System

Abstract

An object of a system according to an embodiment is to predict a future health risk on the basis of a result of a medical checkup and propose an appropriate preventive measure or improvement measure.SOLUTION: A system includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a preventive measure or an improvement measure based on the result obtained by the analysis unit. The providing unit receives the information provided by the proposing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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] Conventional techniques have not yet been developed to effectively utilize the results of health checkups to predict future health risks, and there is room for improvement.

[0005] The system according to the embodiment aims to predict future health risks based on the results of health checkups and to propose appropriate preventive or remedial measures. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The suggestion unit proposes preventive or improvement measures based on the results obtained by the analysis unit. The provision unit receives information provided by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict future health risks based on the results of health checkups and suggest appropriate preventive or remedial measures. [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 non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a 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) A health risk prediction system according to an embodiment of the present invention digitizes and imports past health checkup results, and predicts future health risks by comparing them with information such as gender, age, and lifestyle. The health risk prediction system imports past health checkup results into a database, analyzes them using AI, and predicts future health risks by comparing them with information such as gender, age, and lifestyle. For example, the health risk prediction system imports past health checkup results into a database. This also imports information such as the examinee's gender, age, and lifestyle. For example, health checkup results such as blood pressure, blood sugar, and cholesterol levels are stored in the database. The health risk prediction system then analyzes the imported data using AI. The AI ​​predicts future health risks based on past health checkup results and information such as gender, age, and lifestyle. For example, the AI ​​can identify individuals at high risk for high blood pressure or diabetes from past data and provide them with appropriate health management advice. Furthermore, the health risk prediction system not only predicts future health risks, but also suggests preventive and remedial measures. For example, health risks can be reduced by recommending lifestyle improvements and regular health checkups. This allows the health risk prediction system to enable patients to understand their own health condition and predict future health risks. Furthermore, by implementing appropriate preventive and improvement measures, health risks can be reduced and patients can live healthy lives. This allows the health risk prediction system to enable patients to understand their own health condition and predict future health risks. Furthermore, by implementing appropriate preventive and improvement measures, health risks can be reduced and patients can live healthy lives.

[0029] A health risk prediction system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit imports past health checkup results into a database. Examples of past health checkup results include, but are not limited to, blood pressure, blood glucose levels, and cholesterol levels. The collection unit, for example, automatically imports health checkup results from an electronic medical record system. The collection unit can also import health checkup results manually entered by the patient. The collection unit can also import data acquired from a wearable device. For example, the collection unit automatically acquires data from the electronic medical record system and stores it in a database. The collection unit verifies data manually entered by the patient and stores it in the database. The collection unit imports data acquired from the wearable device in real time and stores it in the database. The analysis unit uses AI to analyze the data collected by the collection unit. The analysis is performed based on, for example, information such as gender, age, and lifestyle habits, but is not limited to, examples. For example, the analysis unit predicts health risks using a machine learning algorithm. The analysis unit can also predict health risks using deep learning. The analysis unit can also predict health risks using statistical analysis. For example, the analysis unit uses a machine learning algorithm to predict the risk of high blood pressure from past data. Deep learning learns from large amounts of data and makes highly accurate predictions. Statistical analysis analyzes data distributions and correlations to predict health risks. The suggestion unit suggests preventive measures and improvement measures based on the results obtained by the analysis unit. Suggestions include, but are not limited to, recommending lifestyle improvements and regular health checkups. For example, the suggestion unit can suggest an exercise program. The suggestion unit can also suggest dietary advice. The suggestion unit can also suggest stress management methods. For example, the suggestion unit can recommend aerobic exercise three times a week as an exercise program. The dietary advice can recommend a balanced diet. The stress management method can recommend relaxation techniques and meditation. The provision unit receives the information provided by the suggestion unit. The provision can be, for example, via email or app notification, but is not limited to, these examples.For example, the providing unit notifies the patient of preventive measures or improvement measures by email. The providing unit can also notify the patient of preventive measures or improvement measures through an app notification. The providing unit can also provide the patient with preventive measures or improvement measures through a web portal. For example, the providing unit notifies the patient of an exercise program by email. The app notification provides information in real time. The web portal functions as an information source that the patient can access at any time. As a result, the health risk prediction system according to the embodiment collects, analyzes, proposes, and provides data in a continuous flow, thereby predicting future health risks and providing appropriate preventive measures and improvement measures.

[0030] The collection unit can import past health checkup results into a database. Past health checkup results include, but are not limited to, blood pressure, blood glucose levels, and cholesterol levels. The collection unit, for example, automatically imports health checkup results from an electronic medical record system. The collection unit can also import health checkup results manually entered by the examinee. For example, the collection unit automatically acquires data from the electronic medical record system and stores it in a database. The collection unit verifies the data manually entered by the examinee and stores it in the database. By importing past health checkup results into the database, data necessary for predicting future health risks can be collected. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input data acquired from the electronic medical record system into a generation AI and have the generation AI verify the data.

[0031] The analysis unit can predict future health risks based on information on gender, age, and lifestyle habits. The analysis unit can predict health risks using, for example, a machine learning algorithm. For example, the analysis unit can predict the risk of high blood pressure based on past data. The analysis unit can also predict health risks using deep learning. For example, deep learning learns from large amounts of data and makes highly accurate predictions. The analysis unit can also predict health risks using statistical analysis. For example, statistical analysis analyzes data distribution and correlations to predict health risks. This makes it possible to predict health risks based on information such as gender, age, and lifestyle habits, thereby providing preventive and remedial measures appropriate for each individual user. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input past health checkup data into the generation AI and have the generation AI perform health risk predictions.

[0032] The suggestion unit can suggest preventive measures and improvement measures. The suggestion unit, for example, suggests an exercise program. For example, the suggestion unit recommends aerobic exercise three times a week. The suggestion unit can also suggest dietary advice. For example, the suggestion unit recommends a balanced diet. The suggestion unit can also suggest stress management methods. For example, the suggestion unit recommends relaxation techniques and meditation. By suggesting preventive measures and improvement measures, the user can take specific actions to reduce health risks. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the health risk prediction results to the generation AI and cause the generation AI to suggest preventive measures and improvement measures.

[0033] The providing unit can provide preventive measures and improvement measures to the patient. The providing unit, for example, notifies the patient of the preventive measures and improvement measures by email. For example, the providing unit notifies the patient of an exercise program by email. The providing unit can also notify the patient of the preventive measures and improvement measures through an app notification. For example, the app notification provides information in real time. The providing unit can also provide the preventive measures and improvement measures to the patient through a web portal. For example, the web portal functions as an information source that the patient can access at any time. This can encourage the patient to take specific actions to reduce health risks by providing the preventive measures and improvement measures to the patient. Some or all of the above-mentioned processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input information about the preventive measures and improvement measures to a generation AI and cause the generation AI to generate information to be provided to the patient.

[0034] The collection unit can analyze the user's past health checkup data and select an appropriate collection method. For example, if the user has previously measured their blood pressure, the collection unit can collect data using the same method. Furthermore, if the user has previously measured their blood glucose level, the collection unit can also collect data using the same method. Furthermore, if the user has previously measured their cholesterol level, the collection unit can also collect data using the same method. This allows the optimal collection method to be selected by analyzing past data, enabling efficient data collection. 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 can input past health checkup data into the generation AI and have the generation AI select the optimal collection method.

[0035] The collection unit can filter the health checkup data based on the user's current health condition and lifestyle habits when collecting the data. For example, if the user currently has high blood pressure, the collection unit can prioritize collecting blood pressure data. Furthermore, if the user currently has diabetes, the collection unit can prioritize collecting blood glucose level data. Furthermore, if the user currently has high cholesterol, the collection unit can prioritize collecting cholesterol level data. This allows important data to be collected preferentially by filtering the data based on the user's current health condition and lifestyle habits. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's current health condition and lifestyle habit data into the generation AI and have the generation AI perform filtering.

[0036] When collecting health checkup data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user desires voice input, the collection unit collects data by voice. Furthermore, if the user desires text input, the collection unit can also collect data by text. Furthermore, if the user desires image input, the collection unit can also collect data by image. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. 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 can input data depending on the user's input method into a generation AI and have the generation AI select the optimal collection means.

[0037] When collecting health checkup data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user lives at high altitude, the collection unit can prioritize collecting data related to health risks specific to high altitudes. Furthermore, if the user lives in an urban area, the collection unit can prioritize collecting data related to health risks specific to urban areas. Furthermore, if the user lives by the sea, the collection unit can prioritize collecting data related to health risks specific to seaside areas. In this way, highly relevant data can be collected preferentially by taking the geographical location information into consideration. 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 can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0038] 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 posts about health on social media, the collection unit collects data related to the content. Furthermore, if the user posts about exercise on social media, the collection unit can collect data related to the content. Furthermore, if the user posts about diet on social media, the collection unit can collect data related to the content. This allows for efficient collection of related data by analyzing social media activity. 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 can input the user's social media activity data into the generation AI and cause the generation AI to collect related data.

[0039] When collecting health checkup data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, if the user has preferred voice input in the past, the collection unit can prioritize voice input. Also, if the user has preferred text input in the past, the collection unit can prioritize text input. Also, if the user has preferred image input in the past, the collection unit can prioritize image input. In this way, by reflecting past feedback, it is possible to provide the optimal collection method for the user. 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 the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health risk. For example, if the risk of high blood pressure is high, the analysis unit can perform a detailed blood pressure analysis. Furthermore, if the risk of diabetes is high, the analysis unit can also perform a detailed blood glucose level analysis. Furthermore, if the risk of high cholesterol is high, the analysis unit can also perform a detailed cholesterol level analysis. In this way, by adjusting the level of detail of the analysis based on the importance of the health risk, detailed analysis can be performed for important risks. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health risk importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the health risk category. For example, the analysis unit can apply a cardiovascular analysis algorithm to cardiovascular risk. The analysis unit can also apply a diabetes analysis algorithm to diabetes risk. The analysis unit can also apply a cancer analysis algorithm to cancer risk. This makes it possible to provide highly accurate analysis results by applying an appropriate analysis algorithm depending on the health risk category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input health risk category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve the accuracy of the current blood pressure analysis by referring to the user's past blood pressure analysis results. The analysis unit can also improve the accuracy of the current blood glucose level analysis by referring to the user's past blood glucose level analysis results. The analysis unit can also improve the accuracy of the current cholesterol level analysis by referring to the user's past cholesterol level analysis results. In this way, by referring to the past analysis results, the accuracy of the current analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the health checkup data. For example, the analysis unit prioritizes analysis of the most recent health checkup data. The analysis unit can also prioritize analysis of important data by referring to past health checkup data. The analysis unit can also postpone analysis of data submitted earlier. In this way, by determining the priority of analysis based on the submission date, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of the health checkup data into the generation AI and have the generation AI determine the analysis priority.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the health checkup data. For example, the analysis unit prioritizes analyzing data related to high blood pressure. The analysis unit can also prioritize analyzing data related to diabetes. The analysis unit can also prioritize analyzing data related to high cholesterol. By adjusting the order of analysis based on the relevance, important data can be prioritized for analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the health checkup data to the generation AI and have the generation AI adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is a medical professional, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user is a layperson, the analysis unit can also provide analysis results that avoid technical terminology. Furthermore, if the user has medical knowledge, the analysis unit can also provide analysis results that use a moderate amount of technical terminology. By adjusting the use of technical terminology according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0046] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the preventive measures and improvement measures. For example, if the risk of high blood pressure is high, the suggestion unit can suggest detailed preventive measures. Furthermore, if the risk of diabetes is high, the suggestion unit can also suggest detailed improvement measures. Furthermore, if the risk of high cholesterol is high, the suggestion unit can also suggest detailed preventive measures. In this way, by adjusting the level of detail of the proposal based on the importance of the preventive measures and improvement measures, detailed suggestions can be made for important risks. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input importance data of the preventive measures and improvement measures to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0047] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the preventive measure or improvement measure. For example, the proposal unit applies an algorithm that proposes cardiovascular preventive measures to cardiovascular risk. The proposal unit can also apply an algorithm that proposes diabetes improvement measures to diabetes risk. The proposal unit can also apply an algorithm that proposes cancer preventive measures to cancer risk. In this way, by applying an appropriate proposal algorithm depending on the category of the preventive measure or improvement measure, highly accurate proposals can be provided. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input category data of the preventive measure or improvement measure to the generation AI and cause the generation AI to apply the proposal algorithm.

[0048] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit can, for example, improve the accuracy of the current proposal by referring to the results of the user's past high blood pressure prevention measures. The suggestion unit can also improve the accuracy of the current proposal by referring to the results of the user's past diabetes improvement measures. The suggestion unit can also improve the accuracy of the current proposal by referring to the results of the user's past cholesterol level improvement measures. In this way, the accuracy of the current proposal can be improved by referring to the past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0049] When making a proposal, the suggestion unit can determine the priority of the proposals based on the submission dates of the preventive measures and improvement measures. For example, the suggestion unit prioritizes the most important preventive measures based on the latest health checkup data. The suggestion unit can also prioritize important improvement measures by referring to past health checkup data. The suggestion unit can also postpone the proposal of data submitted earlier. In this way, by determining the priority of the proposals based on the submission dates, it is possible to prioritize proposals based on the latest data. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs submission date data of preventive measures and improvement measures into the generation AI, and causes the generation AI to determine the priority of the proposals.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of preventive measures and remedial measures when making suggestions. For example, the suggestion unit prioritizes suggesting preventive measures related to high blood pressure. The suggestion unit can also prioritize suggesting remedial measures related to diabetes. The suggestion unit can also prioritize suggesting preventive measures related to high cholesterol. In this way, by adjusting the order of suggestions based on the relevance, important suggestions can be prioritized. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input relevance data of preventive measures and remedial measures into the generation AI and cause the generation AI to adjust the order of suggestions.

[0051] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user is a medical professional, the suggestion unit can provide a proposal that uses a lot of technical terminology. Furthermore, if the user is a layperson, the suggestion unit can also provide a proposal that avoids technical terminology. Furthermore, if the user has medical knowledge, the suggestion unit can also provide a proposal that uses technical terminology moderately. By adjusting the use of technical terminology according to the user's level of expertise, it is possible to provide a proposal that is easy for the user to understand. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0052] The providing unit can adjust the level of detail of the provided information based on the importance of the preventive measures and remedial measures when providing the information. For example, if the risk of high blood pressure is high, the providing unit can provide detailed preventive measures. The providing unit can also provide detailed remedial measures if the risk of diabetes is high. The providing unit can also provide detailed preventive measures if the risk of high cholesterol is high. In this way, by adjusting the level of detail of the provided information based on the importance of the preventive measures and remedial measures, important information can be provided in detail. 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 importance data of the preventive measures and remedial measures to the generating AI and cause the generating AI to adjust the level of detail of the provided information.

[0053] The providing unit can apply different providing algorithms depending on the category of the preventive measures or improvement measures when providing the information. For example, the providing unit applies an algorithm that provides cardiovascular preventive measures to cardiovascular risk. The providing unit can also apply an algorithm that provides diabetes improvement measures to diabetes risk. The providing unit can also apply an algorithm that provides cancer preventive measures to cancer risk. This makes it possible to provide highly accurate information by applying an appropriate providing algorithm depending on the category of the preventive measures or improvement measures. Some or all of the above-mentioned 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 category data of the preventive measures or improvement measures to the generating AI and cause the generating AI to apply the providing algorithm.

[0054] The providing unit can improve the accuracy of the provision at the time of provision by referring to the user's past provision results. The providing unit can improve the accuracy of the current provision by referring to, for example, the results of the user's past high blood pressure prevention measures. The providing unit can also improve the accuracy of the current provision by referring to the results of the user's past diabetes improvement measures. The providing unit can also improve the accuracy of the current provision by referring to the results of the user's past cholesterol level improvement measures. In this way, the accuracy of the current provision can be improved by referring to the past provision results. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0055] At the time of provision, the providing unit can determine the priority of provision based on the submission date of preventive measures and improvement measures. For example, the providing unit can prioritize providing the most important preventive measures based on the latest health checkup data. The providing unit can also prioritize providing important improvement measures by referring to past health checkup data. The providing unit can also postpone providing data that was submitted earlier. In this way, by determining the priority of provision based on the submission date, information based on the latest data can be provided preferentially. Some or all of the above-mentioned 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 submission date data of preventive measures and improvement measures into the generating AI and cause the generating AI to determine the priority of provision.

[0056] The providing unit can adjust the order of provision based on the relevance of preventive measures and remedial measures when providing the information. For example, the providing unit can prioritize providing preventive measures related to high blood pressure. The providing unit can also prioritize providing remedial measures related to diabetes. The providing unit can also prioritize providing preventive measures related to high cholesterol. In this way, by adjusting the order of provision based on the relevance, important information can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of preventive measures and remedial measures into the generating AI and cause the generating AI to adjust the order of provision.

[0057] The providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise. For example, if the user is a medical professional, the providing unit can provide information that uses a lot of technical terminology. Furthermore, if the user is a layperson, the providing unit can also provide information that avoids technical terminology. Furthermore, if the user has medical knowledge, the providing unit can also provide information that uses technical terminology moderately. In this way, by adjusting the use of technical terminology according to the user's level of expertise, it is possible to provide information that is easy for the user to understand. Some or all of the above-mentioned 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 user's level of expertise data into the generating AI and cause the generating AI to adjust the use of technical terminology.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The collection unit can prioritize collection of highly relevant data by taking into account the user's geographical location information. For example, if the user lives at high altitude, data related to health risks specific to high altitudes can be prioritized. Also, if the user lives in an urban area, data related to health risks specific to urban areas can be prioritized. Furthermore, if the user lives by the sea, data related to health risks specific to the seaside can be prioritized. In this way, highly relevant data can be prioritized by taking the geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0060] When collecting health checkup data, the collection unit can analyze the user's social media activity and collect related data. For example, if the user posts about health on social media, data related to that content can be collected. Also, if the user posts about exercise on social media, data related to that content can be collected. Furthermore, if the user posts about diet on social media, data related to that content can be collected. This allows for efficient collection of related data by analyzing social media activity. 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 the user's social media activity data into the generation AI and cause the generation AI to collect related data.

[0061] When collecting health checkup data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, if the user has preferred voice input in the past, the collection unit can provide voice input preferentially. Furthermore, if the user has preferred text input in the past, the collection unit can provide text input preferentially. Furthermore, if the user has preferred image input in the past, the collection unit can provide image input preferentially. In this way, by reflecting past feedback, the optimal collection method can be provided for the user. 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 the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0062] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health risk. For example, if the risk of high blood pressure is high, a detailed blood pressure analysis can be performed. Also, if the risk of diabetes is high, a detailed blood glucose level analysis can be performed. Furthermore, if the risk of high cholesterol is high, a detailed cholesterol level analysis can be performed. In this way, by adjusting the level of detail of the analysis based on the importance of the health risk, detailed analysis can be performed for important risks. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health risk importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0063] During analysis, the analysis unit can apply different analysis algorithms depending on the health risk category. For example, a cardiovascular analysis algorithm can be applied to cardiovascular risk. A diabetes analysis algorithm can also be applied to diabetes risk. Furthermore, a cancer analysis algorithm can be applied to cancer risk. This makes it possible to provide highly accurate analysis results by applying an appropriate analysis algorithm depending on the health risk category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health risk category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection department imports past health checkup results into a database. Specifically, it automatically retrieves data from the electronic medical record system and stores it in the database. It can also retrieve data manually entered by patients and data obtained from wearable devices. In this way, the collection department collects data from a variety of data sources and stores it in the database. Step 2: The analysis unit uses AI to analyze the data collected by the collection unit. The analysis is based on information such as gender, age, and lifestyle habits, and predicts health risks using machine learning algorithms, deep learning, and statistical analysis. This allows the analysis unit to make highly accurate health risk predictions. Step 3: The proposal unit proposes preventive and improvement measures based on the results obtained by the analysis unit. Specifically, it suggests lifestyle improvements, regular health checkups, exercise programs, dietary advice, stress management methods, etc. In this way, the proposal unit provides the examinee with concrete guidelines for action. Step 4: The provider receives the information provided by the suggestion department. This is done via email, app notification, or web portal, allowing patients to quickly receive suggested preventive and improvement measures.

[0066] (Example 2) A health risk prediction system according to an embodiment of the present invention digitizes and imports past health checkup results, and predicts future health risks by comparing them with information such as gender, age, and lifestyle. The health risk prediction system imports past health checkup results into a database, analyzes them using AI, and predicts future health risks by comparing them with information such as gender, age, and lifestyle. For example, the health risk prediction system imports past health checkup results into a database. This also imports information such as the examinee's gender, age, and lifestyle. For example, health checkup results such as blood pressure, blood sugar, and cholesterol levels are stored in the database. The health risk prediction system then analyzes the imported data using AI. The AI ​​predicts future health risks based on past health checkup results and information such as gender, age, and lifestyle. For example, the AI ​​can identify individuals at high risk for high blood pressure or diabetes from past data and provide them with appropriate health management advice. Furthermore, the health risk prediction system not only predicts future health risks, but also suggests preventive and remedial measures. For example, health risks can be reduced by recommending lifestyle improvements and regular health checkups. This allows the health risk prediction system to enable patients to understand their own health condition and predict future health risks. Furthermore, by implementing appropriate preventive and improvement measures, health risks can be reduced and patients can live healthy lives. This allows the health risk prediction system to enable patients to understand their own health condition and predict future health risks. Furthermore, by implementing appropriate preventive and improvement measures, health risks can be reduced and patients can live healthy lives.

[0067] A health risk prediction system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit imports past health checkup results into a database. Examples of past health checkup results include, but are not limited to, blood pressure, blood glucose levels, and cholesterol levels. The collection unit, for example, automatically imports health checkup results from an electronic medical record system. The collection unit can also import health checkup results manually entered by the patient. The collection unit can also import data acquired from a wearable device. For example, the collection unit automatically acquires data from the electronic medical record system and stores it in a database. The collection unit verifies data manually entered by the patient and stores it in the database. The collection unit imports data acquired from the wearable device in real time and stores it in the database. The analysis unit uses AI to analyze the data collected by the collection unit. The analysis is performed based on, for example, information such as gender, age, and lifestyle habits, but is not limited to, examples. For example, the analysis unit predicts health risks using a machine learning algorithm. The analysis unit can also predict health risks using deep learning. The analysis unit can also predict health risks using statistical analysis. For example, the analysis unit uses a machine learning algorithm to predict the risk of high blood pressure from past data. Deep learning learns from large amounts of data and makes highly accurate predictions. Statistical analysis analyzes data distributions and correlations to predict health risks. The suggestion unit suggests preventive measures and improvement measures based on the results obtained by the analysis unit. Suggestions include, but are not limited to, recommending lifestyle improvements and regular health checkups. For example, the suggestion unit can suggest an exercise program. The suggestion unit can also suggest dietary advice. The suggestion unit can also suggest stress management methods. For example, the suggestion unit can recommend aerobic exercise three times a week as an exercise program. The dietary advice can recommend a balanced diet. The stress management method can recommend relaxation techniques and meditation. The provision unit receives the information provided by the suggestion unit. The provision can be, for example, via email or app notification, but is not limited to, these examples.For example, the providing unit notifies the patient of preventive measures or improvement measures by email. The providing unit can also notify the patient of preventive measures or improvement measures through an app notification. The providing unit can also provide the patient with preventive measures or improvement measures through a web portal. For example, the providing unit notifies the patient of an exercise program by email. The app notification provides information in real time. The web portal functions as an information source that the patient can access at any time. As a result, the health risk prediction system according to the embodiment collects, analyzes, proposes, and provides data in a continuous flow, thereby predicting future health risks and providing appropriate preventive measures and improvement measures.

[0068] The collection unit can import past health checkup results into a database. Past health checkup results include, but are not limited to, blood pressure, blood glucose levels, and cholesterol levels. The collection unit, for example, automatically imports health checkup results from an electronic medical record system. The collection unit can also import health checkup results manually entered by the examinee. For example, the collection unit automatically acquires data from the electronic medical record system and stores it in a database. The collection unit verifies the data manually entered by the examinee and stores it in the database. By importing past health checkup results into the database, data necessary for predicting future health risks can be collected. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input data acquired from the electronic medical record system into a generation AI and have the generation AI verify the data.

[0069] The analysis unit can predict future health risks based on information on gender, age, and lifestyle habits. The analysis unit can predict health risks using, for example, a machine learning algorithm. For example, the analysis unit can predict the risk of high blood pressure based on past data. The analysis unit can also predict health risks using deep learning. For example, deep learning learns from large amounts of data and makes highly accurate predictions. The analysis unit can also predict health risks using statistical analysis. For example, statistical analysis analyzes data distribution and correlations to predict health risks. This makes it possible to predict health risks based on information such as gender, age, and lifestyle habits, thereby providing preventive and remedial measures appropriate for each individual user. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input past health checkup data into the generation AI and have the generation AI perform health risk predictions.

[0070] The suggestion unit can suggest preventive measures and improvement measures. The suggestion unit, for example, suggests an exercise program. For example, the suggestion unit recommends aerobic exercise three times a week. The suggestion unit can also suggest dietary advice. For example, the suggestion unit recommends a balanced diet. The suggestion unit can also suggest stress management methods. For example, the suggestion unit recommends relaxation techniques and meditation. By suggesting preventive measures and improvement measures, the user can take specific actions to reduce health risks. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the health risk prediction results to the generation AI and cause the generation AI to suggest preventive measures and improvement measures.

[0071] The providing unit can provide preventive measures and improvement measures to the patient. The providing unit, for example, notifies the patient of the preventive measures and improvement measures by email. For example, the providing unit notifies the patient of an exercise program by email. The providing unit can also notify the patient of the preventive measures and improvement measures through an app notification. For example, the app notification provides information in real time. The providing unit can also provide the preventive measures and improvement measures to the patient through a web portal. For example, the web portal functions as an information source that the patient can access at any time. This can encourage the patient to take specific actions to reduce health risks by providing the preventive measures and improvement measures to the patient. Some or all of the above-mentioned processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input information about the preventive measures and improvement measures to a generation AI and cause the generation AI to generate information to be provided to the patient.

[0072] The collection unit can estimate the user's emotions and adjust the timing of collecting health checkup data based on the emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing to collect data in a relaxed state. Furthermore, if the user is relaxed, the collection unit can collect data immediately to obtain highly accurate data. Furthermore, if the user is in a hurry, the collection unit can collect data in a short time to reduce the user's burden. This allows for more accurate data collection by adjusting the collection timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the collection timing.

[0073] The collection unit can analyze the user's past health checkup data and select an appropriate collection method. For example, if the user has previously measured their blood pressure, the collection unit can collect data using the same method. Furthermore, if the user has previously measured their blood glucose level, the collection unit can also collect data using the same method. Furthermore, if the user has previously measured their cholesterol level, the collection unit can also collect data using the same method. This allows the optimal collection method to be selected by analyzing past data, enabling efficient data collection. 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 can input past health checkup data into the generation AI and have the generation AI select the optimal collection method.

[0074] The collection unit can filter the health checkup data based on the user's current health condition and lifestyle habits when collecting the data. For example, if the user currently has high blood pressure, the collection unit can prioritize collecting blood pressure data. Furthermore, if the user currently has diabetes, the collection unit can prioritize collecting blood glucose level data. Furthermore, if the user currently has high cholesterol, the collection unit can prioritize collecting cholesterol level data. This allows important data to be collected preferentially by filtering the data based on the user's current health condition and lifestyle habits. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's current health condition and lifestyle habit data into the generation AI and have the generation AI perform filtering.

[0075] When collecting health checkup data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user desires voice input, the collection unit collects data by voice. Furthermore, if the user desires text input, the collection unit can also collect data by text. Furthermore, if the user desires image input, the collection unit can also collect data by image. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. 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 can input data depending on the user's input method into a generation AI and have the generation AI select the optimal collection means.

[0076] The collection unit can estimate the user's emotions and determine the priority of health checkup data to be collected based on the emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting stress-related data. Furthermore, if the user is relaxed, the collection unit can also collect overall health data in a balanced manner. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important data. Thus, by determining the priority of data based on the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, 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 collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0077] When collecting health checkup data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, if the user lives at high altitude, the collection unit can prioritize collecting data related to health risks specific to high altitudes. Furthermore, if the user lives in an urban area, the collection unit can prioritize collecting data related to health risks specific to urban areas. Furthermore, if the user lives by the sea, the collection unit can prioritize collecting data related to health risks specific to seaside areas. In this way, highly relevant data can be collected preferentially by taking the geographical location information into consideration. 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 can input the user's geographical location information into the generation AI and cause the generation 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 posts about health on social media, the collection unit collects data related to the content. Furthermore, if the user posts about exercise on social media, the collection unit can collect data related to the content. Furthermore, if the user posts about diet on social media, the collection unit can collect data related to the content. This allows for efficient collection of related data by analyzing social media activity. 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 can input the user's social media activity data into the generation AI and cause the generation AI to collect related data.

[0079] When collecting health checkup data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, if the user has preferred voice input in the past, the collection unit can prioritize voice input. Also, if the user has preferred text input in the past, the collection unit can prioritize text input. Also, if the user has preferred image input in the past, the collection unit can prioritize image input. In this way, by reflecting past feedback, it is possible to provide the optimal collection method for the user. 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 the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0080] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that are easy to understand by adjusting the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health risk. For example, if the risk of high blood pressure is high, the analysis unit can perform a detailed blood pressure analysis. Furthermore, if the risk of diabetes is high, the analysis unit can also perform a detailed blood glucose level analysis. Furthermore, if the risk of high cholesterol is high, the analysis unit can also perform a detailed cholesterol level analysis. In this way, by adjusting the level of detail of the analysis based on the importance of the health risk, detailed analysis can be performed for important risks. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health risk importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the health risk category. For example, the analysis unit can apply a cardiovascular analysis algorithm to cardiovascular risk. The analysis unit can also apply a diabetes analysis algorithm to diabetes risk. The analysis unit can also apply a cancer analysis algorithm to cancer risk. This makes it possible to provide highly accurate analysis results by applying an appropriate analysis algorithm depending on the health risk category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input health risk category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve the accuracy of the current blood pressure analysis by referring to the user's past blood pressure analysis results. The analysis unit can also improve the accuracy of the current blood glucose level analysis by referring to the user's past blood glucose level analysis results. The analysis unit can also improve the accuracy of the current cholesterol level analysis by referring to the user's past cholesterol level analysis results. In this way, by referring to the past analysis results, the accuracy of the current analysis can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the emotions. For example, if the user is nervous, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can quickly provide an analysis result. By adjusting the length of the analysis according to the user's emotions, it is possible to provide an optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0085] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the health checkup data. For example, the analysis unit prioritizes analysis of the most recent health checkup data. The analysis unit can also prioritize analysis of important data by referring to past health checkup data. The analysis unit can also postpone analysis of data submitted earlier. In this way, by determining the priority of analysis based on the submission date, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of the health checkup data into the generation AI and have the generation AI determine the analysis priority.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the health checkup data. For example, the analysis unit prioritizes analyzing data related to high blood pressure. The analysis unit can also prioritize analyzing data related to diabetes. The analysis unit can also prioritize analyzing data related to high cholesterol. By adjusting the order of analysis based on the relevance, important data can be prioritized for analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the health checkup data to the generation AI and have the generation AI adjust the order of analysis.

[0087] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user is a medical professional, the analysis unit can provide analysis results that use a lot of technical terminology. Furthermore, if the user is a layperson, the analysis unit can also provide analysis results that avoid technical terminology. Furthermore, if the user has medical knowledge, the analysis unit can also provide analysis results that use a moderate amount of technical terminology. By adjusting the use of technical terminology according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0088] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that are easy to understand by adjusting the way the suggestions are expressed based on the user's emotions. The emotion estimation is realized using an emotion estimation function, 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 suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0089] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the preventive measures and improvement measures. For example, if the risk of high blood pressure is high, the suggestion unit can suggest detailed preventive measures. Furthermore, if the risk of diabetes is high, the suggestion unit can also suggest detailed improvement measures. Furthermore, if the risk of high cholesterol is high, the suggestion unit can also suggest detailed preventive measures. In this way, by adjusting the level of detail of the proposal based on the importance of the preventive measures and improvement measures, detailed suggestions can be made for important risks. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input importance data of the preventive measures and improvement measures to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0090] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the preventive measure or improvement measure. For example, the proposal unit applies an algorithm that proposes cardiovascular preventive measures to cardiovascular risk. The proposal unit can also apply an algorithm that proposes diabetes improvement measures to diabetes risk. The proposal unit can also apply an algorithm that proposes cancer preventive measures to cancer risk. In this way, by applying an appropriate proposal algorithm depending on the category of the preventive measure or improvement measure, highly accurate proposals can be provided. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input category data of the preventive measure or improvement measure to the generation AI and cause the generation AI to apply the proposal algorithm.

[0091] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit can, for example, improve the accuracy of the current proposal by referring to the results of the user's past high blood pressure prevention measures. The suggestion unit can also improve the accuracy of the current proposal by referring to the results of the user's past diabetes improvement measures. The suggestion unit can also improve the accuracy of the current proposal by referring to the results of the user's past cholesterol level improvement measures. In this way, the accuracy of the current proposal can be improved by referring to the past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0092] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the emotions. For example, if the user is nervous, the suggestion unit can provide short and to-the-point suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can also provide quick suggestions. By adjusting the length of the suggestions according to the user's emotions, the suggestion unit can provide optimal suggestions for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0093] When making a proposal, the suggestion unit can determine the priority of the proposals based on the submission dates of the preventive measures and improvement measures. For example, the suggestion unit prioritizes the most important preventive measures based on the latest health checkup data. The suggestion unit can also prioritize important improvement measures by referring to past health checkup data. The suggestion unit can also postpone the proposal of data submitted earlier. In this way, by determining the priority of the proposals based on the submission dates, it is possible to prioritize proposals based on the latest data. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit inputs submission date data of preventive measures and improvement measures into the generation AI, and causes the generation AI to determine the priority of the proposals.

[0094] The suggestion unit can adjust the order of suggestions based on the relevance of preventive measures and remedial measures when making suggestions. For example, the suggestion unit prioritizes suggesting preventive measures related to high blood pressure. The suggestion unit can also prioritize suggesting remedial measures related to diabetes. The suggestion unit can also prioritize suggesting preventive measures related to high cholesterol. In this way, by adjusting the order of suggestions based on the relevance, important suggestions can be prioritized. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input relevance data of preventive measures and remedial measures into the generation AI and cause the generation AI to adjust the order of suggestions.

[0095] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user is a medical professional, the suggestion unit can provide a proposal that uses a lot of technical terminology. Furthermore, if the user is a layperson, the suggestion unit can also provide a proposal that avoids technical terminology. Furthermore, if the user has medical knowledge, the suggestion unit can also provide a proposal that uses technical terminology moderately. By adjusting the use of technical terminology according to the user's level of expertise, it is possible to provide a proposal that is easy for the user to understand. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0096] The providing unit can estimate the user's emotions and adjust the way the information is presented based on the emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible information. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide information that is easy to understand by adjusting the way the information is presented based on the user's emotions. The emotion estimation is realized using an emotion estimation function, 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 can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the information is presented.

[0097] The providing unit can adjust the level of detail of the provided information based on the importance of the preventive measures and remedial measures when providing the information. For example, if the risk of high blood pressure is high, the providing unit can provide detailed preventive measures. The providing unit can also provide detailed remedial measures if the risk of diabetes is high. The providing unit can also provide detailed preventive measures if the risk of high cholesterol is high. In this way, by adjusting the level of detail of the provided information based on the importance of the preventive measures and remedial measures, important information can be provided in detail. 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 importance data of the preventive measures and remedial measures to the generating AI and cause the generating AI to adjust the level of detail of the provided information.

[0098] The providing unit can apply different providing algorithms depending on the category of the preventive measures or improvement measures when providing the information. For example, the providing unit applies an algorithm that provides cardiovascular preventive measures to cardiovascular risk. The providing unit can also apply an algorithm that provides diabetes improvement measures to diabetes risk. The providing unit can also apply an algorithm that provides cancer preventive measures to cancer risk. This makes it possible to provide highly accurate information by applying an appropriate providing algorithm depending on the category of the preventive measures or improvement measures. Some or all of the above-mentioned 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 category data of the preventive measures or improvement measures to the generating AI and cause the generating AI to apply the providing algorithm.

[0099] The providing unit can improve the accuracy of the provision at the time of provision by referring to the user's past provision results. The providing unit can improve the accuracy of the current provision by referring to, for example, the results of the user's past high blood pressure prevention measures. The providing unit can also improve the accuracy of the current provision by referring to the results of the user's past diabetes improvement measures. The providing unit can also improve the accuracy of the current provision by referring to the results of the user's past cholesterol level improvement measures. In this way, the accuracy of the current provision can be improved by referring to the past provision results. Some or all of the above-described processing in the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input past provision result data into the generation AI and cause the generation AI to improve the accuracy of the provision.

[0100] The providing unit can estimate the user's emotions and adjust the length of the information to be provided based on the emotions. For example, if the user is nervous, the providing unit can provide short, to-the-point information. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can quickly provide information. By adjusting the length of information according to the user's emotions, optimal information can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the information.

[0101] At the time of provision, the providing unit can determine the priority of provision based on the submission date of preventive measures and improvement measures. For example, the providing unit can prioritize providing the most important preventive measures based on the latest health checkup data. The providing unit can also prioritize providing important improvement measures by referring to past health checkup data. The providing unit can also postpone providing data that was submitted earlier. In this way, by determining the priority of provision based on the submission date, information based on the latest data can be provided preferentially. Some or all of the above-mentioned 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 submission date data of preventive measures and improvement measures into the generating AI and cause the generating AI to determine the priority of provision.

[0102] The providing unit can adjust the order of provision based on the relevance of preventive measures and remedial measures when providing the information. For example, the providing unit can prioritize providing preventive measures related to high blood pressure. The providing unit can also prioritize providing remedial measures related to diabetes. The providing unit can also prioritize providing preventive measures related to high cholesterol. In this way, by adjusting the order of provision based on the relevance, important information can be provided preferentially. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of preventive measures and remedial measures into the generating AI and cause the generating AI to adjust the order of provision.

[0103] The providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise. For example, if the user is a medical professional, the providing unit can provide information that uses a lot of technical terminology. Furthermore, if the user is a layperson, the providing unit can also provide information that avoids technical terminology. Furthermore, if the user has medical knowledge, the providing unit can also provide information that uses technical terminology moderately. In this way, by adjusting the use of technical terminology according to the user's level of expertise, it is possible to provide information that is easy for the user to understand. Some or all of the above-mentioned 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 user's level of expertise data into the generating AI and cause the generating AI to adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and provision 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 can capture health checkup results using the camera 42 and microphone 38B of the smart device 14 and store the results in a database by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using AI. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, suggests preventive measures and improvement measures based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides information to the examinee via email or app notification. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and provision 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 can capture health checkup results using the camera 42 and microphone 238 of the smart glasses 214 and store the results in a database by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data using AI. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, suggests preventive measures and improvement measures based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides information to the examinee via email or app notification. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and provision 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 can capture health checkup results using the camera 42 and microphone 238 of the headset-type terminal 314 and store the results in a database by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests preventive measures and improvement measures based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides information to the examinee via email or app notification. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and provision 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 can capture health checkup results using the camera 42 and microphone 238 of the robot 414 and store the results in a database by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests preventive measures or improvement measures based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides information to the examinee via email or app notification.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The collection unit can estimate the user's emotions and adjust the timing of collecting health checkup data based on the emotions. For example, if the user is feeling stressed, the collection timing can be delayed to collect data in a relaxed state. Also, if the user is relaxed, data can be collected immediately to obtain highly accurate data. Furthermore, if the user is in a hurry, data can be collected in a short time, reducing the user's burden. This allows for more accurate data collection by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, 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 collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the collection timing.

[0106] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide analysis results that are concise. By adjusting the way the analysis is presented based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0107] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the emotions. For example, if the user is nervous, a simple, highly visible suggestion can be provided. If the user is relaxed, a detailed suggestion can be provided. Furthermore, if the user is in a hurry, a suggestion that focuses on the main points can be provided. By adjusting the way suggestions are expressed according to the user's emotions, suggestions that are easy for the user to understand can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way suggestions are expressed.

[0108] The providing unit can estimate the user's emotions and adjust the way information is presented based on the emotions. For example, if the user is nervous, simple, highly visible information can be provided. If the user is relaxed, detailed information can be provided. Furthermore, if the user is in a hurry, information that is concise can be provided. By adjusting the way information is presented based on the user's emotions, it is possible to provide information that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, 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 can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the way information is presented.

[0109] The collection unit can estimate the user's emotions and prioritize the health checkup data to be collected based on the emotions. For example, if the user is feeling stressed, stress-related data can be collected first. Also, if the user is relaxed, overall health data can be collected in a balanced manner. Furthermore, if the user is in a hurry, only important data can be collected first. Thus, by prioritizing data based on the user's emotions, important data can be collected first. Emotion estimation is achieved using an emotion estimation function, 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 collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the data priorities.

[0110] The collection unit can prioritize collection of highly relevant data by taking into account the user's geographical location information. For example, if the user lives at high altitude, data related to health risks specific to high altitudes can be prioritized. Also, if the user lives in an urban area, data related to health risks specific to urban areas can be prioritized. Furthermore, if the user lives by the sea, data related to health risks specific to the seaside can be prioritized. In this way, highly relevant data can be prioritized by taking the geographical location information into account. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0111] When collecting health checkup data, the collection unit can analyze the user's social media activity and collect related data. For example, if the user posts about health on social media, data related to that content can be collected. Also, if the user posts about exercise on social media, data related to that content can be collected. Furthermore, if the user posts about diet on social media, data related to that content can be collected. This allows for efficient collection of related data by analyzing social media activity. 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 the user's social media activity data into the generation AI and cause the generation AI to collect related data.

[0112] When collecting health checkup data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, if the user has preferred voice input in the past, the collection unit can provide voice input preferentially. Furthermore, if the user has preferred text input in the past, the collection unit can provide text input preferentially. Furthermore, if the user has preferred image input in the past, the collection unit can provide image input preferentially. In this way, by reflecting past feedback, the optimal collection method can be provided for the user. 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 the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0113] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health risk. For example, if the risk of high blood pressure is high, a detailed blood pressure analysis can be performed. Also, if the risk of diabetes is high, a detailed blood glucose level analysis can be performed. Furthermore, if the risk of high cholesterol is high, a detailed cholesterol level analysis can be performed. In this way, by adjusting the level of detail of the analysis based on the importance of the health risk, detailed analysis can be performed for important risks. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health risk importance data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0114] During analysis, the analysis unit can apply different analysis algorithms depending on the health risk category. For example, a cardiovascular analysis algorithm can be applied to cardiovascular risk. A diabetes analysis algorithm can also be applied to diabetes risk. Furthermore, a cancer analysis algorithm can be applied to cancer risk. This makes it possible to provide highly accurate analysis results by applying an appropriate analysis algorithm depending on the health risk category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input health risk category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection department imports past health checkup results into a database. Specifically, it automatically retrieves data from the electronic medical record system and stores it in the database. It can also retrieve data manually entered by patients and data obtained from wearable devices. In this way, the collection department collects data from a variety of data sources and stores it in the database. Step 2: The analysis unit uses AI to analyze the data collected by the collection unit. The analysis is based on information such as gender, age, and lifestyle habits, and predicts health risks using machine learning algorithms, deep learning, and statistical analysis. This allows the analysis unit to make highly accurate health risk predictions. Step 3: The proposal unit proposes preventive and improvement measures based on the results obtained by the analysis unit. Specifically, it suggests lifestyle improvements, regular health checkups, exercise programs, dietary advice, stress management methods, etc. In this way, the proposal unit provides the examinee with concrete guidelines for action. Step 4: The provider receives the information provided by the suggestion department. This is done via email, app notification, or web portal, allowing patients to quickly receive suggested preventive and improvement measures.

[0117] 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.

[0118] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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, in order to avoid confusion and to 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 data; an analysis unit that analyzes the data collected by the collection unit; a suggestion unit that proposes preventive measures or improvement measures based on the results obtained by the analysis unit; a providing unit that receives the information provided by the suggesting unit; Equipped with A system characterized by:

2. The collecting unit Import past health checkup results into a database 2. The system of claim 1.

3. The analysis unit Predict future health risks based on gender, age, and lifestyle information 2. The system of claim 1.

4. The proposal unit Suggest preventative and remedial measures 2. The system of claim 1.

5. The providing unit Providing preventative and remedial measures to patients 2. The system of claim 1.

6. 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.

7. The collecting unit Analyze the user's past health checkup data and select the appropriate collection method 2. The system of claim 1.

8. The collecting unit When collecting health checkup data, filtering is performed based on the user's current health condition and lifestyle habits.

2. The system of claim 1.

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