system

Abstract

The system of the embodiment aims to personalize and optimize diabetes risk assessment and management. [Solution] A system according to an embodiment includes a collection unit, a prediction unit, a provision unit, a monitoring unit, and a platform unit. The collection unit collects health checkup data. The prediction unit analyzes the data collected by the collection unit to predict a diabetes risk score. The provision unit provides individual risk assessment and advice based on the risk score predicted by the prediction unit. The monitoring unit collects and analyzes data using monitoring devices. The platform unit provides a patient community platform.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Current techniques do not individually optimize diabetes risk assessment and management, and there is room for improvement.

[0005] The system of the embodiment aims to personalize and optimize diabetes risk assessment and management. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a prediction unit, a provision unit, a monitoring unit, and a platform unit. The collection unit collects health checkup data. The prediction unit analyzes the data collected by the collection unit to predict a diabetes risk score. The provision unit provides individual risk assessment and advice based on the risk score predicted by the prediction unit. The monitoring unit collects and analyzes data using monitoring devices. The platform unit provides a patient community platform. [Effects of the Invention]

[0007] Systems according to embodiments can personalize diabetes risk assessment and management. [Brief explanation of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol 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 including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

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

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) The voice conversation diabetes management AI coaching app service according to an embodiment of the present invention is a system that comprehensively supports the health management of diabetes patients. This system supports patient health management through AI-based early prevention testing, individualized risk assessment and advice, data collection and analysis using monitoring devices, and a patient community platform. Specifically, the AI ​​first predicts a diabetes risk score using health checkup data. Then, it provides individualized risk assessment and advice based on the predicted risk score. Furthermore, monitoring devices automatically record data such as blood glucose levels, diet, and exercise, and the AI ​​then provides personalized advice. Finally, it provides a patient community platform that promotes information exchange and support among patients and also provides access to experts. For example, the AI ​​analyzes health checkup data to calculate a diabetes risk score, including data such as blood glucose levels, weight, blood pressure, and cholesterol levels. This allows patients to understand their risk and take early preventative measures. Next, the AI ​​provides specific advice, such as dietary improvements and exercise recommendations, based on the predicted risk score. This allows patients to take appropriate measures according to their health status. Furthermore, daily health data is collected using devices such as blood glucose meters and smartwatches, and the AI ​​analyzes it to provide personalized advice. For example, specific advice is provided on the timing and content of meals, and the type and frequency of exercise. Finally, a community platform is provided where patients can exchange information and support each other. For example, they can share information such as meal recipes and exercise methods. Access to experts is also provided, allowing them to receive professional advice as needed. This allows patients to manage their health within the community, without isolation. In this way, the voice conversation diabetes management AI coaching app service can comprehensively support the health management of diabetes patients.

[0029] A voice conversation diabetes management AI coaching app service according to an embodiment includes a collection unit, a prediction unit, a provision unit, a monitoring unit, and a platform unit. The collection unit collects health checkup data. The health checkup data includes, but is not limited to, blood glucose levels, weight, blood pressure, and cholesterol levels. The collection unit can collect daily health data using a device such as a blood glucose meter or a smartwatch. The collection unit can also build a system that automatically collects health checkup data. For example, the collection unit periodically collects health checkup data and converts it into a format that is easy for AI to analyze. The prediction unit analyzes the data collected by the collection unit to predict a diabetes risk score. For example, the prediction unit uses AI to analyze the health checkup data and calculate a diabetes risk score. The AI ​​uses an algorithm to assess diabetes risk based on the health checkup data. For example, the AI ​​analyzes data such as blood glucose levels, weight, blood pressure, and cholesterol levels to calculate a risk score. The provision unit provides a personalized risk assessment and advice based on the risk score predicted by the prediction unit. For example, the provision unit generates personalized advice based on the risk score using AI. Based on the risk score, the AI ​​provides specific advice such as recommendations for dietary improvements and exercise. For example, if the risk score is high, the AI ​​provides detailed recommendations for dietary improvements and exercise. The monitoring unit collects and analyzes data using monitoring equipment. The monitoring unit collects daily health data using devices such as blood glucose meters and smartwatches, and the AI ​​analyzes it. The AI ​​provides individually tailored advice based on the collected data. For example, the AI ​​provides specific advice on the timing and content of meals, and the type and frequency of exercise. The platform unit provides a patient community platform. The platform unit provides a community platform where patients can exchange information and support each other, for example. Patients can share information such as meal recipes and exercise methods. The platform unit also provides access to experts so that patients can receive professional advice as needed.As a result, the voice conversation diabetes management AI coaching app service according to the embodiment can comprehensively support the health management of diabetes patients.

[0030] The collection unit can collect health checkup data including blood glucose levels, weight, blood pressure, and cholesterol levels. Examples of health checkup data include, but are not limited to, blood glucose levels, weight, blood pressure, and cholesterol levels. The collection unit can collect blood glucose levels using, for example, a blood glucose meter. For example, the collection unit periodically measures blood glucose levels using the blood glucose meter and collects the data. The collection unit can also collect weight using a scale. For example, the collection unit periodically measures weight using a scale and collects the data. The collection unit can also collect blood pressure using a sphygmomanometer. For example, the collection unit periodically measures blood pressure using a sphygmomanometer and collects the data. This enables the collection of health checkup data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired by the blood glucose meter into AI and have the AI ​​analyze the data.

[0031] The prediction unit can analyze the collected health checkup data and predict a diabetes risk score. The prediction unit, for example, uses AI to analyze the health checkup data and calculate a diabetes risk score. The AI ​​uses an algorithm to assess diabetes risk based on the health checkup data. For example, the AI ​​analyzes data such as blood glucose level, weight, blood pressure, and cholesterol level and calculates a risk score. The AI ​​builds a model for assessing diabetes risk based on this data. For example, the AI ​​learns from past health checkup data and trains a model for predicting a risk score. This makes it possible to predict a diabetes risk score. Some or all of the above-mentioned processing in the prediction unit may be performed using AI, for example, or may be performed without using AI. For example, the prediction unit can input the collected health checkup data into AI and have the AI ​​predict a risk score.

[0032] The providing unit can provide individualized risk assessment and advice, including dietary improvements and exercise recommendations, based on the predicted risk score. The providing unit generates individualized advice based on the risk score, for example, using AI. The AI ​​provides specific advice, such as dietary improvements and exercise recommendations, based on the risk score. For example, the AI ​​provides detailed dietary improvements and exercise recommendations when the risk score is high. The AI ​​can also provide simplified advice when the risk score is low. For example, the AI ​​provides specific advice, such as dietary content, type and frequency of exercise, based on the risk score. This makes it possible to provide individualized risk assessment and advice. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the risk score into AI and have the AI ​​generate advice.

[0033] The monitoring unit can collect and analyze daily health data using devices including a blood glucose meter and a smart watch. The monitoring unit can collect blood glucose levels using, for example, a blood glucose meter. For example, the monitoring unit can periodically measure blood glucose levels using the blood glucose meter and collect the data. The monitoring unit can also collect weight and exercise data using a smart watch. For example, the monitoring unit can collect daily exercise data using the smart watch and analyze the data. The monitoring unit can also collect blood pressure using a blood pressure monitor. For example, the monitoring unit can periodically measure blood pressure using the blood pressure monitor and collect the data. This allows daily health data to be collected and analyzed. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input data acquired by the blood glucose meter into AI and have the AI ​​analyze the data.

[0034] The platform unit can provide a community platform where patients can exchange information and support each other. The platform unit, for example, provides a community platform where patients can exchange information and support each other. Patients can share information such as meal recipes and exercise methods. The platform unit also provides access to experts, allowing patients to receive professional advice as needed. For example, the platform unit provides access to experts such as doctors, nutritionists, and exercise instructors, allowing patients to receive professional advice. This allows patients to exchange information and support each other. Some or all of the above-mentioned processing in the platform unit may be performed using, or without, AI, for example. For example, the platform unit can input patient information into AI and have the AI ​​analyze and provide the information.

[0035] The platform unit can provide access to experts and provide professional advice as needed. The platform unit provides access to experts such as doctors, nutritionists, and exercise instructors. By receiving advice from experts, patients can learn more specific health management methods. For example, the platform unit can communicate directly with experts through online consultations or chat functions. The platform unit can also host webinars and seminars by experts, providing patients with opportunities to learn the latest health information. This allows patients to access experts and receive professional advice. Some or all of the above-mentioned processing in the platform unit may be performed using, for example, AI, or may be performed without AI. For example, the platform unit can input patients' questions into AI, which can generate answers based on expert advice.

[0036] The collection unit can analyze the user's past health checkup data and select the optimal collection method. For example, the collection unit analyzes the user's past health checkup data and selects the most effective collection method. For example, the collection unit selects a collection method that focuses on specific health checkup items from the past data. The collection unit can also adjust the collection frequency based on the past data. For example, the collection unit optimizes the collection frequency from the past data and efficiently collects necessary data. The collection unit can also analyze the past data and select a collection method that focuses on specific items. For example, the collection unit prioritizes collecting data related to specific health risks from the past data. This makes it possible to select the optimal collection method based on the user's past health checkup data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past health checkup data into AI and have the AI ​​select the optimal collection method.

[0037] When collecting health checkup data, the collection unit can filter the data based on the user's current lifestyle and areas of interest. For example, if the user is on a diet, the collection unit can focus on collecting data related to their diet. For example, the collection unit can collect information on the user's diet and calorie intake to monitor the progress of the diet. Furthermore, if the user has just started exercising, the collection unit can focus on collecting data related to their exercise. For example, the collection unit can collect information on the amount and type of exercise the user does and monitor the effectiveness of the exercise. Furthermore, if the user is feeling stressed, the collection unit can focus on collecting data related to stress management. For example, the collection unit can collect information on the user's heart rate and stress level and monitor the effectiveness of stress management. This allows data to be filtered based on the user's lifestyle and areas of interest. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data related to the user's lifestyle and areas of interest into AI and have the AI ​​filter the data.

[0038] When collecting health checkup data, the collection unit can prioritize collection of highly relevant data taking into account the user's geographical location information. For example, if the user lives at high altitude, the collection unit collects data related to health risks specific to high altitudes. For example, the collection unit collects data related to the oxygen saturation and cardiopulmonary function of the user living at high altitude. Furthermore, if the user lives in an urban area, the collection unit can also collect data related to health risks specific to urban areas. For example, the collection unit collects data related to air pollution and stress levels of users living in urban areas. Furthermore, if the user lives in a rural area, the collection unit can also collect data related to health risks specific to rural areas. For example, the collection unit collects data related to pesticide exposure and exercise volume of users living in rural areas. This makes it possible to collect highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's geographical location information into AI and cause the AI ​​to collect highly relevant data.

[0039] The collection unit can analyze the user's social media activity and collect related data when collecting health checkup data. For example, if the user frequently posts health-related information on social media, the collection unit can collect data based on the content of the posts. For example, the collection unit can analyze the content of the user's posts and collect health-related data. Furthermore, if the user has expressed interest in a particular health topic on social media, the collection unit can also collect data related to the topic. For example, the collection unit can collect data related to health topics of the user's interest. Furthermore, if the user has shared information about a particular health risk on social media, the collection unit can also collect data related to the risk. For example, the collection unit can collect data related to the health risk shared by the user. This allows related data to be collected based on the user's social media activity. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's social media activity into AI and have the AI ​​collect related data.

[0040] The prediction unit can adjust the level of detail of the prediction based on the importance of the health checkup data during prediction. The prediction unit adjusts the level of detail of the prediction based on, for example, the importance of the health checkup data. For example, the prediction unit predicts a detailed risk score when there is a lot of important health checkup data. The prediction unit can also predict a simplified risk score when there is a little important health checkup data. For example, when specific health checkup data is important, the prediction unit predicts the risk score by prioritizing that data. This makes it possible to adjust the level of detail of the prediction based on the importance of the health checkup data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input health checkup data into AI and have the AI ​​adjust the level of detail of the prediction.

[0041] The prediction unit can apply different prediction algorithms depending on the category of health checkup data when making predictions. For example, the prediction unit predicts a risk score by applying a specific algorithm to blood glucose level data. For example, the prediction unit predicts a risk score using regression analysis on the blood glucose level data. The prediction unit can also predict a risk score by applying a different algorithm to weight data. For example, the prediction unit predicts a risk score using a neural network on weight data. Furthermore, the prediction unit can predict a risk score by applying yet another algorithm to blood pressure data. For example, the prediction unit predicts a risk score using a decision tree on blood pressure data. This makes it possible to apply different prediction algorithms depending on the category of health checkup data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input health checkup data into AI and have the AI ​​apply different prediction algorithms.

[0042] The prediction unit can determine the priority of prediction based on the submission date of the health checkup data when making predictions. For example, the prediction unit can prioritize the use of recently submitted health checkup data to predict the risk score. For example, the prediction unit can predict the risk score based on the most recent data. The prediction unit can also use older health checkup data with lower priority. For example, the prediction unit can predict the risk score by lowering the priority of data submitted earlier. Furthermore, the prediction unit can predict the risk score by prioritizing data submitted within a specific period. For example, the prediction unit can predict the risk score by prioritizing data submitted more recently. This makes it possible to determine the priority of prediction based on the submission date of the health checkup data. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or without AI. For example, the prediction unit can input the submission date of the health checkup data into AI and have the AI ​​determine the prediction priority.

[0043] The prediction unit can adjust the order of predictions based on the relevance of the health checkup data during prediction. For example, the prediction unit predicts the risk score by prioritizing data with high relevance. For example, the prediction unit predicts the risk score based on data with high relevance. The prediction unit can also use data with low relevance later. For example, the prediction unit predicts the risk score by lowering the priority of data with low relevance. Furthermore, the prediction unit can predict the risk score by prioritizing data with specific relevance. For example, the prediction unit predicts the risk score by prioritizing data with specific relevance. This makes it possible to adjust the order of predictions based on the relevance of the health checkup data. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the relevance of the health checkup data into AI and have the AI ​​adjust the order of predictions.

[0044] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the risk score. For example, in the case of a high risk score, the providing unit provides detailed advice. For example, in the case of a high risk score, the providing unit provides detailed advice on dietary improvements and exercise recommendations. Furthermore, the providing unit can also provide simplified advice in the case of a low risk score. For example, in the case of a low risk score, the providing unit provides simplified advice on dietary improvements and exercise recommendations. Furthermore, the providing unit can also provide advice with an appropriate level of detail based on a specific risk score. For example, the providing unit provides detailed advice based on a specific risk score. This makes it possible to adjust the level of detail of the advice based on the importance of the risk score. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit may input the risk score into AI and cause the AI ​​to adjust the level of detail of the advice.

[0045] When providing advice, the providing unit can apply different advice algorithms depending on the category of the risk score. For example, the providing unit applies a specific advice algorithm to a risk score related to diet. For example, the providing unit applies a rule-based advice algorithm to the risk score related to diet. The providing unit can also apply a different advice algorithm to a risk score related to exercise. For example, the providing unit applies an advice algorithm using machine learning to the risk score related to exercise. Furthermore, the providing unit can apply yet another advice algorithm to a risk score related to stress management. For example, the providing unit applies an advice algorithm using a neural network to the risk score related to stress management. This makes it possible to apply different advice algorithms depending on the category of the risk score. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the risk score to AI and cause the AI ​​to apply different advice algorithms.

[0046] When providing advice, the providing unit can determine the priority of advice based on the time of submission of the risk score. The providing unit, for example, prioritizes providing advice based on the most recently submitted risk score. For example, the providing unit provides advice based on the latest risk score. The providing unit can also provide advice by lowering the priority of older risk scores. For example, the providing unit can provide advice by lowering the priority of risk scores that were submitted earlier. Furthermore, the providing unit can provide advice by placing emphasis on risk scores that were submitted within a specific period. For example, the providing unit provides advice by placing emphasis on risk scores that were submitted more recently. This makes it possible to determine the priority of advice based on the time of submission of the risk score. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time of submission of the risk score into AI and cause the AI ​​to determine the priority of advice.

[0047] When providing advice, the providing unit can adjust the order of advice based on the relevance of the risk scores. For example, the providing unit prioritizes providing advice based on risk scores with high relevance. For example, the providing unit provides advice based on risk scores with high relevance. The providing unit can also provide advice after risk scores with low relevance. For example, the providing unit provides advice by lowering the priority of risk scores with low relevance. Furthermore, the providing unit can provide advice by prioritizing risk scores with specific relevance. For example, the providing unit provides advice by prioritizing risk scores with specific relevance. This makes it possible to adjust the order of advice based on the relevance of the risk scores. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the relevance of the risk scores into AI and cause the AI ​​to adjust the order of advice.

[0048] During monitoring, the monitoring unit can analyze the user's past health data and select the optimal monitoring method. For example, the monitoring unit analyzes the user's past health data and selects the most effective monitoring method. For example, the monitoring unit selects a monitoring method that focuses on specific health items from the past data. The monitoring unit can also adjust the monitoring frequency based on the past data. For example, the monitoring unit optimizes the monitoring frequency from the past data and efficiently collects necessary data. The monitoring unit can also analyze the past data and select a monitoring method that focuses on specific items. For example, the monitoring unit prioritizes monitoring data related to specific health risks from the past data. This makes it possible to select the optimal monitoring method based on the user's past health data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past health data into AI and have the AI ​​select the optimal monitoring method.

[0049] During monitoring, the monitoring unit can customize the monitoring means based on the user's current lifestyle. For example, if the user is on a diet, the monitoring unit can focus on monitoring dietary intake. For example, the monitoring unit can monitor the user's dietary intake and calorie intake to evaluate the user's progress. Furthermore, if the user has just started exercising, the monitoring unit can focus on monitoring exercise. For example, the monitoring unit can monitor the user's exercise amount and type to evaluate the effectiveness of the exercise. Furthermore, if the user is feeling stressed, the monitoring unit can focus on monitoring stress management. For example, the monitoring unit can monitor the user's heart rate and stress level to evaluate the effectiveness of stress management. This allows the monitoring means to be customized based on the user's lifestyle. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input data about the user's lifestyle into AI and have the AI ​​customize the monitoring means.

[0050] During monitoring, the monitoring unit can select the optimal monitoring method by taking into account the user's geographical location information. For example, if the user lives at high altitude, the monitoring unit performs monitoring related to health risks specific to high altitudes. For example, the monitoring unit monitors data related to the oxygen saturation and cardiopulmonary function of the user living at high altitude. Furthermore, if the user lives in an urban area, the monitoring unit can also perform monitoring related to health risks specific to urban areas. For example, the monitoring unit monitors data related to air pollution and stress levels of users living in urban areas. Furthermore, if the user lives in a rural area, the monitoring unit can also perform monitoring related to health risks specific to rural areas. For example, the monitoring unit monitors data related to pesticide exposure and exercise volume of users living in rural areas. This allows the optimal monitoring method to be selected based on the user's geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input the user's geographical location information into AI and have the AI ​​select the optimal monitoring method.

[0051] During monitoring, the monitoring unit can analyze the user's social media activity and suggest monitoring measures. For example, if the user frequently posts health-related information on social media, the monitoring unit can perform monitoring based on the content of the posts. For example, the monitoring unit can analyze the content of the user's posts and monitor health-related data. Furthermore, if the user shows interest in a specific health topic on social media, the monitoring unit can also perform monitoring related to that topic. For example, the monitoring unit can monitor data related to health topics of the user's interest. Furthermore, if the user shares information about a specific health risk on social media, the monitoring unit can also perform monitoring related to that risk. For example, the monitoring unit can monitor data related to the health risk shared by the user. This makes it possible to suggest monitoring measures based on the user's social media activity. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input the user's social media activity into AI and have the AI ​​suggest monitoring measures.

[0052] When displaying the platform, the platform unit can select the optimal display method by referring to the user's past operation history. For example, the platform unit prioritizes providing a display method that the user has frequently used in the past. For example, the platform unit suggests the most efficient display method based on the user's past operation history. The platform unit can also customize a specific display method based on the user's past operation history. For example, the platform unit analyzes the user's past operation history and customizes a specific display method. This makes it possible to select the optimal display method based on the user's past operation history. Some or all of the above-mentioned processing in the platform unit may be performed using, for example, AI, or may be performed without using AI. For example, the platform unit can input the user's operation history into AI and have the AI ​​select the optimal display method.

[0053] The platform unit can customize the display content based on the user's current areas of interest when displaying the platform. For example, if the user is interested in dieting, the platform unit can prioritize displaying diet-related information. For example, the platform unit can display diet-related information based on the user's areas of interest. Furthermore, if the user is interested in exercise, the platform unit can also prioritize displaying exercise-related information. For example, the platform unit can display exercise-related information based on the user's areas of interest. Furthermore, if the user is interested in stress management, the platform unit can also prioritize displaying stress management-related information. For example, the platform unit can display stress management-related information based on the user's areas of interest. This allows the display content to be customized based on the user's areas of interest. Some or all of the above-described processing in the platform unit may be performed using, for example, AI, or may be performed without using AI. For example, the platform unit can input data related to the user's areas of interest into AI and have the AI ​​customize the display content.

[0054] When displaying the platform, the platform unit can select the optimal display method taking into account the user's device information. For example, if the user is using a smartphone, the platform unit provides a display method tailored to the screen size. For example, the platform unit provides a display method optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the platform unit can also provide a display method optimized for a large screen. For example, the platform unit provides a display method optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the platform unit can also provide a simple and highly visible display method. For example, the platform unit provides a display method optimized for the small screen of a smartwatch. This makes it possible to select the optimal display method based on the user's device information. Some or all of the above-described processing in the platform unit may be performed using, for example, AI, or may be performed without using AI. For example, the platform unit can input the user's device information into AI and have the AI ​​select the optimal display method.

[0055] When displaying on the platform, the platform unit can analyze the user's social media activity and suggest display content. For example, if the user frequently posts health-related content on social media, the platform unit can suggest display content based on that content. For example, the platform unit can analyze the user's posted content and display health-related information. Furthermore, if the user shows interest in a specific health topic on social media, the platform unit can also suggest display content related to that topic. For example, the platform unit can display information related to a health topic of interest to the user. Furthermore, if the user shares information about a specific health risk on social media, the platform unit can also suggest display content related to that risk. For example, the platform unit can display information related to the health risk shared by the user. This makes it possible to suggest display content based on the user's social media activity. Some or all of the above-mentioned processing in the platform unit may be performed using, or without, AI. For example, the platform unit may input the user's social media activity into AI and have the AI ​​suggest display content.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The collection unit can also collect lifestyle habit data of the user and provide it to the prediction unit. For example, the collection unit can collect the user's sleep patterns and stress levels and provide this data to the prediction unit. The prediction unit can use this additional data to predict a more accurate risk score. The collection unit can also record the user's diet and exercise amount in detail and provide this data to the prediction unit. This allows the prediction unit to perform a risk assessment based on the user's lifestyle habits. Furthermore, the collection unit can collect environmental data of the user (e.g., living environment and work environment) and provide it to the prediction unit. This allows the prediction unit to perform a risk assessment that takes into account the user's environmental factors.

[0058] The collection unit can collect data related to region-specific health risks by taking into account the user's geographical location information. For example, if the user lives in a high altitude, the collection unit can collect data related to high altitude-specific health risks. If the user lives in an urban area, the collection unit can also collect data related to urban area-specific health risks. Furthermore, if the user lives in a rural area, the collection unit can also collect data related to rural area-specific health risks. This enables more accurate health risk assessment based on the user's geographical location information.

[0059] The prediction unit can analyze the user's past health checkup data and select the optimal prediction algorithm. For example, the prediction unit selects an algorithm related to a specific health risk from the past data. The prediction unit can also select an algorithm to improve the accuracy of prediction based on the past data. Furthermore, the prediction unit can analyze the past data and select an algorithm that prioritizes the use of data related to a specific health risk. This enables more accurate risk assessment based on the user's past health checkup data.

[0060] The providing unit can customize the content of the advice based on the user's current living situation. For example, if the user is on a diet, the providing unit can provide advice focusing on dietary advice. Also, if the user has just started exercising, the providing unit can provide advice focusing on exercise advice. Furthermore, if the user is feeling stressed, the providing unit can provide advice focusing on stress management advice. This allows more appropriate advice to be provided based on the user's living situation.

[0061] The monitoring unit can analyze the user's social media activity and collect data related to health risks. For example, if the user frequently posts health-related content on social media, the monitoring unit can collect data based on the content. In addition, if the user shows interest in a particular health topic, the monitoring unit can collect data related to that topic. Furthermore, if the user shares information about a particular health risk, the monitoring unit can collect data related to that risk. This enables more accurate health risk assessment based on the user's social media activity.

[0062] The platform unit can select the optimal display method by referring to the user's past operation history. For example, the platform unit can provide a display method that the user has frequently used in the past with priority. The platform unit can also customize a specific display method based on the user's past operation history. Furthermore, the platform unit can analyze the user's past operation history and suggest the most efficient display method. This provides a platform that is easier to use based on the user's past operation history.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The collection unit collects health checkup data. Health checkup data includes blood glucose levels, weight, blood pressure, cholesterol levels, etc. The collection unit can collect daily health data using devices such as blood glucose meters and smartwatches. The collection unit also periodically collects health checkup data and converts it into a format that is easy for AI to analyze. Step 2: The prediction unit analyzes the data collected by the collection unit and predicts a diabetes risk score. The prediction unit uses AI to analyze the health checkup data and calculate a diabetes risk score. The AI ​​analyzes data such as blood glucose levels, weight, blood pressure, and cholesterol levels and calculates a risk score. Step 3: The provider provides personalized risk assessment and advice based on the risk score predicted by the predictor. The provider uses AI to generate personalized advice based on the risk score. Based on the risk score, the AI ​​provides specific advice such as dietary improvements and exercise recommendations. Step 4: The monitoring unit collects and analyzes data using monitoring devices. The monitoring unit uses devices such as blood glucose meters and smartwatches to collect daily health data, which is then analyzed by AI. The AI ​​then provides personalized advice based on the collected data. Step 5: The platform department provides a patient community platform. The platform department provides a community platform where patients can exchange information and support each other. Patients can share information such as meal recipes and exercise methods. The platform department also provides access to experts, so patients can receive professional advice as needed.

[0065] (Example 2) The voice conversation diabetes management AI coaching app service according to an embodiment of the present invention is a system that comprehensively supports the health management of diabetes patients. This system supports patient health management through AI-based early prevention testing, individualized risk assessment and advice, data collection and analysis using monitoring devices, and a patient community platform. Specifically, the AI ​​first predicts a diabetes risk score using health checkup data. Then, it provides individualized risk assessment and advice based on the predicted risk score. Furthermore, monitoring devices automatically record data such as blood glucose levels, diet, and exercise, and the AI ​​then provides personalized advice. Finally, it provides a patient community platform that promotes information exchange and support among patients and also provides access to experts. For example, the AI ​​analyzes health checkup data to calculate a diabetes risk score, including data such as blood glucose levels, weight, blood pressure, and cholesterol levels. This allows patients to understand their risk and take early preventative measures. Next, the AI ​​provides specific advice, such as dietary improvements and exercise recommendations, based on the predicted risk score. This allows patients to take appropriate measures according to their health status. Furthermore, daily health data is collected using devices such as blood glucose meters and smartwatches, and the AI ​​analyzes it to provide personalized advice. For example, specific advice is provided on the timing and content of meals, and the type and frequency of exercise. Finally, a community platform is provided where patients can exchange information and support each other. For example, they can share information such as meal recipes and exercise methods. Access to experts is also provided, allowing them to receive professional advice as needed. This allows patients to manage their health within the community, without isolation. In this way, the voice conversation diabetes management AI coaching app service can comprehensively support the health management of diabetes patients.

[0066] A voice conversation diabetes management AI coaching app service according to an embodiment includes a collection unit, a prediction unit, a provision unit, a monitoring unit, and a platform unit. The collection unit collects health checkup data. The health checkup data includes, but is not limited to, blood glucose levels, weight, blood pressure, and cholesterol levels. The collection unit can collect daily health data using a device such as a blood glucose meter or a smartwatch. The collection unit can also build a system that automatically collects health checkup data. For example, the collection unit periodically collects health checkup data and converts it into a format that is easy for AI to analyze. The prediction unit analyzes the data collected by the collection unit to predict a diabetes risk score. For example, the prediction unit uses AI to analyze the health checkup data and calculate a diabetes risk score. The AI ​​uses an algorithm to assess diabetes risk based on the health checkup data. For example, the AI ​​analyzes data such as blood glucose levels, weight, blood pressure, and cholesterol levels to calculate a risk score. The provision unit provides a personalized risk assessment and advice based on the risk score predicted by the prediction unit. For example, the provision unit generates personalized advice based on the risk score using AI. Based on the risk score, the AI ​​provides specific advice such as recommendations for dietary improvements and exercise. For example, if the risk score is high, the AI ​​provides detailed recommendations for dietary improvements and exercise. The monitoring unit collects and analyzes data using monitoring equipment. The monitoring unit collects daily health data using devices such as blood glucose meters and smartwatches, and the AI ​​analyzes it. The AI ​​provides individually tailored advice based on the collected data. For example, the AI ​​provides specific advice on the timing and content of meals, and the type and frequency of exercise. The platform unit provides a patient community platform. The platform unit provides a community platform where patients can exchange information and support each other, for example. Patients can share information such as meal recipes and exercise methods. The platform unit also provides access to experts so that patients can receive professional advice as needed.As a result, the voice conversation diabetes management AI coaching app service according to the embodiment can comprehensively support the health management of diabetes patients.

[0067] The collection unit can collect health checkup data including blood glucose levels, weight, blood pressure, and cholesterol levels. Examples of health checkup data include, but are not limited to, blood glucose levels, weight, blood pressure, and cholesterol levels. The collection unit can collect blood glucose levels using, for example, a blood glucose meter. For example, the collection unit periodically measures blood glucose levels using the blood glucose meter and collects the data. The collection unit can also collect weight using a scale. For example, the collection unit periodically measures weight using a scale and collects the data. The collection unit can also collect blood pressure using a sphygmomanometer. For example, the collection unit periodically measures blood pressure using a sphygmomanometer and collects the data. This enables the collection of health checkup data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired by the blood glucose meter into AI and have the AI ​​analyze the data.

[0068] The prediction unit can analyze the collected health checkup data and predict a diabetes risk score. The prediction unit, for example, uses AI to analyze the health checkup data and calculate a diabetes risk score. The AI ​​uses an algorithm to assess diabetes risk based on the health checkup data. For example, the AI ​​analyzes data such as blood glucose level, weight, blood pressure, and cholesterol level and calculates a risk score. The AI ​​builds a model for assessing diabetes risk based on this data. For example, the AI ​​learns from past health checkup data and trains a model for predicting a risk score. This makes it possible to predict a diabetes risk score. Some or all of the above-mentioned processing in the prediction unit may be performed using AI, for example, or may be performed without using AI. For example, the prediction unit can input the collected health checkup data into AI and have the AI ​​predict a risk score.

[0069] The providing unit can provide individualized risk assessment and advice, including dietary improvements and exercise recommendations, based on the predicted risk score. The providing unit generates individualized advice based on the risk score, for example, using AI. The AI ​​provides specific advice, such as dietary improvements and exercise recommendations, based on the risk score. For example, the AI ​​provides detailed dietary improvements and exercise recommendations when the risk score is high. The AI ​​can also provide simplified advice when the risk score is low. For example, the AI ​​provides specific advice, such as dietary content, type and frequency of exercise, based on the risk score. This makes it possible to provide individualized risk assessment and advice. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can input the risk score into AI and have the AI ​​generate advice.

[0070] The monitoring unit can collect and analyze daily health data using devices including a blood glucose meter and a smart watch. The monitoring unit can collect blood glucose levels using, for example, a blood glucose meter. For example, the monitoring unit can periodically measure blood glucose levels using the blood glucose meter and collect the data. The monitoring unit can also collect weight and exercise data using a smart watch. For example, the monitoring unit can collect daily exercise data using the smart watch and analyze the data. The monitoring unit can also collect blood pressure using a blood pressure monitor. For example, the monitoring unit can periodically measure blood pressure using the blood pressure monitor and collect the data. This allows daily health data to be collected and analyzed. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input data acquired by the blood glucose meter into AI and have the AI ​​analyze the data.

[0071] The platform unit can provide a community platform where patients can exchange information and support each other. The platform unit, for example, provides a community platform where patients can exchange information and support each other. Patients can share information such as meal recipes and exercise methods. The platform unit also provides access to experts, allowing patients to receive professional advice as needed. For example, the platform unit provides access to experts such as doctors, nutritionists, and exercise instructors, allowing patients to receive professional advice. This allows patients to exchange information and support each other. Some or all of the above-mentioned processing in the platform unit may be performed using, or without, AI, for example. For example, the platform unit can input patient information into AI and have the AI ​​analyze and provide the information.

[0072] The platform unit can provide access to experts and provide professional advice as needed. The platform unit provides access to experts such as doctors, nutritionists, and exercise instructors. By receiving advice from experts, patients can learn more specific health management methods. For example, the platform unit can communicate directly with experts through online consultations or chat functions. The platform unit can also host webinars and seminars by experts, providing patients with opportunities to learn the latest health information. This allows patients to access experts and receive professional advice. Some or all of the above-mentioned processing in the platform unit may be performed using, for example, AI, or may be performed without AI. For example, the platform unit can input patients' questions into AI, which can generate answers based on expert advice.

[0073] The collection unit can estimate the user's emotions and adjust the timing of collecting health checkup data based on the estimated user emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions and adjusts the collection timing. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the collection timing. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations and adjusts the collection timing. This makes it possible to adjust the timing of collecting health checkup data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0074] The collection unit can analyze the user's past health checkup data and select the optimal collection method. For example, the collection unit analyzes the user's past health checkup data and selects the most effective collection method. For example, the collection unit selects a collection method that focuses on specific health checkup items from the past data. The collection unit can also adjust the collection frequency based on the past data. For example, the collection unit optimizes the collection frequency from the past data and efficiently collects necessary data. The collection unit can also analyze the past data and select a collection method that focuses on specific items. For example, the collection unit prioritizes collecting data related to specific health risks from the past data. This makes it possible to select the optimal collection method based on the user's past health checkup data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past health checkup data into AI and have the AI ​​select the optimal collection method.

[0075] When collecting health checkup data, the collection unit can filter the data based on the user's current lifestyle and areas of interest. For example, if the user is on a diet, the collection unit can focus on collecting data related to their diet. For example, the collection unit can collect information on the user's diet and calorie intake to monitor the progress of the diet. Furthermore, if the user has just started exercising, the collection unit can focus on collecting data related to their exercise. For example, the collection unit can collect information on the amount and type of exercise the user does and monitor the effectiveness of the exercise. Furthermore, if the user is feeling stressed, the collection unit can focus on collecting data related to stress management. For example, the collection unit can collect information on the user's heart rate and stress level and monitor the effectiveness of stress management. This allows data to be filtered based on the user's lifestyle and areas of interest. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data related to the user's lifestyle and areas of interest into AI and have the AI ​​filter the data.

[0076] The collection unit can estimate the user's emotions and determine the priority of the health checkup data to be collected based on the estimated user emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions and determines the priority of the data to be collected. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of the data to be collected. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations and determines the priority of the data to be collected. This makes it possible to determine the priority of the health checkup data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0077] When collecting health checkup data, the collection unit can prioritize collection of highly relevant data taking into account the user's geographical location information. For example, if the user lives at high altitude, the collection unit collects data related to health risks specific to high altitudes. For example, the collection unit collects data related to the oxygen saturation and cardiopulmonary function of the user living at high altitude. Furthermore, if the user lives in an urban area, the collection unit can also collect data related to health risks specific to urban areas. For example, the collection unit collects data related to air pollution and stress levels of users living in urban areas. Furthermore, if the user lives in a rural area, the collection unit can also collect data related to health risks specific to rural areas. For example, the collection unit collects data related to pesticide exposure and exercise volume of users living in rural areas. This makes it possible to collect highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's geographical location information into AI and cause the AI ​​to collect highly relevant data.

[0078] The collection unit can analyze the user's social media activity and collect related data when collecting health checkup data. For example, if the user frequently posts health-related information on social media, the collection unit can collect data based on the content of the posts. For example, the collection unit can analyze the content of the user's posts and collect health-related data. Furthermore, if the user has expressed interest in a particular health topic on social media, the collection unit can also collect data related to the topic. For example, the collection unit can collect data related to health topics of the user's interest. Furthermore, if the user has shared information about a particular health risk on social media, the collection unit can also collect data related to the risk. For example, the collection unit can collect data related to the health risk shared by the user. This allows related data to be collected based on the user's social media activity. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's social media activity into AI and have the AI ​​collect related data.

[0079] The prediction unit can estimate the user's emotion and adjust the risk score prediction method based on the estimated user emotion. For example, the prediction unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the prediction unit calculates an emotion score based on changes in facial expression and adjusts the risk score prediction method. The prediction unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the prediction unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the risk score prediction method. The prediction unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the prediction unit calculates an emotion score based on heart rate fluctuations and adjusts the risk score prediction method. This makes it possible to adjust the risk score prediction method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0080] The prediction unit can adjust the level of detail of the prediction based on the importance of the health checkup data during prediction. The prediction unit adjusts the level of detail of the prediction based on, for example, the importance of the health checkup data. For example, the prediction unit predicts a detailed risk score when there is a lot of important health checkup data. The prediction unit can also predict a simplified risk score when there is a little important health checkup data. For example, when specific health checkup data is important, the prediction unit predicts the risk score by prioritizing that data. This makes it possible to adjust the level of detail of the prediction based on the importance of the health checkup data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input health checkup data into AI and have the AI ​​adjust the level of detail of the prediction.

[0081] The prediction unit can apply different prediction algorithms depending on the category of health checkup data when making predictions. For example, the prediction unit predicts a risk score by applying a specific algorithm to blood glucose level data. For example, the prediction unit predicts a risk score using regression analysis on the blood glucose level data. The prediction unit can also predict a risk score by applying a different algorithm to weight data. For example, the prediction unit predicts a risk score using a neural network on weight data. Furthermore, the prediction unit can predict a risk score by applying yet another algorithm to blood pressure data. For example, the prediction unit predicts a risk score using a decision tree on blood pressure data. This makes it possible to apply different prediction algorithms depending on the category of health checkup data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input health checkup data into AI and have the AI ​​apply different prediction algorithms.

[0082] The prediction unit can estimate the user's emotion and adjust the display method of the risk score based on the estimated user emotion. For example, the prediction unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the prediction unit calculates an emotion score based on changes in facial expression and adjusts the display method of the risk score. The prediction unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the prediction unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the risk score. The prediction unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the prediction unit calculates an emotion score based on fluctuations in heart rate and adjusts the display method of the risk score. This makes it possible to adjust the display method of the risk score according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0083] The prediction unit can determine the priority of prediction based on the submission date of the health checkup data when making predictions. For example, the prediction unit can prioritize the use of recently submitted health checkup data to predict the risk score. For example, the prediction unit can predict the risk score based on the most recent data. The prediction unit can also use older health checkup data with lower priority. For example, the prediction unit can predict the risk score by lowering the priority of data submitted earlier. Furthermore, the prediction unit can predict the risk score by prioritizing data submitted within a specific period. For example, the prediction unit can predict the risk score by prioritizing data submitted more recently. This makes it possible to determine the priority of prediction based on the submission date of the health checkup data. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or without AI. For example, the prediction unit can input the submission date of the health checkup data into AI and have the AI ​​determine the prediction priority.

[0084] The prediction unit can adjust the order of predictions based on the relevance of the health checkup data during prediction. For example, the prediction unit predicts the risk score by prioritizing data with high relevance. For example, the prediction unit predicts the risk score based on data with high relevance. The prediction unit can also use data with low relevance later. For example, the prediction unit predicts the risk score by lowering the priority of data with low relevance. Furthermore, the prediction unit can predict the risk score by prioritizing data with specific relevance. For example, the prediction unit predicts the risk score by prioritizing data with specific relevance. This makes it possible to adjust the order of predictions based on the relevance of the health checkup data. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit can input the relevance of the health checkup data into AI and have the AI ​​adjust the order of predictions.

[0085] The providing unit can estimate the user's emotions and adjust the way the advice is presented based on the estimated user's emotions. For example, the providing unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression and adjusts the way the advice is presented. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the way the advice is presented. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on fluctuations in heart rate and adjusts the way the advice is presented. This makes it possible to adjust the way the advice is presented depending on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a user taken by a camera to the generating AI and cause the generating AI to estimate the user's emotions.

[0086] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the risk score. For example, in the case of a high risk score, the providing unit provides detailed advice. For example, in the case of a high risk score, the providing unit provides detailed advice on dietary improvements and exercise recommendations. Furthermore, the providing unit can also provide simplified advice in the case of a low risk score. For example, in the case of a low risk score, the providing unit provides simplified advice on dietary improvements and exercise recommendations. Furthermore, the providing unit can also provide advice with an appropriate level of detail based on a specific risk score. For example, the providing unit provides detailed advice based on a specific risk score. This makes it possible to adjust the level of detail of the advice based on the importance of the risk score. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit may input the risk score into AI and cause the AI ​​to adjust the level of detail of the advice.

[0087] When providing advice, the providing unit can apply different advice algorithms depending on the category of the risk score. For example, the providing unit applies a specific advice algorithm to a risk score related to diet. For example, the providing unit applies a rule-based advice algorithm to the risk score related to diet. The providing unit can also apply a different advice algorithm to a risk score related to exercise. For example, the providing unit applies an advice algorithm using machine learning to the risk score related to exercise. Furthermore, the providing unit can apply yet another advice algorithm to a risk score related to stress management. For example, the providing unit applies an advice algorithm using a neural network to the risk score related to stress management. This makes it possible to apply different advice algorithms depending on the category of the risk score. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the risk score to AI and cause the AI ​​to apply different advice algorithms.

[0088] The providing unit can estimate the user's emotion and adjust the length of the advice based on the estimated user emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression and adjusts the length of the advice. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of the advice. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on fluctuations in heart rate and adjusts the length of the advice. This makes it possible to adjust the length of the advice according to the user's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of a user taken by a camera to the generating AI and cause the generating AI to estimate the user's emotions.

[0089] When providing advice, the providing unit can determine the priority of advice based on the time of submission of the risk score. The providing unit, for example, prioritizes providing advice based on the most recently submitted risk score. For example, the providing unit provides advice based on the latest risk score. The providing unit can also provide advice by lowering the priority of older risk scores. For example, the providing unit can provide advice by lowering the priority of risk scores that were submitted earlier. Furthermore, the providing unit can provide advice by placing emphasis on risk scores that were submitted within a specific period. For example, the providing unit provides advice by placing emphasis on risk scores that were submitted more recently. This makes it possible to determine the priority of advice based on the time of submission of the risk score. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time of submission of the risk score into AI and cause the AI ​​to determine the priority of advice.

[0090] When providing advice, the providing unit can adjust the order of advice based on the relevance of the risk scores. For example, the providing unit prioritizes providing advice based on risk scores with high relevance. For example, the providing unit provides advice based on risk scores with high relevance. The providing unit can also provide advice after risk scores with low relevance. For example, the providing unit provides advice by lowering the priority of risk scores with low relevance. Furthermore, the providing unit can provide advice by prioritizing risk scores with specific relevance. For example, the providing unit provides advice by prioritizing risk scores with specific relevance. This makes it possible to adjust the order of advice based on the relevance of the risk scores. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the relevance of the risk scores into AI and cause the AI ​​to adjust the order of advice.

[0091] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user emotions. For example, the monitoring unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the monitoring unit calculates an emotion score based on changes in facial expressions and adjusts the monitoring method. The monitoring unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the monitoring unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the monitoring method. The monitoring unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the monitoring unit calculates an emotion score based on heart rate fluctuations and adjusts the monitoring method. This makes it possible to adjust the monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0092] During monitoring, the monitoring unit can analyze the user's past health data and select the optimal monitoring method. For example, the monitoring unit analyzes the user's past health data and selects the most effective monitoring method. For example, the monitoring unit selects a monitoring method that focuses on specific health items from the past data. The monitoring unit can also adjust the monitoring frequency based on the past data. For example, the monitoring unit optimizes the monitoring frequency from the past data and efficiently collects necessary data. The monitoring unit can also analyze the past data and select a monitoring method that focuses on specific items. For example, the monitoring unit prioritizes monitoring data related to specific health risks from the past data. This makes it possible to select the optimal monitoring method based on the user's past health data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past health data into AI and have the AI ​​select the optimal monitoring method.

[0093] During monitoring, the monitoring unit can customize the monitoring means based on the user's current lifestyle. For example, if the user is on a diet, the monitoring unit can focus on monitoring dietary intake. For example, the monitoring unit can monitor the user's dietary intake and calorie intake to evaluate the user's progress. Furthermore, if the user has just started exercising, the monitoring unit can focus on monitoring exercise. For example, the monitoring unit can monitor the user's exercise amount and type to evaluate the effectiveness of the exercise. Furthermore, if the user is feeling stressed, the monitoring unit can focus on monitoring stress management. For example, the monitoring unit can monitor the user's heart rate and stress level to evaluate the effectiveness of stress management. This allows the monitoring means to be customized based on the user's lifestyle. Some or all of the above-described processing in the monitoring unit may be performed using, or without, AI. For example, the monitoring unit can input data about the user's lifestyle into AI and have the AI ​​customize the monitoring means.

[0094] The monitoring unit can estimate the user's emotions and determine the monitoring priority based on the estimated user emotions. For example, the monitoring unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the monitoring unit calculates an emotion score based on changes in facial expressions and determines the monitoring priority. The monitoring unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the monitoring unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the monitoring priority. The monitoring unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the monitoring unit calculates an emotion score based on heart rate fluctuations and determines the monitoring priority. This makes it possible to determine the monitoring priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit may input image data of a user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0095] During monitoring, the monitoring unit can select the optimal monitoring method taking into account the user's geographical location information. For example, if the user lives at high altitude, the monitoring unit performs monitoring related to health risks specific to high altitudes. For example, the monitoring unit monitors data related to the oxygen saturation and cardiopulmonary function of the user living at high altitude. Furthermore, if the user lives in an urban area, the monitoring unit can also perform monitoring related to health risks specific to urban areas. For example, the monitoring unit monitors data related to air pollution and stress levels of users living in urban areas. Furthermore, if the user lives in a rural area, the monitoring unit can also perform monitoring related to health risks specific to rural areas. For example, the monitoring unit monitors data related to pesticide exposure and exercise volume of users living in rural areas. This allows the optimal monitoring method to be selected based on the user's geographical location information. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input the user's geographical location information into AI and have the AI ​​select the optimal monitoring method.

[0096] During monitoring, the monitoring unit can analyze the user's social media activity and suggest monitoring measures. For example, if the user frequently posts health-related information on social media, the monitoring unit can perform monitoring based on the content of the posts. For example, the monitoring unit can analyze the content of the user's posts and monitor health-related data. Furthermore, if the user shows interest in a specific health topic on social media, the monitoring unit can also perform monitoring related to that topic. For example, the monitoring unit can monitor data related to health topics of the user's interest. Furthermore, if the user shares information about a specific health risk on social media, the monitoring unit can also perform monitoring related to that risk. For example, the monitoring unit can monitor data related to the health risk shared by the user. This makes it possible to suggest monitoring measures based on the user's social media activity. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can input the user's social media activity into AI and have the AI ​​suggest monitoring measures.

[0097] The platform unit can estimate the user's emotions and adjust the display method of the platform based on the estimated user emotions. For example, the platform unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the platform unit calculates an emotion score based on changes in facial expressions and adjusts the display method of the platform. The platform unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the platform unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the platform. The platform unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the platform unit calculates an emotion score based on heart rate fluctuations and adjusts the display method of the platform. This makes it possible to adjust the display method of the platform according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the platform unit may be performed using, for example, AI, or may be performed without using AI. For example, the platform unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0098] When displaying the platform, the platform unit can select the optimal display method by referring to the user's past operation history. For example, the platform unit prioritizes providing a display method that the user has frequently used in the past. For example, the platform unit suggests the most efficient display method based on the user's past operation history. The platform unit can also customize a specific display method based on the user's past operation history. For example, the platform unit analyzes the user's past operation history and customizes a specific display method. This makes it possible to select the optimal display method based on the user's past operation history. Some or all of the above-mentioned processing in the platform unit may be performed using, for example, AI, or may be performed without using AI. For example, the platform unit can input the user's operation history into AI and have the AI ​​select the optimal display method.

[0099] The platform unit can customize the display content based on the user's current areas of interest when displaying the platform. For example, if the user is interested in dieting, the platform unit can prioritize displaying diet-related information. For example, the platform unit can display diet-related information based on the user's areas of interest. Furthermore, if the user is interested in exercise, the platform unit can also prioritize displaying exercise-related information. For example, the platform unit can display exercise-related information based on the user's areas of interest. Furthermore, if the user is interested in stress management, the platform unit can also prioritize displaying stress management-related information. For example, the platform unit can display stress management-related information based on the user's areas of interest. This allows the display content to be customized based on the user's areas of interest. Some or all of the above-described processing in the platform unit may be performed using, for example, AI, or may be performed without using AI. For example, the platform unit can input data related to the user's areas of interest into AI and have the AI ​​customize the display content.

[0100] The platform unit can estimate the user's emotions and adjust the platform's operation procedures based on the estimated user emotions. For example, the platform unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the platform unit calculates an emotion score based on changes in facial expressions and adjusts the operation procedures. The platform unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the platform unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the operation procedures. The platform unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the platform unit calculates an emotion score based on heart rate fluctuations and adjusts the operation procedures. This makes it possible to adjust the platform's operation procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the platform unit may be performed using, for example, AI, or may be performed without using AI. For example, the platform unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0101] When displaying the platform, the platform unit can select the optimal display method taking into account the user's device information. For example, if the user is using a smartphone, the platform unit provides a display method tailored to the screen size. For example, the platform unit provides a display method optimized for the smartphone screen size. Furthermore, if the user is using a tablet, the platform unit can also provide a display method optimized for a large screen. For example, the platform unit provides a display method optimized for the tablet screen size. Furthermore, if the user is using a smartwatch, the platform unit can also provide a simple and highly visible display method. For example, the platform unit provides a display method optimized for the small screen of a smartwatch. This makes it possible to select the optimal display method based on the user's device information. Some or all of the above-described processing in the platform unit may be performed using, for example, AI, or may be performed without using AI. For example, the platform unit can input the user's device information into AI and have the AI ​​select the optimal display method.

[0102] When displaying on the platform, the platform unit can analyze the user's social media activity and suggest display content. For example, if the user frequently posts health-related content on social media, the platform unit can suggest display content based on that content. For example, the platform unit can analyze the user's posted content and display health-related information. Furthermore, if the user shows interest in a specific health topic on social media, the platform unit can also suggest display content related to that topic. For example, the platform unit can display information related to a health topic of interest to the user. Furthermore, if the user shares information about a specific health risk on social media, the platform unit can also suggest display content related to that risk. For example, the platform unit can display information related to the health risk shared by the user. This makes it possible to suggest display content based on the user's social media activity. Some or all of the above-mentioned processing in the platform unit may be performed using, or without, AI. For example, the platform unit may input the user's social media activity into AI and have the AI ​​suggest display content. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, prediction unit, provision unit, monitoring unit, and platform unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14, and estimates the user's emotions using the control unit 46A. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes health checkup data to calculate a diabetes risk score. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and generates individualized advice based on the risk score. The monitoring unit is realized by the control unit 46A of the smart device 14 and collects daily health data using devices such as a blood glucose meter and a smartwatch, and analyzes the data using AI. The platform unit is realized by the control unit 46A of the smart device 14 and provides a community platform where patients can exchange information with each other. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, prediction unit, provision unit, monitoring unit, and platform unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214, and estimates the user's emotions using the control unit 46A. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes health checkup data to calculate a diabetes risk score. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and generates personalized advice based on the risk score. The monitoring unit is realized by the control unit 46A of the smart glasses 214 and collects daily health data using devices such as a blood glucose meter and a smart watch, and analyzes the data using AI. The platform unit is realized by the control unit 46A of the smart glasses 214 and provides a community platform where patients can exchange information with each other. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, prediction unit, provision unit, monitoring unit, and platform unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the user's facial expressions and voice using the camera 42 and microphone 238 of the headset-type terminal 314 and estimates the user's emotions using the control unit 46A. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes health checkup data to calculate a diabetes risk score. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and generates individualized advice based on the risk score. The monitoring unit is realized by the control unit 46A of the headset-type terminal 314 and collects daily health data using devices such as a blood glucose meter and a smartwatch, and analyzes the data using AI. The platform unit is realized by the control unit 46A of the headset-type terminal 314 and provides a community platform where patients can exchange information with each other. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, prediction unit, provision unit, monitoring unit, and platform unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's facial expressions and voice using the camera 42 and microphone 238 of the robot 414, and estimates the user's emotions using the control unit 46A. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes health checkup data to calculate a diabetes risk score. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and generates individualized advice based on the risk score. The monitoring unit is realized by the control unit 46A of the robot 414 and collects daily health data using devices such as a blood glucose meter and a smartwatch, and analyzes the data using AI. The platform unit is realized by the control unit 46A of the robot 414 and provides a community platform where patients can exchange information with each other.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The collection unit can also collect lifestyle habit data of the user and provide it to the prediction unit. For example, the collection unit can collect the user's sleep patterns and stress levels and provide this data to the prediction unit. The prediction unit can use this additional data to predict a more accurate risk score. The collection unit can also record the user's diet and exercise amount in detail and provide this data to the prediction unit. This allows the prediction unit to perform a risk assessment based on the user's lifestyle habits. Furthermore, the collection unit can collect environmental data of the user (e.g., living environment and work environment) and provide it to the prediction unit. This allows the prediction unit to perform a risk assessment that takes into account the user's environmental factors.

[0105] The prediction unit can estimate the user's emotions and adjust the risk score prediction method based on the estimated user's emotions. For example, if the user is feeling stressed, the prediction unit can predict a risk score that takes the stress level into account. Alternatively, if the user is relaxed, the prediction unit can predict a normal risk score. Furthermore, the prediction unit can monitor the user's emotional fluctuations over the long term and predict a risk score based on the emotional fluctuation pattern. This enables risk assessment according to the user's emotions.

[0106] The providing unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide advice in a gentle tone. Also, if the user is relaxed, the providing unit can provide detailed advice. Furthermore, the providing unit can monitor the user's emotional fluctuations over the long term and adjust the way in which advice is expressed based on the emotional fluctuation pattern. In this way, appropriate advice is provided according to the user's emotions.

[0107] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user's emotions. For example, the monitoring unit can increase the monitoring frequency when the user is feeling stressed. The monitoring unit can also decrease the monitoring frequency when the user is relaxed. Furthermore, the monitoring unit can monitor the user's emotional fluctuations over the long term and adjust the monitoring frequency based on the emotional fluctuation pattern. This allows appropriate monitoring to be performed according to the user's emotions.

[0108] The platform unit can estimate the user's emotions and adjust the display content of the platform based on the estimated user's emotions. For example, if the user is feeling stressed, the platform unit can display relaxing content. Also, if the user is relaxed, the platform unit can display educational content. Furthermore, the platform unit can monitor the user's emotional fluctuations over the long term and adjust the display content based on the emotional fluctuation patterns. This allows appropriate content to be provided according to the user's emotions.

[0109] The collection unit can collect data related to region-specific health risks by taking into account the user's geographical location information. For example, if the user lives in a high altitude, the collection unit can collect data related to high altitude-specific health risks. If the user lives in an urban area, the collection unit can also collect data related to urban area-specific health risks. Furthermore, if the user lives in a rural area, the collection unit can also collect data related to rural area-specific health risks. This enables more accurate health risk assessment based on the user's geographical location information.

[0110] The prediction unit can analyze the user's past health checkup data and select the optimal prediction algorithm. For example, the prediction unit selects an algorithm related to a specific health risk from the past data. The prediction unit can also select an algorithm to improve the accuracy of prediction based on the past data. Furthermore, the prediction unit can analyze the past data and select an algorithm that prioritizes the use of data related to a specific health risk. This enables more accurate risk assessment based on the user's past health checkup data.

[0111] The providing unit can customize the content of the advice based on the user's current living situation. For example, if the user is on a diet, the providing unit can provide advice focusing on dietary advice. Also, if the user has just started exercising, the providing unit can provide advice focusing on exercise advice. Furthermore, if the user is feeling stressed, the providing unit can provide advice focusing on stress management advice. This allows more appropriate advice to be provided based on the user's living situation.

[0112] The monitoring unit can analyze the user's social media activity and collect data related to health risks. For example, if the user frequently posts health-related content on social media, the monitoring unit can collect data based on the content. In addition, if the user shows interest in a particular health topic, the monitoring unit can collect data related to that topic. Furthermore, if the user shares information about a particular health risk, the monitoring unit can collect data related to that risk. This enables more accurate health risk assessment based on the user's social media activity.

[0113] The platform unit can select the optimal display method by referring to the user's past operation history. For example, the platform unit can provide a display method that the user has frequently used in the past with priority. The platform unit can also customize a specific display method based on the user's past operation history. Furthermore, the platform unit can analyze the user's past operation history and suggest the most efficient display method. This provides a platform that is easier to use based on the user's past operation history.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The collection unit collects health checkup data. Health checkup data includes blood glucose levels, weight, blood pressure, cholesterol levels, etc. The collection unit can collect daily health data using devices such as blood glucose meters and smartwatches. The collection unit also periodically collects health checkup data and converts it into a format that is easy for AI to analyze. Step 2: The prediction unit analyzes the data collected by the collection unit and predicts a diabetes risk score. The prediction unit uses AI to analyze the health checkup data and calculate a diabetes risk score. The AI ​​analyzes data such as blood glucose levels, weight, blood pressure, and cholesterol levels and calculates a risk score. Step 3: The provider provides personalized risk assessment and advice based on the risk score predicted by the predictor. The provider uses AI to generate personalized advice based on the risk score. Based on the risk score, the AI ​​provides specific advice such as dietary improvements and exercise recommendations. Step 4: The monitoring unit collects and analyzes data using monitoring devices. The monitoring unit uses devices such as blood glucose meters and smartwatches to collect daily health data, which is then analyzed by AI. The AI ​​then provides personalized advice based on the collected data. Step 5: The platform department provides a patient community platform. The platform department provides a community platform where patients can exchange information and support each other. Patients can share information such as meal recipes and exercise methods. The platform department also provides access to experts, so patients can receive professional advice as needed.

[0116] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0118] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0138] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0150] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0154] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0159] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0160] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0167] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0171] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0172] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0173] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0177] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0178] 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.

[0179] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0180] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0181] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0182] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0184] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0185] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0186] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0187] [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects health checkup data; a prediction unit that analyzes the data collected by the collection unit and predicts a diabetes risk score; a providing unit that provides an individual risk assessment and advice based on the risk score predicted by the prediction unit; A monitoring department that collects and analyzes data using monitoring equipment; A platform unit that provides a patient community platform. A system characterized by:

2. The collecting unit Collect health checkup data including blood sugar, weight, blood pressure, and cholesterol levels 2. The system of claim 1.

3. The prediction unit Analyzing collected health checkup data to predict diabetes risk scores 2. The system of claim 1.

4. The providing unit Based on the predicted risk score, provide personalized risk assessment and advice, including recommendations for dietary modifications and exercise 2. The system of claim 1.

5. The monitoring unit Use devices, including blood glucose monitors and smartwatches, to collect and analyze daily health data 2. The system of claim 1.

6. The platform portion is Providing a community platform where patients can exchange information and support each other 2. The system of claim 1.

7. The platform portion is Providing access to experts and providing specialized advice when needed 2. The system of claim 1.

8. The collecting unit To estimate a user's emotion and adjust the timing of collecting health checkup data based on the estimated user's emotion.

2. The system of claim 1.

Images