of recording lifestyle habits based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recording unit is Analyze the user's past lifestyle data and select the optimal recording method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recording unit is When recording lifestyle habits, filtering is performed based on the user's current health status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, Improve the accuracy of analysis by considering the interrelationships of lifestyle data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Perform analysis while considering user attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, Adjust the level of detail in the proposal based on the importance of the risks. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, Apply different proposed algorithms depending on the risk category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned hospital candidate department, The system estimates the user's emotions and adjusts the hospital candidate selection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned hospital candidate department, When selecting potential hospitals, the system analyzes the user's past medical history to choose the most suitable hospital. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned hospital candidate department, When selecting potential hospitals, the system customizes the list of potential hospitals based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned hospital candidate department, The system estimates the user's emotions and prioritizes potential hospitals based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned hospital candidate department, When selecting potential hospitals, the system will consider the user's geographical location to select the most suitable hospital. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned hospital candidate department, When selecting potential hospitals, we analyze users' social media activity to suggest hospital candidates. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0174] 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 registration unit for registering the user's health check results and medical history, A recording unit records lifestyle habits based on the data registered by the aforementioned registration unit, An analysis unit analyzes the data recorded by the recording unit to determine the risk of lifestyle-related diseases, A proposal unit that proposes specific improvement measures based on the risks determined by the aforementioned analysis unit, The system includes a hospital candidate department that selects hospital candidates based on improvement measures proposed by the aforementioned proposal department. A system characterized by the following features.

2. The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of registering health check results and medical history based on those estimated emotions. The system according to feature 1.

3. The aforementioned registration unit is The system analyzes the user's past health check results and medical history to select the optimal registration method. The system according to feature 1.

4. The aforementioned registration unit is When registering health checkup results or medical history, filtering is performed based on the user's current health status and areas of interest. The system according to feature 1.

5. The recording unit is, IoT devices such as smartwatches and smart scales are used to record users' daily habits. The system according to feature 1.

6. The aforementioned analysis unit, Using the generated AI, the data recorded by the recording unit is analyzed to determine the risk of lifestyle-related diseases. The system according to feature 1.

7. The aforementioned proposal section is, Using the generated AI, specific improvement measures are proposed based on the risk determined by the analysis unit. The system according to feature 1.

8. The aforementioned hospital candidate department, Using a generation AI, hospital candidates are selected based on the improvement measures proposed by the proposal unit. The system according to feature 1.

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